PyCitySchools.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cdd2e00a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the Pandas dependency.\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98346eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First option  Files to load\n",
    "#school_data_to_load = \"Resources/schools_complete.csv\"\n",
    "#student_data_to_load = \"Resources/students_complete.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e13a2d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#When we want to get the indirect path to a file, we use os.path.join() to load a file from somewhere in our directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe1967e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Opotion 2\n",
    "# Add the dependencies.\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b344d2f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#then, use the os.path.join() method to connect to the CSV files: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76c4b8ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Files to load\n",
    "school_data_to_load = os.path.join(\"Resources\", \"schools_complete.csv\")\n",
    "student_data_to_load = os.path.join(\"Resources\", \"students_complete.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d179b9b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School ID</th>\n",
       "      <th>school_name</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Figueroa High School</td>\n",
       "      <td>District</td>\n",
       "      <td>2949</td>\n",
       "      <td>1884411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Shelton High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1761</td>\n",
       "      <td>1056600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hernandez High School</td>\n",
       "      <td>District</td>\n",
       "      <td>4635</td>\n",
       "      <td>3022020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Griffin High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1468</td>\n",
       "      <td>917500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Wilson High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>2283</td>\n",
       "      <td>1319574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Cabrera High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1858</td>\n",
       "      <td>1081356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Bailey High School</td>\n",
       "      <td>District</td>\n",
       "      <td>4976</td>\n",
       "      <td>3124928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Holden High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>427</td>\n",
       "      <td>248087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Pena High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>962</td>\n",
       "      <td>585858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Wright High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1800</td>\n",
       "      <td>1049400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Rodriguez High School</td>\n",
       "      <td>District</td>\n",
       "      <td>3999</td>\n",
       "      <td>2547363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Johnson High School</td>\n",
       "      <td>District</td>\n",
       "      <td>4761</td>\n",
       "      <td>3094650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Ford High School</td>\n",
       "      <td>District</td>\n",
       "      <td>2739</td>\n",
       "      <td>1763916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    School ID            school_name      type  size   budget\n",
       "0           0      Huang High School  District  2917  1910635\n",
       "1           1   Figueroa High School  District  2949  1884411\n",
       "2           2    Shelton High School   Charter  1761  1056600\n",
       "3           3  Hernandez High School  District  4635  3022020\n",
       "4           4    Griffin High School   Charter  1468   917500\n",
       "5           5     Wilson High School   Charter  2283  1319574\n",
       "6           6    Cabrera High School   Charter  1858  1081356\n",
       "7           7     Bailey High School  District  4976  3124928\n",
       "8           8     Holden High School   Charter   427   248087\n",
       "9           9       Pena High School   Charter   962   585858\n",
       "10         10     Wright High School   Charter  1800  1049400\n",
       "11         11  Rodriguez High School  District  3999  2547363\n",
       "12         12    Johnson High School  District  4761  3094650\n",
       "13         13       Ford High School  District  2739  1763916\n",
       "14         14     Thomas High School   Charter  1635  1043130"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the school data file and store it in a Pandas DataFrame.\n",
    "school_data_df = pd.read_csv(school_data_to_load)\n",
    "school_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fcb7f908",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Paul Bradley</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>66</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Victor Smith</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Kevin Rodriguez</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Dr. Richard Scott</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Student ID       student_name gender grade        school_name  \\\n",
       "0           0       Paul Bradley      M   9th  Huang High School   \n",
       "1           1       Victor Smith      M  12th  Huang High School   \n",
       "2           2    Kevin Rodriguez      M  12th  Huang High School   \n",
       "3           3  Dr. Richard Scott      M  12th  Huang High School   \n",
       "4           4         Bonnie Ray      F   9th  Huang High School   \n",
       "\n",
       "   reading_score  math_score  \n",
       "0             66          79  \n",
       "1             94          61  \n",
       "2             90          60  \n",
       "3             67          58  \n",
       "4             97          84  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the student data file and store it in a Pandas DataFrame.\n",
    "student_data_df = pd.read_csv(student_data_to_load)\n",
    "student_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed47aaab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School ID      15\n",
       "school_name    15\n",
       "type           15\n",
       "size           15\n",
       "budget         15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.5.1 Find Missing Values \n",
    "#Determine if there are any missing values in the school data.\n",
    "school_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9bd2f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student ID       39170\n",
       "student_name     39170\n",
       "gender           39170\n",
       "grade            39170\n",
       "school_name      39170\n",
       "reading_score    39170\n",
       "math_score       39170\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine if there are any missing values in the student data.\n",
    "student_data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "351fa19b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School ID</th>\n",
       "      <th>school_name</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    School ID  school_name   type   size  budget\n",
       "0       False        False  False  False   False\n",
       "1       False        False  False  False   False\n",
       "2       False        False  False  False   False\n",
       "3       False        False  False  False   False\n",
       "4       False        False  False  False   False\n",
       "5       False        False  False  False   False\n",
       "6       False        False  False  False   False\n",
       "7       False        False  False  False   False\n",
       "8       False        False  False  False   False\n",
       "9       False        False  False  False   False\n",
       "10      False        False  False  False   False\n",
       "11      False        False  False  False   False\n",
       "12      False        False  False  False   False\n",
       "13      False        False  False  False   False\n",
       "14      False        False  False  False   False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The isnull() Method\n",
    "# Determine if there are any missing values in the school data.\n",
    "school_data_df.isnull()\n",
    "\n",
    "#When we execute this code, we see that every row that is not empty is given the Boolean value \"False,\" which tells us that there are no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66daa133",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39165</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39166</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39167</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39168</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39169</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39170 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student ID  student_name  gender  grade  school_name  reading_score  \\\n",
       "0           False         False   False  False        False          False   \n",
       "1           False         False   False  False        False          False   \n",
       "2           False         False   False  False        False          False   \n",
       "3           False         False   False  False        False          False   \n",
       "4           False         False   False  False        False          False   \n",
       "...           ...           ...     ...    ...          ...            ...   \n",
       "39165       False         False   False  False        False          False   \n",
       "39166       False         False   False  False        False          False   \n",
       "39167       False         False   False  False        False          False   \n",
       "39168       False         False   False  False        False          False   \n",
       "39169       False         False   False  False        False          False   \n",
       "\n",
       "       math_score  \n",
       "0           False  \n",
       "1           False  \n",
       "2           False  \n",
       "3           False  \n",
       "4           False  \n",
       "...           ...  \n",
       "39165       False  \n",
       "39166       False  \n",
       "39167       False  \n",
       "39168       False  \n",
       "39169       False  \n",
       "\n",
       "[39170 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine if there are any missing values in the student data.\n",
    "student_data_df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8960c5d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student ID       0\n",
       "student_name     0\n",
       "gender           0\n",
       "grade            0\n",
       "school_name      0\n",
       "reading_score    0\n",
       "math_score       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To get the total number of empty rows, or rows that are \"True,\" \n",
    "#we can use the Pandas sum() method after the isnull() method, like this\n",
    "\n",
    "# Determine if there are any missing values in the student data.\n",
    "student_data_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ee53e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The notnull() Method\n",
    "#Another method that we can use to find missing values is the notnull() method. When you apply the notnull() method \n",
    "#to a column, Series, or a DataFrame, a Boolean will be returned: \"True\" for the row or rows that are not empty, \n",
    "#or \"False\" for the row or rows that are empty. This method returns the opposite output of the isnull() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f8a1f7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "    School ID  school_name  type  size  budget\n",
       "0        True         True  True  True    True\n",
       "1        True         True  True  True    True\n",
       "2        True         True  True  True    True\n",
       "3        True         True  True  True    True\n",
       "4        True         True  True  True    True\n",
       "5        True         True  True  True    True\n",
       "6        True         True  True  True    True\n",
       "7        True         True  True  True    True\n",
       "8        True         True  True  True    True\n",
       "9        True         True  True  True    True\n",
       "10       True         True  True  True    True\n",
       "11       True         True  True  True    True\n",
       "12       True         True  True  True    True\n",
       "13       True         True  True  True    True\n",
       "14       True         True  True  True    True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's use the notnull() method on the school_data_df DataFrame. \n",
    "# Determine if there are not any missing values in the school data.\n",
    "school_data_df.notnull()\n",
    "\n",
    "#When we run this code, the output returns a copy of our school_data_df DataFrame, \n",
    "#where all the rows that do not have any missing values are labeled \"True.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e3c0981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student ID       39170\n",
       "student_name     39170\n",
       "gender           39170\n",
       "grade            39170\n",
       "school_name      39170\n",
       "reading_score    39170\n",
       "math_score       39170\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine if there are not any missing values in the student data.\n",
    "student_data_df.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54dddf01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.5.3 Determine Data Types\n",
    "#You have been tasked with getting key metrics from the datasets. This will involve performing calculations to get sums, averages, and percentages. \n",
    "#Before you perform these calculations, though, you should check if the numbers in these datasets are the correct data type for making those calculations, as this is considered a best practice.\n",
    "#Here are six common data types that we may encounter in this module and that you may come across in the future:\n",
    "#If integers store values from –32,768 to 32,767, they are 16 bits or int16.\n",
    "#If integer store values from –2,147,483,648 to 2,147,483,647, they are 32 bits or int32.\n",
    "#If integer store values from\n",
    "#–9,223,372,036,854,775,808 to 9,223,372,036,854,775,807, they are 64 bits, or int64."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6763177d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School ID       int64\n",
       "school_name    object\n",
       "type           object\n",
       "size            int64\n",
       "budget          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine data types (dtypes) for the school DataFrame.\n",
    "school_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c87c75f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student ID        int64\n",
       "student_name     object\n",
       "gender           object\n",
       "grade            object\n",
       "school_name      object\n",
       "reading_score     int64\n",
       "math_score        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine data types for the student DataFrame.\n",
    "student_data_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d482423b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The code returns each column name and the data type of each column. As shown in the output above, \"Student ID,\" \"reading_score,\" \n",
    "#and \"math_score\" are integers, which is the correct data type for performing arithmetic calculations.\n",
    "#Based on the output, we determined that all of the columns we need to use for calculations are integers. Therefore, \n",
    "#we won't need to change the data types for these columns. However, there may be instances in which it's necessary to change the data \n",
    "#type. Some CSV and text files, for example, may contain numbers as strings (or objects) rather than integers. These numbers would need to be converted to integers or floats.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "565cec52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.5.7 Fix the Students' Names\n",
    "#To initialize the list, add the following code to a new cell in your PyCitySchools.ipynb file. \n",
    "#Make sure that there is a space after the prefix and before the suffix.\n",
    "\n",
    "# Add each prefix and suffix to remove to a list.\n",
    "prefixes_suffixes = [\"Dr. \", \"Mr. \",\"Ms. \", \"Mrs. \", \"Miss \", \" MD\", \" DDS\", \" DVM\", \" PhD\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d452be00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/66/5p235c5s195_bk7dc39664bh0000gn/T/ipykernel_50427/1925828618.py:6: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  student_data_df[\"student_name\"] = student_data_df[\"student_name\"].str.replace(word,\"\")\n"
     ]
    }
   ],
   "source": [
    "#Next, add the following code below the prefixes_suffixes list. \n",
    "#This is similar to the code we used in the cleaning_student_names.ipynb file.\n",
    "\n",
    "# Iterate through the words in the \"prefixes_suffixes\" list and replace them with an empty space, \"\".\n",
    "for word in prefixes_suffixes:\n",
    "    student_data_df[\"student_name\"] = student_data_df[\"student_name\"].str.replace(word,\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d074f0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>M</td>\n",
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       "      <td>Huang High School</td>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
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       "      <th>4</th>\n",
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       "      <td>12th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39166</th>\n",
       "      <td>39166</td>\n",
       "      <td>Dawn Bell</td>\n",
       "      <td>F</td>\n",
       "      <td>10th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>95</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39167</th>\n",
       "      <td>39167</td>\n",
       "      <td>Rebecca Tanner</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>73</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39168</th>\n",
       "      <td>39168</td>\n",
       "      <td>Desiree Kidd</td>\n",
       "      <td>F</td>\n",
       "      <td>10th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39169</th>\n",
       "      <td>39169</td>\n",
       "      <td>Carolyn Jackson</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39170 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student ID     student_name gender grade         school_name  \\\n",
       "0               0     Paul Bradley      M   9th   Huang High School   \n",
       "1               1     Victor Smith      M  12th   Huang High School   \n",
       "2               2  Kevin Rodriguez      M  12th   Huang High School   \n",
       "3               3    Richard Scott      M  12th   Huang High School   \n",
       "4               4       Bonnie Ray      F   9th   Huang High School   \n",
       "...           ...              ...    ...   ...                 ...   \n",
       "39165       39165     Donna Howard      F  12th  Thomas High School   \n",
       "39166       39166        Dawn Bell      F  10th  Thomas High School   \n",
       "39167       39167   Rebecca Tanner      F   9th  Thomas High School   \n",
       "39168       39168     Desiree Kidd      F  10th  Thomas High School   \n",
       "39169       39169  Carolyn Jackson      F  11th  Thomas High School   \n",
       "\n",
       "       reading_score  math_score  \n",
       "0                 66          79  \n",
       "1                 94          61  \n",
       "2                 90          60  \n",
       "3                 67          58  \n",
       "4                 97          84  \n",
       "...              ...         ...  \n",
       "39165             99          90  \n",
       "39166             95          70  \n",
       "39167             73          84  \n",
       "39168             99          90  \n",
       "39169             95          75  \n",
       "\n",
       "[39170 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3af6a41c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.1 Merge DataFrames\n",
    "#We'll merge school_data_df and student_data_dfon a shared column using the merge() method. The column that these DataFrames have in common is school_name. Inside the parentheses of the merge() method, we'll do the following:\n",
    "#Add the DataFrames to be merged.\n",
    "#Add the shared column to each DataFrame so that the merge can occur.\n",
    "#Define how the DataFrames should be merged: left, right, inner, or outer. The default is inner. (You will learn more about merging later in this course.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "09e11635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
       "      <th>School ID</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Paul Bradley</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>66</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Victor Smith</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Kevin Rodriguez</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Richard Scott</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID     student_name gender grade        school_name  reading_score  \\\n",
       "0           0     Paul Bradley      M   9th  Huang High School             66   \n",
       "1           1     Victor Smith      M  12th  Huang High School             94   \n",
       "2           2  Kevin Rodriguez      M  12th  Huang High School             90   \n",
       "3           3    Richard Scott      M  12th  Huang High School             67   \n",
       "4           4       Bonnie Ray      F   9th  Huang High School             97   \n",
       "\n",
       "   math_score  School ID      type  size   budget  \n",
       "0          79          0  District  2917  1910635  \n",
       "1          61          0  District  2917  1910635  \n",
       "2          60          0  District  2917  1910635  \n",
       "3          58          0  District  2917  1910635  \n",
       "4          84          0  District  2917  1910635  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data into a single dataset.\n",
    "school_data_complete_df = pd.merge(student_data_df, school_data_df, on=[\"school_name\", \"school_name\"])\n",
    "school_data_complete_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3631e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's break down how the DataFrames are being merged with this code.\n",
    "#We create a new DataFrame for the merged DataFrames, called school_data_complete_df.\n",
    "#The new DataFrame is created as a result of merging DataFrame #2 (school_data_df), which is the \"right\" DataFrame, into DataFrame #1 (student_data_df), which is the \"left\" DataFrame. We refer to the DataFrames as \"left\" and \"right\" to reflect the order they appear inside the parentheses.\n",
    "#We use the parameter \"on,\" which is equal to a list of the columns that are identical from each DataFrame, in this\n",
    "#case, \"school_name.\" We can also use the column name like this: on=\"school_name\".\n",
    "#--------\n",
    "#The data from student_data_df takes up the first seven columns, and the four columns from the school_data_df are added at the end: School ID, type, size, and budget. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "77650c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.2 Get the Number of Students\n",
    "#Recall that we counted the number of missing values in each DataFrame using the count() method. We can use the same method on the school_data_complete_df DataFrame to get the counts of specific columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a2b5e480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student ID       39170\n",
       "student_name     39170\n",
       "gender           39170\n",
       "grade            39170\n",
       "school_name      39170\n",
       "reading_score    39170\n",
       "math_score       39170\n",
       "School ID        39170\n",
       "type             39170\n",
       "size             39170\n",
       "budget           39170\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If we add the count() method to the school_data_complete_df DataFrame, we will count all the items in each row for each column that is not null. Run the following code:\n",
    "# Get the total number of students.\n",
    "student_count = school_data_complete_df.count()\n",
    "student_count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0af4e6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To assign the \"student_count\" to a column that identifies with students, we will use the \"Student ID\" column.\n",
    "#Edit the code in the previous cell to look like this:\n",
    "#school_data_complete_df[column].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "68d66d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39170"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_data_complete_df[\"Student ID\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "01284f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.3 Get the Number of Schools\n",
    "#Now it's time to get the total number of schools in the school district.\n",
    "\n",
    "#Remember we found the total number of schools by using the count() method on the school_data_df DataFrame. The output was 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f45a1142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are two ways we can get the number of schools. One is to use the school_data_df DataFrame and assign a variable to one of the columns in the DataFrame, like this:\n",
    "\n",
    "# Calculate the total number of schools.\n",
    "school_count = school_data_df[\"school_name\"].count()\n",
    "school_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4c1ea6da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can't use the count() method on the school_data_complete_df[\"school_name\"] column because this would give us a value of 39,170. If we want to use school_data_complete_df, we first need to get the unique items in the [\"school_name\"] \n",
    "#column by using the unique() method. This method will return a \"ndarray\", or n-dimensional array of all the unique values of that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a5f056f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Huang High School', 'Figueroa High School', 'Shelton High School',\n",
       "       'Hernandez High School', 'Griffin High School',\n",
       "       'Wilson High School', 'Cabrera High School', 'Bailey High School',\n",
       "       'Holden High School', 'Pena High School', 'Wright High School',\n",
       "       'Rodriguez High School', 'Johnson High School', 'Ford High School',\n",
       "       'Thomas High School'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total number of schools\n",
    "school_count_2 = school_data_complete_df[\"school_name\"].unique()\n",
    "school_count_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "73f9e0df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The output of using the unique() method on a Series is a ndarray of all the high schools—and you know how to get the number of items in an array!\n",
    "#Using Python, how would you get the number of high schools from the array school_count_2?\n",
