데이터 분석을 위한 파이썬 12장~.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7590109",
   "metadata": {},
   "source": [
    "# 12장 Introduction to Modeling Libraries in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb13497",
   "metadata": {},
   "source": [
    "## 12.1 Interfacing Between pandas and Model Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "953ed096",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e0a5cac",
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-4.10</td>\n",
       "      <td>1.3</td>\n",
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       "      <td>5</td>\n",
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      "text/plain": [
       "   x0    x1    y\n",
       "0   1  0.01 -1.5\n",
       "1   2 -0.01  0.0\n",
       "2   3  0.25  3.6\n",
       "3   4 -4.10  1.3\n",
       "4   5  0.00 -2.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    'x0': [1, 2, 3, 4, 5],\n",
    "    'x1': [0.01, -0.01, 0.25, -4.1, 0.],\n",
    "    'y': [-1.5, 0., 3.6, 1.3, -2.]})\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b6c8f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['x0', 'x1', 'y'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a1ca598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  0.01, -1.5 ],\n",
       "       [ 2.  , -0.01,  0.  ],\n",
       "       [ 3.  ,  0.25,  3.6 ],\n",
       "       [ 4.  , -4.1 ,  1.3 ],\n",
       "       [ 5.  ,  0.  , -2.  ]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e923a85b",
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
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       "   one   two  three\n",
       "0  1.0  0.01   -1.5\n",
       "1  2.0 -0.01    0.0\n",
       "2  3.0  0.25    3.6\n",
       "3  4.0 -4.10    1.3\n",
       "4  5.0  0.00   -2.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(data.to_numpy(), columns=['one', 'two', 'three'])\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdb9ded5",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   x0    x1    y strings\n",
       "0   1  0.01 -1.5       a\n",
       "1   2 -0.01  0.0       b\n",
       "2   3  0.25  3.6       c\n",
       "3   4 -4.10  1.3       d\n",
       "4   5  0.00 -2.0       e"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = data.copy()\n",
    "\n",
    "df3['strings'] = ['a', 'b', 'c', 'd', 'e']\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2605a911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0.01, -1.5, 'a'],\n",
       "       [2, -0.01, 0.0, 'b'],\n",
       "       [3, 0.25, 3.6, 'c'],\n",
       "       [4, -4.1, 1.3, 'd'],\n",
       "       [5, 0.0, -2.0, 'e']], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d16541f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  0.01],\n",
       "       [ 2.  , -0.01],\n",
       "       [ 3.  ,  0.25],\n",
       "       [ 4.  , -4.1 ],\n",
       "       [ 5.  ,  0.  ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_cols = ['x0', 'x1']\n",
    "\n",
    "data.loc[:, model_cols].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c166bda",
   "metadata": {},
   "outputs": [
    {
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       "      <td>b</td>\n",
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      "text/plain": [
       "   x0    x1    y category\n",
       "0   1  0.01 -1.5        a\n",
       "1   2 -0.01  0.0        b\n",
       "2   3  0.25  3.6        a\n",
       "3   4 -4.10  1.3        a\n",
       "4   5  0.00 -2.0        b"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['category'] = pd.Categorical(['a', 'b', 'a', 'a', 'b'],\n",
    "                                  categories=['a', 'b'])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72a6beb2",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
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      "text/plain": [
       "   x0    x1    y  category_a  category_b\n",
       "0   1  0.01 -1.5           1           0\n",
       "1   2 -0.01  0.0           0           1\n",
       "2   3  0.25  3.6           1           0\n",
       "3   4 -4.10  1.3           1           0\n",
       "4   5  0.00 -2.0           0           1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(data.category, prefix='category')\n",
    "\n",
    "data_with_dummies = data.drop('category', axis=1).join(dummies)\n",
    "\n",
    "data_with_dummies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b8ea432",
   "metadata": {},
   "source": [
    "## 12.2 Creating Model Descriptions with Patsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92e82861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b23ca298",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.25</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>-4.10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>-2.0</td>\n",
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      "text/plain": [
       "   x0    x1    y\n",
       "0   1  0.01 -1.5\n",
       "1   2 -0.01  0.0\n",
       "2   3  0.25  3.6\n",
       "3   4 -4.10  1.3\n",
       "4   5  0.00 -2.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    'x0': [1, 2, 3, 4, 5],\n",
    "    'x1': [0.01, -0.01, 0.25, -4.1, 0.],\n",
    "    'y': [-1.5, 0., 3.6, 1.3, -2.]})\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "08c5f636",
   "metadata": {},
   "outputs": [],
   "source": [
    "import patsy\n",
    "\n",
    "y, X = patsy.dmatrices('y ~ x0 + x1', data) #patsy.dmatrices function takes a formula string along with a dataset and produces design matrices for a linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfe33036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 1)\n",
       "     y\n",
       "  -1.5\n",
       "   0.0\n",
       "   3.6\n",
       "   1.3\n",
       "  -2.0\n",
       "  Terms:\n",
       "    'y' (column 0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d837bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 3)\n",
       "  Intercept  x0     x1\n",
       "          1   1   0.01\n",
       "          1   2  -0.01\n",
       "          1   3   0.25\n",
       "          1   4  -4.10\n",
       "          1   5   0.00\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'x0' (column 1)\n",
       "    'x1' (column 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3d5ca7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.5],\n",
       "       [ 0. ],\n",
       "       [ 3.6],\n",
       "       [ 1.3],\n",
       "       [-2. ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a12a6020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.  ,  1.  ,  0.01],\n",
       "       [ 1.  ,  2.  , -0.01],\n",
       "       [ 1.  ,  3.  ,  0.25],\n",
       "       [ 1.  ,  4.  , -4.1 ],\n",
       "       [ 1.  ,  5.  ,  0.  ]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "24320383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 2)\n",
       "  x0     x1\n",
       "   1   0.01\n",
       "   2  -0.01\n",
       "   3   0.25\n",
       "   4  -4.10\n",
       "   5   0.00\n",
       "  Terms:\n",
       "    'x0' (column 0)\n",
       "    'x1' (column 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices('y ~ x0 + x1 + 0', data)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eecbcfbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-ef4372856a50>:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  coef, resid, _, _ = np.linalg.lstsq(X, y)\n"
     ]
    }
   ],
   "source": [
    "coef, resid, _, _ = np.linalg.lstsq(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0865d856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.31290976],\n",
       "       [-0.07910564],\n",
       "       [-0.26546384]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "54af8915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    0.312910\n",
       "x0          -0.079106\n",
       "x1          -0.265464\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef = pd.Series(coef.squeeze(), index=X.design_info.column_names)\n",
    "\n",
    "coef"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a701c2ad",
   "metadata": {},
   "source": [
    "###### 12.2.1 Data Transformations in Patsy Formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e8758752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 3)\n",
       "  Intercept  x0  np.log(np.abs(x1) + 1)\n",
       "          1   1                 0.00995\n",
       "          1   2                 0.00995\n",
       "          1   3                 0.22314\n",
       "          1   4                 1.62924\n",
       "          1   5                 0.00000\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'x0' (column 1)\n",
       "    'np.log(np.abs(x1) + 1)' (column 2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = patsy.dmatrices('y ~ x0 + np.log(np.abs(x1) + 1)', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "547d1cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 3)\n",
       "  Intercept  standardize(x0)  center(x1)\n",
       "          1         -1.41421        0.78\n",
       "          1         -0.70711        0.76\n",
       "          1          0.00000        1.02\n",
       "          1          0.70711       -3.33\n",
       "          1          1.41421        0.77\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'standardize(x0)' (column 1)\n",
