05_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qn42qVpHSu4A"
   },
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7r0QIhb4Su4A"
   },
   "source": [
    "At the very basic level, Pandas objects can be thought of as enhanced versions of NumPy structured arrays in which the rows and columns are identified with labels rather than simple integer indices.\n",
    "\n",
    "https://www.datacamp.com/tutorial/pandas-multi-index\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy:\n",
    "Data WITHOUT indexes ---> Arrays (1D or many dimensions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src=\"img/numpy_arrays.png\" width=\"600\"/> </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas:\n",
    "Data WITH indexes ---> Series (1 column), DataFrame (several columns), with MultIndex (different layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src=\"img/pandas_df.png\" width=\"700\"/> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "Y2xC2KTeSu4B"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas data structures: the ``Series``, ``DataFrame``, and ``Index``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src=\"img/pandas_series_df.png\" width=\"700\"/> </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PBK-FlIISu4B"
   },
   "source": [
    "## The Pandas Series Object\n",
    "\n",
    "A Pandas ``Series`` is a one-dimensional array of indexed data.\n",
    "It can be created from a list or array as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "C7RIZCnzSu4C",
    "outputId": "83423ce4-3064-406a-fd82-b994dc7ae4f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.50\n",
       "2    0.75\n",
       "3    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datanp = np.array([0.25, 0.5, 0.75, 1.0])\n",
    "datanp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mlD_KMu0Su4D"
   },
   "source": [
    "As we see in the output, the ``Series`` wraps both a sequence of values and a sequence of indices, which we can access with the ``values`` and ``index`` attributes.\n",
    "The ``values`` are simply a familiar NumPy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "xOfZg_bVSu4D",
    "outputId": "8f90f752-b457-4791-8162-ebfaa4640095"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YpE83FD_Su4E"
   },
   "source": [
    "The ``index`` is an array-like object of type ``pd.Index``, which we'll discuss in more detail momentarily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "DvMd-SnYSu4E",
    "outputId": "d867a59d-8048-47a5-b79c-8821ef77fb87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gZPhRWJESu4E"
   },
   "source": [
    "Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "4TwaRTUNSu4F",
    "outputId": "69dc314d-06b3-4c9f-c463-c280d7ad982a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "TaGMB45zSu4F",
    "outputId": "6695f63a-c239-421c-b48c-2a5e0e9aac47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.50\n",
       "2    0.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "41GMycT5Su4H",
    "outputId": "8e013cdf-c256-4ba3-cfa5-6f2c422dd43a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.25\n",
       "b    0.50\n",
       "c    0.75\n",
       "d    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "                 index=['a', 'b', 'c', 'd'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1] # CHECH data[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# loc, iloc\n",
    "\n",
    "### loc --> access your series/df by index (rows, columns)\n",
    "### iloc --> access your series/df by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "5u91HO9FSu4L",
    "outputId": "3a555178-46c6-4820-e520-72093d9d2270"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1i_tL3HySu4M"
   },
   "source": [
    "We can even use non-contiguous or non-sequential indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "6jE_lYcYSu4N",
    "outputId": "0c936935-235f-4861-96ed-389ddff57445"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.25\n",
       "5    0.50\n",
       "3    0.75\n",
       "7    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([0.25, 0.5, 0.75, 1.0],\n",
    "                 index=[2, 5, 3, 7])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 5, 3, 7], dtype='int64')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "RqoRi6sLSu4e",
    "outputId": "3af601eb-868a-48c3-95a2-8a31f10df5c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "b1J3QzaaSu4f",
    "outputId": "4be94a6f-d055-485c-c2d1-f4a689801377"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_dict = {'California': 38332521,\n",
    "                   'Texas': 26448193,\n",
    "                   'New York': 19651127,\n",
    "                   'Florida': 19552860,\n",
    "                   'Illinois': 12882135}\n",
    "population = pd.Series(population_dict)\n",
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B7cEjprrSu4f"
   },
   "source": [
    "By default, a ``Series`` will be created where the index is drawn from the sorted keys.\n",
    "From here, typical dictionary-style item access can be performed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "Tkd3u7MMSu4f",
    "outputId": "750bef72-8b7f-482d-f12c-bb057405f9d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38332521"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38332521, 26448193, 19651127, 19552860, 12882135])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GB6ZQhV5Su4f"
   },
   "source": [
    "Unlike a dictionary, though, the ``Series`` also supports array-style operations such as slicing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "tRsQdPQZSu4g",
    "outputId": "5dec8c28-8e0d-4f17-a469-1dfb15b72f7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['California':'Illinois']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rf1WZshzSu4h"
   },
   "source": [
    "### Constructing Series objects\n",
    "\n",
    "We've already seen a few ways of constructing a Pandas ``Series`` from scratch; all of them are some version of the following:\n",
    "\n",
    "```python\n",
    ">>> pd.Series(data, index=index)\n",
    "```\n",
    "\n",
    "where ``index`` is an optional argument, and ``data`` can be one of many entities.\n",
    "\n",
    "For example, ``data`` can be a list or NumPy array, in which case ``index`` defaults to an integer sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "5_Kt4YIsSu4h",
    "outputId": "017b0b9a-65e9-450b-9706-27f5899aaf08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([2, 4, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k6dkE8kiSu4i"
   },
   "source": [
    "``data`` can be a scalar, which is repeated to fill the specified index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "XcI4kRMVSu4i",
    "outputId": "a6e34d10-fc51-4d7b-bdb1-67376c3dbed5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    5\n",
       "200    5\n",
       "300    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(5, index=[100, 200, 300])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mLU5pnf7Su4k"
   },
   "source": [
    "``data`` can be a dictionary, in which ``index`` defaults to the sorted dictionary keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "Xph2d9RFSu4l",
    "outputId": "09645480-4de3-44f1-92b8-dbf581ee439b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    a\n",
       "1    b\n",
       "3    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({2:'a', 1:'b', 3:'c'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PJ0LgIxTSu4n"
   },