    "#Running the code: len(school_data_complete_df[\"school_name\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "81a963d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.4 Get the Total Budget\n",
    "#Maria has asked you to calculate the total budget for the whole district."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "15a12ace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24649428"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Instead of using the sum() method on the \"budget\" column of the school_data_complete_df DataFrame, we can apply \n",
    "#the sum() method on the \"budget\" column of the school_data_df DataFrame.\n",
    "\n",
    "# Calculate the total budget.\n",
    "total_budget = school_data_df[\"budget\"].sum()\n",
    "total_budget\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "46472178",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The total budget of the schools in the district is $24,649,428. Your output should match the sum of E3:E16 in the schools_complete.csv file. \n",
    "#This is a good way to check that our answer matches the CSV file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e0d1d549",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.5 Get the Score Averages\n",
    "\n",
    "#So far, you have calculated the total number of students, the total number of schools, and the total budget of the district. \n",
    "#Now you need to get the average math and reading scores.\n",
    "#The Pandas method for getting the average of columns is the mean() method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "43592da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.87784018381414"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the Average Reading Score\n",
    "\n",
    "#Using the school_data_complete_df DataFrame, we can get the average reading score using the following code:\n",
    "# Calculate the average reading score.\n",
    "average_reading_score = school_data_complete_df[\"reading_score\"].mean()\n",
    "average_reading_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a37060b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.98537145774827"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the Average Math Score\n",
    "\n",
    "#Now we'll repurpose the code for the average reading score to find the average math score, replacing average_reading_score with average_math_score.\n",
    "#Run the following code:\n",
    "# Calculate the average math score.\n",
    "average_math_score = school_data_complete_df[\"math_score\"].mean()\n",
    "average_math_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2cf4f369",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.6 Get the Passing Percentages\n",
    "#Now it's time to get the percentage of students who passed math and reading, as well as the overall passing percentage.\n",
    "#---------\n",
    "\n",
    "#To get the percentage of students who passed math and reading, we will write code to:\n",
    "#Determine the passing grade.\n",
    "#Get the number of students who passed math and reading in separate DataFrames.\n",
    "#Calculate the number of students who passed math and reading.\n",
    "#Calculate the percentage of students who passed math and reading.\n",
    "#To get the overall passing percentage, we will write code to:\n",
    "#Get the number of students who passed both math and reading in a DataFrame.\n",
    "#Calculate the number of students who passed both math and reading.\n",
    "#Calculate the percentage of students who passed both math and reading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "53d79625",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine the Passing Grade\n",
    "#For math and reading assessment tests in this school district, the passing score was 70. Therefore, we need to get all the math and reading scores that are greater than or equal to 70. To do this, in a new cell, assign a passing_math variable to the math_score column in school_data_complete_df, where all the math scores are equal to or greater than 70.\n",
    "\n",
    "passing_math = school_data_complete_df[\"math_score\"] >= 70\n",
    "passing_reading = school_data_complete_df[\"reading_score\"] >= 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ff148d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4         True\n",
       "         ...  \n",
       "39165     True\n",
       "39166     True\n",
       "39167     True\n",
       "39168     True\n",
       "39169     True\n",
       "Name: math_score, Length: 39170, dtype: bool"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To find the passing_math variable, run passing_math in a new cell. The result is Boolean values for the rows, where \"True\" means the score is equal to or greater than 70, and \"False\" means the score is not equal to or greater than 70.\n",
    "passing_math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ac12ffa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#When we execute the code for passing_reading, we will get Boolean values for each row, where \"True\" means the score is equal to or greater than 70, and \"False\" means the score is not equal to or greater than 70.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "acaa3833",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
       "      <th>School ID</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Paul Bradley</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>66</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Bryan Miranda</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Sheena Carter</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Michael Roth</td>\n",
       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>95</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID   student_name gender grade        school_name  reading_score  \\\n",
       "0           0   Paul Bradley      M   9th  Huang High School             66   \n",
       "4           4     Bonnie Ray      F   9th  Huang High School             97   \n",
       "5           5  Bryan Miranda      M   9th  Huang High School             94   \n",
       "6           6  Sheena Carter      F  11th  Huang High School             82   \n",
       "8           8   Michael Roth      M  10th  Huang High School             95   \n",
       "\n",
       "   math_score  School ID      type  size   budget  \n",
       "0          79          0  District  2917  1910635  \n",
       "4          84          0  District  2917  1910635  \n",
       "5          94          0  District  2917  1910635  \n",
       "6          80          0  District  2917  1910635  \n",
       "8          87          0  District  2917  1910635  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the Number of Students Who Passed Math and Reading\n",
    "\n",
    "#To get all the students who passed math and all the students who passed reading, we need to filter our school_data_complete_df DataFrame for the \"True\" cases. In other words, get only the students who have a grade is equal or greater to 70.\n",
    "#We can filter the school_data_complete_df DataFrame by adding the school_data_complete_df[\"math_score\"] >= 70 within brackets, like this:\n",
    "\n",
    "# Get all the students who are passing math in a new DataFrame.\n",
    "passing_math = school_data_complete_df[school_data_complete_df[\"math_score\"] >= 70]\n",
    "passing_math.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "97596b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we can repurpose the code we wrote to calculate passing_math in order to find passing_reading, but we'll switch out math_score with reading_score, as shown in the following code:\n",
    "# Get all the students that are passing reading in a new DataFrame.\n",
    "passing_reading = school_data_complete_df[school_data_complete_df[\"reading_score\"] >= 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "047c0fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To get the number of students who passed math and reading, apply the count() method to the student_name column of the passing_math and passing_reading DataFrames, like this:\n",
    "\n",
    "# Calculate the number of students passing math.\n",
    "passing_math_count = passing_math[\"student_name\"].count()\n",
    "\n",
    "# Calculate the number of students passing reading.\n",
    "passing_reading_count = passing_reading[\"student_name\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e6479fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29370\n",
      "33610\n"
     ]
    }
   ],
   "source": [
    "#When we execute this cell and print out the passing_math_count and the passing_reading_count, we get the number of students who passed math and the number of students who passed reading:\n",
    "print(passing_math_count)\n",
    "\n",
    "print(passing_reading_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "324d5c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since we are calculating a percentage, we need to convert the student_count to a number with a decimal, or floating-point decimal, by using float().\n",
    "#The final calculation should look like this:\n",
    "# Calculate the percent that passed math.\n",
    "passing_math_percentage = passing_math_count / student_count * 100\n",
    "\n",
    "# Calculate the percent that passed reading.\n",
    "passing_reading_percentage = passing_reading_count / student_count * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "73318b49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student ID       74.980853\n",
      "student_name     74.980853\n",
      "gender           74.980853\n",
      "grade            74.980853\n",
      "school_name      74.980853\n",
      "reading_score    74.980853\n",
      "math_score       74.980853\n",
      "School ID        74.980853\n",
      "type             74.980853\n",
      "size             74.980853\n",
      "budget           74.980853\n",
      "dtype: float64\n",
      "Student ID       85.805463\n",
      "student_name     85.805463\n",
      "gender           85.805463\n",
      "grade            85.805463\n",
      "school_name      85.805463\n",
      "reading_score    85.805463\n",
      "math_score       85.805463\n",
      "School ID        85.805463\n",
      "type             85.805463\n",
      "size             85.805463\n",
      "budget           85.805463\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(passing_math_percentage)\n",
    "print(passing_reading_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fc854836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
       "      <th>School ID</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Bryan Miranda</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Sheena Carter</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Michael Roth</td>\n",
       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>95</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Matthew Greene</td>\n",
       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>96</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID    student_name gender grade        school_name  reading_score  \\\n",
       "4           4      Bonnie Ray      F   9th  Huang High School             97   \n",
       "5           5   Bryan Miranda      M   9th  Huang High School             94   \n",
       "6           6   Sheena Carter      F  11th  Huang High School             82   \n",
       "8           8    Michael Roth      M  10th  Huang High School             95   \n",
       "9           9  Matthew Greene      M  10th  Huang High School             96   \n",
       "\n",
       "   math_score  School ID      type  size   budget  \n",
       "4          84          0  District  2917  1910635  \n",
       "5          94          0  District  2917  1910635  \n",
       "6          80          0  District  2917  1910635  \n",
       "8          87          0  District  2917  1910635  \n",
       "9          84          0  District  2917  1910635  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the Overall Passing Percentage\n",
    "#To get the overall passing percentage, we need to get all the students who passed both math and reading and divide by the total number of students.\n",
    "#We can filter the school_data_complete_df DataFrame by adding the school_data_complete_df[\"math_score\"] >= 70 and school_data_complete_df[\"reading_score\"] >= 70 with the logical operator \"&\" within brackets, like this:\n",
    "\n",
    "# Calculate the students who passed both math and reading.\n",
    "passing_math_reading = school_data_complete_df[(school_data_complete_df[\"math_score\"] >= 70) & (school_data_complete_df[\"reading_score\"] >= 70)]\n",
    "\n",
    "passing_math_reading.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f91755c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Next, we'll get the total number of students who passed both math and reading.\n",
    "#How would you get the total number of students that passed math and reading?\n",
    "#(Select all that apply)\n",
    "\n",
    "#passing_math_reading[\"student_name\"].count()\n",
    "\n",
    "#passing_math_reading.student_name.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "85f81df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To get the total number of students who passed both math and reading, we apply the count()method to the passing_math_reading \n",
    "#DataFrame with the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "41ce78bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25528"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the number of students who passed both math and reading.\n",
    "overall_passing_math_reading_count = passing_math_reading[\"student_name\"].count()\n",
    "overall_passing_math_reading_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4b9711fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student ID       65.172326\n",
       "student_name     65.172326\n",
       "gender           65.172326\n",
       "grade            65.172326\n",
       "school_name      65.172326\n",
       "reading_score    65.172326\n",
       "math_score       65.172326\n",
       "School ID        65.172326\n",
       "type             65.172326\n",
       "size             65.172326\n",
       "budget           65.172326\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finally, we calculate the percentage of students who passed both math and reading by dividing the total number of students and multiplying by 100, using the following code.\n",
    "# Calculate the overall passing percentage.\n",
    "overall_passing_percentage = overall_passing_math_reading_count / student_count * 100\n",
    "overall_passing_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cf70d877",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.7 Create a District Summary DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "07e2aabc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now that we have performed all the calculations needed for the district summary, let's add the following values and columns to a new DataFrame named district_summary_df.\n",
    "#Total number of schools in the column \"Total Schools\"\n",
    "#Total number of students in the column \"Total Students\"\n",
    "#Total budget in the column \"Total Budget\"\n",
    "#Average reading score in the column \"Average Reading Score\"\n",
    "#Average math score in the column \"Average Math Score\"\n",
    "#Percentage of students passing reading in the column \"% Passing Reading\"\n",
    "#Percentage of students passing math in the column \"% Passing Math\"\n",
    "#Overall passing percentage in the column \"% Overall Passing\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "84d322f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Remember, one way to create a new DataFrame is to convert a list of dictionaries to a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "319a0382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Schools</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>Student ID       39170\n",
       "student_name     39170\n",
       "...</td>\n",
       "      <td>24649428</td>\n",
       "      <td>78.985371</td>\n",
       "      <td>81.87784</td>\n",
       "      <td>Student ID       74.980853\n",
       "student_name     74...</td>\n",
       "      <td>Student ID       85.805463\n",
       "student_name     85...</td>\n",
       "      <td>Student ID       65.172326\n",
       "student_name     65...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Schools                                     Total Students  \\\n",
       "0             15  Student ID       39170\n",
       "student_name     39170\n",
       "...   \n",
       "\n",
       "   Total Budget  Average Math Score  Average Reading Score  \\\n",
       "0      24649428           78.985371               81.87784   \n",
       "\n",
       "                                      % Passing Math  \\\n",
       "0  Student ID       74.980853\n",
       "student_name     74...   \n",
       "\n",
       "                                   % Passing Reading  \\\n",
       "0  Student ID       85.805463\n",
       "student_name     85...   \n",
       "\n",
       "                                   % Overall Passing  \n",
       "0  Student ID       65.172326\n",
       "student_name     65...  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To create district_summary_df DataFrame, we can create a list of dictionaries, where the keys are column names and the values are the metrics we calculated. We do this because the DataFrame has no index, and lists have natural indexing.\n",
    "#Add the following code to a new cell and run the cell.\n",
    "\n",
    "# Adding a list of values with keys to create a new DataFrame.\n",
    "district_summary_df = pd.DataFrame(\n",
    "          [{\"Total Schools\": school_count,\n",
    "          \"Total Students\": student_count,\n",
    "          \"Total Budget\": total_budget,\n",
    "          \"Average Math Score\": average_math_score,\n",
    "          \"Average Reading Score\": average_reading_score,\n",
    "          \"% Passing Math\": passing_math_percentage,\n",
    "         \"% Passing Reading\": passing_reading_percentage,\n",
    "        \"% Overall Passing\": overall_passing_percentage}])\n",
    "district_summary_df\n",
    "\n",
    "##OR\n",
    "#district_summary_df[\"Total Students\"] = pd.DataFrame([\"Total Students\"])\n",
    "#district_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cc86f1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writing Functions for the School District Data\n",
    "# Define a function that calculates the percentage of students that passed both \n",
    "# math and reading and returns the passing percentage when the function is called.\n",
    "\n",
    "def passing_math_percent(pass_math_count, student_count):\n",
    "    return pass_math_count / float(student_count) * 100\n",
    "\n",
    "#Let's go over what is happening in this function.\n",
    "#We added two values to the passing_math_percent function: pass_math_count and student_count.\n",
    "#We added return in front of the calculation for the passing percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dbec80a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "passing_math_count = 29370\n",
    "total_student_count = 39170"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b3f18de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.9808526933878"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the function.\n",
    "passing_math_percent(passing_math_count, total_student_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a3edc6f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Student ID       39170\n",
       "student_name     39170\n",
       "...\n",
       "Name: Total Students, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's apply this formatting technique to format the Total Students column with a thousands separator using the basic syntax:\n",
    "#df[\"column\"] = df[\"column\"].map(\"{:,}\".format)\n",
    "#Now let's replace df[\"column\"] with district_summary_df[\"Total Students\"], so our code looks like the following. Add this code to youPyCitySchools.ipynb file below the output of your district_summary_df DataFrame, and run the cell.\n",
    "\n",
    "# Format the \"Total Students\" to have the comma for a thousands separator.\n",
    "#district_summary_df = district_summary_df[\"Total Students\"]\n",
    "\n",
    "district_summary_df[\"Total Students\"] = district_summary_df[\"Total Students\"]\n",
    "district_summary_df[\"Total Students\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "709e3a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Format the \"Total Students\" to have the comma for a thousands separator.\n",
    "#district_summary_df[\"Total Students\"] = district_summary_df[\"Total Students\"].map(\"{:,1}\".format)\n",
    "\n",
    "#district_summary_df[\"Total Students\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1ab21708",
   "metadata": {},
   "outputs": [],
   "source": [
    "#donot use \n",
    "#district_summary_df[\"Total Budget\"] = total_budget\n",
    "#district_summary_df[\"Total Budget\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9e222dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    $24,649,428.00\n",
       "Name: Total Budget, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format \"Total Budget\" to have the comma for a thousands separator, a decimal separator, and a \"$\".\n",
    "\n",
    "district_summary_df[\"Total Budget\"] = district_summary_df[\"Total Budget\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "district_summary_df[\"Total Budget\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4e245a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We need to format the remaining columns accordingly:\n",
    "#The \"Average Reading Score\" column will be formatted to one decimal place.\n",
    "#The \"Average Math Score\" column will be formatted to one decimal place.\n",
    "#The \"% Passing Reading\" column will be formatted to the nearest whole number percentage.\n",
    "#The \"% Passing Math\" column will be formatted to the nearest whole number percentage\n",
    "#The \"% Overall Passing\" column will be formatted to the nearest whole number percentage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7483f55b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    79.0\n",
       "Name: Average Math Score, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format the columns.\n",
    "district_summary_df[\"Average Math Score\"] = district_summary_df[\"Average Math Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "#Display DataFrame\n",
    "district_summary_df[\"Average Math Score\"] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7d317eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    81.9\n",
       "Name: Average Reading Score, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_summary_df[\"Average Reading Score\"] = district_summary_df[\"Average Reading Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "district_summary_df[\"Average Reading Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "eba06fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Student ID       74.980853\n",
       "student_name     74...\n",
       "Name: % Passing Math, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_summary_df[\"% Passing Math\"] = district_summary_df[\"% Passing Math\"]\n",
    "district_summary_df[\"% Passing Math\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "92226a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Student ID       85.805463\n",
       "student_name     85...\n",
       "Name: % Passing Reading, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_summary_df[\"% Passing Reading\"] = district_summary_df[\"% Passing Reading\"]\n",
    "\n",
    "district_summary_df[\"% Passing Reading\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e1a4a1d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Student ID       65.172326\n",
       "student_name     65...\n",
       "Name: % Overall Passing, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district_summary_df[\"% Overall Passing\"] = district_summary_df[\"% Overall Passing\"]\n",
    "district_summary_df[\"% Overall Passing\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d139258f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Schools</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>Student ID       39170\n",
       "student_name     39170\n",
       "...</td>\n",
       "      <td>$24,649,428.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>81.9</td>\n",
       "      <td>Student ID       74.980853\n",
       "student_name     74...</td>\n",
       "      <td>Student ID       85.805463\n",
       "student_name     85...</td>\n",
       "      <td>Student ID       65.172326\n",
       "student_name     65...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Schools                                     Total Students  \\\n",
       "0             15  Student ID       39170\n",
       "student_name     39170\n",
       "...   \n",
       "\n",
       "     Total Budget Average Math Score Average Reading Score  \\\n",
       "0  $24,649,428.00               79.0                  81.9   \n",
       "\n",
       "                                      % Passing Math  \\\n",
       "0  Student ID       74.980853\n",
       "student_name     74...   \n",
       "\n",
       "                                   % Passing Reading  \\\n",
       "0  Student ID       85.805463\n",
       "student_name     85...   \n",
       "\n",
       "                                   % Overall Passing  \n",
       "0  Student ID       65.172326\n",
       "student_name     65...  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format the columns.\n",
    "district_summary_df[\"Average Math Score\"] = district_summary_df[\"Average Math Score\"]\n",
    "\n",
    "district_summary_df[\"Average Reading Score\"] = district_summary_df[\"Average Reading Score\"]\n",
    "\n",
    "district_summary_df[\"% Passing Math\"] = district_summary_df[\"% Passing Math\"]\n",
    "\n",
    "district_summary_df[\"% Passing Reading\"] = district_summary_df[\"% Passing Reading\"]\n",
    "\n",
    "district_summary_df[\"% Overall Passing\"] = district_summary_df[\"% Overall Passing\"]\n",
    "\n",
    "district_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8e169acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.7.9 Reorder Columns\n",
    "\n",
    "## Reorder the columns in the order you want them to appear.\n",
    "#new_column_order = [\"column2\", \"column4\", \"column1\"]\n",
    "\n",
    "# Assign a new or the same DataFrame the new column order.\n",
    "#df = df[new_column_order]\n",
    "\n",
    "#This technique can also be used to filter out columns that you don't need and select the columns in the order that you want. \n",
    "#For example, we didn't include column3 in the new order; this is how we can filter out columns we don't want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f213d84e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Schools</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>Student ID       39170\n",
       "student_name     39170\n",
       "...</td>\n",
       "      <td>$24,649,428.00</td>\n",
       "      <td>79.0</td>\n",
       "      <td>81.9</td>\n",
       "      <td>Student ID       74.980853\n",
       "student_name     74...</td>\n",
       "      <td>Student ID       85.805463\n",
       "student_name     85...</td>\n",
       "      <td>Student ID       65.172326\n",
       "student_name     65...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Total Schools                                     Total Students  \\\n",
