Create_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e26b9549",
   "metadata": {},
   "source": [
    "# How to Install <p style=\"color:#83E74E;font-size:35px\"><b>Pandas package</b></p> into <u><b>Jupyter Notebook</b></u> or any of the other <u>Python Packages</u>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6674e57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (1.3.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from pandas) (1.20.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "221ae694",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6fc67903",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "['T',\n",
       " '_AXIS_LEN',\n",
       " '_AXIS_NAMES',\n",
       " '_AXIS_NUMBERS',\n",
       " '_AXIS_ORDERS',\n",
       " '_AXIS_REVERSED',\n",
       " '_AXIS_TO_AXIS_NUMBER',\n",
       " '_HANDLED_TYPES',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__annotations__',\n",
       " '__array__',\n",
       " '__array_priority__',\n",
       " '__array_ufunc__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__finalize__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattr__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__ifloordiv__',\n",
       " '__imod__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
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       " '__ipow__',\n",
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       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__weakref__',\n",
       " '__xor__',\n",
       " '_accessors',\n",
       " '_accum_func',\n",
       " '_add_numeric_operations',\n",
       " '_agg_by_level',\n",
       " '_agg_examples_doc',\n",
       " '_agg_see_also_doc',\n",
       " '_align_frame',\n",
       " '_align_series',\n",
       " '_arith_method',\n",
       " '_as_manager',\n",
       " '_binop',\n",
       " '_can_hold_na',\n",
       " '_check_inplace_and_allows_duplicate_labels',\n",
       " '_check_inplace_setting',\n",
       " '_check_is_chained_assignment_possible',\n",
       " '_check_label_or_level_ambiguity',\n",
       " '_check_setitem_copy',\n",
       " '_clear_item_cache',\n",
       " '_clip_with_one_bound',\n",
       " '_clip_with_scalar',\n",
       " '_cmp_method',\n",
       " '_consolidate',\n",
       " '_consolidate_inplace',\n",
       " '_construct_axes_dict',\n",
       " '_construct_axes_from_arguments',\n",
       " '_construct_result',\n",
       " '_constructor',\n",
       " '_constructor_expanddim',\n",
       " '_convert',\n",
       " '_convert_dtypes',\n",
       " '_data',\n",
       " '_dir_additions',\n",
       " '_dir_deletions',\n",
       " '_drop_axis',\n",
       " '_drop_labels_or_levels',\n",
       " '_duplicated',\n",
       " '_find_valid_index',\n",
       " '_from_mgr',\n",
       " '_get_axis',\n",
       " '_get_axis_name',\n",
       " '_get_axis_number',\n",
       " '_get_axis_resolvers',\n",
       " '_get_block_manager_axis',\n",
       " '_get_bool_data',\n",
       " '_get_cacher',\n",
       " '_get_cleaned_column_resolvers',\n",
       " '_get_index_resolvers',\n",
       " '_get_label_or_level_values',\n",
       " '_get_numeric_data',\n",
       " '_get_value',\n",
       " '_get_values',\n",
       " '_get_values_tuple',\n",
       " '_get_with',\n",
       " '_gotitem',\n",
       " '_hidden_attrs',\n",
       " '_index',\n",
       " '_indexed_same',\n",
       " '_info_axis',\n",
       " '_info_axis_name',\n",
       " '_info_axis_number',\n",
       " '_init_dict',\n",
       " '_init_mgr',\n",
       " '_inplace_method',\n",
       " '_internal_names',\n",
       " '_internal_names_set',\n",
       " '_is_cached',\n",
       " '_is_copy',\n",
       " '_is_label_or_level_reference',\n",
       " '_is_label_reference',\n",
       " '_is_level_reference',\n",
       " '_is_mixed_type',\n",
       " '_is_view',\n",
       " '_ixs',\n",
       " '_logical_func',\n",
       " '_logical_method',\n",
       " '_map_values',\n",
       " '_maybe_update_cacher',\n",
       " '_memory_usage',\n",
       " '_metadata',\n",
       " '_min_count_stat_function',\n",
       " '_needs_reindex_multi',\n",
       " '_protect_consolidate',\n",
       " '_reduce',\n",
       " '_reindex_axes',\n",
       " '_reindex_indexer',\n",
       " '_reindex_multi',\n",
       " '_reindex_with_indexers',\n",
       " '_replace_single',\n",
       " '_repr_data_resource_',\n",
       " '_repr_latex_',\n",
       " '_reset_cache',\n",
       " '_reset_cacher',\n",
       " '_set_as_cached',\n",
       " '_set_axis',\n",
       " '_set_axis_name',\n",
       " '_set_axis_nocheck',\n",
       " '_set_is_copy',\n",
       " '_set_labels',\n",
       " '_set_name',\n",
       " '_set_value',\n",
       " '_set_values',\n",
       " '_set_with',\n",
       " '_set_with_engine',\n",
       " '_slice',\n",
       " '_stat_axis',\n",
       " '_stat_axis_name',\n",
       " '_stat_axis_number',\n",
       " '_stat_function',\n",
       " '_stat_function_ddof',\n",
       " '_take_with_is_copy',\n",
       " '_typ',\n",
       " '_update_inplace',\n",
       " '_validate_dtype',\n",
       " '_values',\n",
       " '_where',\n",
       " 'abs',\n",
       " 'add',\n",
       " 'add_prefix',\n",
       " 'add_suffix',\n",
       " 'agg',\n",
       " 'aggregate',\n",
       " 'align',\n",
       " 'all',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argsort',\n",
       " 'array',\n",
       " 'asfreq',\n",
       " 'asof',\n",
       " 'astype',\n",
       " 'at',\n",
       " 'at_time',\n",
       " 'attrs',\n",
       " 'autocorr',\n",
       " 'axes',\n",
       " 'backfill',\n",
       " 'between',\n",
       " 'between_time',\n",
       " 'bfill',\n",
       " 'bool',\n",
       " 'cat',\n",
       " 'clip',\n",
       " 'combine',\n",
       " 'combine_first',\n",
       " 'compare',\n",
       " 'convert_dtypes',\n",
       " 'copy',\n",
       " 'corr',\n",
       " 'count',\n",
       " 'cov',\n",
       " 'cummax',\n",
       " 'cummin',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'describe',\n",
       " 'diff',\n",
       " 'div',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'drop',\n",
       " 'drop_duplicates',\n",
       " 'droplevel',\n",
       " 'dropna',\n",
       " 'dt',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'duplicated',\n",
       " 'empty',\n",
       " 'eq',\n",
       " 'equals',\n",