       "    'center(x1)' (column 2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = patsy.dmatrices('y ~ standardize(x0) + center(x1)', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7a871319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DesignMatrix with shape (4, 3)\n",
       "   Intercept  standardize(x0)  center(x1)\n",
       "           1          2.12132        3.87\n",
       "           1          2.82843        0.27\n",
       "           1          3.53553        0.77\n",
       "           1          4.24264        3.07\n",
       "   Terms:\n",
       "     'Intercept' (column 0)\n",
       "     'standardize(x0)' (column 1)\n",
       "     'center(x1)' (column 2)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = pd.DataFrame({\n",
    "    'x0': [6, 7, 8, 9],\n",
    "    'x1': [3.1, -0.5, 0, 2.3],\n",
    "    'y': [1, 2, 3, 4]})\n",
    "new_X = patsy.build_design_matrices([X.design_info], new_data)\n",
    "\n",
    "new_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "17cc248b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 2)\n",
       "  Intercept  I(x0 + x1)\n",
       "          1        1.01\n",
       "          1        1.99\n",
       "          1        3.25\n",
       "          1       -0.10\n",
       "          1        5.00\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'I(x0 + x1)' (column 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = patsy.dmatrices('y ~ I(x0 + x1)', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32bd834e",
   "metadata": {},
   "source": [
    "###### 12.2.2 Categorical Data and Patsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b64c0bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (8, 2)\n",
       "  Intercept  key1[T.b]\n",
       "          1          0\n",
       "          1          0\n",
       "          1          1\n",
       "          1          1\n",
       "          1          0\n",
       "          1          1\n",
       "          1          0\n",
       "          1          1\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'key1' (column 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    'key1': ['a', 'a', 'b', 'b', 'a', 'b', 'a', 'b'],\n",
    "    'key2': [0, 1, 0, 1, 0, 1, 0, 0],\n",
    "    'v1': [1, 2, 3, 4, 5, 6, 7, 8],\n",
    "    'v2': [-1, 0, 2.5, -0.5, 4.0, -1.2, 0.2, -1.7]\n",
    "})\n",
    "\n",
    "y, X = patsy.dmatrices('v2 ~ key1', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7e7a9108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (8, 2)\n",
       "  key1[a]  key1[b]\n",
       "        1        0\n",
       "        1        0\n",
       "        0        1\n",
       "        0        1\n",
       "        1        0\n",
       "        0        1\n",
       "        1        0\n",
       "        0        1\n",
       "  Terms:\n",
       "    'key1' (columns 0:2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = patsy.dmatrices('v2 ~ key1 + 0', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3ba7b377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (8, 2)\n",
       "  Intercept  C(key2)[T.1]\n",
       "          1             0\n",
       "          1             1\n",
       "          1             0\n",
       "          1             1\n",
       "          1             0\n",
       "          1             1\n",
       "          1             0\n",
       "          1             0\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'C(key2)' (column 1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = patsy.dmatrices('v2 ~ C(key2)', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "17dc8fdc",
   "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>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>zero</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>zero</td>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>zero</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td>zero</td>\n",
       "      <td>7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b</td>\n",
       "      <td>zero</td>\n",
       "      <td>8</td>\n",
       "      <td>-1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1  key2  v1   v2\n",
       "0    a  zero   1 -1.0\n",
       "1    a   one   2  0.0\n",
       "2    b  zero   3  2.5\n",
       "3    b   one   4 -0.5\n",
       "4    a  zero   5  4.0\n",
       "5    b   one   6 -1.2\n",
       "6    a  zero   7  0.2\n",
       "7    b  zero   8 -1.7"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['key2'] = data['key2'].map({0: 'zero', 1: 'one'})\n",
    "\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "48787143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (8, 3)\n",
       "  Intercept  key1[T.b]  key2[T.zero]\n",
       "          1          0             1\n",
       "          1          0             0\n",
       "          1          1             1\n",
       "          1          1             0\n",
       "          1          0             1\n",
       "          1          1             0\n",
       "          1          0             1\n",
       "          1          1             1\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'key1' (column 1)\n",
       "    'key2' (column 2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = patsy.dmatrices('v2 ~ key1 + key2', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c0d6a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (8, 4)\n",
       "  Intercept  key1[T.b]  key2[T.zero]  key1[T.b]:key2[T.zero]\n",
       "          1          0             1                       0\n",
       "          1          0             0                       0\n",
       "          1          1             1                       1\n",
       "          1          1             0                       0\n",
       "          1          0             1                       0\n",
       "          1          1             0                       0\n",
       "          1          0             1                       0\n",
       "          1          1             1                       1\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'key1' (column 1)\n",
       "    'key2' (column 2)\n",
       "    'key1:key2' (column 3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = patsy.dmatrices('v2 ~ key1 + key2 + key1:key2', data)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448e641e",
   "metadata": {},
   "source": [
    "## 12.3 Introduction to statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d87e1f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "conda install statsmodels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4959c8ea",
   "metadata": {},
   "source": [
    "###### 12.3.1 Estimating Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "682a3e22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "28b1d073",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make the example reproducible\n",
    "rng = np.random.default_rng(seed=12345)\n",
    "\n",
    "def dnorm(mean, variance, size=1):\n",
    "    if isinstance(size, int):\n",
    "        size = size,\n",
    "    return mean + np.sqrt(variance) * rng.standard_normal(*size)\n",
    "\n",
    "N = 100\n",
    "X = np.c_[dnorm(0, 0.4, size=N),\n",
    "          dnorm(0, 0.6, size=N),\n",
    "          dnorm(0, 0.2, size=N)]\n",
    "eps = dnorm(0, 0.1, size=N)\n",
    "beta = [0.1, 0.3, 0.5]\n",
    "\n",
    "y = np.dot(X, beta) + eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a18131c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.90050602, -0.18942958, -1.0278702 ],\n",
       "       [ 0.79925205, -1.54598388, -0.32739708],\n",
       "       [-0.55065483, -0.12025429,  0.32935899],\n",
       "       [-0.16391555,  0.82403985,  0.20827485],\n",
       "       [-0.04765129, -0.21314698, -0.04824364]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6c3904d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.59952668, -0.58845445,  0.18563386, -0.00747657, -0.01537445])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8d226aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.90050602, -0.18942958, -1.0278702 ],\n",
       "       [ 1.        ,  0.79925205, -1.54598388, -0.32739708],\n",
       "       [ 1.        , -0.55065483, -0.12025429,  0.32935899],\n",
       "       [ 1.        , -0.16391555,  0.82403985,  0.20827485],\n",
       "       [ 1.        , -0.04765129, -0.21314698, -0.04824364]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_model = sm.add_constant(X)\n",
    "\n",
    "X_model[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d8958d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06681503, 0.26803235, 0.45052319])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(y, X)\n",
    "\n",
    "results = model.fit()\n",
    "\n",
    "results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0b9c17ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                      y   R-squared (uncentered):                   0.469\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.452\n",
      "Method:                 Least Squares   F-statistic:                              28.51\n",
      "Date:                Thu, 26 Jan 2023   Prob (F-statistic):                    2.66e-13\n",
      "Time:                        03:52:48   Log-Likelihood:                         -25.611\n",
      "No. Observations:                 100   AIC:                                      57.22\n",