   "source": [
    "In each case, the index can be explicitly set if a different result is preferred:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qHrWyexNSu4o"
   },
   "source": [
    "## The Pandas DataFrame Object\n",
    "\n",
    "The next fundamental structure in Pandas is the ``DataFrame``.\n",
    "Like the ``Series`` object discussed in the previous section, the ``DataFrame`` can be thought of either as a generalization of a NumPy array, or as a specialization of a Python dictionary.\n",
    "We'll now take a look at each of these perspectives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AcmvCo-BSu4o"
   },
   "source": [
    "### DataFrame as a generalized NumPy array\n",
    "If a ``Series`` is an analog of a one-dimensional array with flexible indices, a ``DataFrame`` is an analog of a two-dimensional array with both flexible row indices and flexible column names.\n",
    "Just as you might think of a two-dimensional array as an ordered sequence of aligned one-dimensional columns, you can think of a ``DataFrame`` as a sequence of aligned ``Series`` objects.\n",
    "Here, by \"aligned\" we mean that they share the same index.\n",
    "\n",
    "To demonstrate this, let's first construct a new ``Series`` listing the area of each of the five states discussed in the previous section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "IzE-vDjCSu4o",
    "outputId": "518ab49f-b7d8-4503-fa03-a3f5a8301eaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Illinois      149995\n",
       "Texas         695662\n",
       "New York      141297\n",
       "Florida       170312\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_dict = {'California': 423967, 'Illinois': 149995, 'Texas': 695662, 'New York': 141297,\n",
    "             'Florida': 170312, }\n",
    "area = pd.Series(area_dict)\n",
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "Illinois      12882135\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoFYUSHcSu4p"
   },
   "source": [
    "Now that we have this along with the ``population`` Series from before, we can use a dictionary to construct a single two-dimensional object containing this information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "Jq8Ekh5mSu4p",
    "outputId": "e3306a53-990d-47ff-dd7c-f8cbe04856b7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>149995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area\n",
       "California    38332521  423967\n",
       "Florida       19552860  170312\n",
       "Illinois      12882135  149995\n",
       "New York      19651127  141297\n",
       "Texas         26448193  695662"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = pd.DataFrame({'population': population,\n",
    "                       'area': area})\n",
    "states"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EzJmlRviSu4q"
   },
   "source": [
    "Like the ``Series`` object, the ``DataFrame`` has an ``index`` attribute that gives access to the index labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "P8IK2s7jSu4q",
    "outputId": "6bc9d81b-286b-46aa-a375-84b8cf8918bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['California', 'Florida', 'Illinois', 'New York', 'Texas'], dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xj4JyClZSu4q"
   },
   "source": [
    "Additionally, the ``DataFrame`` has a ``columns`` attribute, which is an ``Index`` object holding the column labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "BSozJY5RSu4q",
    "outputId": "2d050572-731a-4dcd-fc97-7f8b76dfa3b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['population', 'area'], dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1NKsJLbhSu4q"
   },
   "source": [
    "Thus the ``DataFrame`` can be thought of as a generalization of a two-dimensional NumPy array, where both the rows and columns have a generalized index for accessing the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YMAP-5l9Su4r"
   },
   "source": [
    "### Constructing DataFrame objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O7AGZg4jSu4r"
   },
   "source": [
    "#### From a single Series object\n",
    "\n",
    "A ``DataFrame`` is a collection of ``Series`` objects, and a single-column ``DataFrame`` can be constructed from a single ``Series``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "b6zFSxWYSu4r",
    "outputId": "56a83996-7f9f-45a4-bc0c-bfe6fad437f3"
   },
   "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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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population\n",
       "California    38332521\n",
       "Texas         26448193\n",
       "New York      19651127\n",
       "Florida       19552860\n",
       "Illinois      12882135"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(population, columns=['population'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iBWa2QxuSu4r"
   },
   "source": [
    "#### From a list of dicts\n",
    "\n",
    "Any list of dictionaries can be made into a ``DataFrame``.\n",
    "We'll use a simple list comprehension to create some data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "7RXDi2E4Su4r",
    "outputId": "744751d5-74ab-4074-f445-2833b457f370"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>aa</th>\n",
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       "      <th>bb</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cc</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "    a  b\n",
       "aa  0  0\n",
       "bb  1  2\n",
       "cc  2  4"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [{'a': i, 'b': 2 * i} for i in range(3)]\n",
    "data = pd.DataFrame(data, index=['aa', 'bb', 'cc'])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "In9hbdqiSu4r"
   },
   "source": [
    "Even if some keys in the dictionary are missing, Pandas will fill them in with ``NaN`` (i.e., \"not a number\") values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "_aSWOmzLSu4r",
    "outputId": "1468ffa1-e03b-4fc3-91d0-87f051103835"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>d</th>\n",
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       "      <th>0</th>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     a    b    c    d\n",
       "0  1.0  2.0  NaN  NaN\n",
       "1  NaN  3.0  4.0  NaN\n",
       "2  NaN  NaN  NaN  1.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}, {'d':1}])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.0\n",
       "1    3.0\n",
       "2    NaN\n",
       "Name: b, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, 'b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    </tr>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a  b    c\n",
       "0  1.0  2  6.0\n",
       "1  NaN  3  4.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ecPeb2TXSu4r"
   },
   "source": [
    "#### From a dictionary of Series objects\n",
    "\n",
    "As we saw before, a ``DataFrame`` can be constructed from a dictionary of ``Series`` objects as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "qtpWQu6vSu4r",
    "outputId": "672c6003-510f-4124-a7f0-5e0437da2186"
   },
   "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>population</th>\n",
       "      <th>area</th>\n",
       "      <th>area2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>38332521</td>\n",
       "      <td>423967</td>\n",
       "      <td>423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>26448193</td>\n",
       "      <td>695662</td>\n",