       " 'ewm',\n",
       " 'expanding',\n",
       " 'explode',\n",
       " 'factorize',\n",
       " 'ffill',\n",
       " 'fillna',\n",
       " 'filter',\n",
       " 'first',\n",
       " 'first_valid_index',\n",
       " 'flags',\n",
       " 'floordiv',\n",
       " 'ge',\n",
       " 'get',\n",
       " 'groupby',\n",
       " 'gt',\n",
       " 'hasnans',\n",
       " 'head',\n",
       " 'hist',\n",
       " 'iat',\n",
       " 'idxmax',\n",
       " 'idxmin',\n",
       " 'iloc',\n",
       " 'index',\n",
       " 'infer_objects',\n",
       " 'interpolate',\n",
       " 'is_monotonic',\n",
       " 'is_monotonic_decreasing',\n",
       " 'is_monotonic_increasing',\n",
       " 'is_unique',\n",
       " 'isin',\n",
       " 'isna',\n",
       " 'isnull',\n",
       " 'item',\n",
       " 'items',\n",
       " 'iteritems',\n",
       " 'keys',\n",
       " 'kurt',\n",
       " 'kurtosis',\n",
       " 'last',\n",
       " 'last_valid_index',\n",
       " 'le',\n",
       " 'loc',\n",
       " 'lt',\n",
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       " 'map',\n",
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       " 'mean',\n",
       " 'median',\n",
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       " 'min',\n",
       " 'mod',\n",
       " 'mode',\n",
       " 'mul',\n",
       " 'multiply',\n",
       " 'name',\n",
       " 'nbytes',\n",
       " 'ndim',\n",
       " 'ne',\n",
       " 'nlargest',\n",
       " 'notna',\n",
       " 'notnull',\n",
       " 'nsmallest',\n",
       " 'nunique',\n",
       " 'pad',\n",
       " 'pct_change',\n",
       " 'pipe',\n",
       " 'plot',\n",
       " 'pop',\n",
       " 'pow',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'quantile',\n",
       " 'radd',\n",
       " 'rank',\n",
       " 'ravel',\n",
       " 'rdiv',\n",
       " 'rdivmod',\n",
       " 'reindex',\n",
       " 'reindex_like',\n",
       " 'rename',\n",
       " 'rename_axis',\n",
       " 'reorder_levels',\n",
       " 'repeat',\n",
       " 'replace',\n",
       " 'resample',\n",
       " 'reset_index',\n",
       " 'rfloordiv',\n",
       " 'rmod',\n",
       " 'rmul',\n",
       " 'rolling',\n",
       " 'round',\n",
       " 'rpow',\n",
       " 'rsub',\n",
       " 'rtruediv',\n",
       " 'sample',\n",
       " 'searchsorted',\n",
       " 'sem',\n",
       " 'set_axis',\n",
       " 'set_flags',\n",
       " 'shape',\n",
       " 'shift',\n",
       " 'size',\n",
       " 'skew',\n",
       " 'slice_shift',\n",
       " 'sort_index',\n",
       " 'sort_values',\n",
       " 'sparse',\n",
       " 'squeeze',\n",
       " 'std',\n",
       " 'str',\n",
       " 'sub',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'swaplevel',\n",
       " 'tail',\n",
       " 'take',\n",
       " 'to_clipboard',\n",
       " 'to_csv',\n",
       " 'to_dict',\n",
       " 'to_excel',\n",
       " 'to_frame',\n",
       " 'to_hdf',\n",
       " 'to_json',\n",
       " 'to_latex',\n",
       " 'to_list',\n",
       " 'to_markdown',\n",
       " 'to_numpy',\n",
       " 'to_period',\n",
       " 'to_pickle',\n",
       " 'to_sql',\n",
       " 'to_string',\n",
       " 'to_timestamp',\n",
       " 'to_xarray',\n",
       " 'tolist',\n",
       " 'transform',\n",
       " 'transpose',\n",
       " 'truediv',\n",
       " 'truncate',\n",
       " 'tshift',\n",
       " 'tz_convert',\n",
       " 'tz_localize',\n",
       " 'unique',\n",
       " 'unstack',\n",
       " 'update',\n",
       " 'value_counts',\n",
       " 'values',\n",
       " 'var',\n",
       " 'view',\n",
       " 'where',\n",
       " 'xs']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(pd.Series)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c80a8857",
   "metadata": {},
   "source": [
    "# Create a Series Object from a Python List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81724567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Chocolate\n",
       "1       Vanilla\n",
       "2     Strawbery\n",
       "3    Rum Raisen\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice_cream = [\"Chocolate\", \"Vanilla\", \"Strawbery\", \"Rum Raisen\"]\n",
    "s1 = pd.Series(ice_cream)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af2825ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     8\n",
       "2    15\n",
       "3    16\n",
       "4    23\n",
       "5    42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = [4,8,15,16,23,42]\n",
    "pd.Series(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7bf36104",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Monday           Apple\n",
       "Tuesday         Orange\n",
       "Wednesday         Plum\n",
       "Thursday         Grape\n",
       "Friday       Blueberry\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruits = [\"Apple\",\"Orange\",\"Plum\",\"Grape\",\"Blueberry\"]\n",
    "Weekdays = ['Monday','Tuesday','Wednesday','Thursday','Friday']\n",
    "pd.Series(data=fruits, index=Weekdays)\n",
    "\n",
    "#pd.Series(fruits, Weekdays)\n",
    "#pd.Series(data=fruits, index=Weekdays)\n",
    "#pd.Series(fruits, index=Weekdays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "354bb469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pd.Series(fruits,Weekdays)'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"pd.Series(fruits,Weekdays)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a9727cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     8\n",
       "2    15\n",
       "3    16\n",
       "4    23\n",
       "5    42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=[4,8,15,16,23,42]\n",
    "pd.Series(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1561ade",
   "metadata": {},
   "source": [
    "# Create a Series Object from Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b1139f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "german_dict=pd.Series({'Cat':'Katze',\n",
    "           'Snake':'Schlange',\n",
    "            'Horse':'Pferd',\n",
    "            'Dog': 'Hund',\n",
    "            'Queue':'Schlange'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31c1f450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat         Katze\n",
       "Snake    Schlange\n",
       "Horse       Pferd\n",
       "Dog          Hund\n",