      "Df Residuals:                      97   BIC:                                      65.04\n",
      "Df Model:                           3                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1             0.0668      0.054      1.243      0.217      -0.040       0.174\n",
      "x2             0.2680      0.042      6.313      0.000       0.184       0.352\n",
      "x3             0.4505      0.068      6.605      0.000       0.315       0.586\n",
      "==============================================================================\n",
      "Omnibus:                        0.435   Durbin-Watson:                   1.869\n",
      "Prob(Omnibus):                  0.805   Jarque-Bera (JB):                0.301\n",
      "Skew:                           0.134   Prob(JB):                        0.860\n",
      "Kurtosis:                       2.995   Cond. No.                         1.64\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results.summary()) #음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2360061d",
   "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>col0</th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900506</td>\n",
       "      <td>-0.189430</td>\n",
       "      <td>-1.027870</td>\n",
       "      <td>-0.599527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.799252</td>\n",
       "      <td>-1.545984</td>\n",
       "      <td>-0.327397</td>\n",
       "      <td>-0.588454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.550655</td>\n",
       "      <td>-0.120254</td>\n",
       "      <td>0.329359</td>\n",
       "      <td>0.185634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.163916</td>\n",
       "      <td>0.824040</td>\n",
       "      <td>0.208275</td>\n",
       "      <td>-0.007477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.047651</td>\n",
       "      <td>-0.213147</td>\n",
       "      <td>-0.048244</td>\n",
       "      <td>-0.015374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col0      col1      col2         y\n",
       "0 -0.900506 -0.189430 -1.027870 -0.599527\n",
       "1  0.799252 -1.545984 -0.327397 -0.588454\n",
       "2 -0.550655 -0.120254  0.329359  0.185634\n",
       "3 -0.163916  0.824040  0.208275 -0.007477\n",
       "4 -0.047651 -0.213147 -0.048244 -0.015374"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(X, columns=['col0', 'col1', 'col2'])\n",
    "\n",
    "data['y'] = y\n",
    "\n",
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "df94633b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -0.020799\n",
       "col0         0.065813\n",
       "col1         0.268970\n",
       "col2         0.449419\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = smf.ols('y ~ col0 + col1 + col2', data=data).fit()\n",
    "\n",
    "results.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "221a7890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -0.652501\n",
       "col0         1.219768\n",
       "col1         6.312369\n",
       "col2         6.567428\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.tvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b7bc62a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.592959\n",
       "1   -0.531160\n",
       "2    0.058636\n",
       "3    0.283658\n",
       "4   -0.102947\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.predict(data[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c1f660",
   "metadata": {},
   "source": [
    "###### 12.3.2 Estimating TIme Series Processes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1a30c7d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "init_x = 4\n",
    "\n",
    "values = [init_x, init_x]\n",
    "N = 1000\n",
    "\n",
    "b0 = 0.8\n",
    "b1 = -0.4\n",
    "noise = dnorm(0, 0.1, N)\n",
    "for i in range(N):\n",
    "    new_x = values[-1] * b0 + values[-2] * b1 + noise[i]\n",
    "    values.append(new_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "836b3413",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/ar_model.py:248: FutureWarning: The parameter names will change after 0.12 is released. Set old_names to False to use the new names now. Set old_names to True to use the old names. \n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.ar_model import AutoReg\n",
    "\n",
    "MAXLAGS = 5\n",
    "\n",
    "model = AutoReg(values, MAXLAGS)\n",
    "\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d881c516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.02346612,  0.8096828 , -0.42865278, -0.03336517,  0.04267874,\n",
       "       -0.05671529])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3948c70",
   "metadata": {},
   "source": [
    "## 12.4 Introduction to scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9c6750d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9591814d",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('datasets/titanic/train.csv')\n",
    "\n",
    "test = pd.read_csv('datasets/titanic/test.csv')\n",
    "\n",
    "train.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "08e11dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ec93a0ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "50c85693",
   "metadata": {},
   "outputs": [],
   "source": [
    "impute_value = train['Age'].median()\n",
    "\n",
    "train['Age'] = train['Age'].fillna(impute_value)\n",
    "\n",
    "test['Age'] = test['Age'].fillna(impute_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "71de52b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['IsFemale'] = (train['Sex'] == 'female').astype(int)\n",
    "\n",
    "test['IsFemale'] = (test['Sex'] == 'female').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3f36a882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  0., 22.],\n",
       "       [ 1.,  1., 38.],\n",
       "       [ 3.,  1., 26.],\n",
       "       [ 1.,  1., 35.],\n",
       "       [ 3.,  0., 35.]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = ['Pclass', 'IsFemale', 'Age']\n",
    "\n",
    "X_train = train[predictors].to_numpy()\n",
    "\n",
    "X_test = test[predictors].to_numpy()\n",
    "\n",
    "y_train = train['Survived'].to_numpy()\n",
    "\n",
    "X_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a908344e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6c2d4f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4619fd90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "05928211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = model.predict(X_test)\n",
    "\n",
    "y_predict[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dc3738b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "model_cv = LogisticRegressionCV(Cs=10)\n",
    "\n",
    "model_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4db040ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77578475, 0.79820628, 0.77578475, 0.78828829])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = LogisticRegression(C=10)\n",
    "\n",
    "scores = cross_val_score(model, X_train, y_train, cv=4)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1a9fb1",
   "metadata": {},
   "source": [
    "# 13장 Data Analysis Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb48b04",
   "metadata": {},
   "source": [
    "## 13.1장 Bitly Data from 1.USA.gov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25746b4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ \"a\": \"Mozilla\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\/535.11 (KHTML, like Gecko) Chrome\\/17.0.963.78 Safari\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\/\\/www.facebook.com\\/l\\/7AQEFzjSi\\/1.usa.gov\\/wfLQtf\", \"u\": \"http:\\/\\/www.ncbi.nlm.nih.gov\\/pubmed\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\n",
      "\n"
     ]
    }
   ],
   "source": [
    "path = \"datasets/bitly_usagov/example.txt\"\n",
    "\n",
    "with open(path) as f:\n",
    "    print(f.readline())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff879a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open(path) as f:\n",
    "    records = [json.loads(line) for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "226b0b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " 'c': 'US',\n",
       " 'nk': 1,\n",
       " 'tz': 'America/New_York',\n",
       " 'gr': 'MA',\n",
       " 'g': 'A6qOVH',\n",
       " 'h': 'wfLQtf',\n",
       " 'l': 'orofrog',\n",
       " 'al': 'en-US,en;q=0.8',\n",
       " 'hh': '1.usa.gov',\n",
       " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991',\n",
       " 't': 1331923247,\n",
       " 'hc': 1331822918,\n",
       " 'cy': 'Danvers',\n",
       " 'll': [42.576698, -70.954903]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05fbdd68",
   "metadata": {},
   "source": [
    "###### 13.1.1 Counting Time Zones in Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8780756a",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'tz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-abdeba901c13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_zones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tz\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-6-abdeba901c13>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_zones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tz\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'tz'"
     ]