       "0             15  Student ID       39170\n",
       "student_name     39170\n",
       "...   \n",
       "\n",
       "     Total Budget Average Math Score Average Reading Score  \\\n",
       "0  $24,649,428.00               79.0                  81.9   \n",
       "\n",
       "                                      % Passing Math  \\\n",
       "0  Student ID       74.980853\n",
       "student_name     74...   \n",
       "\n",
       "                                   % Passing Reading  \\\n",
       "0  Student ID       85.805463\n",
       "student_name     85...   \n",
       "\n",
       "                                   % Overall Passing  \n",
       "0  Student ID       65.172326\n",
       "student_name     65...  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reorder the columns in the order you want them to appear.\n",
    "new_column_order = [\"Total Schools\", \"Total Students\", \"Total Budget\",\"Average Math Score\", \"Average Reading Score\", \"% Passing Math\", \"% Passing Reading\", \"% Overall Passing\"]\n",
    "\n",
    "# Assign district summary df the new column order.\n",
    "district_summary_df = district_summary_df[new_column_order]\n",
    "district_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "090e0585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Huang High School        District\n",
       "Figueroa High School     District\n",
       "Shelton High School       Charter\n",
       "Hernandez High School    District\n",
       "Griffin High School       Charter\n",
       "Wilson High School        Charter\n",
       "Cabrera High School       Charter\n",
       "Bailey High School       District\n",
       "Holden High School        Charter\n",
       "Pena High School          Charter\n",
       "Wright High School        Charter\n",
       "Rodriguez High School    District\n",
       "Johnson High School      District\n",
       "Ford High School         District\n",
       "Thomas High School        Charter\n",
       "Name: type, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the school type.\n",
    "per_school_types = school_data_df.set_index([\"school_name\"])[\"type\"]\n",
    "per_school_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "05b83cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Huang High School</th>\n",
       "      <td>District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shelton High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernandez High School</th>\n",
       "      <td>District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holden High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pena High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wright High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rodriguez High School</th>\n",
       "      <td>District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson High School</th>\n",
       "      <td>District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas High School</th>\n",
       "      <td>Charter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           type\n",
       "school_name                    \n",
       "Huang High School      District\n",
       "Figueroa High School   District\n",
       "Shelton High School     Charter\n",
       "Hernandez High School  District\n",
       "Griffin High School     Charter\n",
       "Wilson High School      Charter\n",
       "Cabrera High School     Charter\n",
       "Bailey High School     District\n",
       "Holden High School      Charter\n",
       "Pena High School        Charter\n",
       "Wright High School      Charter\n",
       "Rodriguez High School  District\n",
       "Johnson High School    District\n",
       "Ford High School       District\n",
       "Thomas High School      Charter"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the per_school_types into a DataFrame for testing.\n",
    "df = pd.DataFrame(per_school_types)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9fb52fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.8.2 Get the Student Count Per School"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ce049552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2917\n",
       "1     2949\n",
       "2     1761\n",
       "3     4635\n",
       "4     1468\n",
       "5     2283\n",
       "6     1858\n",
       "7     4976\n",
       "8      427\n",
       "9      962\n",
       "10    1800\n",
       "11    3999\n",
       "12    4761\n",
       "13    2739\n",
       "14    1635\n",
       "Name: size, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total student count.\n",
    "per_school_counts = school_data_df[\"size\"]\n",
    "per_school_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "213b408e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Unfortunately, this Series doesn't have an index with \"school_name.\" Therefore, we can't use the \"size\" column from school_data_df to get the count of the student population.\n",
    "#To fix this, we can use the set_index() method on the \"school_name\" column, and then select the \"size\" column to display the student count for each school."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4edef631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Huang High School        2917\n",
       "Figueroa High School     2949\n",
       "Shelton High School      1761\n",
       "Hernandez High School    4635\n",
       "Griffin High School      1468\n",
       "Wilson High School       2283\n",
       "Cabrera High School      1858\n",
       "Bailey High School       4976\n",
       "Holden High School        427\n",
       "Pena High School          962\n",
       "Wright High School       1800\n",
       "Rodriguez High School    3999\n",
       "Johnson High School      4761\n",
       "Ford High School         2739\n",
       "Thomas High School       1635\n",
       "Name: size, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total student count.\n",
    "per_school_counts = school_data_df.set_index([\"school_name\"])[\"size\"]\n",
    "per_school_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "82ca735c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To get the number of students from the school_data_complete_df DataFrame, we can count the number of times a high school appears using value_counts() on the \"school_name\" column. \n",
    "#The value_counts() method will return a Series of data with the number of times each school_name appears in a row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d00f8601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       4976\n",
       "Johnson High School      4761\n",
       "Hernandez High School    4635\n",
       "Rodriguez High School    3999\n",
       "Figueroa High School     2949\n",
       "Huang High School        2917\n",
       "Ford High School         2739\n",
       "Wilson High School       2283\n",
       "Cabrera High School      1858\n",
       "Wright High School       1800\n",
       "Shelton High School      1761\n",
       "Thomas High School       1635\n",
       "Griffin High School      1468\n",
       "Pena High School          962\n",
       "Holden High School        427\n",
       "Name: school_name, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total student count.\n",
    "per_school_counts = school_data_complete_df[\"school_name\"].value_counts()\n",
    "per_school_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "65081550",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we have two methods to get the student count. As long as we make sure the index is the \"school_name\" column, we can use either method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a5b32dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.8.3 Get the Budget Per Student\n",
    "#Maria wants you to add the budget per student for each school.  First you'll need to get the budget for each school \n",
    "#and then divide by the total students per school, which you already calculated as per_school_counts.\n",
    "#We can use the set_index() method on the school_name column of the school_data_df DataFrame to get the school_name as the index, \n",
    "#like we did when we calculated the total students from each school, with school_data_df.set_index([\"school_name\"])[\"size\"]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "859b20e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Huang High School        1910635\n",
       "Figueroa High School     1884411\n",
       "Shelton High School      1056600\n",
       "Hernandez High School    3022020\n",
       "Griffin High School       917500\n",
       "Wilson High School       1319574\n",
       "Cabrera High School      1081356\n",
       "Bailey High School       3124928\n",
       "Holden High School        248087\n",
       "Pena High School          585858\n",
       "Wright High School       1049400\n",
       "Rodriguez High School    2547363\n",
       "Johnson High School      3094650\n",
       "Ford High School         1763916\n",
       "Thomas High School       1043130\n",
       "Name: budget, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total school budget.\n",
    "per_school_budget = school_data_df.set_index([\"school_name\"])[\"budget\"]\n",
    "per_school_budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5ef10ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       628.0\n",
       "Cabrera High School      582.0\n",
       "Figueroa High School     639.0\n",
       "Ford High School         644.0\n",
       "Griffin High School      625.0\n",
       "Hernandez High School    652.0\n",
       "Holden High School       581.0\n",
       "Huang High School        655.0\n",
       "Johnson High School      650.0\n",
       "Pena High School         609.0\n",
       "Rodriguez High School    637.0\n",
       "Shelton High School      600.0\n",
       "Thomas High School       638.0\n",
       "Wilson High School       578.0\n",
       "Wright High School       583.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The data type for the budget column is int64, which is suitable for calculations that we need to perform in order to find the budget per student.\n",
    "#To get the budget per student, we'll divide the per_school_budget by the per_school_counts.\n",
    "#Add the following code to a new cell and run the cell.\n",
    "\n",
    "# Calculate the per capita spending.\n",
    "per_school_capita = per_school_budget / per_school_counts\n",
    "per_school_capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "08a936b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.8.4 Get the Score Averages Per School\n",
    "#To calculate the average math and reading test scores for each school, what method should you use for the math or reading score columns?\n",
    "#mean() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "df26089f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Huang High School     79\n",
       "Huang High School     61\n",
       "Huang High School     60\n",
       "Huang High School     58\n",
       "Huang High School     84\n",
       "                      ..\n",
       "Thomas High School    90\n",
       "Thomas High School    70\n",
       "Thomas High School    84\n",
       "Thomas High School    90\n",
       "Thomas High School    75\n",
       "Name: math_score, Length: 39170, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We've used the set_index() method on the school_name column in student_data_df to get data from another column, just like how we retrieved the school budget using school_data_df.set_index([\"school_name\"])[\"budget\"].\n",
    "#Let's use this procedure to replace budget with math_score. Add the following code to a new cell and run the cell.\n",
    "\n",
    "# Calculate the math scores.\n",
    "student_school_math = student_data_df.set_index([\"school_name\"])[\"math_score\"]\n",
    "student_school_math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1fdf8161",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The output from the code will look like the following, where we get every occurrence of the high school as the index, \n",
    "#and the math grade from each student in that school.\n",
    "\n",
    "#Unfortunately, we can't use the school_data_df DataFrame, as there aren't any columns containing grades. We also \n",
    "#can't use the set_index() method on the school_name column in student_data_df because there are too many occurrences of the school_name column.\n",
    "\n",
    "#Instead, we need to use the Pandas groupby() function. The groupby() function will split an object (like a DataFrame), \n",
    "#apply a mathematical operation, and combine the results. This can be used to group large amounts of data when we want to compute mathematical \n",
    "#operations on these groups.\n",
    "\n",
    "#The mathematical operation we will apply to the groupby() function is the mean() method. Let's see how this will look when we apply it to \n",
    "#school_data_complete_df to get the grade averages for each column. Add the following code to a new cell and run the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "829ebdac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
       "      <th>School ID</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>20358.5</td>\n",
       "      <td>81.033963</td>\n",
       "      <td>77.048432</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4976.0</td>\n",
       "      <td>3124928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>16941.5</td>\n",
       "      <td>83.975780</td>\n",
       "      <td>83.061895</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1081356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>4391.0</td>\n",
       "      <td>81.158020</td>\n",
       "      <td>76.711767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2949.0</td>\n",
       "      <td>1884411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>36165.0</td>\n",
       "      <td>80.746258</td>\n",
       "      <td>77.102592</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2739.0</td>\n",
       "      <td>1763916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>12995.5</td>\n",
       "      <td>83.816757</td>\n",
       "      <td>83.351499</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>917500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernandez High School</th>\n",
       "      <td>9944.0</td>\n",
       "      <td>80.934412</td>\n",
       "      <td>77.289752</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4635.0</td>\n",
       "      <td>3022020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holden High School</th>\n",
       "      <td>23060.0</td>\n",
       "      <td>83.814988</td>\n",
       "      <td>83.803279</td>\n",
       "      <td>8.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>248087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huang High School</th>\n",
       "      <td>1458.0</td>\n",
       "      <td>81.182722</td>\n",
       "      <td>76.629414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2917.0</td>\n",
       "      <td>1910635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson High School</th>\n",
       "      <td>32415.0</td>\n",
       "      <td>80.966394</td>\n",
       "      <td>77.072464</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4761.0</td>\n",
       "      <td>3094650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pena High School</th>\n",
       "      <td>23754.5</td>\n",
       "      <td>84.044699</td>\n",
       "      <td>83.839917</td>\n",
       "      <td>9.0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>585858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rodriguez High School</th>\n",
       "      <td>28035.0</td>\n",
       "      <td>80.744686</td>\n",
       "      <td>76.842711</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3999.0</td>\n",
       "      <td>2547363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shelton High School</th>\n",
       "      <td>6746.0</td>\n",
       "      <td>83.725724</td>\n",
       "      <td>83.359455</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1761.0</td>\n",
       "      <td>1056600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas High School</th>\n",
       "      <td>38352.0</td>\n",
       "      <td>83.848930</td>\n",
       "      <td>83.418349</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>1043130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson High School</th>\n",
       "      <td>14871.0</td>\n",
       "      <td>83.989488</td>\n",
       "      <td>83.274201</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2283.0</td>\n",
       "      <td>1319574.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wright High School</th>\n",
       "      <td>25135.5</td>\n",
       "      <td>83.955000</td>\n",
       "      <td>83.682222</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1049400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Student ID  reading_score  math_score  School ID  \\\n",
       "school_name                                                               \n",
       "Bailey High School        20358.5      81.033963   77.048432        7.0   \n",
       "Cabrera High School       16941.5      83.975780   83.061895        6.0   \n",
       "Figueroa High School       4391.0      81.158020   76.711767        1.0   \n",
       "Ford High School          36165.0      80.746258   77.102592       13.0   \n",
       "Griffin High School       12995.5      83.816757   83.351499        4.0   \n",
       "Hernandez High School      9944.0      80.934412   77.289752        3.0   \n",
       "Holden High School        23060.0      83.814988   83.803279        8.0   \n",
       "Huang High School          1458.0      81.182722   76.629414        0.0   \n",
       "Johnson High School       32415.0      80.966394   77.072464       12.0   \n",
       "Pena High School          23754.5      84.044699   83.839917        9.0   \n",
       "Rodriguez High School     28035.0      80.744686   76.842711       11.0   \n",
       "Shelton High School        6746.0      83.725724   83.359455        2.0   \n",
       "Thomas High School        38352.0      83.848930   83.418349       14.0   \n",
       "Wilson High School        14871.0      83.989488   83.274201        5.0   \n",
       "Wright High School        25135.5      83.955000   83.682222       10.0   \n",
       "\n",
       "                         size     budget  \n",
       "school_name                               \n",
       "Bailey High School     4976.0  3124928.0  \n",
       "Cabrera High School    1858.0  1081356.0  \n",
       "Figueroa High School   2949.0  1884411.0  \n",
       "Ford High School       2739.0  1763916.0  \n",
       "Griffin High School    1468.0   917500.0  \n",
       "Hernandez High School  4635.0  3022020.0  \n",
       "Holden High School      427.0   248087.0  \n",
       "Huang High School      2917.0  1910635.0  \n",
       "Johnson High School    4761.0  3094650.0  \n",
       "Pena High School        962.0   585858.0  \n",
       "Rodriguez High School  3999.0  2547363.0  \n",
       "Shelton High School    1761.0  1056600.0  \n",
       "Thomas High School     1635.0  1043130.0  \n",
       "Wilson High School     2283.0  1319574.0  \n",
       "Wright High School     1800.0  1049400.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average math scores.\n",
    "per_school_averages = school_data_complete_df.groupby([\"school_name\"]).mean()\n",
    "per_school_averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ae225d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       77.048432\n",
       "Cabrera High School      83.061895\n",
       "Figueroa High School     76.711767\n",
       "Ford High School         77.102592\n",
       "Griffin High School      83.351499\n",
       "Hernandez High School    77.289752\n",
       "Holden High School       83.803279\n",
       "Huang High School        76.629414\n",
       "Johnson High School      77.072464\n",
       "Pena High School         83.839917\n",
       "Rodriguez High School    76.842711\n",
       "Shelton High School      83.359455\n",
       "Thomas High School       83.418349\n",
       "Wilson High School       83.274201\n",
       "Wright High School       83.682222\n",
       "Name: math_score, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#But we don't want all of this data in the school summary DataFrame, just the reading and math scores. \n",
    "#To get the average math score and reading score for each school, we can add the math_score and reading_score columns at the end. Add the following code to a new cell and run the cell.\n",
    "\n",
    "# Calculate the average test scores.\n",
    "per_school_math = school_data_complete_df.groupby([\"school_name\"]).mean()[\"math_score\"]\n",
    "per_school_math\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "bb00a7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       81.033963\n",
       "Cabrera High School      83.975780\n",
       "Figueroa High School     81.158020\n",
       "Ford High School         80.746258\n",
       "Griffin High School      83.816757\n",
       "Hernandez High School    80.934412\n",
       "Holden High School       83.814988\n",
       "Huang High School        81.182722\n",
       "Johnson High School      80.966394\n",
       "Pena High School         84.044699\n",
       "Rodriguez High School    80.744686\n",
       "Shelton High School      83.725724\n",
       "Thomas High School       83.848930\n",
       "Wilson High School       83.989488\n",
       "Wright High School       83.955000\n",
       "Name: reading_score, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_school_reading = school_data_complete_df.groupby([\"school_name\"]).mean()[\"reading_score\"]\n",
    "per_school_reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b19ecb33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.8.5 Get the Passing Percentages Per School\n",
    "#Maria would like you to continue gathering key data by calculating the passing percentages for math and reading for each school, as well as get the overall passing percentage for each school. She goes over what you will need in order to perform these calculations, writing the pseudocode on a whiteboard.\n",
    "#Here's the pseudocode from Maria: \n",
    " # To get the passing percentages, we need to:\n",
    " # 1. Determine what is the passing grade.\n",
    " # 2. Get the number of students who passed math and reading.\n",
    " # 3. Get the students who passed math and passed reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8ae04765",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determine the Passing Grade\n",
    "\n",
    "#Remember, for assessment tests, the passing score is 70, with the >= 70 statement to filter the grades that are passing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c540d98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the Number of Students Who Passed Math and Reading\n",
    "\n",
    "#We determined the number of students who passed math and reading for the district summary using the following code:\n",
    "#passing_math = school_data_complete_df[(school_data_complete_df[\"math_score\"] >= 70)]\n",
    "#and\n",
    "#passing_reading = school_data_complete_df[(school_data_complete_df[\"reading_score\"] >= 70)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "942c07dc",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
       "      <th>School ID</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Paul Bradley</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>66</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Bryan Miranda</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Sheena Carter</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Michael Roth</td>\n",
       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>95</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID   student_name gender grade        school_name  reading_score  \\\n",
       "0           0   Paul Bradley      M   9th  Huang High School             66   \n",
       "4           4     Bonnie Ray      F   9th  Huang High School             97   \n",
       "5           5  Bryan Miranda      M   9th  Huang High School             94   \n",
       "6           6  Sheena Carter      F  11th  Huang High School             82   \n",
       "8           8   Michael Roth      M  10th  Huang High School             95   \n",
       "\n",
       "   math_score  School ID      type  size   budget  \n",
       "0          79          0  District  2917  1910635  \n",
       "4          84          0  District  2917  1910635  \n",
       "5          94          0  District  2917  1910635  \n",
       "6          80          0  District  2917  1910635  \n",
       "8          87          0  District  2917  1910635  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the passing scores by creating a filtered DataFrame.\n",
    "per_school_passing_math = school_data_complete_df[(school_data_complete_df[\"math_score\"] >= 70)]\n",
    "per_school_passing_math.head() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e99e778d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
       "      <th>School ID</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Victor Smith</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Kevin Rodriguez</td>\n",
       "      <td>M</td>\n",
       "      <td>12th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Bryan Miranda</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Sheena Carter</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID     student_name gender grade        school_name  reading_score  \\\n",
       "1           1     Victor Smith      M  12th  Huang High School             94   \n",
       "2           2  Kevin Rodriguez      M  12th  Huang High School             90   \n",
       "4           4       Bonnie Ray      F   9th  Huang High School             97   \n",
       "5           5    Bryan Miranda      M   9th  Huang High School             94   \n",
       "6           6    Sheena Carter      F  11th  Huang High School             82   \n",
       "\n",
       "   math_score  School ID      type  size   budget  \n",
       "1          61          0  District  2917  1910635  \n",
       "2          60          0  District  2917  1910635  \n",
       "4          84          0  District  2917  1910635  \n",
       "5          94          0  District  2917  1910635  \n",
       "6          80          0  District  2917  1910635  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_school_passing_reading = school_data_complete_df[(school_data_complete_df[\"reading_score\"] >= 70)]\n",
    "per_school_passing_reading.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f9a1bf1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#However, we need to get the average reading and math scores for each school. So, the index needs to be the school_name, \n",
    "#and we need to get the number of students in the per_school_passing_math and the per_school_passing_reading DataFrames.\n",
    "\n",
    "#From the per_school_passing_math and the per_school_passing_reading DataFrames. How can we get the index to be the school_name?\n",
    "#Use groupby([\"school_name\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "053aa325",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To create a Series that has school_name as the index, we'll use groupby([\"school_name\"]) on the per_school_passing_math and the per_school_passing_math DataFrames.\n",
    "\n",
    "# Using the groupby() function will split an object (like a DataFrame) and apply a mathematical operation.\n",
    "\n",