       "Queue    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c06f2aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Katze', 'Schlange', 'Pferd', 'Hund', 'Schlange'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f1d1b1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cat', 'Snake', 'Horse', 'Dog', 'Queue'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef43cc2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Katze'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict['Cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d692212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cat in german is called Katze\n"
     ]
    }
   ],
   "source": [
    "print('Cat in german is called',german_dict['Cat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d29370",
   "metadata": {},
   "source": [
    "# Import Series with the read_csv Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45119a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Universities.csv')\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2901a223",
   "metadata": {},
   "outputs": [
    {
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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>Univ</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Top10</th>\n",
       "      <th>Accept</th>\n",
       "      <th>SFRatio</th>\n",
       "      <th>Expenses</th>\n",
       "      <th>GradRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown</td>\n",
       "      <td>1310</td>\n",
       "      <td>89</td>\n",
       "      <td>22</td>\n",
       "      <td>13</td>\n",
       "      <td>22704</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CalTech</td>\n",
       "      <td>1415</td>\n",
       "      <td>100</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>63575</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMU</td>\n",
       "      <td>1260</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>25026</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Columbia</td>\n",
       "      <td>1310</td>\n",
       "      <td>76</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>31510</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cornell</td>\n",
       "      <td>1280</td>\n",
       "      <td>83</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>21864</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dartmouth</td>\n",
       "      <td>1340</td>\n",
       "      <td>89</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>32162</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duke</td>\n",
       "      <td>1315</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>31585</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Georgetown</td>\n",
       "      <td>1255</td>\n",
       "      <td>74</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>20126</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harvard</td>\n",
       "      <td>1400</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>39525</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JohnsHopkins</td>\n",
       "      <td>1305</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>58691</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MIT</td>\n",
       "      <td>1380</td>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>34870</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwestern</td>\n",
       "      <td>1260</td>\n",
       "      <td>85</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>28052</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NotreDame</td>\n",
       "      <td>1255</td>\n",
       "      <td>81</td>\n",
       "      <td>42</td>\n",
       "      <td>13</td>\n",
       "      <td>15122</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PennState</td>\n",
       "      <td>1081</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>10185</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Princeton</td>\n",
       "      <td>1375</td>\n",
       "      <td>91</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>30220</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Purdue</td>\n",
       "      <td>1005</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>9066</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stanford</td>\n",
       "      <td>1360</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>36450</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TexasA&amp;M</td>\n",
       "      <td>1075</td>\n",
       "      <td>49</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8704</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UCBerkeley</td>\n",
       "      <td>1240</td>\n",
       "      <td>95</td>\n",
       "      <td>40</td>\n",
       "      <td>17</td>\n",
       "      <td>15140</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UChicago</td>\n",
       "      <td>1290</td>\n",
       "      <td>75</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>38380</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UMichigan</td>\n",
       "      <td>1180</td>\n",
       "      <td>65</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>15470</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>UPenn</td>\n",
       "      <td>1285</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>27553</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UVA</td>\n",
       "      <td>1225</td>\n",
       "      <td>77</td>\n",
       "      <td>44</td>\n",
       "      <td>14</td>\n",
       "      <td>13349</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UWisconsin</td>\n",
       "      <td>1085</td>\n",
       "      <td>40</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>11857</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yale</td>\n",
       "      <td>1375</td>\n",
       "      <td>95</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>43514</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate\n",
       "0          Brown  1310     89      22       13     22704        94\n",
       "1        CalTech  1415    100      25        6     63575        81\n",
       "2            CMU  1260     62      59        9     25026        72\n",
       "3       Columbia  1310     76      24       12     31510        88\n",
       "4        Cornell  1280     83      33       13     21864        90\n",
       "5      Dartmouth  1340     89      23       10     32162        95\n",
       "6           Duke  1315     90      30       12     31585        95\n",