       "      <td>695662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19651127</td>\n",
       "      <td>141297</td>\n",
       "      <td>141297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>19552860</td>\n",
       "      <td>170312</td>\n",
       "      <td>170312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12882135</td>\n",
       "      <td>149995</td>\n",
       "      <td>149995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            population    area   area2\n",
       "California    38332521  423967  423967\n",
       "Texas         26448193  695662  695662\n",
       "New York      19651127  141297  141297\n",
       "Florida       19552860  170312  170312\n",
       "Illinois      12882135  149995  149995"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'population': population,\n",
    "              'area': area, 'area2': area})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T_pWcG1PSu4r"
   },
   "source": [
    "#### From a two-dimensional NumPy array\n",
    "\n",
    "Given a two-dimensional array of data, we can create a ``DataFrame`` with any specified column and index names.\n",
    "If omitted, an integer index will be used for each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "-Maro4qlSu4s",
    "outputId": "ce1126f6-d07b-413b-8f46-76f28f7a7f7b"
   },
   "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>foo</th>\n",
       "      <th>bar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.621707</td>\n",
       "      <td>0.483119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.744376</td>\n",
       "      <td>0.726758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.326315</td>\n",
       "      <td>0.923784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        foo       bar\n",
       "1  0.621707  0.483119\n",
       "2  0.744376  0.726758\n",
       "5  0.326315  0.923784"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.random.rand(3, 2),\n",
    "                    columns=['foo', 'bar'],\n",
    "                    index=[1, 2, 5])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.84009305, 0.87375535],\n",
       "       [0.9367475 , 0.03970539],\n",
       "       [0.60339727, 0.19718966]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(3, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nekpe2ErSu4s"
   },
   "source": [
    "## The Pandas Index Object\n",
    "\n",
    "We have seen here that both the ``Series`` and ``DataFrame`` objects contain an explicit *index* that lets you reference and modify data.\n",
    "This ``Index`` object is an interesting structure in itself, and it can be thought of either as an *immutable array* or as an *ordered set* (technically a multi-set, as ``Index`` objects may contain repeated values).\n",
    "Those views have some interesting consequences in the operations available on ``Index`` objects.\n",
    "As a simple example, let's construct an ``Index`` from a list of integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "xejpnvmtSu4s",
    "outputId": "ad5a4e10-a0c8-48e3-9cda-0e0a3685e4e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 3, 5, 7, 11], dtype='int64')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind = pd.Index([2, 3, 5, 7, 11])\n",
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "rufVcSrqSu4t",
    "outputId": "93461359-ad40-46ec-d011-088c9e939e26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 (5,) 1 int64\n"
     ]
    }
   ],
   "source": [
    "print(ind.size, ind.shape, ind.ndim, ind.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oVplaUEySu4t"
   },
   "source": [
    "One difference between ``Index`` objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:\n",
    "\n",
    "\n",
    "ind[1] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Selection in DataFrame\n",
    "\n",
    "Recall that a ``DataFrame`` acts in many ways like a two-dimensional or structured array, and in other ways like a dictionary of ``Series`` structures sharing the same index.\n",
    "\n",
    "loc, iloc, "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, the ``loc`` attribute allows indexing and slicing that always references the explicit index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.621707</td>\n",
       "      <td>0.483119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.744376</td>\n",
       "      <td>0.726758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.326315</td>\n",
       "      <td>0.923784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        foo       bar\n",
       "1  0.621707  0.483119\n",
       "2  0.744376  0.726758\n",
       "5  0.326315  0.923784"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "XiYsLx3WSu4t",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foo    0.621707\n",
       "bar    0.483119\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.621707\n",
       "2    0.744376\n",
       "5    0.326315\n",
       "Name: foo, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:, 'foo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6217074918853102"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0].loc['foo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.483119\n",
       "2    0.726758\n",
       "5    0.923784\n",
       "Name: bar, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['bar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.483119\n",
       "2    0.726758\n",
       "5    0.923784\n",
       "Name: bar, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## col = 'a' (position 2)\n",
    "\n",
    "## df.a, df['a'], df.loc[:,'a'], df.iloc[:, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>foo</th>\n",
       "      <th>bar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.968457</td>\n",
       "      <td>0.992146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.812544</td>\n",
       "      <td>0.292530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        foo       bar\n",
       "1  0.968457  0.992146\n",
       "2  0.812544  0.292530"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The iloc attribute allows indexing and slicing that always references the implicit Python-style index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "1  1  2\n",
       "2  2  4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operating on Null Values\n",
    "\n",
    "As we have seen, Pandas treats ``None`` and ``NaN`` as essentially interchangeable for indicating missing or null values.\n",
    "To facilitate this convention, there are several useful methods for detecting, removing, and replacing null values in Pandas data structures.\n",
    "They are:\n",
    "\n",
    "- ``isnull()``: Generate a boolean mask indicating missing values\n",
    "- ``notnull()``: Opposite of ``isnull()``\n",
    "- ``dropna()``: Return a filtered version of the data\n",
    "- ``fillna()``: Return a copy of the data with missing values filled or imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1      NaN\n",
       "2    hello\n",
       "3     None\n",
       "dtype: object"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([1, np.nan, 'hello', None])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = data.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.Series([2, 3, 98, 187], index=[0, 1, 2, 3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      2\n",
       "b      3\n",
       "c     98\n",