    "#Next, we need to perform a mathematical operation on the groupby() object. Remember, we need to get the number of students who passed, \n",
    "#so we'll use the count() method on the student_name column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "44dafaba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       3318\n",
       "Cabrera High School      1749\n",
       "Figueroa High School     1946\n",
       "Ford High School         1871\n",
       "Griffin High School      1371\n",
       "Hernandez High School    3094\n",
       "Holden High School        395\n",
       "Huang High School        1916\n",
       "Johnson High School      3145\n",
       "Pena High School          910\n",
       "Rodriguez High School    2654\n",
       "Shelton High School      1653\n",
       "Thomas High School       1525\n",
       "Wilson High School       2143\n",
       "Wright High School       1680\n",
       "Name: student_name, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the number of students passing math and passing reading by school.\n",
    "per_school_passing_math = per_school_passing_math.groupby([\"school_name\"]).count()[\"student_name\"]\n",
    "per_school_passing_math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "796d399b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       4077\n",
       "Cabrera High School      1803\n",
       "Figueroa High School     2381\n",
       "Ford High School         2172\n",
       "Griffin High School      1426\n",
       "Hernandez High School    3748\n",
       "Holden High School        411\n",
       "Huang High School        2372\n",
       "Johnson High School      3867\n",
       "Pena High School          923\n",
       "Rodriguez High School    3208\n",
       "Shelton High School      1688\n",
       "Thomas High School       1591\n",
       "Wilson High School       2204\n",
       "Wright High School       1739\n",
       "Name: student_name, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_school_passing_reading = per_school_passing_reading.groupby([\"school_name\"]).count()[\"student_name\"]\n",
    "per_school_passing_reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4ad2fcd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       66.680064\n",
       "Cabrera High School      94.133477\n",
       "Figueroa High School     65.988471\n",
       "Ford High School         68.309602\n",
       "Griffin High School      93.392371\n",
       "Hernandez High School    66.752967\n",
       "Holden High School       92.505855\n",
       "Huang High School        65.683922\n",
       "Johnson High School      66.057551\n",
       "Pena High School         94.594595\n",
       "Rodriguez High School    66.366592\n",
       "Shelton High School      93.867121\n",
       "Thomas High School       93.272171\n",
       "Wilson High School       93.867718\n",
       "Wright High School       93.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Determine the Percentage of Students Passing Math and Reading\n",
    "\n",
    "#To determine the percentage of students passing math and reading, we must divide per_school_passing_math and per_school_passing_reading by the per_school_counts, and then multiply by 100, as shown in the following code:\n",
    "# Calculate the percentage of passing math and reading scores per school.\n",
    "\n",
    "per_school_passing_math = per_school_passing_math / per_school_counts * 100\n",
    "\n",
    "per_school_passing_math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c1e69d36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       81.933280\n",
       "Cabrera High School      97.039828\n",
       "Figueroa High School     80.739234\n",
       "Ford High School         79.299014\n",
       "Griffin High School      97.138965\n",
       "Hernandez High School    80.862999\n",
       "Holden High School       96.252927\n",
       "Huang High School        81.316421\n",
       "Johnson High School      81.222432\n",
       "Pena High School         95.945946\n",
       "Rodriguez High School    80.220055\n",
       "Shelton High School      95.854628\n",
       "Thomas High School       97.308869\n",
       "Wilson High School       96.539641\n",
       "Wright High School       96.611111\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_school_passing_reading = per_school_passing_reading / per_school_counts * 100\n",
    "\n",
    "per_school_passing_reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7b5d4b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the Overall Passing Percentage for All Students for Each School\n",
    "\n",
    "# REWIND\n",
    "#To get the overall passing percentage, we need to get all the students who passed both math and reading and then divide by the total number of students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "616586e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student ID</th>\n",
       "      <th>student_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>grade</th>\n",
       "      <th>school_name</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>math_score</th>\n",
       "      <th>School ID</th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Bryan Miranda</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Sheena Carter</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Michael Roth</td>\n",
       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>95</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Matthew Greene</td>\n",
       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>96</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Student ID    student_name gender grade        school_name  reading_score  \\\n",
       "4           4      Bonnie Ray      F   9th  Huang High School             97   \n",
       "5           5   Bryan Miranda      M   9th  Huang High School             94   \n",
       "6           6   Sheena Carter      F  11th  Huang High School             82   \n",
       "8           8    Michael Roth      M  10th  Huang High School             95   \n",
       "9           9  Matthew Greene      M  10th  Huang High School             96   \n",
       "\n",
       "   math_score  School ID      type  size   budget  \n",
       "4          84          0  District  2917  1910635  \n",
       "5          94          0  District  2917  1910635  \n",
       "6          80          0  District  2917  1910635  \n",
       "8          87          0  District  2917  1910635  \n",
       "9          84          0  District  2917  1910635  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the students who passed both math and reading.\n",
    "per_passing_math_reading = school_data_complete_df[(school_data_complete_df[\"math_score\"] >= 70) & (school_data_complete_df[\"reading_score\"] >= 70)]\n",
    "\n",
    "per_passing_math_reading.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "94b3b537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       54.642283\n",
       "Cabrera High School      91.334769\n",
       "Figueroa High School     53.204476\n",
       "Ford High School         54.289887\n",
       "Griffin High School      90.599455\n",
       "Hernandez High School    53.527508\n",
       "Holden High School       89.227166\n",
       "Huang High School        53.513884\n",
       "Johnson High School      53.539172\n",
       "Pena High School         90.540541\n",
       "Rodriguez High School    52.988247\n",
       "Shelton High School      89.892107\n",
       "Thomas High School       90.948012\n",
       "Wilson High School       90.582567\n",
       "Wright High School       90.333333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To get the total number of students who passed both math and reading on the per_passing_math_reading DataFrame, we use the following code, which sets the index to school_name, and then use the count() method for the student_name.\n",
    "\n",
    "# Calculate the number of students who passed both math and reading.\n",
    "per_passing_math_reading = per_passing_math_reading.groupby([\"school_name\"]).count()[\"student_name\"]\n",
    "\n",
    "# Calculate the overall passing percentage.\n",
    "per_overall_passing_percentage = per_passing_math_reading / per_school_counts * 100\n",
    "\n",
    "per_overall_passing_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4bdb4f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.8.6 Create the School Summary DataFrame\n",
    "#Now that we have performed all the calculations needed for the school summary, we can add the following values to a new DataFrame named per_school_summary_df. \n",
    "#The data and columns of the DataFrame will be:\n",
    "#Type of school in the \"School Type\" column\n",
    "#Total students per school in the \"Total Students\" column\n",
    "#Total budget per school in the \"Total School Budget\" column\n",
    "#Total budget per student for each school in the \"Per Student Budget\" column\n",
    "#Average math score for each school in the \"Average Math Score\" column\n",
    "#Average reading score for each school in the \"Average Reading Score\" column\n",
    "#Percentage of students passing math for each school in the \"% Passing Math\" column\n",
    "#Percentage of students passing reading for each school in the \"% Passing Reading\" column\n",
    "#Overall passing percentage for each school in the \"% Overall Passing\" column\n",
    "# REWIND\n",
    "#To create per_school_summary_df, the column names will be the keys, and the values will be each piece of data we retrieved or calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6dbfe0cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School Type</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total School Budget</th>\n",
       "      <th>Per Student Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4976</td>\n",
       "      <td>3124928</td>\n",
       "      <td>628.0</td>\n",
       "      <td>77.048432</td>\n",
       "      <td>81.033963</td>\n",
       "      <td>66.680064</td>\n",
       "      <td>81.933280</td>\n",
       "      <td>54.642283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1858</td>\n",
       "      <td>1081356</td>\n",
       "      <td>582.0</td>\n",
       "      <td>83.061895</td>\n",
       "      <td>83.975780</td>\n",
       "      <td>94.133477</td>\n",
       "      <td>97.039828</td>\n",
       "      <td>91.334769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2949</td>\n",
       "      <td>1884411</td>\n",
       "      <td>639.0</td>\n",
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       "      <td>81.158020</td>\n",
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       "      <td>80.739234</td>\n",
       "      <td>53.204476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2739</td>\n",
       "      <td>1763916</td>\n",
       "      <td>644.0</td>\n",
       "      <td>77.102592</td>\n",
       "      <td>80.746258</td>\n",
       "      <td>68.309602</td>\n",
       "      <td>79.299014</td>\n",
       "      <td>54.289887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1468</td>\n",
       "      <td>917500</td>\n",
       "      <td>625.0</td>\n",
       "      <td>83.351499</td>\n",
       "      <td>83.816757</td>\n",
       "      <td>93.392371</td>\n",
       "      <td>97.138965</td>\n",
       "      <td>90.599455</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                     School Type  Total Students  Total School Budget  \\\n",
       "Bailey High School      District            4976              3124928   \n",
       "Cabrera High School      Charter            1858              1081356   \n",
       "Figueroa High School    District            2949              1884411   \n",
       "Ford High School        District            2739              1763916   \n",
       "Griffin High School      Charter            1468               917500   \n",
       "\n",
       "                      Per Student Budget  Average Math Score  \\\n",
       "Bailey High School                 628.0           77.048432   \n",
       "Cabrera High School                582.0           83.061895   \n",
       "Figueroa High School               639.0           76.711767   \n",
       "Ford High School                   644.0           77.102592   \n",
       "Griffin High School                625.0           83.351499   \n",
       "\n",
       "                      Average Reading Score  % Passing Math  \\\n",
       "Bailey High School                81.033963       66.680064   \n",
       "Cabrera High School               83.975780       94.133477   \n",
       "Figueroa High School              81.158020       65.988471   \n",
       "Ford High School                  80.746258       68.309602   \n",
       "Griffin High School               83.816757       93.392371   \n",
       "\n",
       "                      % Passing Reading  % Overall Passing  \n",
       "Bailey High School            81.933280          54.642283  \n",
       "Cabrera High School           97.039828          91.334769  \n",
       "Figueroa High School          80.739234          53.204476  \n",
       "Ford High School              79.299014          54.289887  \n",
       "Griffin High School           97.138965          90.599455  "
      ]
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     "execution_count": 110,
     "metadata": {},
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    }
   ],
   "source": [
    "# Adding a list of values with keys to create a new DataFrame.\n",
    "\n",
    "per_school_summary_df = pd.DataFrame({\n",
    "             \"School Type\": per_school_types,\n",
    "             \"Total Students\": per_school_counts,\n",
    "             \"Total School Budget\": per_school_budget,\n",
    "             \"Per Student Budget\": per_school_capita,\n",
    "             \"Average Math Score\": per_school_math,\n",
    "           \"Average Reading Score\": per_school_reading,\n",
    "           \"% Passing Math\": per_school_passing_math,\n",
    "           \"% Passing Reading\": per_school_passing_reading,\n",
    "           \"% Overall Passing\": per_overall_passing_percentage})\n",
    "per_school_summary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ee04f342",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.8.7 Clean Up the DataFrame\n",
    "#Maria wants you to format the budget columns by adding dollar signs and converting the amounts to two decimal places.\n",
    "#In the per_school_summary_df DataFrame, we will format the Total School Budget and Per Student Budget columns to include:\n",
    "#A U.S. dollar sign \n",
    "#Two decimal places\n",
    "#A thousands separator\n",
    "#Format Columns\n",
    "\n",
    "# REWIND\n",
    "#To format every row in the column, use the map() function. Inside the parentheses, apply formatting with {\" \"}.format. \n",
    "#Inside the quotations, pass the formatting specification to the value in the row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bb28c3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School Type</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total School Budget</th>\n",
       "      <th>Per Student Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
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       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4976</td>\n",
       "      <td>$3,124,928.00</td>\n",
       "      <td>$628.00</td>\n",
       "      <td>77.048432</td>\n",
       "      <td>81.033963</td>\n",
       "      <td>66.680064</td>\n",
       "      <td>81.933280</td>\n",
       "      <td>54.642283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1858</td>\n",
       "      <td>$1,081,356.00</td>\n",
       "      <td>$582.00</td>\n",
       "      <td>83.061895</td>\n",
       "      <td>83.975780</td>\n",
       "      <td>94.133477</td>\n",
       "      <td>97.039828</td>\n",
       "      <td>91.334769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2949</td>\n",
       "      <td>$1,884,411.00</td>\n",
       "      <td>$639.00</td>\n",
       "      <td>76.711767</td>\n",
       "      <td>81.158020</td>\n",
       "      <td>65.988471</td>\n",
       "      <td>80.739234</td>\n",
       "      <td>53.204476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2739</td>\n",
       "      <td>$1,763,916.00</td>\n",
       "      <td>$644.00</td>\n",
       "      <td>77.102592</td>\n",
       "      <td>80.746258</td>\n",
       "      <td>68.309602</td>\n",
       "      <td>79.299014</td>\n",
       "      <td>54.289887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1468</td>\n",
       "      <td>$917,500.00</td>\n",
       "      <td>$625.00</td>\n",
       "      <td>83.351499</td>\n",
       "      <td>83.816757</td>\n",
       "      <td>93.392371</td>\n",
       "      <td>97.138965</td>\n",
       "      <td>90.599455</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     School Type  Total Students Total School Budget  \\\n",
       "Bailey High School      District            4976       $3,124,928.00   \n",
       "Cabrera High School      Charter            1858       $1,081,356.00   \n",
       "Figueroa High School    District            2949       $1,884,411.00   \n",
       "Ford High School        District            2739       $1,763,916.00   \n",
       "Griffin High School      Charter            1468         $917,500.00   \n",
       "\n",
       "                     Per Student Budget  Average Math Score  \\\n",
       "Bailey High School              $628.00           77.048432   \n",
       "Cabrera High School             $582.00           83.061895   \n",
       "Figueroa High School            $639.00           76.711767   \n",
       "Ford High School                $644.00           77.102592   \n",
       "Griffin High School             $625.00           83.351499   \n",
       "\n",
       "                      Average Reading Score  % Passing Math  \\\n",
       "Bailey High School                81.033963       66.680064   \n",
       "Cabrera High School               83.975780       94.133477   \n",
       "Figueroa High School              81.158020       65.988471   \n",
       "Ford High School                  80.746258       68.309602   \n",
       "Griffin High School               83.816757       93.392371   \n",
       "\n",
       "                      % Passing Reading  % Overall Passing  \n",
       "Bailey High School            81.933280          54.642283  \n",
       "Cabrera High School           97.039828          91.334769  \n",
       "Figueroa High School          80.739234          53.204476  \n",
       "Ford High School              79.299014          54.289887  \n",
       "Griffin High School           97.138965          90.599455  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Format the Total School Budget and the Per Student Budget columns.\n",
    "per_school_summary_df[\"Total School Budget\"] = per_school_summary_df[\"Total School Budget\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "per_school_summary_df[\"Per Student Budget\"] = per_school_summary_df[\"Per Student Budget\"].map(\"${:,.2f}\".format)\n",
    "\n",
    "\n",
    "# Display the data frame\n",
    "per_school_summary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "115da19d",
   "metadata": {},
   "outputs": [],
   "source": [
    "##TO REORDER in case it does print\n",
    "#To reorder columns, use the following format:\n",
    "#new_column_order = [\"column2\", \"column4\", \"column1\"]\n",
    "#Assign the same DataFrame to the new column order:\n",
    "#df = df[new_column_order]\n",
    "#The code for reordering the columns will look like this:\n",
    "# Reorder the columns in the order you want them to appear.\n",
    "#new_column_order = [\"School Type\", \"Total Students\", \"Total School Budget\", \"Per Student Budget\", \"Average Math Score\", \"Average Reading Score\", \"% Passing Math\", \"% Passing Reading\", \"% Overall Passing\"]\n",
    "\n",
    "# Assign district summary df the new column order.\n",
    "#per_school_summary_df = per_school_summary_df[new_column_order]\n",
    "\n",
    "#per_school_summary_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ae0d7854",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.9.1 Find the Highest Performing Schools\n",
    "#To display the top five highest-performing schools, we can sort the per_school_summary_df DataFrame on the % Overall Passing column.\n",
    "# REWIND\n",
    "#To view the first five rows of a DataFrame, use the df.head() method.\n",
    "#To view the last five rows of a DataFrame, use the df.tail() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "027995f5",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>Cabrera High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1858</td>\n",
       "      <td>$1,081,356.00</td>\n",
       "      <td>$582.00</td>\n",
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       "      <td>91.334769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>$1,043,130.00</td>\n",
       "      <td>$638.00</td>\n",
       "      <td>83.418349</td>\n",
       "      <td>83.848930</td>\n",
       "      <td>93.272171</td>\n",
       "      <td>97.308869</td>\n",
       "      <td>90.948012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1468</td>\n",
       "      <td>$917,500.00</td>\n",
       "      <td>$625.00</td>\n",
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       "      <td>97.138965</td>\n",
       "      <td>90.599455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>2283</td>\n",
       "      <td>$1,319,574.00</td>\n",
       "      <td>$578.00</td>\n",
       "      <td>83.274201</td>\n",
       "      <td>83.989488</td>\n",
       "      <td>93.867718</td>\n",
       "      <td>96.539641</td>\n",
       "      <td>90.582567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pena High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>962</td>\n",
       "      <td>$585,858.00</td>\n",
       "      <td>$609.00</td>\n",
       "      <td>83.839917</td>\n",
       "      <td>84.044699</td>\n",
       "      <td>94.594595</td>\n",
       "      <td>95.945946</td>\n",
       "      <td>90.540541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    School Type  Total Students Total School Budget  \\\n",
       "Cabrera High School     Charter            1858       $1,081,356.00   \n",
       "Thomas High School      Charter            1635       $1,043,130.00   \n",
       "Griffin High School     Charter            1468         $917,500.00   \n",
       "Wilson High School      Charter            2283       $1,319,574.00   \n",
       "Pena High School        Charter             962         $585,858.00   \n",
       "\n",
       "                    Per Student Budget  Average Math Score  \\\n",
       "Cabrera High School            $582.00           83.061895   \n",
       "Thomas High School             $638.00           83.418349   \n",
       "Griffin High School            $625.00           83.351499   \n",
       "Wilson High School             $578.00           83.274201   \n",
       "Pena High School               $609.00           83.839917   \n",
       "\n",
       "                     Average Reading Score  % Passing Math  % Passing Reading  \\\n",
       "Cabrera High School              83.975780       94.133477          97.039828   \n",
       "Thomas High School               83.848930       93.272171          97.308869   \n",
       "Griffin High School              83.816757       93.392371          97.138965   \n",
       "Wilson High School               83.989488       93.867718          96.539641   \n",
       "Pena High School                 84.044699       94.594595          95.945946   \n",
       "\n",
       "                     % Overall Passing  \n",
       "Cabrera High School          91.334769  \n",
       "Thomas High School           90.948012  \n",
       "Griffin High School          90.599455  \n",
       "Wilson High School           90.582567  \n",
       "Pena High School             90.540541  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using the Pandas sort_values() function, we can sort a DataFrame or Series for a given text, index, or column that is passed within the parentheses. In addition, we can add the parameter ascending=False to sort from highest to lowest, or ascending=True to sort from lowest to highest the value referenced in the sort_values() function. If we don't add the ascending parameter, the default is ascending=True.\n",
    "#To sort the schools on the % Overall Passing column from highest to lowest, add the following code and run the cell.\n",
    "\n",
    "# Sort and show top five schools.\n",
    "top_schools = per_school_summary_df.sort_values([\"% Overall Passing\"], ascending=False)\n",
    "\n",
    "top_schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c10f15e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#When we execute the code, we are going to sort the per_school_summary_df DataFrame on the % Overall Passing column from the highest % Overall Passing to the lowest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "6fa0715f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.9.2 Find the Lowest Performing Schools\n",
    "# To find the five lowest-performing schools based on the overall percentage of students who passed in the per_school_summary_df DataFrame, \n",
    "#we can use the ascending=True parameter. Since ascending=True is the default parameter for the sort_values() function, we don't need to add it. \n",
    "#However, it's beneficial to add this parameter so we know how we are sorting.\n",
    "#Which code would you use to sort the results in order to determine the bottom five performing schools in ascending order? (Select all that apply.)\n",
    "\n",
    "#bottom_schools = per_school_summary_df.sort_values([\"% Overall Passing\"])\n",
    "#bottom_schools.head()\n",
    "\n",
    "#bottom_schools = per_school_summary_df.sort_values([\"% Overall Passing\"],\n",
    "#ascending=True)\n",
    "#bottom_schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "0c183b78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School Type</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total School Budget</th>\n",
       "      <th>Per Student Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rodriguez High School</th>\n",
       "      <td>District</td>\n",
       "      <td>3999</td>\n",
       "      <td>$2,547,363.00</td>\n",
       "      <td>$637.00</td>\n",
       "      <td>76.842711</td>\n",
       "      <td>80.744686</td>\n",
       "      <td>66.366592</td>\n",
       "      <td>80.220055</td>\n",
       "      <td>52.988247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2949</td>\n",
       "      <td>$1,884,411.00</td>\n",
       "      <td>$639.00</td>\n",
       "      <td>76.711767</td>\n",
       "      <td>81.158020</td>\n",
       "      <td>65.988471</td>\n",
       "      <td>80.739234</td>\n",
       "      <td>53.204476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huang High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>$1,910,635.00</td>\n",
       "      <td>$655.00</td>\n",
       "      <td>76.629414</td>\n",
       "      <td>81.182722</td>\n",
       "      <td>65.683922</td>\n",
       "      <td>81.316421</td>\n",
       "      <td>53.513884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernandez High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4635</td>\n",
       "      <td>$3,022,020.00</td>\n",
       "      <td>$652.00</td>\n",
       "      <td>77.289752</td>\n",
       "      <td>80.934412</td>\n",
       "      <td>66.752967</td>\n",
       "      <td>80.862999</td>\n",
       "      <td>53.527508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4761</td>\n",
       "      <td>$3,094,650.00</td>\n",
       "      <td>$650.00</td>\n",