       "7     Georgetown  1255     74      24       12     20126        92\n",
       "8        Harvard  1400     91      14       11     39525        97\n",
       "9   JohnsHopkins  1305     75      44        7     58691        87\n",
       "10           MIT  1380     94      30       10     34870        91\n",
       "11  Northwestern  1260     85      39       11     28052        89\n",
       "12     NotreDame  1255     81      42       13     15122        94\n",
       "13     PennState  1081     38      54       18     10185        80\n",
       "14     Princeton  1375     91      14        8     30220        95\n",
       "15        Purdue  1005     28      90       19      9066        69\n",
       "16      Stanford  1360     90      20       12     36450        93\n",
       "17      TexasA&M  1075     49      67       25      8704        67\n",
       "18    UCBerkeley  1240     95      40       17     15140        78\n",
       "19      UChicago  1290     75      50       13     38380        87\n",
       "20     UMichigan  1180     65      68       16     15470        85\n",
       "21         UPenn  1285     80      36       11     27553        90\n",
       "22           UVA  1225     77      44       14     13349        92\n",
       "23    UWisconsin  1085     40      69       15     11857        71\n",
       "24          Yale  1375     95      19       11     43514        96"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d25e5844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Brown\n",
       "1          CalTech\n",
       "2              CMU\n",
       "3         Columbia\n",
       "4          Cornell\n",
       "5        Dartmouth\n",
       "6             Duke\n",
       "7       Georgetown\n",
       "8          Harvard\n",
       "9     JohnsHopkins\n",
       "10             MIT\n",
       "11    Northwestern\n",
       "12       NotreDame\n",
       "13       PennState\n",
       "14       Princeton\n",
       "15          Purdue\n",
       "16        Stanford\n",
       "17        TexasA&M\n",
       "18      UCBerkeley\n",
       "19        UChicago\n",
       "20       UMichigan\n",
       "21           UPenn\n",
       "22             UVA\n",
       "23      UWisconsin\n",
       "24            Yale\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ = pd.read_csv('Universities.csv', usecols = ['Univ'], squeeze=True)\n",
    "type(univ)\n",
    "univ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ae949db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(univ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9729a36d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Univ\n",
      "0          Brown\n",
      "1        CalTech\n",
      "2            CMU\n",
      "3       Columbia\n",
      "4        Cornell\n",
      "5      Dartmouth\n",
      "6           Duke\n",
      "7     Georgetown\n",
      "8        Harvard\n",
      "9   JohnsHopkins\n",
      "10           MIT\n",
      "11  Northwestern\n",
      "12     NotreDame\n",
      "13     PennState\n",
      "14     Princeton\n",
      "15        Purdue\n",
      "16      Stanford\n",
      "17      TexasA&M\n",
      "18    UCBerkeley\n",
      "19      UChicago\n",
      "20     UMichigan\n",
      "21         UPenn\n",
      "22           UVA\n",
      "23    UWisconsin\n",
      "24          Yale\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Univdataframexample = pd.read_csv('Universities.csv', usecols = ['Univ'])\n",
    "print(Univdataframexample)\n",
    "type(Univdataframexample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2fcda382",
   "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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       "    }\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>Univ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CalTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Columbia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cornell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dartmouth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Georgetown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Harvard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>JohnsHopkins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MIT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwestern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NotreDame</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PennState</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Princeton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Purdue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stanford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TexasA&amp;M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>UCBerkeley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>UChicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>UMichigan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>UPenn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>UWisconsin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yale</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Univ\n",
       "0          Brown\n",
       "1        CalTech\n",
       "2            CMU\n",
       "3       Columbia\n",
       "4        Cornell\n",
       "5      Dartmouth\n",
       "6           Duke\n",
       "7     Georgetown\n",
       "8        Harvard\n",
       "9   JohnsHopkins\n",
       "10           MIT\n",
       "11  Northwestern\n",
       "12     NotreDame\n",
       "13     PennState\n",
       "14     Princeton\n",
       "15        Purdue\n",
       "16      Stanford\n",
       "17      TexasA&M\n",
       "18    UCBerkeley\n",
       "19      UChicago\n",
       "20     UMichigan\n",
       "21         UPenn\n",
       "22           UVA\n",
       "23    UWisconsin\n",
       "24          Yale"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(Univdataframexample)\n",
    "type(Univdataframexample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c1ec9bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>58691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>36450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>38380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>43514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Expenses\n",
       "0      22704\n",
       "1      63575\n",
       "2      25026\n",