       "d    187\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "2    98\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1      NaN\n",
       "2    hello\n",
       "3     None\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pp[~np.isnan(pp)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1      NaN\n",
       "2    hello\n",
       "3     None\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True) # inplace = True !!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE ON .COPY()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([1, 2, 3])\n",
    "data_c = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_c[0] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 3])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### END NOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data.dropna()\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1      NaN\n",
       "2    hello\n",
       "3     None\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "2    hello\n",
       "dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2\n",
       "a  1.0  NaN  2\n",
       "b  2.0  3.0  6\n",
       "c  NaN  4.0  6"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1,      np.nan, 2],\n",
    "                   [2,      3,      6],\n",
    "                   [np.nan, 4,      6]], index=['a', 'b', 'c'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cannot drop single values from a DataFrame; we can only drop full rows or full columns. Depending on the application, you might want one or the other, so dropna() gives a number of options for a DataFrame.\n",
    "By default, dropna() will drop all rows in which any null value is present:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2\n",
       "a  2\n",
       "b  6\n",
       "c  6"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2\n",
       "a  1.0  NaN  2\n",
       "b  2.0  3.0  6\n",
       "c  NaN  4.0  6"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis='columns', how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this drops some good data as well; you might rather be interested in dropping rows or columns with *all* NA values, or a majority of NA values.\n",
    "This can be specified through the ``how`` or ``thresh`` parameters, which allow fine control of the number of nulls to allow through.\n",
    "\n",
    "The default is ``how='any'``, such that any row or column (depending on the ``axis`` keyword) containing a null value will be dropped.\n",
    "You can also specify ``how='all'``, which will only drop rows/columns that are *all* null values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>3</th>\n",
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       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2   3\n",
       "a  1.0  NaN  2 NaN\n",
       "b  2.0  3.0  6 NaN\n",
       "c  NaN  4.0  6 NaN"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1  2\n",
       "a  1.0  NaN  2\n",
       "b  2.0  3.0  6\n",
       "c  NaN  4.0  6"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis='columns', how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For finer-grained control, the ``thresh`` parameter lets you specify a minimum number of non-null values for the row/column to be kept:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     0    1  2   3\n",
       "b  2.0  3.0  6 NaN"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis='rows', thresh=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can fill NA entries with a single value, such as zero:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1.0</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-999.0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>6</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-999.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "       0      1  2      3\n",
       "a    1.0 -999.0  2 -999.0\n",
       "b    2.0    3.0  6 -999.0\n",
       "c -999.0    4.0  6 -999.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc['d']=np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>d</th>\n",
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       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>897.0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>897.0</td>\n",
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       "      <th>c</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>897.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0      1    2      d\n",
       "a  1.0  897.0  2.0  897.0\n",
       "b  2.0    3.0  6.0  897.0\n",
       "c  2.0    4.0  6.0  897.0\n",
       "d  2.0    4.0  6.0  897.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill', limit=2).fillna(897)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>2</th>\n",
       "      <th>d</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    d\n",
       "a  1.0  1.0  2.0  2.0\n",
       "b  2.0  3.0  6.0  6.0\n",
       "c  NaN  4.0  6.0  6.0\n",
       "d  NaN  NaN  NaN  NaN"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='ffill', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Index\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <img src=\"img/pandas_df_multindex.png\" width=\"500\"/> </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill    False\n",
       "tip           False\n",
       "sex           False\n",
       "smoker        False\n",
       "day           False\n",
       "time          False\n",
       "size          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x14fa94610>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 856x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme() # sns.set_theme()\n",
    "sns.pairplot(tips, hue='sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yr/jscdd90s2rdcvd490226gpqc0000gn/T/ipykernel_27895/2776829344.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df = tips.loc[:, ['total_bill', 'tip', 'size', 'smoker']].groupby('smoker').mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>20.756344</td>\n",
       "      <td>3.008710</td>\n",
       "      <td>2.408602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>19.188278</td>\n",
       "      <td>2.991854</td>\n",
       "      <td>2.668874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bill       tip      size\n",
       "smoker                                \n",
       "Yes      20.756344  3.008710  2.408602\n",
       "No       19.188278  2.991854  2.668874"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get mean of smoker/non-smoker groups\n",
    "df = tips.loc[:, ['total_bill', 'tip', 'size', 'smoker']].groupby('smoker').mean()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.1'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "pandas.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>20.756344</td>\n",
       "      <td>3.008710</td>\n",
       "      <td>2.408602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>19.188278</td>\n",
       "      <td>2.991854</td>\n",
       "      <td>2.668874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bill       tip      size\n",
       "smoker                                \n",
       "Yes      20.756344  3.008710  2.408602\n",
       "No       19.188278  2.991854  2.668874"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>smoker</th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>20.756344</td>\n",
       "      <td>3.008710</td>\n",
       "      <td>2.408602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>19.188278</td>\n",
       "      <td>2.991854</td>\n",