       "      <td>77.072464</td>\n",
       "      <td>80.966394</td>\n",
       "      <td>66.057551</td>\n",
       "      <td>81.222432</td>\n",
       "      <td>53.539172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      School Type  Total Students Total School Budget  \\\n",
       "Rodriguez High School    District            3999       $2,547,363.00   \n",
       "Figueroa High School     District            2949       $1,884,411.00   \n",
       "Huang High School        District            2917       $1,910,635.00   \n",
       "Hernandez High School    District            4635       $3,022,020.00   \n",
       "Johnson High School      District            4761       $3,094,650.00   \n",
       "\n",
       "                      Per Student Budget  Average Math Score  \\\n",
       "Rodriguez High School            $637.00           76.842711   \n",
       "Figueroa High School             $639.00           76.711767   \n",
       "Huang High School                $655.00           76.629414   \n",
       "Hernandez High School            $652.00           77.289752   \n",
       "Johnson High School              $650.00           77.072464   \n",
       "\n",
       "                       Average Reading Score  % Passing Math  \\\n",
       "Rodriguez High School              80.744686       66.366592   \n",
       "Figueroa High School               81.158020       65.988471   \n",
       "Huang High School                  81.182722       65.683922   \n",
       "Hernandez High School              80.934412       66.752967   \n",
       "Johnson High School                80.966394       66.057551   \n",
       "\n",
       "                       % Passing Reading  % Overall Passing  \n",
       "Rodriguez High School          80.220055          52.988247  \n",
       "Figueroa High School           80.739234          53.204476  \n",
       "Huang High School              81.316421          53.513884  \n",
       "Hernandez High School          80.862999          53.527508  \n",
       "Johnson High School            81.222432          53.539172  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort and show top five schools.\n",
    "bottom_schools = per_school_summary_df.sort_values([\"% Overall Passing\"], ascending=True)\n",
    "\n",
    "bottom_schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "2559be8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.10.1 Create Grade-Level DataFrames\n",
    "\n",
    "#In this DataFrame, each grade—9th, 10th, 11th and 12th—is a Series of data. To get each grade as a Series, we need to filter school_data_complete_df for each grade.\n",
    "# REWIND\n",
    "#To get the passing scores for the district and school summary, we used the following code:\n",
    "#passing_math = school_data_complete_df[\"math_score\"] >= 70\n",
    "#When we executed this code, a Series was returned where rows with a score equal to or greater than 70 were \"True,\" and rows with scores not equal to or greater than 70 were \"False.\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d9bb2d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's first get the ninth graders in a Series. Set the variable ninth_graders equal to school_data_complete_df[\"grade\"] == \"9th\". This will retrieve all rows where this statement is \"True.\"\n",
    "#Using the code school_data_complete_df[\"grade\"] == \"9th\", we can filter school_data_complete_df for the \"True\" cases as follows:\n",
    "#school_data_complete_df[(school_data_complete_df[\"grade\"] == \"9th\")]\n",
    "#Now repurpose this code for each grade by replacing \"9th\" with each grade. Assign a grade-level variable to each grade-level DataFrame; for example, ninth_graders is for the ninth grade data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "55ca578f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Paul Bradley</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>66</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Bonnie Ray</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>97</td>\n",
       "      <td>84</td>\n",
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       "      <td>2917</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Bryan Miranda</td>\n",
       "      <td>M</td>\n",
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       "      <td>Huang High School</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
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       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Brittney Walker</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>64</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>William Long</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39152</th>\n",
       "      <td>39152</td>\n",
       "      <td>Lori Moore</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>98</td>\n",
       "      <td>84</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39153</th>\n",
       "      <td>39153</td>\n",
       "      <td>William Hubbard</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39157</th>\n",
       "      <td>39157</td>\n",
       "      <td>Kristen Gonzalez</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>79</td>\n",
       "      <td>94</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39164</th>\n",
       "      <td>39164</td>\n",
       "      <td>Joseph Anthony</td>\n",
       "      <td>M</td>\n",
       "      <td>9th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>97</td>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39167</th>\n",
       "      <td>39167</td>\n",
       "      <td>Rebecca Tanner</td>\n",
       "      <td>F</td>\n",
       "      <td>9th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>73</td>\n",
       "      <td>84</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11408 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student ID      student_name gender grade         school_name  \\\n",
       "0               0      Paul Bradley      M   9th   Huang High School   \n",
       "4               4        Bonnie Ray      F   9th   Huang High School   \n",
       "5               5     Bryan Miranda      M   9th   Huang High School   \n",
       "12             12   Brittney Walker      F   9th   Huang High School   \n",
       "13             13      William Long      M   9th   Huang High School   \n",
       "...           ...               ...    ...   ...                 ...   \n",
       "39152       39152        Lori Moore      F   9th  Thomas High School   \n",
       "39153       39153   William Hubbard      M   9th  Thomas High School   \n",
       "39157       39157  Kristen Gonzalez      F   9th  Thomas High School   \n",
       "39164       39164    Joseph Anthony      M   9th  Thomas High School   \n",
       "39167       39167    Rebecca Tanner      F   9th  Thomas High School   \n",
       "\n",
       "       reading_score  math_score  School ID      type  size   budget  \n",
       "0                 66          79          0  District  2917  1910635  \n",
       "4                 97          84          0  District  2917  1910635  \n",
       "5                 94          94          0  District  2917  1910635  \n",
       "12                64          79          0  District  2917  1910635  \n",
       "13                71          79          0  District  2917  1910635  \n",
       "...              ...         ...        ...       ...   ...      ...  \n",
       "39152             98          84         14   Charter  1635  1043130  \n",
       "39153             80          75         14   Charter  1635  1043130  \n",
       "39157             79          94         14   Charter  1635  1043130  \n",
       "39164             97          76         14   Charter  1635  1043130  \n",
       "39167             73          84         14   Charter  1635  1043130  \n",
       "\n",
       "[11408 rows x 11 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a grade level DataFrames.\n",
    "ninth_graders = school_data_complete_df[(school_data_complete_df[\"grade\"] == \"9th\")]\n",
    "\n",
    "ninth_graders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "bfbbb0d8",
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
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       "      <td>Michael Roth</td>\n",
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       "      <td>95</td>\n",
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       "      <td>10th</td>\n",
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       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>78</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Tammy Hebert</td>\n",
       "      <td>F</td>\n",
       "      <td>10th</td>\n",
       "      <td>Huang High School</td>\n",
       "      <td>85</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>1910635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39155</th>\n",
       "      <td>39155</td>\n",
       "      <td>John Brooks</td>\n",
       "      <td>M</td>\n",
       "      <td>10th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39158</th>\n",
       "      <td>39158</td>\n",
       "      <td>Kari Holloway</td>\n",
       "      <td>F</td>\n",
       "      <td>10th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>87</td>\n",
       "      <td>90</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39161</th>\n",
       "      <td>39161</td>\n",
       "      <td>April Reyes</td>\n",
       "      <td>F</td>\n",
       "      <td>10th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>70</td>\n",
       "      <td>84</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39166</th>\n",
       "      <td>39166</td>\n",
       "      <td>Dawn Bell</td>\n",
       "      <td>F</td>\n",
       "      <td>10th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>95</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39168</th>\n",
       "      <td>39168</td>\n",
       "      <td>Desiree Kidd</td>\n",
       "      <td>F</td>\n",
       "      <td>10th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10168 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student ID      student_name gender grade         school_name  \\\n",
       "8               8      Michael Roth      M  10th   Huang High School   \n",
       "9               9    Matthew Greene      M  10th   Huang High School   \n",
       "10             10  Andrew Alexander      M  10th   Huang High School   \n",
       "11             11     Daniel Cooper      M  10th   Huang High School   \n",
       "14             14      Tammy Hebert      F  10th   Huang High School   \n",
       "...           ...               ...    ...   ...                 ...   \n",
       "39155       39155       John Brooks      M  10th  Thomas High School   \n",
       "39158       39158     Kari Holloway      F  10th  Thomas High School   \n",
       "39161       39161       April Reyes      F  10th  Thomas High School   \n",
       "39166       39166         Dawn Bell      F  10th  Thomas High School   \n",
       "39168       39168      Desiree Kidd      F  10th  Thomas High School   \n",
       "\n",
       "       reading_score  math_score  School ID      type  size   budget  \n",
       "8                 95          87          0  District  2917  1910635  \n",
       "9                 96          84          0  District  2917  1910635  \n",
       "10                90          70          0  District  2917  1910635  \n",
       "11                78          77          0  District  2917  1910635  \n",
       "14                85          67          0  District  2917  1910635  \n",
       "...              ...         ...        ...       ...   ...      ...  \n",
       "39155             92          98         14   Charter  1635  1043130  \n",
       "39158             87          90         14   Charter  1635  1043130  \n",
       "39161             70          84         14   Charter  1635  1043130  \n",
       "39166             95          70         14   Charter  1635  1043130  \n",
       "39168             99          90         14   Charter  1635  1043130  \n",
       "\n",
       "[10168 rows x 11 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenth_graders = school_data_complete_df[(school_data_complete_df[\"grade\"] == \"10th\")]\n",
    "\n",
    "tenth_graders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "d587fd82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Student ID</th>\n",
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       "      <td>39156</td>\n",
       "      <td>Stephanie Contreras</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>79</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>39159</th>\n",
       "      <td>39159</td>\n",
       "      <td>Kimberly Cabrera</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Thomas High School</td>\n",
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       "      <td>Katie Weaver</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
       "      <td>Thomas High School</td>\n",
       "      <td>89</td>\n",
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       "      <td>John Reese</td>\n",
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       "      <td>11th</td>\n",
       "      <td>Thomas High School</td>\n",
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       "      <td>Carolyn Jackson</td>\n",
       "      <td>F</td>\n",
       "      <td>11th</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9695 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student ID         student_name gender grade         school_name  \\\n",
       "6               6        Sheena Carter      F  11th   Huang High School   \n",
       "15             15        Jordan Carson      M  11th   Huang High School   \n",
       "22             22     Kimberly Stewart      F  11th   Huang High School   \n",
       "24             24     Chelsea Griffith      F  11th   Huang High School   \n",
       "28             28          Kelly James      F  11th   Huang High School   \n",
       "...           ...                  ...    ...   ...                 ...   \n",
       "39156       39156  Stephanie Contreras      F  11th  Thomas High School   \n",
       "39159       39159     Kimberly Cabrera      F  11th  Thomas High School   \n",
       "39160       39160         Katie Weaver      F  11th  Thomas High School   \n",
       "39163       39163           John Reese      M  11th  Thomas High School   \n",
       "39169       39169      Carolyn Jackson      F  11th  Thomas High School   \n",
       "\n",
       "       reading_score  math_score  School ID      type  size   budget  \n",
       "6                 82          80          0  District  2917  1910635  \n",
       "15                94          88          0  District  2917  1910635  \n",
       "22                99          84          0  District  2917  1910635  \n",
       "24                85          73          0  District  2917  1910635  \n",
       "28                73          55          0  District  2917  1910635  \n",
       "...              ...         ...        ...       ...   ...      ...  \n",
       "39156             79          95         14   Charter  1635  1043130  \n",
       "39159             85          72         14   Charter  1635  1043130  \n",
       "39160             89          86         14   Charter  1635  1043130  \n",
       "39163             90          75         14   Charter  1635  1043130  \n",
       "39169             95          75         14   Charter  1635  1043130  \n",
       "\n",
       "[9695 rows x 11 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eleventh_graders = school_data_complete_df[(school_data_complete_df[\"grade\"] == \"11th\")]\n",
    "\n",
    "eleventh_graders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "24f6bc1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>39137</th>\n",
       "      <td>39137</td>\n",
       "      <td>Victoria Bailey</td>\n",
       "      <td>F</td>\n",
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       "      <td>Thomas High School</td>\n",
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       "    <tr>\n",
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       "      <td>39144</td>\n",
       "      <td>Cristian Webster</td>\n",
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       "      <td>Derek Weeks</td>\n",
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       "      <td>Donna Howard</td>\n",
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       "      <td>Thomas High School</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>14</td>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>1043130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7899 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student ID      student_name gender grade         school_name  \\\n",
       "1               1      Victor Smith      M  12th   Huang High School   \n",
       "2               2   Kevin Rodriguez      M  12th   Huang High School   \n",
       "3               3     Richard Scott      M  12th   Huang High School   \n",
       "7               7      Nicole Baker      F  12th   Huang High School   \n",
       "29             29      Nicole Brown      F  12th   Huang High School   \n",
       "...           ...               ...    ...   ...                 ...   \n",
       "39137       39137   Victoria Bailey      F  12th  Thomas High School   \n",
       "39144       39144  Cristian Webster      M  12th  Thomas High School   \n",
       "39154       39154   Bradley Johnson      M  12th  Thomas High School   \n",
       "39162       39162       Derek Weeks      M  12th  Thomas High School   \n",
       "39165       39165      Donna Howard      F  12th  Thomas High School   \n",
       "\n",
       "       reading_score  math_score  School ID      type  size   budget  \n",
       "1                 94          61          0  District  2917  1910635  \n",
       "2                 90          60          0  District  2917  1910635  \n",
       "3                 67          58          0  District  2917  1910635  \n",
       "7                 96          69          0  District  2917  1910635  \n",
       "29                90          88          0  District  2917  1910635  \n",
       "...              ...         ...        ...       ...   ...      ...  \n",
       "39137             75          70         14   Charter  1635  1043130  \n",
       "39144             77          87         14   Charter  1635  1043130  \n",
       "39154             91          71         14   Charter  1635  1043130  \n",
       "39162             94          77         14   Charter  1635  1043130  \n",
       "39165             99          90         14   Charter  1635  1043130  \n",
       "\n",
       "[7899 rows x 11 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twelfth_graders = school_data_complete_df[(school_data_complete_df[\"grade\"] == \"12th\")]\n",
    "\n",
    "twelfth_graders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7e4c4ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.10.2 Score Averages Grouped by School Name\n",
    "\n",
    "#To get the average math and reading scores for each grade level and for each school, we need to do the following: \n",
    "#Use the groupby function to group by the school_name column\n",
    "#Calculate the mean math_score and reading_score\n",
    "# REWIND\n",
    "#When we use the groupby() function on a DataFrame, we need to perform a mathematical operation on the groupby() object by selecting a column from the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "58d8b0c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       77.083676\n",
       "Cabrera High School      83.094697\n",
       "Figueroa High School     76.403037\n",
       "Ford High School         77.361345\n",
       "Griffin High School      82.044010\n",
       "Hernandez High School    77.438495\n",
       "Holden High School       83.787402\n",
       "Huang High School        77.027251\n",
       "Johnson High School      77.187857\n",
       "Pena High School         83.625455\n",
       "Rodriguez High School    76.859966\n",
       "Shelton High School      83.420755\n",
       "Thomas High School       83.590022\n",
       "Wilson High School       83.085578\n",
       "Wright High School       83.264706\n",
       "Name: math_score, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the Average Math Scores by School\n",
    "\n",
    "#First, we will get the average math score for each school. For each grade level DataFrame, we will use the groupby() function on the school_name column and apply the mean() on the math_score column.\n",
    "#To get the math_score averages for each grade level, add the following code in a new cell and run the cell.\n",
    "\n",
    "## Group each grade level DataFrame by the school name for the average math score.\n",
    "ninth_grade_math_scores = ninth_graders.groupby([\"school_name\"]).mean()[\"math_score\"]\n",
    "\n",
    "ninth_grade_math_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "57e31001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       76.996772\n",
       "Cabrera High School      83.154506\n",
       "Figueroa High School     76.539974\n",
       "Ford High School         77.672316\n",
       "Griffin High School      84.229064\n",
       "Hernandez High School    77.337408\n",
       "Holden High School       83.429825\n",
       "Huang High School        75.908735\n",
       "Johnson High School      76.691117\n",
       "Pena High School         83.372000\n",
       "Rodriguez High School    76.612500\n",
       "Shelton High School      82.917411\n",
       "Thomas High School       83.087886\n",
       "Wilson High School       83.724422\n",
       "Wright High School       84.010288\n",
       "Name: math_score, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenth_grade_math_scores = tenth_graders.groupby([\"school_name\"]).mean()[\"math_score\"]\n",
    "\n",
    "tenth_grade_math_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ff36fec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       77.515588\n",
       "Cabrera High School      82.765560\n",
       "Figueroa High School     76.884344\n",
       "Ford High School         76.918058\n",
       "Griffin High School      83.842105\n",
       "Hernandez High School    77.136029\n",
       "Holden High School       85.000000\n",
       "Huang High School        76.446602\n",
       "Johnson High School      77.491653\n",
       "Pena High School         84.328125\n",
       "Rodriguez High School    76.395626\n",
       "Shelton High School      83.383495\n",
       "Thomas High School       83.498795\n",
       "Wilson High School       83.195326\n",
       "Wright High School       83.836782\n",
       "Name: math_score, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eleventh_grade_math_scores = eleventh_graders.groupby([\"school_name\"]).mean()[\"math_score\"]\n",
    "\n",
    "eleventh_grade_math_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "2fc5cd32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       76.492218\n",
       "Cabrera High School      83.277487\n",
       "Figueroa High School     77.151369\n",
       "Ford High School         76.179963\n",
       "Griffin High School      83.356164\n",
       "Hernandez High School    77.186567\n",
       "Holden High School       82.855422\n",
       "Huang High School        77.225641\n",
       "Johnson High School      76.863248\n",
       "Pena High School         84.121547\n",
       "Rodriguez High School    77.690748\n",
       "Shelton High School      83.778976\n",
       "Thomas High School       83.497041\n",
       "Wilson High School       83.035794\n",
       "Wright High School       83.644986\n",
       "Name: math_score, dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twelfth_grade_math_scores = twelfth_graders.groupby([\"school_name\"]).mean()[\"math_score\"]\n",
    "\n",
    "twelfth_grade_math_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "8f60e75c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       81.303155\n",
       "Cabrera High School      83.676136\n",
       "Figueroa High School     81.198598\n",
       "Ford High School         80.632653\n",
       "Griffin High School      83.369193\n",
       "Hernandez High School    80.866860\n",
       "Holden High School       83.677165\n",
       "Huang High School        81.290284\n",
       "Johnson High School      81.260714\n",
       "Pena High School         83.807273\n",
       "Rodriguez High School    80.993127\n",
       "Shelton High School      84.122642\n",
       "Thomas High School       83.728850\n",
       "Wilson High School       83.939778\n",
       "Wright High School       83.833333\n",
       "Name: reading_score, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the Average Reading Scores by School\n",
    "\n",
    "#We can repurpose this code to find the average reading scores for each grade level by replacing  math_score with reading_score. Add the following code in a new cell and run the cell.\n",
    "# Group each grade level DataFrame by the school name for the average reading score.\n",
    "ninth_grade_reading_scores = ninth_graders.groupby([\"school_name\"]).mean()[\"reading_score\"]\n",
    "\n",
    "ninth_grade_reading_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "0a6df17e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       80.907183\n",
       "Cabrera High School      84.253219\n",
       "Figueroa High School     81.408912\n",
       "Ford High School         81.262712\n",
       "Griffin High School      83.706897\n",
       "Hernandez High School    80.660147\n",
       "Holden High School       83.324561\n",
       "Huang High School        81.512386\n",
       "Johnson High School      80.773431\n",
       "Pena High School         83.612000\n",
       "Rodriguez High School    80.629808\n",
       "Shelton High School      83.441964\n",
       "Thomas High School       84.254157\n",
       "Wilson High School       84.021452\n",
       "Wright High School       83.812757\n",
       "Name: reading_score, dtype: float64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenth_grade_reading_scores = tenth_graders.groupby([\"school_name\"]).mean()[\"reading_score\"]\n",
    "\n",
    "tenth_grade_reading_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "1a283290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       80.945643\n",
       "Cabrera High School      83.788382\n",
       "Figueroa High School     80.640339\n",
       "Ford High School         80.403642\n",
       "Griffin High School      84.288089\n",
       "Hernandez High School    81.396140\n",
       "Holden High School       83.815534\n",
       "Huang High School        81.417476\n",
       "Johnson High School      80.616027\n",
       "Pena High School         84.335938\n",
       "Rodriguez High School    80.864811\n",
       "Shelton High School      84.373786\n",
       "Thomas High School       83.585542\n",
       "Wilson High School       83.764608\n",
       "Wright High School       84.156322\n",
       "Name: reading_score, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eleventh_grade_reading_scores = eleventh_graders.groupby([\"school_name\"]).mean()[\"reading_score\"]\n",
    "\n",
    "eleventh_grade_reading_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "ec1045a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school_name\n",
       "Bailey High School       80.912451\n",
       "Cabrera High School      84.287958\n",
       "Figueroa High School     81.384863\n",
       "Ford High School         80.662338\n",
       "Griffin High School      84.013699\n",
       "Hernandez High School    80.857143\n",
       "Holden High School       84.698795\n",