       "3      31510\n",
       "4      21864\n",
       "5      32162\n",
       "6      31585\n",
       "7      20126\n",
       "8      39525\n",
       "9      58691\n",
       "10     34870\n",
       "11     28052\n",
       "12     15122\n",
       "13     10185\n",
       "14     30220\n",
       "15      9066\n",
       "16     36450\n",
       "17      8704\n",
       "18     15140\n",
       "19     38380\n",
       "20     15470\n",
       "21     27553\n",
       "22     13349\n",
       "23     11857\n",
       "24     43514"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anotherexample = pd.read_csv('Universities.csv', usecols = ['Expenses'],squeeze=False)\n",
    "anotherexample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a7e29d",
   "metadata": {},
   "source": [
    "# Intro to<p style=\"color:red;font-size:30px;\">Attributes </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1cc5c4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat         Katze\n",
       "Snake    Schlange\n",
       "Horse       Pferd\n",
       "Dog          Hund\n",
       "Queue    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d2a34530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Katze', 'Schlange', 'Pferd', 'Hund', 'Schlange'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "31a692df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Cat', 'Snake', 'Horse', 'Dog', 'Queue'], dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1fabc63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3c52a024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0967498d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "69877740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e379c080",
   "metadata": {},
   "source": [
    "# Built-In Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cc9448",
   "metadata": {},
   "source": [
    "## The <p style=\"color:#83E74E;font-size:25px\">.head()</p> and <p style=\"color:#83E74E;font-size:25px\">.tail()</p> Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ac345357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Brown\n",
       "1     CalTech\n",
       "2         CMU\n",
       "3    Columbia\n",
       "4     Cornell\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "372574b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Brown\n",
       "1      CalTech\n",
       "2          CMU\n",
       "3     Columbia\n",
       "4      Cornell\n",
       "5    Dartmouth\n",
       "6         Duke\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b434ab3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Brown\n",
       "1    CalTech\n",
       "2        CMU\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ff346c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20     UMichigan\n",
       "21         UPenn\n",
       "22           UVA\n",
       "23    UWisconsin\n",
       "24          Yale\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "28362e4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15        Purdue\n",
       "16      Stanford\n",
       "17      TexasA&M\n",
       "18    UCBerkeley\n",
       "19      UChicago\n",
       "20     UMichigan\n",
       "21         UPenn\n",
       "22           UVA\n",
       "23    UWisconsin\n",
       "24          Yale\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa783314",
   "metadata": {},
   "source": [
    "## <p style=\"color:red;\">Sorting</p> Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "310af1cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat         Katze\n",
       "Snake    Schlange\n",
       "Horse       Pferd\n",
       "Dog          Hund\n",
       "Queue    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b95795e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat         Katze\n",
       "Dog          Hund\n",
       "Horse       Pferd\n",
       "Queue    Schlange\n",
       "Snake    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e3bf31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat         Katze\n",
       "Dog          Hund\n",
       "Horse       Pferd\n",
       "Queue    Schlange\n",
       "Snake    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b90b381a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Snake    Schlange\n",
       "Queue    Schlange\n",
       "Horse       Pferd\n",
       "Dog          Hund\n",
       "Cat         Katze\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5ce7f0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dog          Hund\n",
       "Cat         Katze\n",
       "Horse       Pferd\n",
       "Snake    Schlange\n",
       "Queue    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fc3f162f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Snake    Schlange\n",
       "Queue    Schlange\n",
       "Horse       Pferd\n",
       "Cat         Katze\n",
       "Dog          Hund\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f38a9f9",
   "metadata": {},
   "source": [
    "## <p style=\"color:red;font-size:25px\">Important notes</p>\n",
    "### <p style='color:yellow'>1.Whenever You see an output of any code it means a duplicate has been created inplace of the orginal dataframe, series or dictionary whereas original has been unaffected </p>\n",
    "### <p style='color:yellow'>2.But if you do not get any output by writting a code then the original dataframe, series or dictionary has been affected by the new code written for example check below two codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f5e7bb16",
   "metadata": {},
   "outputs": [],
   "source": [
    "german_dict.sort_values(inplace=True, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "25fe18cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dog          Hund\n",
       "Cat         Katze\n",
       "Horse       Pferd\n",
       "Snake    Schlange\n",
       "Queue    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5557f3d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Snake    Schlange\n",
       "Queue    Schlange\n",
       "Horse       Pferd\n",
       "Cat         Katze\n",
       "Dog          Hund\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4b8cb3",
   "metadata": {},
   "source": [