    }
   ],
   "source": [
    "time_zones = [rec[\"tz\"] for rec in records]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "937832a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones = [rec[\"tz\"] for rec in records if \"tz\" in rec]\n",
    "\n",
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "87314e0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_counts(sequence):\n",
    "    counts = {}\n",
    "    for x in sequence:\n",
    "        if x in counts:\n",
    "            counts[x] += 1\n",
    "        else:\n",
    "            counts[x] = 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "61e8fda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def get_counts2(sequence):\n",
    "    counts = defaultdict(int) # values will initialize to 0\n",
    "    for x in sequence:\n",
    "        counts[x] += 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d74865cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = get_counts(time_zones)\n",
    "\n",
    "counts[\"America/New_York\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6b92aa25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b9f955d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_counts(count_dict, n=10):\n",
    "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "568535dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33, 'America/Sao_Paulo'),\n",
       " (35, 'Europe/Madrid'),\n",
       " (36, 'Pacific/Honolulu'),\n",
       " (37, 'Asia/Tokyo'),\n",
       " (74, 'Europe/London'),\n",
       " (191, 'America/Denver'),\n",
       " (382, 'America/Los_Angeles'),\n",
       " (400, 'America/Chicago'),\n",
       " (521, ''),\n",
       " (1251, 'America/New_York')]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0b94228e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('America/New_York', 1251),\n",
       " ('', 521),\n",
       " ('America/Chicago', 400),\n",
       " ('America/Los_Angeles', 382),\n",
       " ('America/Denver', 191),\n",
       " ('Europe/London', 74),\n",
       " ('Asia/Tokyo', 37),\n",
       " ('Pacific/Honolulu', 36),\n",
       " ('Europe/Madrid', 35),\n",
       " ('America/Sao_Paulo', 33)]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(time_zones)\n",
    "\n",
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646927a6",
   "metadata": {},
   "source": [
    "###### 13.1.2 Counting Time Zones with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7efdae6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3560 entries, 0 to 3559\n",
      "Data columns (total 18 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   a            3440 non-null   object \n",
      " 1   c            2919 non-null   object \n",
      " 2   nk           3440 non-null   float64\n",
      " 3   tz           3440 non-null   object \n",
      " 4   gr           2919 non-null   object \n",
      " 5   g            3440 non-null   object \n",
      " 6   h            3440 non-null   object \n",
      " 7   l            3440 non-null   object \n",
      " 8   al           3094 non-null   object \n",
      " 9   hh           3440 non-null   object \n",
      " 10  r            3440 non-null   object \n",
      " 11  u            3440 non-null   object \n",
      " 12  t            3440 non-null   float64\n",
      " 13  hc           3440 non-null   float64\n",
      " 14  cy           2919 non-null   object \n",
      " 15  ll           2919 non-null   object \n",
      " 16  _heartbeat_  120 non-null    float64\n",
      " 17  kw           93 non-null     object \n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 500.8+ KB\n"
     ]
    }
   ],
   "source": [
    "frame = pd.DataFrame(records)\n",
    "\n",
    "frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a8783079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     America/New_York\n",
       "1       America/Denver\n",
       "2     America/New_York\n",
       "3    America/Sao_Paulo\n",
       "4     America/New_York\n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[\"tz\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "07f4ba2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = frame[\"tz\"].value_counts()\n",
    "\n",
    "tz_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5fcf5a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "Unknown                 521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz = frame[\"tz\"].fillna(\"Missing\")\n",
    "\n",
    "clean_tz[clean_tz == \"\"] = \"Unknown\" #\"\"은 missing data가 아님, 그냥 공백 스트링\n",
    "\n",
    "tz_counts = clean_tz.value_counts()\n",
    "\n",
    "tz_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "809f11a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "subset = tz_counts.head()\n",
    "\n",
    "sns.barplot(y=subset.index, x=subset.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9270f5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[\"a\"][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c86e7706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[\"a\"][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "67a877d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P9'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[\"a\"][51][:50]  # long line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b8d341b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Mozilla/5.0\n",
       "1    GoogleMaps/RochesterNY\n",
       "2               Mozilla/4.0\n",
       "3               Mozilla/5.0\n",
       "4               Mozilla/5.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.Series([x.split()[0] for x in frame[\"a\"].dropna()])\n",
    "\n",
    "results.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b9e14638",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0                 2594\n",
       "Mozilla/4.0                  601\n",
       "GoogleMaps/RochesterNY       121\n",
       "Opera/9.80                    34\n",
       "TEST_INTERNET_AGENT           24\n",
       "GoogleProducer                21\n",
       "Mozilla/6.0                    5\n",
       "BlackBerry8520/5.0.0.681       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts().head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c5f75ccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe = frame[frame[\"a\"].notna()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ef970137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Windows\n",
       "1    Not Windows\n",
       "2        Windows\n",
       "3    Not Windows\n",
       "4        Windows\n",
       "Name: os, dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe[\"os\"] = np.where(cframe[\"a\"].str.contains(\"Windows\"),\n",
    "                        \"Windows\", \"Not Windows\")\n",
    "\n",
    "cframe[\"os\"].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1e601bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby([\"tz\", \"os\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "373c3d9b",
   "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>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "                           245.0    276.0\n",
       "Africa/Cairo                 0.0      3.0\n",
       "Africa/Casablanca            0.0      1.0\n",
       "Africa/Ceuta                 0.0      2.0\n",
       "Africa/Johannesburg          0.0      1.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "\n",
    "agg_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e59f5a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24, 20, 21, 92, 87, 53, 54, 57, 26, 55])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer = agg_counts.sum(\"columns\").argsort()\n",
    "\n",
    "indexer.values[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "7be322d1",
   "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>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer[-10:])\n",
    "\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "f677c0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251.0\n",
       "                        521.0\n",
       "America/Chicago         400.0\n",
       "America/Los_Angeles     382.0\n",
       "America/Denver          191.0\n",
       "Europe/London            74.0\n",
       "Asia/Tokyo               37.0\n",
       "Pacific/Honolulu         36.0\n",
       "Europe/Madrid            35.0\n",
       "America/Sao_Paulo        33.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.sum(axis=\"columns\").nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e2b12dc6",
   "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>tz</th>\n",
       "      <th>os</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Windows</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Windows</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Windows</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tz           os  total\n",
       "0  America/Sao_Paulo  Not Windows   13.0\n",
       "1  America/Sao_Paulo      Windows   20.0\n",
       "2      Europe/Madrid  Not Windows   16.0\n",
       "3      Europe/Madrid      Windows   19.0\n",
       "4   Pacific/Honolulu  Not Windows    0.0\n",
       "5   Pacific/Honolulu      Windows   36.0\n",
       "6         Asia/Tokyo  Not Windows    2.0\n",
       "7         Asia/Tokyo      Windows   35.0\n",
       "8      Europe/London  Not Windows   43.0\n",