       "Huang High School        80.305983\n",
       "Johnson High School      81.227564\n",
       "Pena High School         84.591160\n",
       "Rodriguez High School    80.376426\n",
       "Shelton High School      82.781671\n",
       "Thomas High School       83.831361\n",
       "Wilson High School       84.317673\n",
       "Wright High School       84.073171\n",
       "Name: reading_score, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twelfth_grade_reading_scores = twelfth_graders.groupby([\"school_name\"]).mean()[\"reading_score\"]\n",
    "\n",
    "twelfth_grade_reading_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "d04b6e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>9th</th>\n",
       "      <th>10th</th>\n",
       "      <th>11th</th>\n",
       "      <th>12th</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>77.083676</td>\n",
       "      <td>76.996772</td>\n",
       "      <td>77.515588</td>\n",
       "      <td>76.492218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>83.094697</td>\n",
       "      <td>83.154506</td>\n",
       "      <td>82.765560</td>\n",
       "      <td>83.277487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>76.403037</td>\n",
       "      <td>76.539974</td>\n",
       "      <td>76.884344</td>\n",
       "      <td>77.151369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>77.361345</td>\n",
       "      <td>77.672316</td>\n",
       "      <td>76.918058</td>\n",
       "      <td>76.179963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>82.044010</td>\n",
       "      <td>84.229064</td>\n",
       "      <td>83.842105</td>\n",
       "      <td>83.356164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            9th       10th       11th       12th\n",
       "school_name                                                     \n",
       "Bailey High School    77.083676  76.996772  77.515588  76.492218\n",
       "Cabrera High School   83.094697  83.154506  82.765560  83.277487\n",
       "Figueroa High School  76.403037  76.539974  76.884344  77.151369\n",
       "Ford High School      77.361345  77.672316  76.918058  76.179963\n",
       "Griffin High School   82.044010  84.229064  83.842105  83.356164"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.10.3 Combine each grade level Series into a DataFrame\n",
    "\n",
    "#Now that we have performed all the necessary calculations to get the average math and reading scores by grade level for each school, we can add them to separate DataFrames.\n",
    "#First, let's add the math scores for each grade level to a new DataFrame. To do this, add the following code in a new cell and run the cell.\n",
    "\n",
    "# Combine each grade level Series for average math scores by school into a single DataFrame.\n",
    "math_scores_by_grade = pd.DataFrame({\n",
    "               \"9th\": ninth_grade_math_scores,\n",
    "               \"10th\": tenth_grade_math_scores,\n",
    "               \"11th\": eleventh_grade_math_scores,\n",
    "               \"12th\": twelfth_grade_math_scores})\n",
    "\n",
    "math_scores_by_grade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "2e154802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>9th</th>\n",
       "      <th>10th</th>\n",
       "      <th>11th</th>\n",
       "      <th>12th</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>81.303155</td>\n",
       "      <td>80.907183</td>\n",
       "      <td>80.945643</td>\n",
       "      <td>80.912451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>83.676136</td>\n",
       "      <td>84.253219</td>\n",
       "      <td>83.788382</td>\n",
       "      <td>84.287958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>81.198598</td>\n",
       "      <td>81.408912</td>\n",
       "      <td>80.640339</td>\n",
       "      <td>81.384863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>80.632653</td>\n",
       "      <td>81.262712</td>\n",
       "      <td>80.403642</td>\n",
       "      <td>80.662338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>83.369193</td>\n",
       "      <td>83.706897</td>\n",
       "      <td>84.288089</td>\n",
       "      <td>84.013699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            9th       10th       11th       12th\n",
       "school_name                                                     \n",
       "Bailey High School    81.303155  80.907183  80.945643  80.912451\n",
       "Cabrera High School   83.676136  84.253219  83.788382  84.287958\n",
       "Figueroa High School  81.198598  81.408912  80.640339  81.384863\n",
       "Ford High School      80.632653  81.262712  80.403642  80.662338\n",
       "Griffin High School   83.369193  83.706897  84.288089  84.013699"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine each grade level Series for average reading scores by school into a single DataFrame.\n",
    "reading_scores_by_grade = pd.DataFrame({\n",
    "              \"9th\": ninth_grade_reading_scores,\n",
    "              \"10th\": tenth_grade_reading_scores,\n",
    "              \"11th\": eleventh_grade_reading_scores,\n",
    "              \"12th\": twelfth_grade_reading_scores})\n",
    "\n",
    "reading_scores_by_grade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f4988d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.10.4 Format the Averages and Remove the Index Name\n",
    "#we'll format the grade-level averages to one decimal place, as well as remove the name of the index column, school_name.\n",
    "# REWIND\n",
    "#To format every row in a column, use the map() function. Inside the parentheses, apply the formatting using \"{ }\".format.\n",
    "#To format float64 data types to one decimal place, add {:.1f} inside the quotations, like this: map(\"{:.1f}\".format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "37821e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>9th</th>\n",
       "      <th>10th</th>\n",
       "      <th>11th</th>\n",
       "      <th>12th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>77.1</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>83.1</td>\n",
       "      <td>83.2</td>\n",
       "      <td>82.8</td>\n",
       "      <td>83.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>76.4</td>\n",
       "      <td>76.5</td>\n",
       "      <td>76.9</td>\n",
       "      <td>77.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>77.4</td>\n",
       "      <td>77.7</td>\n",
       "      <td>76.9</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>82.0</td>\n",
       "      <td>84.2</td>\n",
       "      <td>83.8</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       9th  10th  11th  12th\n",
       "Bailey High School    77.1  77.0  77.5  76.5\n",
       "Cabrera High School   83.1  83.2  82.8  83.3\n",
       "Figueroa High School  76.4  76.5  76.9  77.2\n",
       "Ford High School      77.4  77.7  76.9  76.2\n",
       "Griffin High School   82.0  84.2  83.8  83.4"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To format the reading and math averages for each grade level we'll use map(\"{:.1f}\".format. \n",
    "#And we'll remove the index, school_name, by setting the index name to \"None\" with the following syntax: index.name = None.\n",
    "\n",
    " # Format each grade column.\n",
    "\n",
    "math_scores_by_grade[\"9th\"] = math_scores_by_grade[\"9th\"].map(\"{:.1f}\".format)\n",
    "\n",
    "math_scores_by_grade[\"10th\"] = math_scores_by_grade[\"10th\"].map(\"{:.1f}\".format)\n",
    "\n",
    "math_scores_by_grade[\"11th\"] = math_scores_by_grade[\"11th\"].map(\"{:.1f}\".format)\n",
    "\n",
    "math_scores_by_grade[\"12th\"] = math_scores_by_grade[\"12th\"].map(\"{:.1f}\".format)\n",
    "\n",
    "  # Make sure the columns are in the correct order.\n",
    "math_scores_by_grade = math_scores_by_grade[[\"9th\", \"10th\", \"11th\", \"12th\"]]\n",
    "\n",
    "  # Remove the index name.\n",
    "math_scores_by_grade.index.name = None\n",
    "  # Display the DataFrame.\n",
    "math_scores_by_grade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "00f23640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>9th</th>\n",
       "      <th>10th</th>\n",
       "      <th>11th</th>\n",
       "      <th>12th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>81.3</td>\n",
       "      <td>80.9</td>\n",
       "      <td>80.9</td>\n",
       "      <td>80.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>83.7</td>\n",
       "      <td>84.3</td>\n",
       "      <td>83.8</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>81.2</td>\n",
       "      <td>81.4</td>\n",
       "      <td>80.6</td>\n",
       "      <td>81.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>80.6</td>\n",
       "      <td>81.3</td>\n",
       "      <td>80.4</td>\n",
       "      <td>80.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>83.4</td>\n",
       "      <td>83.7</td>\n",
       "      <td>84.3</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       9th  10th  11th  12th\n",
       "Bailey High School    81.3  80.9  80.9  80.9\n",
       "Cabrera High School   83.7  84.3  83.8  84.3\n",
       "Figueroa High School  81.2  81.4  80.6  81.4\n",
       "Ford High School      80.6  81.3  80.4  80.7\n",
       "Griffin High School   83.4  83.7  84.3  84.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Format each grade column.\n",
    "\n",
    "reading_scores_by_grade[\"9th\"] = reading_scores_by_grade[\"9th\"].map(\"{:,.1f}\".format)\n",
    "\n",
    "reading_scores_by_grade[\"10th\"] = reading_scores_by_grade[\"10th\"].map(\"{:,.1f}\".format)\n",
    "\n",
    "reading_scores_by_grade[\"11th\"] = reading_scores_by_grade[\"11th\"].map(\"{:,.1f}\".format)\n",
    "\n",
    "reading_scores_by_grade[\"12th\"] = reading_scores_by_grade[\"12th\"].map(\"{:,.1f}\".format)\n",
    "\n",
    "  # Make sure the columns are in the correct order.\n",
    "reading_scores_by_grade = reading_scores_by_grade[\n",
    "                 [\"9th\", \"10th\", \"11th\", \"12th\"]]\n",
    "\n",
    "  # Remove the index name.\n",
    "reading_scores_by_grade.index.name = None\n",
    "  # Display the data frame.\n",
    "reading_scores_by_grade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ebe90fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.11.1 Establish the Spending Ranges per Student\n",
    "#For this analysis, we will need to sort the school spending per student into four spending \"bins,\" or ranges. The four bins will be dollar amounts that range from the lowest amount ($578) to the highest amount ($655) a school spends on a student.\n",
    "# REWIND\n",
    "# We determined the budget per student in the school summary when we retrieved the per_school_capita Series \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d1660c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To create this DataFrame, we need to do the following:\n",
    "#Group the schools into four bins, or ranges, based on the budget per student.\n",
    "#For each spending range, get the following data:\n",
    "#Average math and reading scores\n",
    "#The percentage of students passing math and reading\n",
    "#The overall passing percentage, which is the average of the percentage of students passing math and reading\n",
    "#Before we aggregate the school spending per student into four bins, we must establish the spending ranges. \n",
    "#We can determine the spending ranges by using the per_school_capita Series that was used to get the budget per student."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a8d92753",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the Spending Ranges\n",
    "\n",
    "#Before we create the bins, we need to determine the distribution of spending per student. \n",
    "#We can find the distribution for school spending per student by using the describe() method.\n",
    "#When we apply the describe() method to a DataFrame or Series, it will return the following descriptive statistics for the DataFrame or Series:\n",
    "#The number of rows in the DataFrame or Series as count; #The average of the rows as mean\n",
    "#The standard deviation of the rows as std; #The minimum value of the rows as min\n",
    "#The 25th percentile as 25%; #The 50th percentile as 50%; #The 75th percentile as 75%; #The minimum value of the rows as max\n",
    "#Using the describe() method will help us determine the spending bins we should use, based on the descriptive statistics. \n",
    "#To get the descriptive statistics on the per_school_capita, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "0185a75c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     15.000000\n",
       "mean     620.066667\n",
       "std       28.544368\n",
       "min      578.000000\n",
       "25%      591.500000\n",
       "50%      628.000000\n",
       "75%      641.500000\n",
       "max      655.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the descriptive statistics for the per_school_capita.\n",
    "per_school_capita.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "6ca96d6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#As you can see, the minimum is 578 and the maximum is 655. The standard deviation is 28.5, or approximately 30. \n",
    "#We don't want the lowest bin to be $578 because there is only one school at or below $578, which is Wilson High School. \n",
    "#However, there are four schools that spend less than $585 per student, so $585 will be our lowest bin. \n",
    "#Also, because the standard deviation is about 30, we will increase the bins by $30, up to $675. \n",
    "#Therefore, the four bins will be: $585, $615, $645, and $675.\n",
    "#The bins will define a range of spending. For the $585 bin, the range is all values at or below $585, the range for the $615 bin is $586–615, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "eff9dab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In Pandas, we can write the ranges for the bins as follows: spending_bins = [0, 585, 615, 645, 675].\n",
    "#Let's go over why these spending bins have five numbers instead of four, which is the number of bins we want. \n",
    "#When Pandas creates the lower edge, it needs a value lower than the lowest value in the column of the per_school_capita Series, which happens to be 578. \n",
    "#In our DataFrame, we make the lower edge equal to 0. If we don't include the 0 for the spending bins, the lowest bin becomes $586–615, which means that the schools that spend less than $586 are not considered. \n",
    "#First we'll use the correct spending bins for our ranges, and then we'll leave out the 0 to see how that affects the bins.\n",
    "#Let's address one more item If you look at the spending_bins list, you'll notice that the dollar sign ($) isn't included with the ranges. \n",
    "#This is because the numbers in the  per_school_capita Series are the float64 data type, so adding a dollar sign would cause an error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "9e013607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       (615, 645]\n",
       "Cabrera High School        (0, 585]\n",
       "Figueroa High School     (615, 645]\n",
       "Ford High School         (615, 645]\n",
       "Griffin High School      (615, 645]\n",
       "Hernandez High School    (645, 675]\n",
       "Holden High School         (0, 585]\n",
       "Huang High School        (645, 675]\n",
       "Johnson High School      (645, 675]\n",
       "Pena High School         (585, 615]\n",
       "Rodriguez High School    (615, 645]\n",
       "Shelton High School      (585, 615]\n",
       "Thomas High School       (615, 645]\n",
       "Wilson High School         (0, 585]\n",
       "Wright High School         (0, 585]\n",
       "dtype: category\n",
       "Categories (4, interval[int64, right]): [(0, 585] < (585, 615] < (615, 645] < (645, 675]]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group the Series on the Spending Ranges\n",
    "\n",
    "#To group the spending ranges, we use the Pandas cut() function. The cut() function segments and sorts data values into bins. We'll use the cut() function to create our spending bins.\n",
    "#To apply the cut() function to a DataFrame or Series, we use pd.cut(df, ranges). This creates a new DataFrame or Series on the given ranges.\n",
    "#We can \"cut\" the per_school_capita into the spending_ranges using the following code. \n",
    "\n",
    "# Cut the per_school_capita into the spending ranges.\n",
    "spending_bins = [0, 585, 615, 645, 675]\n",
    "pd.cut(per_school_capita, spending_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d1ebf845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       (615.0, 645.0]\n",
       "Cabrera High School                 NaN\n",
       "Figueroa High School     (615.0, 645.0]\n",
       "Ford High School         (615.0, 645.0]\n",
       "Griffin High School      (615.0, 645.0]\n",
       "Hernandez High School    (645.0, 675.0]\n",
       "Holden High School                  NaN\n",
       "Huang High School        (645.0, 675.0]\n",
       "Johnson High School      (645.0, 675.0]\n",
       "Pena High School         (585.0, 615.0]\n",
       "Rodriguez High School    (615.0, 645.0]\n",
       "Shelton High School      (585.0, 615.0]\n",
       "Thomas High School       (615.0, 645.0]\n",
       "Wilson High School                  NaN\n",
       "Wright High School                  NaN\n",
       "dtype: category\n",
       "Categories (3, interval[int64, right]): [(585, 615] < (615, 645] < (645, 675]]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The cut() function places each value of the per_school_capita Series into one of four bins. \n",
    "#The bins are created by a list of five numbers given as the spending_bins that define the edges.\n",
    "#What happens if we don't add 0 as the lower edge for the first bin? Copy the following code in a new cell and run the cell.\n",
    "\n",
    "# Cut the per_school_capita into the spending ranges.\n",
    "spending_bins = [585, 615, 645, 675]\n",
    "pd.cut(per_school_capita, spending_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "998c8ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 585]      4\n",
       "(585, 615]    2\n",
       "(615, 645]    6\n",
       "(645, 675]    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we'll determine how many schools are in each range by grouping the spending bins as the index using the groupby() function on the per_school_capita Series. Remember that we need to perform a mathematical operation when we use the groupby() function.\n",
    "#To find out how many schools are in those bins, use the count() method on the groupby() function. Edit your previous code so that it looks like the following: \n",
    "\n",
    "# Cut the per_school_capita into the spending ranges.\n",
    "spending_bins = [0, 585, 615, 645, 675]\n",
    "per_school_capita.groupby(pd.cut(per_school_capita, spending_bins)).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "5c9b2935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 585]      4\n",
       "(585, 630]    4\n",
       "(630, 645]    4\n",
       "(645, 675]    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at this output, it seems that we didn't group the schools fairly. We need to adjust our ranges so that we have three or four schools in each range. Let's increase the [585, 615] range to [585, 630] and change the third range to [630, 645]. The spending_bins will look like this: \n",
    "#[0, 585, 630, 645, 675]\n",
    "#Edit your previous code and run the cell again. The code should look like this: \n",
    "\n",
    "# Cut the per_school_capita into the spending ranges.\n",
    "spending_bins = [0, 585, 630, 645, 675]\n",
    "per_school_capita.groupby(pd.cut(per_school_capita, spending_bins)).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "4b4d5107",
   "metadata": {},
   "outputs": [],
   "source": [
    "#These ranges look better! Now let's name each range. We can label the ranges using a list of string values. Add the following code to a new cell and run the cell.\n",
    "# Establish the spending bins and group names.\n",
    "spending_bins = [0, 585, 630, 645, 675]\n",
    "group_names = [\"<$586\", \"$586-630\", \"$631-645\", \"$646-675\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "46a215f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School Type</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total School Budget</th>\n",
       "      <th>Per Student Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "      <th>Spending Ranges (Per Student)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4976</td>\n",
       "      <td>$3,124,928.00</td>\n",
       "      <td>$628.00</td>\n",
       "      <td>77.048432</td>\n",
       "      <td>81.033963</td>\n",
       "      <td>66.680064</td>\n",
       "      <td>81.933280</td>\n",
       "      <td>54.642283</td>\n",
       "      <td>$586-630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1858</td>\n",
       "      <td>$1,081,356.00</td>\n",
       "      <td>$582.00</td>\n",
       "      <td>83.061895</td>\n",
       "      <td>83.975780</td>\n",
       "      <td>94.133477</td>\n",
       "      <td>97.039828</td>\n",
       "      <td>91.334769</td>\n",
       "      <td>&lt;$586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2949</td>\n",
       "      <td>$1,884,411.00</td>\n",
       "      <td>$639.00</td>\n",
       "      <td>76.711767</td>\n",
       "      <td>81.158020</td>\n",
       "      <td>65.988471</td>\n",
       "      <td>80.739234</td>\n",
       "      <td>53.204476</td>\n",
       "      <td>$631-645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2739</td>\n",
       "      <td>$1,763,916.00</td>\n",
       "      <td>$644.00</td>\n",
       "      <td>77.102592</td>\n",
       "      <td>80.746258</td>\n",
       "      <td>68.309602</td>\n",
       "      <td>79.299014</td>\n",
       "      <td>54.289887</td>\n",
       "      <td>$631-645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1468</td>\n",
       "      <td>$917,500.00</td>\n",
       "      <td>$625.00</td>\n",
       "      <td>83.351499</td>\n",
       "      <td>83.816757</td>\n",
       "      <td>93.392371</td>\n",
       "      <td>97.138965</td>\n",
       "      <td>90.599455</td>\n",
       "      <td>$586-630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernandez High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4635</td>\n",
       "      <td>$3,022,020.00</td>\n",
       "      <td>$652.00</td>\n",
       "      <td>77.289752</td>\n",
       "      <td>80.934412</td>\n",
       "      <td>66.752967</td>\n",
       "      <td>80.862999</td>\n",
       "      <td>53.527508</td>\n",
       "      <td>$646-675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holden High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>427</td>\n",
       "      <td>$248,087.00</td>\n",
       "      <td>$581.00</td>\n",
       "      <td>83.803279</td>\n",
       "      <td>83.814988</td>\n",
       "      <td>92.505855</td>\n",
       "      <td>96.252927</td>\n",
       "      <td>89.227166</td>\n",
       "      <td>&lt;$586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huang High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>$1,910,635.00</td>\n",
       "      <td>$655.00</td>\n",
       "      <td>76.629414</td>\n",
       "      <td>81.182722</td>\n",
       "      <td>65.683922</td>\n",
       "      <td>81.316421</td>\n",
       "      <td>53.513884</td>\n",
       "      <td>$646-675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4761</td>\n",
       "      <td>$3,094,650.00</td>\n",
       "      <td>$650.00</td>\n",
       "      <td>77.072464</td>\n",
       "      <td>80.966394</td>\n",
       "      <td>66.057551</td>\n",
       "      <td>81.222432</td>\n",
       "      <td>53.539172</td>\n",
       "      <td>$646-675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pena High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>962</td>\n",
       "      <td>$585,858.00</td>\n",
       "      <td>$609.00</td>\n",
       "      <td>83.839917</td>\n",
       "      <td>84.044699</td>\n",
       "      <td>94.594595</td>\n",
       "      <td>95.945946</td>\n",
       "      <td>90.540541</td>\n",
       "      <td>$586-630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rodriguez High School</th>\n",
       "      <td>District</td>\n",
       "      <td>3999</td>\n",
       "      <td>$2,547,363.00</td>\n",
       "      <td>$637.00</td>\n",
       "      <td>76.842711</td>\n",
       "      <td>80.744686</td>\n",
       "      <td>66.366592</td>\n",
       "      <td>80.220055</td>\n",
       "      <td>52.988247</td>\n",
       "      <td>$631-645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shelton High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1761</td>\n",
       "      <td>$1,056,600.00</td>\n",
       "      <td>$600.00</td>\n",
       "      <td>83.359455</td>\n",
       "      <td>83.725724</td>\n",
       "      <td>93.867121</td>\n",
       "      <td>95.854628</td>\n",
       "      <td>89.892107</td>\n",
       "      <td>$586-630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>$1,043,130.00</td>\n",
       "      <td>$638.00</td>\n",
       "      <td>83.418349</td>\n",
       "      <td>83.848930</td>\n",
       "      <td>93.272171</td>\n",
       "      <td>97.308869</td>\n",
       "      <td>90.948012</td>\n",
       "      <td>$631-645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>2283</td>\n",
       "      <td>$1,319,574.00</td>\n",
       "      <td>$578.00</td>\n",
       "      <td>83.274201</td>\n",
       "      <td>83.989488</td>\n",
       "      <td>93.867718</td>\n",
       "      <td>96.539641</td>\n",
       "      <td>90.582567</td>\n",
       "      <td>&lt;$586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wright High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1800</td>\n",
       "      <td>$1,049,400.00</td>\n",
       "      <td>$583.00</td>\n",
       "      <td>83.682222</td>\n",
       "      <td>83.955000</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>96.611111</td>\n",
       "      <td>90.333333</td>\n",
       "      <td>&lt;$586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      School Type  Total Students Total School Budget  \\\n",
       "Bailey High School       District            4976       $3,124,928.00   \n",
       "Cabrera High School       Charter            1858       $1,081,356.00   \n",
       "Figueroa High School     District            2949       $1,884,411.00   \n",
       "Ford High School         District            2739       $1,763,916.00   \n",
       "Griffin High School       Charter            1468         $917,500.00   \n",
       "Hernandez High School    District            4635       $3,022,020.00   \n",
       "Holden High School        Charter             427         $248,087.00   \n",
       "Huang High School        District            2917       $1,910,635.00   \n",
       "Johnson High School      District            4761       $3,094,650.00   \n",
       "Pena High School          Charter             962         $585,858.00   \n",
       "Rodriguez High School    District            3999       $2,547,363.00   \n",
       "Shelton High School       Charter            1761       $1,056,600.00   \n",
       "Thomas High School        Charter            1635       $1,043,130.00   \n",
       "Wilson High School        Charter            2283       $1,319,574.00   \n",
       "Wright High School        Charter            1800       $1,049,400.00   \n",
       "\n",
       "                      Per Student Budget  Average Math Score  \\\n",
       "Bailey High School               $628.00           77.048432   \n",
       "Cabrera High School              $582.00           83.061895   \n",