    "### <p style='color:yellow'>3.Another Example of above notes is that if you assign the values, index, dataframe, series or dictionary into new assigned Variable then the desired output will be stored into the new variable and output wont show up untill print, display or variable is coded "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1770e7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "german_sorted = german_dict.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2fb1ae6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat         Katze\n",
       "Dog          Hund\n",
       "Horse       Pferd\n",
       "Queue    Schlange\n",
       "Snake    Schlange\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798a4254",
   "metadata": {},
   "source": [
    "# <p style='color:blue'>Membership Test- in Keyword</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03e71b05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (1.3.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from pandas) (1.20.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\moin dalvi\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1314da6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59c1aee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Cat\" in german_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5dd383dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"katze\" in german_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a345506c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"cat\" in german_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "18c1c582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Cat\" in german_dict.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5180b4c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Cat' in german_dict.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1001a073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Katze' in german_sorted.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420a4267",
   "metadata": {},
   "source": [
    "<p style='font-size:30px>Extract values by Index Position"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387e5111",
   "metadata": {},
   "source": [
    "# Extract Values by Index Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98690e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "univ = pd.read_csv(\"Universities.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3bdc701a",
   "metadata": {},
   "outputs": [],
   "source": [
    "univ_extract = pd.read_csv(\"Universities.csv\", usecols=[\"Univ\"], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "420b8d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Brown\n",
       "1          CalTech\n",
       "2              CMU\n",
       "3         Columbia\n",
       "4          Cornell\n",
       "5        Dartmouth\n",
       "6             Duke\n",
       "7       Georgetown\n",
       "8          Harvard\n",
       "9     JohnsHopkins\n",
       "10             MIT\n",
       "11    Northwestern\n",
       "12       NotreDame\n",
       "13       PennState\n",
       "14       Princeton\n",
       "15          Purdue\n",
       "16        Stanford\n",
       "17        TexasA&M\n",
       "18      UCBerkeley\n",
       "19        UChicago\n",
       "20       UMichigan\n",
       "21           UPenn\n",
       "22             UVA\n",
       "23      UWisconsin\n",
       "24            Yale\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "09145170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CalTech'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "facbd6e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10          MIT\n",
       "12    NotreDame\n",
       "17     TexasA&M\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract[[10,12,17]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bd1024f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3        Columbia\n",
       "4         Cornell\n",
       "5       Dartmouth\n",
       "6            Duke\n",
       "7      Georgetown\n",
       "8         Harvard\n",
       "9    JohnsHopkins\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract[3:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "deaf9f20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Brown\n",
       "2            CMU\n",
       "4        Cornell\n",
       "6           Duke\n",
       "8        Harvard\n",
       "10           MIT\n",
       "12     NotreDame\n",
       "14     Princeton\n",
       "16      Stanford\n",
       "18    UCBerkeley\n",
       "20     UMichigan\n",
       "22           UVA\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract[0:24:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1da8db68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            Brown\n",
       "3         Columbia\n",
       "6             Duke\n",
       "9     JohnsHopkins\n",
       "12       NotreDame\n",
       "15          Purdue\n",
       "18      UCBerkeley\n",
       "21           UPenn\n",
       "24            Yale\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract[::3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b268cb28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           Brown\n",
       "1         CalTech\n",
       "2             CMU\n",
       "3        Columbia\n",
       "4         Cornell\n",
       "5       Dartmouth\n",
       "6            Duke\n",
       "7      Georgetown\n",
       "8         Harvard\n",
       "9    JohnsHopkins\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0285da76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10             MIT\n",
       "11    Northwestern\n",
       "12       NotreDame\n",
       "13       PennState\n",
       "14       Princeton\n",
       "15          Purdue\n",
       "16        Stanford\n",
       "17        TexasA&M\n",
       "18      UCBerkeley\n",
       "19        UChicago\n",
       "20       UMichigan\n",
       "21           UPenn\n",
       "22             UVA\n",
       "23      UWisconsin\n",
       "24            Yale\n",
       "Name: Univ, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ_extract[10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12dc57d",
   "metadata": {},
   "source": [
    "## 1. Extract values by index Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6a7fa52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Katze'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_sorted['Cat']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbaa4eeb",
   "metadata": {},