       "      <td>2.668874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  smoker  total_bill       tip      size\n",
       "0    Yes   20.756344  3.008710  2.408602\n",
       "1     No   19.188278  2.991854  2.668874"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yes</th>\n",
       "      <th>Lunch</th>\n",
       "      <td>17.399130</td>\n",
       "      <td>2.834348</td>\n",
       "      <td>2.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinner</th>\n",
       "      <td>21.859429</td>\n",
       "      <td>3.066000</td>\n",
       "      <td>2.471429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">No</th>\n",
       "      <th>Lunch</th>\n",
       "      <td>17.050889</td>\n",
       "      <td>2.673778</td>\n",
       "      <td>2.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinner</th>\n",
       "      <td>20.095660</td>\n",
       "      <td>3.126887</td>\n",
       "      <td>2.735849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_bill       tip      size\n",
       "smoker time                                  \n",
       "Yes    Lunch    17.399130  2.834348  2.217391\n",
       "       Dinner   21.859429  3.066000  2.471429\n",
       "No     Lunch    17.050889  2.673778  2.511111\n",
       "       Dinner   20.095660  3.126887  2.735849"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by two columns\n",
    "df = tips.groupby(['smoker','time']).mean()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('Yes',  'Lunch'),\n",
       "            ('Yes', 'Dinner'),\n",
       "            ( 'No',  'Lunch'),\n",
       "            ( 'No', 'Dinner')],\n",
       "           names=['smoker', 'time'])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out index\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yr/jscdd90s2rdcvd490226gpqc0000gn/T/ipykernel_27895/1847143587.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  df = tips.loc[:, ['total_bill', 'tip', 'size', 'smoker', 'time']].groupby(['smoker','time']).size()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "smoker  time  \n",
       "Yes     Lunch      23\n",
       "        Dinner     70\n",
       "No      Lunch      45\n",
       "        Dinner    106\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = tips.loc[:, ['total_bill', 'tip', 'size', 'smoker', 'time']].groupby(['smoker','time']).size()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time    smoker\n",
       "Lunch   Yes        23\n",
       "Dinner  Yes        70\n",
       "Lunch   No         45\n",
       "Dinner  No        106\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Swap levels of multi-index\n",
    "df.swaplevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker\n",
       "Yes    23\n",
       "No     45\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unstack your multi-index\n",
    "df.xs('Lunch', level='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time\n",
       "Lunch     23\n",
       "Dinner    70\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs('Yes', level='smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lunch</th>\n",
       "      <td>17.399130</td>\n",
       "      <td>2.834348</td>\n",
       "      <td>2.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinner</th>\n",
       "      <td>21.859429</td>\n",
       "      <td>3.066000</td>\n",
       "      <td>2.471429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bill       tip      size\n",
       "time                                  \n",
       "Lunch    17.399130  2.834348  2.217391\n",
       "Dinner   21.859429  3.066000  2.471429"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Yes', :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill    39.258559\n",
       "tip            5.900348\n",
       "size           4.688820\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Yes', :].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_bill    17.399130\n",
       "tip            2.834348\n",
       "size           2.217391\n",
       "Name: (Yes, Lunch), dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[('Yes', 'Lunch'), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_bill</th>\n",
       "      <th colspan=\"2\" halign=\"left\">tip</th>\n",
       "      <th colspan=\"2\" halign=\"left\">size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lunch</th>\n",
       "      <td>17.399130</td>\n",
       "      <td>17.050889</td>\n",
       "      <td>2.834348</td>\n",
       "      <td>2.673778</td>\n",
       "      <td>2.217391</td>\n",
       "      <td>2.511111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dinner</th>\n",
       "      <td>21.859429</td>\n",
       "      <td>20.095660</td>\n",
       "      <td>3.066000</td>\n",
       "      <td>3.126887</td>\n",
       "      <td>2.471429</td>\n",
       "      <td>2.735849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_bill                  tip                size          \n",
       "smoker        Yes         No       Yes        No       Yes        No\n",
       "time                                                                \n",
       "Lunch   17.399130  17.050889  2.834348  2.673778  2.217391  2.511111\n",
       "Dinner  21.859429  20.095660  3.066000  3.126887  2.471429  2.735849"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unsstack the outer index\n",
    "df.unstack(level=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging, concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(\n",
    "       {\n",
    "           \"A\": [\"A0\", \"A1\", \"A2\", \"A3\"],\n",
    "           \"B\": [\"B0\", \"B1\", \"B2\", \"B3\"],\n",
    "           \"C\": [\"C0\", \"C1\", \"C2\", \"C3\"],\n",
    "           \"D\": [\"D0\", \"D1\", \"D2\", \"D3\"],\n",
    "       },\n",
    "       index=[0, 1, 2, 3],\n",
    "   )\n",
    "   \n",
    "df2 = pd.DataFrame(\n",
    "        {\n",
    "            \"A\": [\"A4\", \"A5\", \"A6\", \"A7\"],\n",
    "            \"B\": [\"B4\", \"B5\", \"B6\", \"B7\"],\n",
    "            \"C\": [\"C4\", \"C5\", \"C6\", \"C7\"],\n",
    "            \"D\": [\"D4\", \"D5\", \"D6\", \"D7\"],\n",
    "        },\n",
    "        index=[0, 1, 2, 3],\n",
    "    )\n",
    "    \n",
    "\n",
    "df3 = pd.DataFrame(\n",
    "       {\n",
    "           \"A\": [\"A8\", \"A9\", \"A10\", \"A11\"],\n",
    "           \"B\": [\"B8\", \"B9\", \"B10\", \"B11\"],\n",
    "           \"C\": [\"C8\", \"C9\", \"C10\", \"C11\"],\n",
    "           \"D\": [\"D8\", \"D9\", \"D10\", \"D11\"],\n",
    "       },\n",
    "       index=[8, 9, 10, 11],\n",
    "   )\n",
    "   \n",
    "frames = [df2,df1,  df3]\n",
    "result = pd.concat([df2, df1, df3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1\n",
       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "    A   B   C   D\n",
       "0  A4  B4  C4  D4\n",
       "1  A5  B5  C5  D5\n",
       "2  A6  B6  C6  D6\n",
       "3  A7  B7  C7  D7"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
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       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
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       "      <th>10</th>\n",
       "      <td>A10</td>\n",
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       "      <td>C10</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "      A    B    C    D\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 54,
     "metadata": {},
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    "df3"