       "9      Europe/London      Windows   31.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = count_subset.stack()\n",
    "\n",
    "count_subset.name = \"total\"\n",
    "\n",
    "count_subset = count_subset.reset_index()\n",
    "count_subset.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3a616454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total', ylabel='tz'>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"total\", y=\"tz\", hue=\"os\",  data=count_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "25dec097",
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm_total(group):\n",
    "    group[\"normed_total\"] = group[\"total\"] / group[\"total\"].sum()\n",
    "    return group\n",
    "\n",
    "results = count_subset.groupby(\"tz\").apply(norm_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4591d0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='normed_total', ylabel='tz'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"normed_total\", y=\"tz\", hue=\"os\",  data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "94b99c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = count_subset.groupby(\"tz\")\n",
    "\n",
    "results2 = count_subset[\"total\"] / g[\"total\"].transform(\"sum\") #apply의 series 버전"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1551cd36",
   "metadata": {},
   "source": [
    "## 13.2 MovieLens 1M Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "efe8018b",
   "metadata": {},
   "outputs": [],
   "source": [
    "unames = [\"user_id\", \"gender\", \"age\", \"occupation\", \"zip\"]\n",
    "users = pd.read_table(\"datasets/movielens/users.dat\", sep=\"::\",\n",
    "                      header=None, names=unames, engine=\"python\")\n",
    "\n",
    "\n",
    "rnames = [\"user_id\", \"movie_id\", \"rating\", \"timestamp\"]\n",
    "ratings = pd.read_table(\"datasets/movielens/ratings.dat\", sep=\"::\",\n",
    "                        header=None, names=rnames, engine=\"python\")\n",
    "\n",
    "\n",
    "mnames = [\"movie_id\", \"title\", \"genres\"]\n",
    "movies = pd.read_table(\"datasets/movielens/movies.dat\", sep=\"::\",\n",
    "                       header=None, names=mnames, engine=\"python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2c1c1ef",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'movies' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-cd5e25d8a7bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"genre\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmovies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"genres\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"|\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'movies' is not defined"
     ]
    }
   ],
   "source": [
    "movies[\"genre\"] = movies.pop(\"genres\").str.split(\"|\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6bf068",
   "metadata": {},
   "source": [
    "## 13.3 US Baby Names 1880–2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e46a4434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary,F,7065\r",
      "\r\n",
      "Anna,F,2604\r",
      "\r\n",
      "Emma,F,2003\r",
      "\r\n",
      "Elizabeth,F,1939\r",
      "\r\n",
      "Minnie,F,1746\r",
      "\r\n",
      "Margaret,F,1578\r",
      "\r\n",
      "Ida,F,1472\r",
      "\r\n",
      "Alice,F,1414\r",
      "\r\n",
      "Bertha,F,1320\r",
      "\r\n",
      "Sarah,F,1288\r",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!head -n 10 datasets/babynames/yob1880.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca580568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>Woodie</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>Worthy</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Wright</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>York</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>Zachariah</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births\n",
       "0          Mary   F    7065\n",
       "1          Anna   F    2604\n",
       "2          Emma   F    2003\n",
       "3     Elizabeth   F    1939\n",
       "4        Minnie   F    1746\n",
       "...         ...  ..     ...\n",
       "1995     Woodie   M       5\n",
       "1996     Worthy   M       5\n",
       "1997     Wright   M       5\n",
       "1998       York   M       5\n",
       "1999  Zachariah   M       5\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880 = pd.read_csv(\"datasets/babynames/yob1880.txt\",\n",
    "                        names=[\"name\", \"sex\", \"births\"])\n",
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e05137be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby(\"sex\")[\"births\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f488262",
   "metadata": {},
   "outputs": [],
   "source": [
    "pieces = [] #각연도의 정보가 하나의 덩어리로 들어감\n",
    "for year in range(1880, 2011):\n",
    "    path = f\"datasets/babynames/yob{year}.txt\"\n",
    "    frame = pd.read_csv(path, names=[\"name\", \"sex\", \"births\"])\n",
    "\n",
    "    # Add a column for the year\n",
    "    frame[\"year\"] = year\n",
    "    pieces.append(frame)\n",
    "\n",
    "\n",
    "names = pd.concat(pieces, ignore_index=True) #합치기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "479c7fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year\n",
       "0             Mary   F    7065  1880\n",
       "1             Anna   F    2604  1880\n",
       "2             Emma   F    2003  1880\n",
       "3        Elizabeth   F    1939  1880\n",
       "4           Minnie   F    1746  1880\n",
       "...            ...  ..     ...   ...\n",
       "1690779    Zymaire   M       5  2010\n",
       "1690780     Zyonne   M       5  2010\n",
       "1690781  Zyquarius   M       5  2010\n",
       "1690782      Zyran   M       5  2010\n",
       "1690783      Zzyzx   M       5  2010\n",
       "\n",
       "[1690784 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "24141d67",
   "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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = names.pivot_table(\"births\", index=\"year\",\n",
    "                                 columns=\"sex\", aggfunc=sum)\n",
    "\n",
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "69b6df73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total births by sex and year'}, xlabel='year'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title=\"Total births by sex and year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f24810a3",
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</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>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1690784 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year      prop\n",
       "0             Mary   F    7065  1880  0.077643\n",
       "1             Anna   F    2604  1880  0.028618\n",
       "2             Emma   F    2003  1880  0.022013\n",
       "3        Elizabeth   F    1939  1880  0.021309\n",
       "4           Minnie   F    1746  1880  0.019188\n",
       "...            ...  ..     ...   ...       ...\n",
       "1690779    Zymaire   M       5  2010  0.000003\n",
       "1690780     Zyonne   M       5  2010  0.000003\n",
       "1690781  Zyquarius   M       5  2010  0.000003\n",
       "1690782      Zyran   M       5  2010  0.000003\n",
       "1690783      Zzyzx   M       5  2010  0.000003\n",
       "\n",
       "[1690784 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_prop(group):\n",
    "    group[\"prop\"] = group[\"births\"] / group[\"births\"].sum()\n",
    "    return group\n",
    "names = names.groupby([\"year\", \"sex\"]).apply(add_prop)\n",
    "\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8349b12f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "            ... \n",
       "2008  M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.groupby([\"year\", \"sex\"])[\"prop\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f4560ffb",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2010</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>1677639</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677640</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677641</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677642</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677645</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261877 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name sex  births  year      prop\n",
       "year sex                                               \n",
       "1880 F   0             Mary   F    7065  1880  0.077643\n",
       "         1             Anna   F    2604  1880  0.028618\n",
       "         2             Emma   F    2003  1880  0.022013\n",
       "         3        Elizabeth   F    1939  1880  0.021309\n",
       "         4           Minnie   F    1746  1880  0.019188\n",
       "...                     ...  ..     ...   ...       ...\n",
       "2010 M   1677639     Camilo   M     194  2010  0.000102\n",
       "         1677640     Destin   M     194  2010  0.000102\n",
       "         1677641     Jaquan   M     194  2010  0.000102\n",
       "         1677642     Jaydan   M     194  2010  0.000102\n",
       "         1677645     Maxton   M     193  2010  0.000102\n",
       "\n",
       "[261877 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values(\"births\", ascending=False)[:1000]\n",