   "source": [
    "# The <p style='color:red'>.get()</p> method on a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4d3c194f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat    Katze\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.get(key=['Cat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9a1eda33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cat    Katze\n",
       "Dog     Hund\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.get(key=['Cat','Dog'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f617b2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "german_dict.get(key='Otter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f0f9531a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Not found'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.get(key='Otter', default='Not found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48af2467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Not Found'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_dict.get('Katze','Not Found')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157409f6",
   "metadata": {},
   "source": [
    "# <p style='color:red'><b>Mathematics</b></p> methods on series datatype objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "855cae07",
   "metadata": {},
   "outputs": [],
   "source": [
    "ser = pd.read_csv('Salaries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b40a148b",
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>105450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>AssocProf</td>\n",
       "      <td>B</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>104542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Female</td>\n",
       "      <td>124312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>109954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>109646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         rank discipline  phd  service     sex  salary\n",
       "0        Prof          B   56       49    Male  186960\n",
       "1        Prof          A   12        6    Male   93000\n",
       "2        Prof          A   23       20    Male  110515\n",
       "3        Prof          A   40       31    Male  131205\n",
       "4        Prof          B   20       18    Male  104800\n",
       "..        ...        ...  ...      ...     ...     ...\n",
       "73       Prof          B   18       10  Female  105450\n",
       "74  AssocProf          B   19        6  Female  104542\n",
       "75       Prof          B   17       17  Female  124312\n",
       "76       Prof          A   28       14  Female  109954\n",
       "77       Prof          A   23       15  Female  109646\n",
       "\n",
       "[78 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab655c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "ser_salary = pd.read_csv('Salaries.csv', usecols = [\"salary\"], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15171a4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     186960\n",
       "1      93000\n",
       "2     110515\n",
       "3     131205\n",
       "4     104800\n",
       "5     122400\n",
       "6      81285\n",
       "7     126300\n",
       "8      94350\n",
       "9      57800\n",
       "10    128250\n",
       "11    134778\n",
       "12     88000\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "637e16ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "ser_sample = pd.series([10,20,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "00384c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d503d87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8425855"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4c9abdf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108023.78205128205"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7d579c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28293.661022264165"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "743b8810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57800"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "efe34e96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186960"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b0ada243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104671.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d9eb8494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    92000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b5b0d421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        78.000000\n",
       "mean     108023.782051\n",
       "std       28293.661022\n",
       "min       57800.000000\n",
       "25%       88612.500000\n",
       "50%      104671.000000\n",
       "75%      126774.750000\n",
       "max      186960.000000\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66febc1",
   "metadata": {},
   "source": [
    "# The <p style='color:red'>.idmax()</p> and <p style='color:red'>.idmax()</p> Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b70f380e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank            Prof\n",
       "discipline         B\n",
       "phd               56\n",
       "service           51\n",
       "sex             Male\n",
       "salary        186960\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f53f93c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186960"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8c7c64cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank          AssocProf\n",
       "discipline            A\n",
       "phd                   1\n",
       "service               0\n",
       "sex              Female\n",
       "salary            57800\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "55cec0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "73fea518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.idxmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bab4002",
   "metadata": {},
   "source": [
    "# The <p style='color:red;font-size:30px'>.value_counts()</p> Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba216ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs = pd.read_csv(\"Salaries.csv\", usecols = ['rank'], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3e064d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74d06d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Prof\n",
       "1          Prof\n",