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       "      A    B    C    D\n",
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       "2    A6   B6   C6   D6\n",
       "3    A7   B7   C7   D7\n",
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       "1    A1   B1   C1   D1\n",
       "2    A2   B2   C2   D2\n",
       "3    A3   B3   C3   D3\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
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     "execution_count": 56,
     "metadata": {},
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    "result"
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   "cell_type": "code",
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       "</table>\n",
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      "text/plain": [
       "        A    B    C    D\n",
       "x 0    A4   B4   C4   D4\n",
       "  1    A5   B5   C5   D5\n",
       "  2    A6   B6   C6   D6\n",
       "  3    A7   B7   C7   D7\n",
       "y 0    A0   B0   C0   D0\n",
       "  1    A1   B1   C1   D1\n",
       "  2    A2   B2   C2   D2\n",
       "  3    A3   B3   C3   D3\n",
       "z 8    A8   B8   C8   D8\n",
       "  9    A9   B9   C9   D9\n",
       "  10  A10  B10  C10  D10\n",
       "  11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pd.concat(frames, keys=[\"x\", \"y\", \"z\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    A   B   C   D\n",
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     "metadata": {},
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    "pd.concat(frames, keys=[\"x\", \"y\", \"z\"]).xs(8, level=1)"
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   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.DataFrame(\n",
    "      {\n",
    "          \"B\": [\"B2\", \"B3\", \"B6\", \"B7\"],\n",
    "          \"D\": [\"D2\", \"D3\", \"D6\", \"D7\"],\n",
    "          \"F\": [\"F2\", \"F3\", \"F6\", \"F7\"],\n",
    "      },\n",
    "      index=[2, 3, 6, 7],\n",
    "  )\n",
    "  \n",
    "\n",
    "result = pd.concat([df1, df4], axis=1, join='inner')#, keys=['1', '2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
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       "      <td>D3</td>\n",
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      "text/plain": [
       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1\n",
       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
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       "      <td>F2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B3</td>\n",
       "      <td>D3</td>\n",
       "      <td>F3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B6</td>\n",
       "      <td>D6</td>\n",
       "      <td>F6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B7</td>\n",
       "      <td>D7</td>\n",
       "      <td>F7</td>\n",
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      "text/plain": [
       "    B   D   F\n",
       "2  B2  D2  F2\n",
       "3  B3  D3  F3\n",
       "6  B6  D6  F6\n",
       "7  B7  D7  F7"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>B</th>\n",
       "      <th>B</th>\n",
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       "      <th>2</th>\n",
       "      <td>B2</td>\n",
       "      <td>B2</td>\n",
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       "      <th>3</th>\n",
       "      <td>B3</td>\n",
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      "text/plain": [
       "    B   B\n",
       "2  B2  B2\n",
       "3  B3  B3"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.loc[:, 'B']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "https://towardsdatascience.com/how-to-use-multiindex-in-pandas-to-level-up-your-analysis-aeac7f451fce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "df = pd.read_csv('data/WordsByCharacter.csv')\n",
    "# pd.read_csv, PD.READ_TABLE, pd.read_xls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Film</th>\n",
       "      <th>Chapter</th>\n",
       "      <th>Character</th>\n",
       "      <th>Race</th>\n",
       "      <th>Words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>01: Prologue</td>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>01: Prologue</td>\n",
       "      <td>Elrond</td>\n",
       "      <td>Elf</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>01: Prologue</td>\n",
       "      <td>Galadriel</td>\n",
       "      <td>Elf</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>01: Prologue</td>\n",
       "      <td>Gollum</td>\n",
       "      <td>Gollum</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>02: Concerning Hobbits</td>\n",
       "      <td>Bilbo</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>The Return Of The King</td>\n",
       "      <td>76: The Grey Havens</td>\n",
       "      <td>Elrond</td>\n",
       "      <td>Elf</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>The Return Of The King</td>\n",
       "      <td>76: The Grey Havens</td>\n",
       "      <td>Frodo</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>The Return Of The King</td>\n",
       "      <td>76: The Grey Havens</td>\n",
       "      <td>Galadriel</td>\n",
       "      <td>Elf</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>The Return Of The King</td>\n",
       "      <td>76: The Grey Havens</td>\n",
       "      <td>Gandalf</td>\n",
       "      <td>Ainur</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>The Return Of The King</td>\n",
       "      <td>76: The Grey Havens</td>\n",
       "      <td>Sam</td>\n",
       "      <td>Hobbit</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Film                 Chapter  Character    Race  \\\n",
       "0    The Fellowship Of The Ring            01: Prologue      Bilbo  Hobbit   \n",
       "1    The Fellowship Of The Ring            01: Prologue     Elrond     Elf   \n",
       "2    The Fellowship Of The Ring            01: Prologue  Galadriel     Elf   \n",
       "3    The Fellowship Of The Ring            01: Prologue     Gollum  Gollum   \n",
       "4    The Fellowship Of The Ring  02: Concerning Hobbits      Bilbo  Hobbit   \n",
       "..                          ...                     ...        ...     ...   \n",
       "726      The Return Of The King     76: The Grey Havens     Elrond     Elf   \n",
       "727      The Return Of The King     76: The Grey Havens      Frodo  Hobbit   \n",
       "728      The Return Of The King     76: The Grey Havens  Galadriel     Elf   \n",
       "729      The Return Of The King     76: The Grey Havens    Gandalf   Ainur   \n",
       "730      The Return Of The King     76: The Grey Havens        Sam  Hobbit   \n",
       "\n",
       "     Words  \n",
       "0        4  \n",
       "1        5  \n",
       "2      460  \n",
       "3       20  \n",
       "4      214  \n",
       "..     ...  \n",
       "726      6  \n",
       "727    132  \n",
       "728     17  \n",
       "729     42  \n",
       "730     14  \n",
       "\n",
       "[731 rows x 5 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi = df.set_index(['Film', 'Chapter', 'Race', 'Character']).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th>Words</th>\n",
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       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th>Chapter</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">The Fellowship Of The Ring</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">01: Prologue</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Elf</th>\n",