    "\n",
    "grouped = names.groupby([\"year\", \"sex\"])\n",
    "\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "\n",
    "top1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea3cb493",
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000 = top1000.reset_index(drop=True)\n",
    "\n",
    "top1000.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac70980",
   "metadata": {},
   "source": [
    "###### Analyzing Naming Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9534d785",
   "metadata": {},
   "outputs": [],
   "source": [
    "boys = top1000[top1000[\"sex\"] == \"M\"]\n",
    "\n",
    "girls = top1000[top1000[\"sex\"] == \"F\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f25e8119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "total_births = top1000.pivot_table(\"births\", index=\"year\",\n",
    "                                   columns=\"name\",\n",
    "                                   aggfunc=sum)\n",
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b53550e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='year'>, <AxesSubplot:xlabel='year'>,\n",
       "       <AxesSubplot:xlabel='year'>, <AxesSubplot:xlabel='year'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset = total_births[[\"John\", \"Harry\", \"Mary\", \"Marilyn\"]]\n",
    "subset.plot(subplots=True, figsize=(12, 10),\n",
    "            title=\"Number of births per year\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2baade",
   "metadata": {},
   "source": [
    "###### Measuring the increase in naming diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1521019f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = top1000.pivot_table(\"prop\", index=\"year\",\n",
    "                            columns=\"sex\", aggfunc=sum)\n",
    "\n",
    "In [132]: table.plot(title=\"Sum of table1000.prop by year and sex\",\n",
    "                     yticks=np.linspace(0, 1.2, 13))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "825e3788",
   "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>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</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>261872</th>\n",
       "      <td>Camilo</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261873</th>\n",
       "      <td>Destin</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261874</th>\n",
       "      <td>Jaquan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261875</th>\n",
       "      <td>Jaydan</td>\n",
       "      <td>M</td>\n",
       "      <td>194</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261876</th>\n",
       "      <td>Maxton</td>\n",
       "      <td>M</td>\n",
       "      <td>193</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name sex  births  year      prop\n",
       "260877    Jacob   M   21875  2010  0.011523\n",
       "260878    Ethan   M   17866  2010  0.009411\n",
       "260879  Michael   M   17133  2010  0.009025\n",
       "260880   Jayden   M   17030  2010  0.008971\n",
       "260881  William   M   16870  2010  0.008887\n",
       "...         ...  ..     ...   ...       ...\n",
       "261872   Camilo   M     194  2010  0.000102\n",
       "261873   Destin   M     194  2010  0.000102\n",
       "261874   Jaquan   M     194  2010  0.000102\n",
       "261875   Jaydan   M     194  2010  0.000102\n",
       "261876   Maxton   M     193  2010  0.000102\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys[\"year\"] == 2010]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "133d263a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum = df[\"prop\"].sort_values(ascending=False).cumsum()\n",
    "\n",
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "6dfc2e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 1900]\n",
    "\n",
    "in1900 = df.sort_values(\"prop\", ascending=False).prop.cumsum()\n",
    "\n",
    "in1900.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "133c75c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(\"prop\", ascending=False)\n",
    "    return group.prop.cumsum().searchsorted(q) + 1\n",
    "\n",
    "diversity = top1000.groupby([\"year\", \"sex\"]).apply(get_quantile_count)\n",
    "diversity = diversity.unstack()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "bdda26e6",
   "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>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "999287d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397d919e",
   "metadata": {},
   "source": [
    "###### The \"last letter\" revolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "ec0c0b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_last_letter(x):\n",
    "    return x[-1]\n",
    "last_letters = names[\"name\"].map(get_last_letter)\n",
    "last_letters.name = \"last_letter\"\n",
    "\n",
    "table = names.pivot_table(\"births\", index=last_letters,\n",
    "                          columns=[\"sex\", \"year\"], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "15061239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</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>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level=\"year\")\n",
    "\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1089a0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "3b59f8da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</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>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.001404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.075852</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>0.037907</td>\n",
       "      <td>0.051670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.039965</td>\n",
       "      <td>0.031734</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.022628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000356</td>\n",
       "      <td>0.036581</td>\n",
       "      <td>0.049384</td>\n",
       "      <td>0.018541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>0.043189</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>0.026356</td>\n",
       "      <td>0.065016</td>\n",
       "      <td>0.104904</td>\n",
       "      <td>0.070367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.008613</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.058044</td>\n",
       "      <td>0.033827</td>\n",
       "      <td>0.024657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>0.079240</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>0.140210</td>\n",
       "      <td>0.143415</td>\n",
       "      <td>0.152522</td>\n",
       "      <td>0.362771</td>\n",
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       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.017065</td>\n",
       "      <td>0.012829</td>\n",
       "      <td>0.042681</td>\n",
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       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.005675</td>\n",
       "      <td>0.001269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.018025</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.031034</td>\n",
       "      <td>0.087477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.039042</td>\n",
       "      <td>0.012764</td>\n",
       "      <td>0.013332</td>\n",
       "      <td>0.130815</td>\n",
       "      <td>0.102730</td>\n",
       "      <td>0.065145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.027438</td>\n",
       "      <td>0.015201</td>\n",
       "      <td>0.007830</td>\n",
       "      <td>0.072879</td>\n",
       "      <td>0.065655</td>\n",
       "      <td>0.022861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>0.000684</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.001434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.006329</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>0.016148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>0.008614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.110972</td>\n",
       "      <td>0.152569</td>\n",
       "      <td>0.116828</td>\n",
       "      <td>0.077349</td>\n",
       "      <td>0.160987</td>\n",
       "      <td>0.058168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.001831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
       "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
       "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
       "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
       "j                 NaN       NaN  0.000090       NaN       NaN  0.000769\n",
       "k            0.000121  0.000156  0.000356  0.036581  0.049384  0.018541\n",
       "l            0.043189  0.033867  0.026356  0.065016  0.104904  0.070367\n",
       "m            0.001201  0.008613  0.002588  0.058044  0.033827  0.024657\n",
       "n            0.079240  0.130687  0.140210  0.143415  0.152522  0.362771\n",
       "o            0.001660  0.002439  0.001243  0.017065  0.012829  0.042681\n",
       "p            0.000018  0.000023  0.000020  0.003172  0.005675  0.001269\n",
       "q                 NaN       NaN  0.000030       NaN       NaN  0.000180\n",
       "r            0.013390  0.006764  0.018025  0.064481  0.031034  0.087477\n",
       "s            0.039042  0.012764  0.013332  0.130815  0.102730  0.065145\n",
       "t            0.027438  0.015201  0.007830  0.072879  0.065655  0.022861\n",