       "2          Prof\n",
       "3          Prof\n",
       "4          Prof\n",
       "        ...    \n",
       "73         Prof\n",
       "74    AssocProf\n",
       "75         Prof\n",
       "76         Prof\n",
       "77         Prof\n",
       "Name: rank, Length: 78, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03da3697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " jobs.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9413544b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof         46\n",
       "AsstProf     19\n",
       "AssocProf    13\n",
       "Name: rank, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jobs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82130caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AssocProf    13\n",
       "AsstProf     19\n",
       "Prof         46\n",
       "Name: rank, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jobs.value_counts(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47af328d",
   "metadata": {},
   "source": [
    "# The <p style='color:red;font-size:30px'>.apply()</p> Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3766aa7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perfrom(number):\n",
    "    if number < 60000:\n",
    "        return \"Ok\"\n",
    "    elif number >= 60000 and number <= 120000:\n",
    "        return \"Satisfactory\"\n",
    "    else:\n",
    "        return \"Excellent\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "42c3e71a",
   "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>rank</th>\n",
       "      <th>discipline</th>\n",
       "      <th>phd</th>\n",
       "      <th>service</th>\n",
       "      <th>sex</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>186960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>Male</td>\n",
       "      <td>93000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>110515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prof</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>131205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Prof</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>104800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank discipline  phd  service   sex  salary\n",
       "0  Prof          B   56       49  Male  186960\n",
       "1  Prof          A   12        6  Male   93000\n",
       "2  Prof          A   23       20  Male  110515\n",
       "3  Prof          A   40       31  Male  131205\n",
       "4  Prof          B   20       18  Male  104800"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a59ed626",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    186960\n",
       "1     93000\n",
       "2    110515\n",
       "3    131205\n",
       "4    104800\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3eb715ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Excellent\n",
       "1    Satisfactory\n",
       "2    Satisfactory\n",
       "3       Excellent\n",
       "4    Satisfactory\n",
       "Name: salary, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.apply(perfrom).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "be5c8141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73    Satisfactory\n",
       "74    Satisfactory\n",
       "75       Excellent\n",
       "76    Satisfactory\n",
       "77    Satisfactory\n",
       "Name: salary, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_salary.apply(perfrom).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d6100aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance(numbers):\n",
    "    if numbers > 60000:\n",
    "        return 'Ok'\n",
    "    elif numbers [60000:120000]:\n",
    "        return \"Satisfactory\"\n",
    "    else:\n",
    "        return \"Excellent!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "66aae4fb",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_14952/3995825870.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mser_salary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mperformance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[0;32m   4355\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4356\u001b[0m         \"\"\"\n\u001b[1;32m-> 4357\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mSeriesApply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4359\u001b[0m     def _reduce(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1041\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1043\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1044\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1045\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1096\u001b[0m                 \u001b[1;31m# List[Union[Callable[..., Any], str]]]]]\"; expected\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1097\u001b[0m                 \u001b[1;31m# \"Callable[[Any], Any]\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1098\u001b[1;33m                 mapped = lib.map_infer(\n\u001b[0m\u001b[0;32m   1099\u001b[0m                     \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                     \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# type: ignore[arg-type]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\MOINDA~1\\AppData\\Local\\Temp/ipykernel_14952/3864411856.py\u001b[0m in \u001b[0;36mperformance\u001b[1;34m(numbers)\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnumbers\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m60000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m'Ok'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[1;32melif\u001b[0m \u001b[0mnumbers\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m60000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m120000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"Satisfactory\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'int' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "ser_salary.apply(performance).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b2e3865",
   "metadata": {},
   "outputs": [],
   "source": [
    "ser.apply(lambda sal : sal + 0.15*sal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6676e7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a41eb26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5b233cf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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