       "      <th>Elrond</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galadriel</th>\n",
       "      <td>460</td>\n",
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       "    <tr>\n",
       "      <th>Gollum</th>\n",
       "      <th>Gollum</th>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>Hobbit</th>\n",
       "      <th>Bilbo</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>02: Concerning Hobbits</th>\n",
       "      <th>Hobbit</th>\n",
       "      <th>Bilbo</th>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">The Two Towers</th>\n",
       "      <th>65: The Battle For Middle Earth Is About To Begin</th>\n",
       "      <th>Hobbit</th>\n",
       "      <th>Sam</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">66: Gollum's Plan</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Gollum</th>\n",
       "      <th>Gollum</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smeagol</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Hobbit</th>\n",
       "      <th>Frodo</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                               Words\n",
       "Film                       Chapter                                           Race   Character       \n",
       "The Fellowship Of The Ring 01: Prologue                                      Elf    Elrond         5\n",
       "                                                                                    Galadriel    460\n",
       "                                                                             Gollum Gollum        20\n",
       "                                                                             Hobbit Bilbo          4\n",
       "                           02: Concerning Hobbits                            Hobbit Bilbo        214\n",
       "...                                                                                              ...\n",
       "The Two Towers             65: The Battle For Middle Earth Is About To Begin Hobbit Sam           69\n",
       "                           66: Gollum's Plan                                 Gollum Gollum        75\n",
       "                                                                                    Smeagol       53\n",
       "                                                                             Hobbit Frodo         11\n",
       "                                                                                    Sam           12\n",
       "\n",
       "[731 rows x 1 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which characters speak in the first chapter of “The Fellowship of the Ring”? Find the total number of words per characters' race in the first chapter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Elrond\n",
       "1    Galadriel\n",
       "2       Gollum\n",
       "3        Bilbo\n",
       "Name: Character, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi.xs('The Fellowship Of The Ring').xs('01: Prologue').reset_index()['Character']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Words</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Elf</th>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gollum</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hobbit</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Words\n",
       "Race         \n",
       "Elf       465\n",
       "Gollum     20\n",
       "Hobbit      4"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi.xs('The Fellowship Of The Ring').xs('01: Prologue').reset_index().loc[:, ['Words', 'Race']].groupby('Race').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>Film</th>\n",
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       "      <th>0</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
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       "      <th>1</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>01: Prologue</td>\n",
       "      <td>Elrond</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>01: Prologue</td>\n",
       "      <td>Galadriel</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Fellowship Of The Ring</td>\n",
       "      <td>01: Prologue</td>\n",
       "      <td>Gollum</td>\n",
       "      <td>Gollum</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Film       Chapter  Character    Race  Words\n",
       "0  The Fellowship Of The Ring  01: Prologue      Bilbo  Hobbit      4\n",
       "1  The Fellowship Of The Ring  01: Prologue     Elrond     Elf      5\n",
       "2  The Fellowship Of The Ring  01: Prologue  Galadriel     Elf    460\n",
       "3  The Fellowship Of The Ring  01: Prologue     Gollum  Gollum     20"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppp = df[df.Film == 'The Fellowship Of The Ring']\n",
    "ppp2 = ppp[ppp.Chapter == '01: Prologue']\n",
    "# ppp3 = "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Who are the first three elves to speak in the “The Fellowship of the Ring”?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Words</th>\n",
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       "    <tr>\n",
       "      <th>Chapter</th>\n",
       "      <th>Character</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">01: Prologue</th>\n",
       "      <th>Elrond</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Galadriel</th>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21: Flight To The Ford</th>\n",
       "      <th>Arwen</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Words\n",
       "Chapter                Character       \n",
       "01: Prologue           Elrond         5\n",
       "                       Galadriel    460\n",
       "21: Flight To The Ford Arwen        131"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi.xs('The Fellowship Of The Ring').xs('Elf', level='Race').head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much do Gandalf and Saruman talk in each chapter of “The Two Towers”?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "gandalf = multi.xs('The Two Towers').xs('Gandalf', level='Character')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Chapter</th>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01: The Foundations Of Stone</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15: The White Rider</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>298</td>\n",
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       "    <tr>\n",
       "      <th>17: The Heir Of Númenor</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20: The King Of The Golden Hall</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22: Simbelmynë on the Burial Mounds</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23: The King's Decision</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58: Forth Eorlingas</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65: The Battle For Middle Earth Is About To Begin</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Words\n",
       "Chapter                                           Race        \n",
       "01: The Foundations Of Stone                      Ainur     39\n",
       "15: The White Rider                               Ainur    298\n",
       "17: The Heir Of Númenor                           Ainur    226\n",
       "20: The King Of The Golden Hall                   Ainur    151\n",
       "22: Simbelmynë on the Burial Mounds               Ainur     28\n",
