code/03-data-analysis-with-pandas/09A-dataframes-from-lists.ipynb

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   "source": [
    "[back](./09-pandas-dataframes.ipynb)\n",
    "\n",
    "---\n",
    "### `Creating DataFrames from lists`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing pandas and numpy\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0\n",
      "0  1\n",
      "1  2\n",
      "2  3\n"
     ]
    }
   ],
   "source": [
    "# Creating DataFrame from list\n",
    "\n",
    "df = pd.DataFrame([1, 2, 3])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0\n",
      "0  1.0\n",
      "1  2.0\n",
      "2  3.0\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# Creating DataFrame with list and providing DataType\n",
    "\n",
    "df = pd.DataFrame([1, 2, 3], dtype=np.float16)\n",
    "print(df)\n",
    "print(type(df))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0\n",
      "0  A\n",
      "1  B\n",
      "2  C\n",
      "       0\n",
      "0   True\n",
      "1  False\n",
      "2   True\n"
     ]
    }
   ],
   "source": [
    "# Creating DataFrame with strings and booleans\n",
    "\n",
    "df = pd.DataFrame(['A', 'B', 'C'])\n",
    "print(df)\n",
    "\n",
    "df = pd.DataFrame([True, False, True])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2\n",
      "0  1  2  3\n",
      "1  4  5  6\n",
      "2  7  8  9\n"
     ]
    }
   ],
   "source": [
    "# Multidimensional DataFrame\n",
    "\n",
    "df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1    2    3\n",
      "0  1  2  3.0  4.0\n",
      "1  5  6  NaN  NaN\n",
      "2  7  8  9.0  NaN\n"
     ]
    }
   ],
   "source": [
    "# Uneven multidimensional DataFrame\n",
    "\n",
    "df = pd.DataFrame([[1, 2, 3, 4], [5, 6], [7, 8, 9]])\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1  2\n",
      "0  5  5  5\n",
      "1  5  5  5\n"
     ]
    }
   ],
   "source": [
    "# Shaping DataFrame with a scalar value \n",
    "\n",
    "df = pd.DataFrame(5, index=[0,1], columns=[0,1,2])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   col1  col2  col3\n",
      "a     5     5     5\n",
      "b     5     5     5\n"
     ]
    }
   ],
   "source": [
    "# Shaping DataFrame with a scalar value with labels\n",
    "\n",
    "df = pd.DataFrame(5, index=['a', 'b'], columns=['col1', 'col2', 'col3'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0\n",
      "a  1\n",
      "b  2\n",
      "c  3\n"
     ]
    }
   ],
   "source": [
    "# DataFrame from list, with label\n",
    "\n",
    "df = pd.DataFrame([1, 2, 3], index=['a', 'b', 'c'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b\n",
      "0  1  2\n",
      "1  3  4\n",
      "2  5  6\n"
     ]
    }
   ],
   "source": [
    "# This is a wrong syntax\n",
    "# df = pd.DataFrame([1, 2, 3], columns=['a', 'b', 'c'])\n",
    "\n",
    "# But, this is valid as there is at least one value specified for each column label provided\n",
    "df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], columns=['a', 'b'])\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      a  b\n",
      "row1  1  2\n",
      "row2  3  4\n",
      "row3  5  6\n"
     ]
    }
   ],
   "source": [
    "# Providing explicitly mentioned labels for both columns and rows\n",
    "\n",
    "df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], index=['row1', 'row2', 'row3'], columns=['a', 'b'])\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "---\n",
    "[next](./09B-dataframes-from-dictionary.ipynb)"
   ]
  }
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