       "Figueroa High School             $639.00           76.711767   \n",
       "Ford High School                 $644.00           77.102592   \n",
       "Griffin High School              $625.00           83.351499   \n",
       "Hernandez High School            $652.00           77.289752   \n",
       "Holden High School               $581.00           83.803279   \n",
       "Huang High School                $655.00           76.629414   \n",
       "Johnson High School              $650.00           77.072464   \n",
       "Pena High School                 $609.00           83.839917   \n",
       "Rodriguez High School            $637.00           76.842711   \n",
       "Shelton High School              $600.00           83.359455   \n",
       "Thomas High School               $638.00           83.418349   \n",
       "Wilson High School               $578.00           83.274201   \n",
       "Wright High School               $583.00           83.682222   \n",
       "\n",
       "                       Average Reading Score  % Passing Math  \\\n",
       "Bailey High School                 81.033963       66.680064   \n",
       "Cabrera High School                83.975780       94.133477   \n",
       "Figueroa High School               81.158020       65.988471   \n",
       "Ford High School                   80.746258       68.309602   \n",
       "Griffin High School                83.816757       93.392371   \n",
       "Hernandez High School              80.934412       66.752967   \n",
       "Holden High School                 83.814988       92.505855   \n",
       "Huang High School                  81.182722       65.683922   \n",
       "Johnson High School                80.966394       66.057551   \n",
       "Pena High School                   84.044699       94.594595   \n",
       "Rodriguez High School              80.744686       66.366592   \n",
       "Shelton High School                83.725724       93.867121   \n",
       "Thomas High School                 83.848930       93.272171   \n",
       "Wilson High School                 83.989488       93.867718   \n",
       "Wright High School                 83.955000       93.333333   \n",
       "\n",
       "                       % Passing Reading  % Overall Passing  \\\n",
       "Bailey High School             81.933280          54.642283   \n",
       "Cabrera High School            97.039828          91.334769   \n",
       "Figueroa High School           80.739234          53.204476   \n",
       "Ford High School               79.299014          54.289887   \n",
       "Griffin High School            97.138965          90.599455   \n",
       "Hernandez High School          80.862999          53.527508   \n",
       "Holden High School             96.252927          89.227166   \n",
       "Huang High School              81.316421          53.513884   \n",
       "Johnson High School            81.222432          53.539172   \n",
       "Pena High School               95.945946          90.540541   \n",
       "Rodriguez High School          80.220055          52.988247   \n",
       "Shelton High School            95.854628          89.892107   \n",
       "Thomas High School             97.308869          90.948012   \n",
       "Wilson High School             96.539641          90.582567   \n",
       "Wright High School             96.611111          90.333333   \n",
       "\n",
       "                      Spending Ranges (Per Student)  \n",
       "Bailey High School                         $586-630  \n",
       "Cabrera High School                           <$586  \n",
       "Figueroa High School                       $631-645  \n",
       "Ford High School                           $631-645  \n",
       "Griffin High School                        $586-630  \n",
       "Hernandez High School                      $646-675  \n",
       "Holden High School                            <$586  \n",
       "Huang High School                          $646-675  \n",
       "Johnson High School                        $646-675  \n",
       "Pena High School                           $586-630  \n",
       "Rodriguez High School                      $631-645  \n",
       "Shelton High School                        $586-630  \n",
       "Thomas High School                         $631-645  \n",
       "Wilson High School                            <$586  \n",
       "Wright High School                            <$586  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.11.2 Categorize the Spending Bins\n",
    "#Using our spending bins and ranges, we can create a new column in the per_school_summary_df DataFrame which will be assigned the spending bins from the per_school_capita Series.\n",
    "#To do this, we will need to do the following:\n",
    "#Use the cut() function on the per_school_capita Series.\n",
    "#Add the bin data to a new column in the per_school_summary_df DataFrame.\n",
    "#Add the following code to a new cell and run the cell.\n",
    "\n",
    "# Categorize spending based on the bins.\n",
    "per_school_summary_df[\"Spending Ranges (Per Student)\"] = pd.cut(per_school_capita, spending_bins, labels=group_names)\n",
    "\n",
    "per_school_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "1823d1f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#There's a lot going on in this code, so let's break it down.\n",
    "#To the left of the equals sign, we add a new column called \"Spending Ranges (Per Student)\" to the per_school_summary_df DataFrame. This column will contain the values specified by using the cut() function.\n",
    "#Inside the cut() function, we add the data (per_school_capita) we are going to use for \"Spending Ranges (Per Student)\". The data must be a one-dimensional array, like a list or the per_school_capitaSeries.\n",
    "#Inside the parentheses, we add the spending_bins and the labels for the bins using labels=group_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "1f165d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.11.3 Group by the Spending Ranges\n",
    "#The final DataFrame will contain the average math and reading scores, the percentage of students who passed math and reading, and the overall percentage of students who passed for each spending bin. To get these averages and percentages, we'll use the groupby() function to group the data on the spending bins in the index column, \"Spending Ranges (Per Student).\" \n",
    "#First, let's create four new Series with the following data:\n",
    "#Average Math Score; Average Reading Score; Average % Passing Math; Average % Passing Reading; Average % Overall Passing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "5295ffe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We'll create each Series using the groupby() function on the \"Spending Ranges (Per Student)\" column. \n",
    "#For each Series, we'll use the mean() method to get the averages of the Average Math Score column, \n",
    "#Average Reading Score column, % Passing Math column, and % Passing Reading column. \n",
    "#For the average \"% Overall Passing,\" we'll add the \"% Passing Math\" and \"% Passing Reading\" columns and then divide by 2. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "1f98ae26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spending Ranges (Per Student)\n",
       "<$586       83.455399\n",
       "$586-630    81.899826\n",
       "$631-645    78.518855\n",
       "$646-675    76.997210\n",
       "Name: Average Math Score, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate averages for the desired columns.\n",
    "spending_math_scores = per_school_summary_df.groupby([\"Spending Ranges (Per Student)\"]).mean()[\"Average Math Score\"]\n",
    "\n",
    "spending_math_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "8d5d269b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spending Ranges (Per Student)\n",
       "<$586       83.933814\n",
       "$586-630    83.155286\n",
       "$631-645    81.624473\n",
       "$646-675    81.027843\n",
       "Name: Average Reading Score, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_reading_scores = per_school_summary_df.groupby([\"Spending Ranges (Per Student)\"]).mean()[\"Average Reading Score\"]\n",
    "\n",
    "spending_reading_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "27a25ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spending Ranges (Per Student)\n",
       "<$586       93.460096\n",
       "$586-630    87.133538\n",
       "$631-645    73.484209\n",
       "$646-675    66.164813\n",
       "Name: % Passing Math, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_passing_math = per_school_summary_df.groupby([\"Spending Ranges (Per Student)\"]).mean()[\"% Passing Math\"]\n",
    "\n",
    "spending_passing_math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "80e76903",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spending Ranges (Per Student)\n",
       "<$586       96.610877\n",
       "$586-630    92.718205\n",
       "$631-645    84.391793\n",
       "$646-675    81.133951\n",
       "Name: % Passing Reading, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_passing_reading = per_school_summary_df.groupby([\"Spending Ranges (Per Student)\"]).mean()[\"% Passing Reading\"]\n",
    "\n",
    "spending_passing_reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "b7116cec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spending Ranges (Per Student)\n",
       "<$586       90.369459\n",
       "$586-630    81.418596\n",
       "$631-645    62.857656\n",
       "$646-675    53.526855\n",
       "Name: % Overall Passing, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_passing_spending = per_school_summary_df.groupby([\"Spending Ranges (Per Student)\"]).mean()[\"% Overall Passing\"]\n",
    "\n",
    "overall_passing_spending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "709d4f7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spending Ranges (Per Student)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;$586</th>\n",
       "      <td>83.455399</td>\n",
       "      <td>83.933814</td>\n",
       "      <td>93.460096</td>\n",
       "      <td>96.610877</td>\n",
       "      <td>90.369459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$586-630</th>\n",
       "      <td>81.899826</td>\n",
       "      <td>83.155286</td>\n",
       "      <td>87.133538</td>\n",
       "      <td>92.718205</td>\n",
       "      <td>81.418596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$631-645</th>\n",
       "      <td>78.518855</td>\n",
       "      <td>81.624473</td>\n",
       "      <td>73.484209</td>\n",
       "      <td>84.391793</td>\n",
       "      <td>62.857656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$646-675</th>\n",
       "      <td>76.997210</td>\n",
       "      <td>81.027843</td>\n",
       "      <td>66.164813</td>\n",
       "      <td>81.133951</td>\n",
       "      <td>53.526855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Average Math Score  Average Reading Score  \\\n",
       "Spending Ranges (Per Student)                                              \n",
       "<$586                                   83.455399              83.933814   \n",
       "$586-630                                81.899826              83.155286   \n",
       "$631-645                                78.518855              81.624473   \n",
       "$646-675                                76.997210              81.027843   \n",
       "\n",
       "                               % Passing Math  % Passing Reading  \\\n",
       "Spending Ranges (Per Student)                                      \n",
       "<$586                               93.460096          96.610877   \n",
       "$586-630                            87.133538          92.718205   \n",
       "$631-645                            73.484209          84.391793   \n",
       "$646-675                            66.164813          81.133951   \n",
       "\n",
       "                               % Overall Passing  \n",
       "Spending Ranges (Per Student)                     \n",
       "<$586                                  90.369459  \n",
       "$586-630                               81.418596  \n",
       "$631-645                               62.857656  \n",
       "$646-675                               53.526855  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.11.4 Create a DataFrame for the Scores by School Spending\n",
    "#Now that we have the average math and reading scores, the average percentage of students who passed math and reading, and the average overall percentage as a Series, let's add this data to a new DataFrame.\n",
    "# REWIND\n",
    "#To add a list or Series to a DataFrame, use the following format:\n",
    "#df = pd.DataFrame({\"column name\": column values})\n",
    "#Create the Spending Summary DataFrame\n",
    "\n",
    "#To create the spending summary DataFrame, add the following code to a new cell and run the cell.\n",
    "# Assemble into DataFrame.\n",
    "spending_summary_df = pd.DataFrame({\n",
    "          \"Average Math Score\" : spending_math_scores,\n",
    "          \"Average Reading Score\": spending_reading_scores,\n",
    "          \"% Passing Math\": spending_passing_math,\n",
    "          \"% Passing Reading\": spending_passing_reading,\n",
    "          \"% Overall Passing\": overall_passing_spending})\n",
    "\n",
    "spending_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "a0edbe6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we need to get our DataFrame to adhere to grade-reporting standards by following certain formatting.  \n",
    "#We need to apply the following changes:\n",
    "#Format the average math and reading scores to one decimal place\n",
    "#Format the percent passing math and reading to the nearest whole number\n",
    "#Format the overall passing percentage to the nearest whole number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "6f5672e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spending Ranges (Per Student)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;$586</th>\n",
       "      <td>83.5</td>\n",
       "      <td>83.9</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$586-630</th>\n",
       "      <td>81.9</td>\n",
       "      <td>83.2</td>\n",
       "      <td>87</td>\n",
       "      <td>93</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$631-645</th>\n",
       "      <td>78.5</td>\n",
       "      <td>81.6</td>\n",
       "      <td>73</td>\n",
       "      <td>84</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$646-675</th>\n",
       "      <td>77.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>66</td>\n",
       "      <td>81</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Average Math Score Average Reading Score  \\\n",
       "Spending Ranges (Per Student)                                            \n",
       "<$586                                       83.5                  83.9   \n",
       "$586-630                                    81.9                  83.2   \n",
       "$631-645                                    78.5                  81.6   \n",
       "$646-675                                    77.0                  81.0   \n",
       "\n",
       "                              % Passing Math % Passing Reading  \\\n",
       "Spending Ranges (Per Student)                                    \n",
       "<$586                                     93                97   \n",
       "$586-630                                  87                93   \n",
       "$631-645                                  73                84   \n",
       "$646-675                                  66                81   \n",
       "\n",
       "                              % Overall Passing  \n",
       "Spending Ranges (Per Student)                    \n",
       "<$586                                        90  \n",
       "$586-630                                     81  \n",
       "$631-645                                     63  \n",
       "$646-675                                     54  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Format the DataFrame\n",
    "#To format the spending_summary_df DataFrame, add the following code to a new cell and run the cell:\n",
    "\n",
    "# Formatting\n",
    "spending_summary_df[\"Average Math Score\"] = spending_summary_df[\"Average Math Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "spending_summary_df[\"Average Reading Score\"] = spending_summary_df[\"Average Reading Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "spending_summary_df[\"% Passing Math\"] = spending_summary_df[\"% Passing Math\"].map(\"{:.0f}\".format)\n",
    "\n",
    "spending_summary_df[\"% Passing Reading\"] = spending_summary_df[\"% Passing Reading\"].map(\"{:.0f}\".format)\n",
    "\n",
    "spending_summary_df[\"% Overall Passing\"] = spending_summary_df[\"% Overall Passing\"].map(\"{:.0f}\".format)\n",
    "\n",
    "spending_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d2fbb506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bailey High School       4976\n",
       "Johnson High School      4761\n",
       "Hernandez High School    4635\n",
       "Rodriguez High School    3999\n",
       "Figueroa High School     2949\n",
       "Huang High School        2917\n",
       "Ford High School         2739\n",
       "Wilson High School       2283\n",
       "Cabrera High School      1858\n",
       "Wright High School       1800\n",
       "Shelton High School      1761\n",
       "Thomas High School       1635\n",
       "Griffin High School      1468\n",
       "Pena High School          962\n",
       "Holden High School        427\n",
       "Name: school_name, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.12.1 Create Bins for School Size\n",
    "#Creating the spending bins for the scores by school spending per student was a bit of a challenge, but you did a great job. \n",
    "#Previously, we grouped the scores by school spending per student. Now we'll group the scores by school size. \n",
    "#For this DataFrame we'll create three bins: small, medium, and large. To determine these bins, we can look at the per_school_counts, or total students, from each school from the per_school_summary_df DataFrame.\n",
    "\n",
    "#Calculate the total student count\n",
    "per_school_counts = school_data_complete_df[\"school_name\"].value_counts()\n",
    "per_school_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "80a596c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish the bins.\n",
    "size_bins = [0, 999, 1999, 5000]\n",
    "group_names = [\"Small (<1000)\", \"Medium (1000-1999)\", \"Large (2000-5000)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "ffaefef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>School Type</th>\n",
       "      <th>Total Students</th>\n",
       "      <th>Total School Budget</th>\n",
       "      <th>Per Student Budget</th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "      <th>Spending Ranges (Per Student)</th>\n",
       "      <th>School Size (Per Student)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bailey High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4976</td>\n",
       "      <td>$3,124,928.00</td>\n",
       "      <td>$628.00</td>\n",
       "      <td>77.048432</td>\n",
       "      <td>81.033963</td>\n",
       "      <td>66.680064</td>\n",
       "      <td>81.933280</td>\n",
       "      <td>54.642283</td>\n",
       "      <td>$586-630</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabrera High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1858</td>\n",
       "      <td>$1,081,356.00</td>\n",
       "      <td>$582.00</td>\n",
       "      <td>83.061895</td>\n",
       "      <td>83.975780</td>\n",
       "      <td>94.133477</td>\n",
       "      <td>97.039828</td>\n",
       "      <td>91.334769</td>\n",
       "      <td>&lt;$586</td>\n",
       "      <td>Medium (1000-1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Figueroa High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2949</td>\n",
       "      <td>$1,884,411.00</td>\n",
       "      <td>$639.00</td>\n",
       "      <td>76.711767</td>\n",
       "      <td>81.158020</td>\n",
       "      <td>65.988471</td>\n",
       "      <td>80.739234</td>\n",
       "      <td>53.204476</td>\n",
       "      <td>$631-645</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2739</td>\n",
       "      <td>$1,763,916.00</td>\n",
       "      <td>$644.00</td>\n",
       "      <td>77.102592</td>\n",
       "      <td>80.746258</td>\n",
       "      <td>68.309602</td>\n",
       "      <td>79.299014</td>\n",
       "      <td>54.289887</td>\n",
       "      <td>$631-645</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Griffin High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1468</td>\n",
       "      <td>$917,500.00</td>\n",
       "      <td>$625.00</td>\n",
       "      <td>83.351499</td>\n",
       "      <td>83.816757</td>\n",
       "      <td>93.392371</td>\n",
       "      <td>97.138965</td>\n",
       "      <td>90.599455</td>\n",
       "      <td>$586-630</td>\n",
       "      <td>Medium (1000-1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hernandez High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4635</td>\n",
       "      <td>$3,022,020.00</td>\n",
       "      <td>$652.00</td>\n",
       "      <td>77.289752</td>\n",
       "      <td>80.934412</td>\n",
       "      <td>66.752967</td>\n",
       "      <td>80.862999</td>\n",
       "      <td>53.527508</td>\n",
       "      <td>$646-675</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holden High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>427</td>\n",
       "      <td>$248,087.00</td>\n",
       "      <td>$581.00</td>\n",
       "      <td>83.803279</td>\n",
       "      <td>83.814988</td>\n",
       "      <td>92.505855</td>\n",
       "      <td>96.252927</td>\n",
       "      <td>89.227166</td>\n",
       "      <td>&lt;$586</td>\n",
       "      <td>Small (&lt;1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Huang High School</th>\n",
       "      <td>District</td>\n",
       "      <td>2917</td>\n",
       "      <td>$1,910,635.00</td>\n",
       "      <td>$655.00</td>\n",
       "      <td>76.629414</td>\n",
       "      <td>81.182722</td>\n",
       "      <td>65.683922</td>\n",
       "      <td>81.316421</td>\n",
       "      <td>53.513884</td>\n",
       "      <td>$646-675</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson High School</th>\n",
       "      <td>District</td>\n",
       "      <td>4761</td>\n",
       "      <td>$3,094,650.00</td>\n",
       "      <td>$650.00</td>\n",
       "      <td>77.072464</td>\n",
       "      <td>80.966394</td>\n",
       "      <td>66.057551</td>\n",
       "      <td>81.222432</td>\n",
       "      <td>53.539172</td>\n",
       "      <td>$646-675</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pena High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>962</td>\n",
       "      <td>$585,858.00</td>\n",
       "      <td>$609.00</td>\n",
       "      <td>83.839917</td>\n",
       "      <td>84.044699</td>\n",
       "      <td>94.594595</td>\n",
       "      <td>95.945946</td>\n",
       "      <td>90.540541</td>\n",
       "      <td>$586-630</td>\n",
       "      <td>Small (&lt;1000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rodriguez High School</th>\n",
       "      <td>District</td>\n",
       "      <td>3999</td>\n",
       "      <td>$2,547,363.00</td>\n",
       "      <td>$637.00</td>\n",
       "      <td>76.842711</td>\n",
       "      <td>80.744686</td>\n",
       "      <td>66.366592</td>\n",
       "      <td>80.220055</td>\n",
       "      <td>52.988247</td>\n",
       "      <td>$631-645</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shelton High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1761</td>\n",
       "      <td>$1,056,600.00</td>\n",
       "      <td>$600.00</td>\n",
       "      <td>83.359455</td>\n",
       "      <td>83.725724</td>\n",
       "      <td>93.867121</td>\n",
       "      <td>95.854628</td>\n",
       "      <td>89.892107</td>\n",
       "      <td>$586-630</td>\n",
       "      <td>Medium (1000-1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1635</td>\n",
       "      <td>$1,043,130.00</td>\n",
       "      <td>$638.00</td>\n",
       "      <td>83.418349</td>\n",
       "      <td>83.848930</td>\n",
       "      <td>93.272171</td>\n",
       "      <td>97.308869</td>\n",
       "      <td>90.948012</td>\n",
       "      <td>$631-645</td>\n",
       "      <td>Medium (1000-1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilson High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>2283</td>\n",
       "      <td>$1,319,574.00</td>\n",
       "      <td>$578.00</td>\n",
       "      <td>83.274201</td>\n",
       "      <td>83.989488</td>\n",
       "      <td>93.867718</td>\n",
       "      <td>96.539641</td>\n",
       "      <td>90.582567</td>\n",
       "      <td>&lt;$586</td>\n",
       "      <td>Large (2000-5000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wright High School</th>\n",
       "      <td>Charter</td>\n",
       "      <td>1800</td>\n",
       "      <td>$1,049,400.00</td>\n",
       "      <td>$583.00</td>\n",
       "      <td>83.682222</td>\n",
       "      <td>83.955000</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>96.611111</td>\n",
       "      <td>90.333333</td>\n",
       "      <td>&lt;$586</td>\n",
       "      <td>Medium (1000-1999)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      School Type  Total Students Total School Budget  \\\n",
       "Bailey High School       District            4976       $3,124,928.00   \n",
       "Cabrera High School       Charter            1858       $1,081,356.00   \n",
       "Figueroa High School     District            2949       $1,884,411.00   \n",
       "Ford High School         District            2739       $1,763,916.00   \n",
       "Griffin High School       Charter            1468         $917,500.00   \n",
       "Hernandez High School    District            4635       $3,022,020.00   \n",
       "Holden High School        Charter             427         $248,087.00   \n",
       "Huang High School        District            2917       $1,910,635.00   \n",
       "Johnson High School      District            4761       $3,094,650.00   \n",
       "Pena High School          Charter             962         $585,858.00   \n",
       "Rodriguez High School    District            3999       $2,547,363.00   \n",
       "Shelton High School       Charter            1761       $1,056,600.00   \n",
       "Thomas High School        Charter            1635       $1,043,130.00   \n",
       "Wilson High School        Charter            2283       $1,319,574.00   \n",
       "Wright High School        Charter            1800       $1,049,400.00   \n",
       "\n",
       "                      Per Student Budget  Average Math Score  \\\n",
       "Bailey High School               $628.00           77.048432   \n",
       "Cabrera High School              $582.00           83.061895   \n",
       "Figueroa High School             $639.00           76.711767   \n",
       "Ford High School                 $644.00           77.102592   \n",
       "Griffin High School              $625.00           83.351499   \n",
       "Hernandez High School            $652.00           77.289752   \n",
       "Holden High School               $581.00           83.803279   \n",
       "Huang High School                $655.00           76.629414   \n",
       "Johnson High School              $650.00           77.072464   \n",
       "Pena High School                 $609.00           83.839917   \n",