       "23: The King's Decision                           Ainur    165\n",
       "58: Forth Eorlingas                               Ainur     21\n",
       "65: The Battle For Middle Earth Is About To Begin Ainur     36"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " multi.xs(('The Two Towers', 'Gandalf'), level=('Film', 'Character'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "saruman = multi.xs('The Two Towers').xs('Saruman', level='Character')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Words</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chapter</th>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01: The Foundations Of Stone</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15: The White Rider</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17: The Heir Of Númenor</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20: The King Of The Golden Hall</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22: Simbelmynë on the Burial Mounds</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23: The King's Decision</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58: Forth Eorlingas</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65: The Battle For Middle Earth Is About To Begin</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06: The Burning of the Westfold</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25: The Ring Of Barahir</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27: Exodus From Edoras</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36: Isengard Unleashed</th>\n",
       "      <th>Ainur</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Words\n",
       "Chapter                                           Race        \n",
       "01: The Foundations Of Stone                      Ainur     39\n",
       "15: The White Rider                               Ainur    298\n",
       "17: The Heir Of Númenor                           Ainur    226\n",
       "20: The King Of The Golden Hall                   Ainur    151\n",
       "22: Simbelmynë on the Burial Mounds               Ainur     28\n",
       "23: The King's Decision                           Ainur    165\n",
       "58: Forth Eorlingas                               Ainur     21\n",
       "65: The Battle For Middle Earth Is About To Begin Ainur     36\n",
       "06: The Burning of the Westfold                   Ainur    187\n",
       "25: The Ring Of Barahir                           Ainur     68\n",
       "27: Exodus From Edoras                            Ainur      4\n",
       "36: Isengard Unleashed                            Ainur     50"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([gandalf, saruman])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which hobbits speak the most in each film and across all three films?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra pandas + seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the penguins dataset\n",
    "penguins = sns.load_dataset(\"penguins\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222.0</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0    Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1    Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2    Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3    Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4    Adelie  Torgersen            36.7           19.3              193.0   \n",
       "..      ...        ...             ...            ...                ...   \n",
       "339  Gentoo     Biscoe             NaN            NaN                NaN   \n",
       "340  Gentoo     Biscoe            46.8           14.3              215.0   \n",
       "341  Gentoo     Biscoe            50.4           15.7              222.0   \n",
       "342  Gentoo     Biscoe            45.2           14.8              212.0   \n",
       "343  Gentoo     Biscoe            49.9           16.1              213.0   \n",
       "\n",
       "     body_mass_g     sex  \n",
       "0         3750.0    Male  \n",
       "1         3800.0  Female  \n",
       "2         3250.0  Female  \n",
       "3            NaN     NaN  \n",
       "4         3450.0  Female  \n",
       "..           ...     ...  \n",
       "339          NaN     NaN  \n",
       "340       4850.0  Female  \n",
       "341       5750.0    Male  \n",
       "342       5200.0  Female  \n",
       "343       5400.0    Male  \n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(\n",
    "    data=penguins,\n",
    "    x=\"bill_length_mm\", y=\"bill_depth_mm\", hue=\"species\",\n",
    "    kind=\"kde\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(10.250000000000002, 0.5, '')]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a categorical scatterplot to show each observation\n",
    "ax = sns.swarmplot(data=penguins, x=\"body_mass_g\", y=\"sex\", hue=\"species\")\n",
    "ax.set(ylabel=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x110469180>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1224.72x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpg = sns.load_dataset(\"mpg\")\n",
    "\n",
    "colors = (250, 70, 50), (350, 70, 50)\n",
    "cmap = sns.blend_palette(colors, input=\"husl\", as_cmap=True)\n",
    "sns.displot(\n",
    "    mpg,\n",
    "    x=\"displacement\", col=\"origin\", hue=\"model_year\",\n",
    "    kind=\"ecdf\", aspect=.75, linewidth=2, palette=cmap,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>europe</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0    18.0          8         307.0       130.0    3504          12.0   \n",
       "1    15.0          8         350.0       165.0    3693          11.5   \n",
       "2    18.0          8         318.0       150.0    3436          11.0   \n",
       "3    16.0          8         304.0       150.0    3433          12.0   \n",
       "4    17.0          8         302.0       140.0    3449          10.5   \n",
       "..    ...        ...           ...         ...     ...           ...   \n",
       "393  27.0          4         140.0        86.0    2790          15.6   \n",
       "394  44.0          4          97.0        52.0    2130          24.6   \n",
       "395  32.0          4         135.0        84.0    2295          11.6   \n",
       "396  28.0          4         120.0        79.0    2625          18.6   \n",
       "397  31.0          4         119.0        82.0    2720          19.4   \n",
       "\n",
       "     model_year  origin                       name  \n",
       "0            70     usa  chevrolet chevelle malibu  \n",
       "1            70     usa          buick skylark 320  \n",
       "2            70     usa         plymouth satellite  \n",
       "3            70     usa              amc rebel sst  \n",
       "4            70     usa                ford torino  \n",
       "..          ...     ...                        ...  \n",
       "393          82     usa            ford mustang gl  \n",
       "394          82  europe                  vw pickup  \n",
       "395          82     usa              dodge rampage  \n",
       "396          82     usa                ford ranger  \n",
       "397          82     usa                 chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the LOTR exercise, plot the number of words per character, per film, per chapter --> how would you present the data?"
   ]
  }
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