       "u            0.000684  0.000574  0.000417  0.000124  0.000057  0.001221\n",
       "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
       "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
       "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
       "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
       "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable / subtable.sum()\n",
    "\n",
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d01a1a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Female'}, xlabel='last_letter'>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
    "letter_prop[\"M\"].plot(kind=\"bar\", rot=0, ax=axes[0], title=\"Male\")\n",
    "letter_prop[\"F\"].plot(kind=\"bar\", rot=0, ax=axes[1], title=\"Female\",\n",
    "                      legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ade8d81a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = pd.Series(top1000[\"name\"].unique())\n",
    "\n",
    "lesley_like = all_names[all_names.str.contains(\"Lesl\")]\n",
    "\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "632a13f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000[\"name\"].isin(lesley_like)]\n",
    "\n",
    "filtered.groupby(\"name\")[\"births\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8788b3cb",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-149-ba479d072ff9>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-149-ba479d072ff9>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    \"description\": \"KENTUCKY FRIED CHICKEN, Fried Chicken, EXTRA CRISPY,\u001b[0m\n\u001b[0m                                                                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2877e8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "db = json.load(open(\"datasets/usda_food/database.json\"))\n",
    "\n",
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8a6cb8d6",
   "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>value</th>\n",
       "      <th>units</th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.18</td>\n",
       "      <td>g</td>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.20</td>\n",
       "      <td>g</td>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.06</td>\n",
       "      <td>g</td>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.28</td>\n",
       "      <td>g</td>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>376.00</td>\n",
       "      <td>kcal</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.28</td>\n",
       "      <td>g</td>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1573.00</td>\n",
       "      <td>kJ</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     value units                  description        group\n",
       "0    25.18     g                      Protein  Composition\n",
       "1    29.20     g            Total lipid (fat)  Composition\n",
       "2     3.06     g  Carbohydrate, by difference  Composition\n",
       "3     3.28     g                          Ash        Other\n",
       "4   376.00  kcal                       Energy       Energy\n",
       "5    39.28     g                        Water  Composition\n",
       "6  1573.00    kJ                       Energy       Energy"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients = pd.DataFrame(db[0][\"nutrients\"])\n",
    "\n",
    "\n",
    "nutrients.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5071253f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count    Dtype  \n",
      "---  ------             --------------    -----  \n",
      " 0   cmte_id            1001731 non-null  object \n",
      " 1   cand_id            1001731 non-null  object \n",
      " 2   cand_nm            1001731 non-null  object \n",
      " 3   contbr_nm          1001731 non-null  object \n",
      " 4   contbr_city        1001712 non-null  object \n",
      " 5   contbr_st          1001727 non-null  object \n",
      " 6   contbr_zip         1001620 non-null  object \n",
      " 7   contbr_employer    988002 non-null   object \n",
      " 8   contbr_occupation  993301 non-null   object \n",
      " 9   contb_receipt_amt  1001731 non-null  float64\n",
      " 10  contb_receipt_dt   1001731 non-null  object \n",
      " 11  receipt_desc       14166 non-null    object \n",
      " 12  memo_cd            92482 non-null    object \n",
      " 13  memo_text          97770 non-null    object \n",
      " 14  form_tp            1001731 non-null  object \n",
      " 15  file_num           1001731 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "fec = pd.read_csv(\"datasets/fec/P00000001-ALL.csv\", low_memory=False)\n",
    "\n",
    "fec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "53d9c031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmte_id                             C00431445\n",
       "cand_id                             P80003338\n",
       "cand_nm                         Obama, Barack\n",
       "contbr_nm                         ELLMAN, IRA\n",
       "contbr_city                             TEMPE\n",
       "contbr_st                                  AZ\n",
       "contbr_zip                          852816719\n",
       "contbr_employer      ARIZONA STATE UNIVERSITY\n",
       "contbr_occupation                   PROFESSOR\n",
       "contb_receipt_amt                        50.0\n",
       "contb_receipt_dt                    01-DEC-11\n",
       "receipt_desc                              NaN\n",
       "memo_cd                                   NaN\n",
       "memo_text                                 NaN\n",
       "form_tp                                 SA17A\n",
       "file_num                               772372\n",
       "Name: 123456, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.iloc[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d39ce4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
       "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
       "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands = fec[\"cand_nm\"].unique()\n",
    "\n",
    "unique_cands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b1f9df0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Obama, Barack'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "eb426679",
   "metadata": {},
   "outputs": [],
   "source": [
    "parties = {\"Bachmann, Michelle\": \"Republican\",\n",
    "           \"Cain, Herman\": \"Republican\",\n",
    "           \"Gingrich, Newt\": \"Republican\",\n",
    "           \"Huntsman, Jon\": \"Republican\",\n",
    "           \"Johnson, Gary Earl\": \"Republican\",\n",
    "           \"McCotter, Thaddeus G\": \"Republican\",\n",
    "           \"Obama, Barack\": \"Democrat\",\n",
    "           \"Paul, Ron\": \"Republican\",\n",
    "           \"Pawlenty, Timothy\": \"Republican\",\n",
    "           \"Perry, Rick\": \"Republican\",\n",
    "           \"Roemer, Charles E. 'Buddy' III\": \"Republican\",\n",
    "           \"Romney, Mitt\": \"Republican\",\n",
    "           \"Santorum, Rick\": \"Republican\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b6c5d47e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Obama, Barack\n",
       "123457    Obama, Barack\n",
       "123458    Obama, Barack\n",
       "123459    Obama, Barack\n",
       "123460    Obama, Barack\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"cand_nm\"][123456:123461]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3d0cfb56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Democrat\n",
       "123457    Democrat\n",
       "123458    Democrat\n",
       "123459    Democrat\n",
       "123460    Democrat\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"cand_nm\"][123456:123461].map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9ae870d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"party\"] = fec[\"cand_nm\"].map(parties)\n",
    "\n",
    "fec[\"party\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8fd07e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     991475\n",
       "False     10256\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fec[\"contb_receipt_amt\"] > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "86e4bc01",
   "metadata": {},
   "outputs": [],
   "source": [
    "fec = fec[fec[\"contb_receipt_amt\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "642e8b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "fec_mrbo = fec[fec[\"cand_nm\"].isin([\"Obama, Barack\", \"Romney, Mitt\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a20c810",
   "metadata": {},
   "source": [
    "###### Donation Statistics by Occupation and Employer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fd1cbfe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                                   233990\n",
       "INFORMATION REQUESTED                      35107\n",
       "ATTORNEY                                   34286\n",
       "HOMEMAKER                                  29931\n",
       "PHYSICIAN                                  23432\n",
       "INFORMATION REQUESTED PER BEST EFFORTS     21138\n",
       "ENGINEER                                   14334\n",
       "TEACHER                                    13990\n",
       "CONSULTANT                                 13273\n",