       "Rodriguez High School            $637.00           76.842711   \n",
       "Shelton High School              $600.00           83.359455   \n",
       "Thomas High School               $638.00           83.418349   \n",
       "Wilson High School               $578.00           83.274201   \n",
       "Wright High School               $583.00           83.682222   \n",
       "\n",
       "                       Average Reading Score  % Passing Math  \\\n",
       "Bailey High School                 81.033963       66.680064   \n",
       "Cabrera High School                83.975780       94.133477   \n",
       "Figueroa High School               81.158020       65.988471   \n",
       "Ford High School                   80.746258       68.309602   \n",
       "Griffin High School                83.816757       93.392371   \n",
       "Hernandez High School              80.934412       66.752967   \n",
       "Holden High School                 83.814988       92.505855   \n",
       "Huang High School                  81.182722       65.683922   \n",
       "Johnson High School                80.966394       66.057551   \n",
       "Pena High School                   84.044699       94.594595   \n",
       "Rodriguez High School              80.744686       66.366592   \n",
       "Shelton High School                83.725724       93.867121   \n",
       "Thomas High School                 83.848930       93.272171   \n",
       "Wilson High School                 83.989488       93.867718   \n",
       "Wright High School                 83.955000       93.333333   \n",
       "\n",
       "                       % Passing Reading  % Overall Passing  \\\n",
       "Bailey High School             81.933280          54.642283   \n",
       "Cabrera High School            97.039828          91.334769   \n",
       "Figueroa High School           80.739234          53.204476   \n",
       "Ford High School               79.299014          54.289887   \n",
       "Griffin High School            97.138965          90.599455   \n",
       "Hernandez High School          80.862999          53.527508   \n",
       "Holden High School             96.252927          89.227166   \n",
       "Huang High School              81.316421          53.513884   \n",
       "Johnson High School            81.222432          53.539172   \n",
       "Pena High School               95.945946          90.540541   \n",
       "Rodriguez High School          80.220055          52.988247   \n",
       "Shelton High School            95.854628          89.892107   \n",
       "Thomas High School             97.308869          90.948012   \n",
       "Wilson High School             96.539641          90.582567   \n",
       "Wright High School             96.611111          90.333333   \n",
       "\n",
       "                      Spending Ranges (Per Student) School Size (Per Student)  \n",
       "Bailey High School                         $586-630         Large (2000-5000)  \n",
       "Cabrera High School                           <$586        Medium (1000-1999)  \n",
       "Figueroa High School                       $631-645         Large (2000-5000)  \n",
       "Ford High School                           $631-645         Large (2000-5000)  \n",
       "Griffin High School                        $586-630        Medium (1000-1999)  \n",
       "Hernandez High School                      $646-675         Large (2000-5000)  \n",
       "Holden High School                            <$586             Small (<1000)  \n",
       "Huang High School                          $646-675         Large (2000-5000)  \n",
       "Johnson High School                        $646-675         Large (2000-5000)  \n",
       "Pena High School                           $586-630             Small (<1000)  \n",
       "Rodriguez High School                      $631-645         Large (2000-5000)  \n",
       "Shelton High School                        $586-630        Medium (1000-1999)  \n",
       "Thomas High School                         $631-645        Medium (1000-1999)  \n",
       "Wilson High School                            <$586         Large (2000-5000)  \n",
       "Wright High School                            <$586        Medium (1000-1999)  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorize spending based on the bins.\n",
    "per_school_summary_df[\"School Size (Per Student)\"] = pd.cut(per_school_counts, size_bins, labels=group_names)\n",
    "\n",
    "per_school_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "e976333c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School Size (Per Student)\n",
       "Small (<1000)         83.821598\n",
       "Medium (1000-1999)    83.374684\n",
       "Large (2000-5000)     77.746417\n",
       "Name: Average Math Score, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate averages for the desired columns.\n",
    "spending_math_scores = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"Average Math Score\"]\n",
    "\n",
    "spending_math_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "6e8e7d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School Size (Per Student)\n",
       "Small (<1000)         83.929843\n",
       "Medium (1000-1999)    83.864438\n",
       "Large (2000-5000)     81.344493\n",
       "Name: Average Reading Score, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_reading_scores = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"Average Reading Score\"]\n",
    "\n",
    "spending_reading_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "7628760c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School Size (Per Student)\n",
       "Small (<1000)         93.550225\n",
       "Medium (1000-1999)    93.599695\n",
       "Large (2000-5000)     69.963361\n",
       "Name: % Passing Math, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_passing_math = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"% Passing Math\"]\n",
    "\n",
    "spending_passing_math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "3dd0e1c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School Size (Per Student)\n",
       "Small (<1000)         96.099437\n",
       "Medium (1000-1999)    96.790680\n",
       "Large (2000-5000)     82.766634\n",
       "Name: % Passing Reading, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spending_passing_reading = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"% Passing Reading\"]\n",
    "\n",
    "spending_passing_reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6d713ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "School Size (Per Student)\n",
       "Small (<1000)         89.883853\n",
       "Medium (1000-1999)    90.621535\n",
       "Large (2000-5000)     58.286003\n",
       "Name: % Overall Passing, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_passing_spending = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"% Overall Passing\"]\n",
    "\n",
    "overall_passing_spending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "7c21c85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School Size (Per Student)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Small (&lt;1000)</th>\n",
       "      <td>83.821598</td>\n",
       "      <td>83.929843</td>\n",
       "      <td>93.550225</td>\n",
       "      <td>96.099437</td>\n",
       "      <td>89.883853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium (1000-1999)</th>\n",
       "      <td>83.374684</td>\n",
       "      <td>83.864438</td>\n",
       "      <td>93.599695</td>\n",
       "      <td>96.790680</td>\n",
       "      <td>90.621535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large (2000-5000)</th>\n",
       "      <td>77.746417</td>\n",
       "      <td>81.344493</td>\n",
       "      <td>69.963361</td>\n",
       "      <td>82.766634</td>\n",
       "      <td>58.286003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Average Math Score  Average Reading Score  \\\n",
       "School Size (Per Student)                                              \n",
       "Small (<1000)                       83.821598              83.929843   \n",
       "Medium (1000-1999)                  83.374684              83.864438   \n",
       "Large (2000-5000)                   77.746417              81.344493   \n",
       "\n",
       "                           % Passing Math  % Passing Reading  \\\n",
       "School Size (Per Student)                                      \n",
       "Small (<1000)                   93.550225          96.099437   \n",
       "Medium (1000-1999)              93.599695          96.790680   \n",
       "Large (2000-5000)               69.963361          82.766634   \n",
       "\n",
       "                           % Overall Passing  \n",
       "School Size (Per Student)                     \n",
       "Small (<1000)                      89.883853  \n",
       "Medium (1000-1999)                 90.621535  \n",
       "Large (2000-5000)                  58.286003  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_school_summary_df = pd.DataFrame({\n",
    "          \"Average Math Score\" : spending_math_scores,\n",
    "          \"Average Reading Score\": spending_reading_scores,\n",
    "          \"% Passing Math\": spending_passing_math,\n",
    "          \"% Passing Reading\": spending_passing_reading,\n",
    "          \"% Overall Passing\": overall_passing_spending})\n",
    "\n",
    "per_school_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "47f2b097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School Size (Per Student)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Small (&lt;1000)</th>\n",
       "      <td>83.8</td>\n",
       "      <td>83.9</td>\n",
       "      <td>94</td>\n",
       "      <td>96</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium (1000-1999)</th>\n",
       "      <td>83.4</td>\n",
       "      <td>83.9</td>\n",
       "      <td>94</td>\n",
       "      <td>97</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large (2000-5000)</th>\n",
       "      <td>77.7</td>\n",
       "      <td>81.3</td>\n",
       "      <td>70</td>\n",
       "      <td>83</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Average Math Score Average Reading Score  \\\n",
       "School Size (Per Student)                                            \n",
       "Small (<1000)                           83.8                  83.9   \n",
       "Medium (1000-1999)                      83.4                  83.9   \n",
       "Large (2000-5000)                       77.7                  81.3   \n",
       "\n",
       "                          % Passing Math % Passing Reading % Overall Passing  \n",
       "School Size (Per Student)                                                     \n",
       "Small (<1000)                         94                96                90  \n",
       "Medium (1000-1999)                    94                97                91  \n",
       "Large (2000-5000)                     70                83                58  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Format the DataFrame\n",
    "#To format the spending_summary_df DataFrame, add the following code to a new cell and run the cell:\n",
    "\n",
    "# Formatting\n",
    "per_school_summary_df[\"Average Math Score\"] = per_school_summary_df[\"Average Math Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "per_school_summary_df[\"Average Reading Score\"] = per_school_summary_df[\"Average Reading Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "per_school_summary_df[\"% Passing Math\"] = per_school_summary_df[\"% Passing Math\"].map(\"{:.0f}\".format)\n",
    "\n",
    "per_school_summary_df[\"% Passing Reading\"] = per_school_summary_df[\"% Passing Reading\"].map(\"{:.0f}\".format)\n",
    "\n",
    "per_school_summary_df[\"% Overall Passing\"] = per_school_summary_df[\"% Overall Passing\"].map(\"{:.0f}\".format)\n",
    "\n",
    "per_school_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "2014cccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.12.2 Categorize the Size Bins\n",
    "\n",
    "#Using our school size bins, we can create a new column in the per_school_summary_df DataFrame, which will be assigned the school size bins from the per_school_summary_df DataFrame.\n",
    "#Use the cut() function on the per_school_summary_df DataFrame.\n",
    "#Add the bin data to a new column in the per_school_summary_df DataFrame.\n",
    "#Add the following code to a new cell and run the cell.\n",
    "\n",
    "#We added a new column to per_school_summary_df DataFrame, called \"School Size\".\n",
    "#We used the cut() function on the per_school_summary_df DataFrame column \"Total Students\" and grouped the student size in the size_bins, and then added the labels=group_names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "7751de60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorize spending based on the bins. SEE 164\n",
    "#per_school_summary_df[\"School Size\"] = pd.cut(per_school_summary_df[\"Total Students\"], size_bins, labels=group_names)\n",
    "\n",
    "#per_school_summary_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "ed97eaed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.12.3 Group by School Size\n",
    "\n",
    "#First, let's create four new Series. We need to get the following data:\n",
    "#Average Math Score; #Average Reading Score; #Average % Passing Math; #Average % Passing Reading; #Average % Overall Passing\n",
    "#Create each Series using the groupby() function on the School Size column. The data for each column will be calculated by using the mean() method to get the averages of the following columns: Average Math Score, Average Reading Score, % Passing Math, and % Passing Reading. For the average \"% Overall Passing,\" we'll add the % Passing Math and  % Passing Reading columns, and then divide by 2. \n",
    "#To get these averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "dd671f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate averages for the desired columns.\n",
    "size_math_scores = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"Average Math Score\"]\n",
    "\n",
    "size_reading_scores = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"Average Reading Score\"]\n",
    "\n",
    "size_passing_math = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"% Passing Math\"]\n",
    "\n",
    "size_passing_reading = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"% Passing Reading\"]\n",
    "\n",
    "size_overall_passing = per_school_summary_df.groupby([\"School Size (Per Student)\"]).mean()[\"% Overall Passing\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "db39211a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.12.4 Create a DataFrame for the Scores by School Size\n",
    "\n",
    "#Now it's time to add the average math and reading scores, the average percentage of students who passed math and reading, \n",
    "#and the average overall percentage to a new DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "d62526f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School Size (Per Student)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Small (&lt;1000)</th>\n",
       "      <td>83.8</td>\n",
       "      <td>83.9</td>\n",
       "      <td>94.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium (1000-1999)</th>\n",
       "      <td>83.4</td>\n",
       "      <td>83.9</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large (2000-5000)</th>\n",
       "      <td>77.7</td>\n",
       "      <td>81.3</td>\n",
       "      <td>70.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Average Math Score  Average Reading Score  \\\n",
       "School Size (Per Student)                                              \n",
       "Small (<1000)                            83.8                   83.9   \n",
       "Medium (1000-1999)                       83.4                   83.9   \n",
       "Large (2000-5000)                        77.7                   81.3   \n",
       "\n",
       "                           % Passing Math  % Passing Reading  \\\n",
       "School Size (Per Student)                                      \n",
       "Small (<1000)                        94.0               96.0   \n",
       "Medium (1000-1999)                   94.0               97.0   \n",
       "Large (2000-5000)                    70.0               83.0   \n",
       "\n",
       "                           % Overall Passing  \n",
       "School Size (Per Student)                     \n",
       "Small (<1000)                           90.0  \n",
       "Medium (1000-1999)                      91.0  \n",
       "Large (2000-5000)                       58.0  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create the Size Summary DataFrame\n",
    "\n",
    "#To create the size summary DataFrame, add the following code to a new cell and run the cell.\n",
    "# Assemble into DataFrame.\n",
    "size_summary_df = pd.DataFrame({\n",
    "          \"Average Math Score\" : size_math_scores,\n",
    "          \"Average Reading Score\": size_reading_scores,\n",
    "          \"% Passing Math\": size_passing_math,\n",
    "          \"% Passing Reading\": size_passing_reading,\n",
    "          \"% Overall Passing\": size_overall_passing})\n",
    "\n",
    "size_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "6b8af172",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The results above already have the one decimal place. This was the code in case the one decimal place was not included\n",
    "#for the passing math, passing reading and overall passing\n",
    "#Now we need to apply the proper formatting to ensure the DataFrame adheres to grade-reporting standards. We need to do the following: \n",
    "#Format the average math and reading scores to one decimal place \n",
    "#Format the percentage of students passing math and reading to the nearest whole number \n",
    "#Format the overall passing percentage to the nearest whole number \n",
    "#To do this, we can repurpose some code from the spending_summary_df DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "54ca534c",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Math Score</th>\n",
       "      <th>Average Reading Score</th>\n",
       "      <th>% Passing Math</th>\n",
       "      <th>% Passing Reading</th>\n",
       "      <th>% Overall Passing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>School Size (Per Student)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Small (&lt;1000)</th>\n",
       "      <td>83.8</td>\n",
       "      <td>83.9</td>\n",
       "      <td>94</td>\n",
       "      <td>96</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medium (1000-1999)</th>\n",
       "      <td>83.4</td>\n",
       "      <td>83.9</td>\n",
       "      <td>94</td>\n",
       "      <td>97</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large (2000-5000)</th>\n",
       "      <td>77.7</td>\n",
       "      <td>81.3</td>\n",
       "      <td>70</td>\n",
       "      <td>83</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Average Math Score Average Reading Score  \\\n",
       "School Size (Per Student)                                            \n",
       "Small (<1000)                           83.8                  83.9   \n",
       "Medium (1000-1999)                      83.4                  83.9   \n",
       "Large (2000-5000)                       77.7                  81.3   \n",
       "\n",
       "                          % Passing Math % Passing Reading % Overall Passing  \n",
       "School Size (Per Student)                                                     \n",
       "Small (<1000)                         94                96                90  \n",
       "Medium (1000-1999)                    94                97                91  \n",
       "Large (2000-5000)                     70                83                58  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Format the DataFrame\n",
    "\n",
    "#To format the size_summary_df DataFrame, add the following code to a new cell and run the cell.\n",
    "# Formatting.\n",
    "size_summary_df[\"Average Math Score\"] = size_summary_df[\"Average Math Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "size_summary_df[\"Average Reading Score\"] = size_summary_df[\"Average Reading Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "size_summary_df[\"% Passing Math\"] = size_summary_df[\"% Passing Math\"].map(\"{:.0f}\".format)\n",
    "\n",
    "size_summary_df[\"% Passing Reading\"] = size_summary_df[\"% Passing Reading\"].map(\"{:.0f}\".format)\n",
    "\n",
    "size_summary_df[\"% Overall Passing\"] = size_summary_df[\"% Overall Passing\"].map(\"{:.0f}\".format)\n",
    "\n",
    "size_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "9f64f8d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.13.1 Group By School Type\n",
    "#For this final DataFrame, we'll group the scores by the type of school: district or charter. For each type of school, we'll calculate the average math and reading scores, \n",
    "#math and reading passing percentages, and overall passing percentage for each bin, as shown in the following image:\n",
    "\n",
    "#Since the School Type column is in the per_school_summary_df DataFrame, we won't need to create this column as we did for the school spending and school size DataFrames. \n",
    "#All we need to do is use the groupby() function to group the data on the School Type.\n",
    "#However, we will need to create four new Series, as we did for the two previous DataFrames by getting the following data:\n",
    "#Average Math Score; #Average Reading Score; #Average % Passing Math; #Average % Passing Reading; #Average % Overall Passing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "c9ce94d4",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'School Type'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [181]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Calculate averages for the desired columns.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m type_math_scores \u001b[38;5;241m=\u001b[39m \u001b[43mper_school_summary_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSchool Type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mmean()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAverage Math Score\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      4\u001b[0m type_reading_scores \u001b[38;5;241m=\u001b[39m per_school_summary_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSchool Type\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39mmean()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAverage Reading Score\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      6\u001b[0m type_passing_math \u001b[38;5;241m=\u001b[39m per_school_summary_df\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSchool Type\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39mmean()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m Passing Math\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/PythonData/lib/python3.9/site-packages/pandas/core/frame.py:7712\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m   7707\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n\u001b[1;32m   7709\u001b[0m \u001b[38;5;66;03m# https://github.com/python/mypy/issues/7642\u001b[39;00m\n\u001b[1;32m   7710\u001b[0m \u001b[38;5;66;03m# error: Argument \"squeeze\" to \"DataFrameGroupBy\" has incompatible type\u001b[39;00m\n\u001b[1;32m   7711\u001b[0m \u001b[38;5;66;03m# \"Union[bool, NoDefault]\"; expected \"bool\"\u001b[39;00m\n\u001b[0;32m-> 7712\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   7713\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7715\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mas_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7718\u001b[0m \u001b[43m    \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7719\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7720\u001b[0m \u001b[43m    \u001b[49m\u001b[43msqueeze\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   7721\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7723\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/PythonData/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:882\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgrouper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_grouper\n\u001b[0;32m--> 882\u001b[0m     grouper, exclusions, obj \u001b[38;5;241m=\u001b[39m \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    888\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmutated\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmutated\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    890\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj \u001b[38;5;241m=\u001b[39m obj\n\u001b[1;32m    894\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/PythonData/lib/python3.9/site-packages/pandas/core/groupby/grouper.py:882\u001b[0m, in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m    880\u001b[0m         in_axis, level, gpr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    881\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 882\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[1;32m    883\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    884\u001b[0m     \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[1;32m    885\u001b[0m     exclusions\u001b[38;5;241m.\u001b[39madd(gpr\u001b[38;5;241m.\u001b[39mkey)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'School Type'"
     ]
    }
   ],
   "source": [
    "# Calculate averages for the desired columns.\n",
    "type_math_scores = per_school_summary_df.groupby([\"School Type\"]).mean()[\"Average Math Score\"]\n",
    "\n",
    "type_reading_scores = per_school_summary_df.groupby([\"School Type\"]).mean()[\"Average Reading Score\"]\n",
    "\n",
    "type_passing_math = per_school_summary_df.groupby([\"School Type\"]).mean()[\"% Passing Math\"]\n",
    "\n",
    "type_passing_reading = per_school_summary_df.groupby([\"School Type\"]).mean()[\"% Passing Reading\"]\n",
    "\n",
    "type_overall_passing = per_school_summary_df.groupby([\"School Type\"]).mean()[\"% Overall Passing\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f60a775",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.13.2 Create a DataFrame for the Scores by School Type\n",
    "#We're going to create a new DataFrame called type_summary_df that includes average math and reading scores, \n",
    "#the average percentage of students who passed math and reading, and the average overall percentage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3feadb25",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80edacb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Format the DataFrame\n",
    "\n",
    "#To format the type_summary_df DataFrame, add the following code to a new cell and run the cell.\n",
    "# Formatting\n",
    "type_summary_df[\"Average Math Score\"] = type_summary_df[\"Average Math Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "type_summary_df[\"Average Reading Score\"] = type_summary_df[\"Average Reading Score\"].map(\"{:.1f}\".format)\n",
    "\n",
    "type_summary_df[\"% Passing Math\"] = type_summary_df[\"% Passing Math\"].map(\"{:.0f}\".format)\n",
    "\n",
    "type_summary_df[\"% Passing Reading\"] = type_summary_df[\"% Passing Reading\"].map(\"{:.0f}\".format)\n",
    "\n",
    "type_summary_df[\"% Overall Passing\"] = type_summary_df[\"% Overall Passing\"].map(\"{:.0f}\".format)\n",
    "\n",
    "type_summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58f41768",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d79ef24",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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