       "PROFESSOR                                  12555\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec[\"contbr_occupation\"].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bbb169d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "occ_mapping = {\n",
    "   \"INFORMATION REQUESTED PER BEST EFFORTS\" : \"NOT PROVIDED\",\n",
    "   \"INFORMATION REQUESTED\" : \"NOT PROVIDED\",\n",
    "   \"INFORMATION REQUESTED (BEST EFFORTS)\" : \"NOT PROVIDED\",\n",
    "   \"C.E.O.\": \"CEO\"\n",
    "}\n",
    "\n",
    "def get_occ(x):\n",
    "    # If no mapping provided, return x\n",
    "    return occ_mapping.get(x, x)\n",
    "\n",
    "fec[\"contbr_occupation\"] = fec[\"contbr_occupation\"].map(get_occ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f634accc",
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_mapping = {\n",
    "   \"INFORMATION REQUESTED PER BEST EFFORTS\" : \"NOT PROVIDED\",\n",
    "   \"INFORMATION REQUESTED\" : \"NOT PROVIDED\",\n",
    "   \"SELF\" : \"SELF-EMPLOYED\",\n",
    "   \"SELF EMPLOYED\" : \"SELF-EMPLOYED\",\n",
    "}\n",
    "#employer용\n",
    "def get_emp(x):\n",
    "    # If no mapping provided, return x\n",
    "    return emp_mapping.get(x, x)\n",
    "\n",
    "fec[\"contbr_employer\"] = fec[\"contbr_employer\"].map(get_emp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "78ea6d52",
   "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>party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATTORNEY</th>\n",
       "      <td>11141982.97</td>\n",
       "      <td>7.477194e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEO</th>\n",
       "      <td>2074974.79</td>\n",
       "      <td>4.211041e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSULTANT</th>\n",
       "      <td>2459912.71</td>\n",
       "      <td>2.544725e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGINEER</th>\n",
       "      <td>951525.55</td>\n",
       "      <td>1.818374e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXECUTIVE</th>\n",
       "      <td>1355161.05</td>\n",
       "      <td>4.138850e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <td>4248875.80</td>\n",
       "      <td>1.363428e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVESTOR</th>\n",
       "      <td>884133.00</td>\n",
       "      <td>2.431769e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAWYER</th>\n",
       "      <td>3160478.87</td>\n",
       "      <td>3.912243e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGER</th>\n",
       "      <td>762883.22</td>\n",
       "      <td>1.444532e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOT PROVIDED</th>\n",
       "      <td>4866973.96</td>\n",
       "      <td>2.056547e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWNER</th>\n",
       "      <td>1001567.36</td>\n",
       "      <td>2.408287e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHYSICIAN</th>\n",
       "      <td>3735124.94</td>\n",
       "      <td>3.594320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRESIDENT</th>\n",
       "      <td>1878509.95</td>\n",
       "      <td>4.720924e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROFESSOR</th>\n",
       "      <td>2165071.08</td>\n",
       "      <td>2.967027e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REAL ESTATE</th>\n",
       "      <td>528902.09</td>\n",
       "      <td>1.625902e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RETIRED</th>\n",
       "      <td>25305116.38</td>\n",
       "      <td>2.356124e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SELF-EMPLOYED</th>\n",
       "      <td>672393.40</td>\n",
       "      <td>1.640253e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "party                 Democrat    Republican\n",
       "contbr_occupation                           \n",
       "ATTORNEY           11141982.97  7.477194e+06\n",
       "CEO                 2074974.79  4.211041e+06\n",
       "CONSULTANT          2459912.71  2.544725e+06\n",
       "ENGINEER             951525.55  1.818374e+06\n",
       "EXECUTIVE           1355161.05  4.138850e+06\n",
       "HOMEMAKER           4248875.80  1.363428e+07\n",
       "INVESTOR             884133.00  2.431769e+06\n",
       "LAWYER              3160478.87  3.912243e+05\n",
       "MANAGER              762883.22  1.444532e+06\n",
       "NOT PROVIDED        4866973.96  2.056547e+07\n",
       "OWNER               1001567.36  2.408287e+06\n",
       "PHYSICIAN           3735124.94  3.594320e+06\n",
       "PRESIDENT           1878509.95  4.720924e+06\n",
       "PROFESSOR           2165071.08  2.967027e+05\n",
       "REAL ESTATE          528902.09  1.625902e+06\n",
       "RETIRED            25305116.38  2.356124e+07\n",
       "SELF-EMPLOYED        672393.40  1.640253e+06"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_occupation = fec.pivot_table(\"contb_receipt_amt\",\n",
    "                                index=\"contbr_occupation\",\n",
    "                                columns=\"party\", aggfunc=\"sum\")\n",
    "\n",
    "over_2mm=by_occupation[by_occupation.sum(axis=\"columns\") > 2000000]\n",
    "over_2mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1dab38a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='contbr_occupation'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "over_2mm.plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "da971e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_amounts(group, key, n=5):\n",
    "    totals = group.groupby(key)[\"contb_receipt_amt\"].sum()\n",
    "    return totals.nlargest(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "43f1ef34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_occupation                     \n",
       "Obama, Barack  RETIRED                                   25305116.38\n",
       "               ATTORNEY                                  11141982.97\n",
       "               INFORMATION REQUESTED                      4866973.96\n",
       "               HOMEMAKER                                  4248875.80\n",
       "               PHYSICIAN                                  3735124.94\n",
       "               LAWYER                                     3160478.87\n",
       "               CONSULTANT                                 2459912.71\n",
       "Romney, Mitt   RETIRED                                   11508473.59\n",
       "               INFORMATION REQUESTED PER BEST EFFORTS    11396894.84\n",
       "               HOMEMAKER                                  8147446.22\n",
       "               ATTORNEY                                   5364718.82\n",
       "               PRESIDENT                                  2491244.89\n",
       "               EXECUTIVE                                  2300947.03\n",
       "               C.E.O.                                     1968386.11\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(\"cand_nm\")\n",
    "\n",
    "grouped.apply(get_top_amounts, \"contbr_occupation\", n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "914dab49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_employer                       \n",
       "Obama, Barack  RETIRED                                   22694358.85\n",
       "               SELF-EMPLOYED                             17080985.96\n",
       "               NOT EMPLOYED                               8586308.70\n",
       "               INFORMATION REQUESTED                      5053480.37\n",
       "               HOMEMAKER                                  2605408.54\n",
       "               SELF                                       1076531.20\n",
       "               SELF EMPLOYED                               469290.00\n",
       "               STUDENT                                     318831.45\n",
       "               VOLUNTEER                                   257104.00\n",
       "               MICROSOFT                                   215585.36\n",
       "Romney, Mitt   INFORMATION REQUESTED PER BEST EFFORTS    12059527.24\n",
       "               RETIRED                                   11506225.71\n",
       "               HOMEMAKER                                  8147196.22\n",
       "               SELF-EMPLOYED                              7409860.98\n",
       "               STUDENT                                     496490.94\n",
       "               CREDIT SUISSE                               281150.00\n",
       "               MORGAN STANLEY                              267266.00\n",
       "               GOLDMAN SACH & CO.                          238250.00\n",
       "               BARCLAYS CAPITAL                            162750.00\n",
       "               H.I.G. CAPITAL                              139500.00\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_top_amounts, \"contbr_employer\", n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3ba01250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411         (10, 100]\n",
       "412       (100, 1000]\n",
       "413       (100, 1000]\n",
       "414         (10, 100]\n",
       "415         (10, 100]\n",
       "             ...     \n",
       "701381      (10, 100]\n",
       "701382    (100, 1000]\n",
       "701383        (1, 10]\n",
       "701384      (10, 100]\n",
       "701385    (100, 1000]\n",
       "Name: contb_receipt_amt, Length: 694282, dtype: category\n",
       "Categories (8, interval[int64]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.array([0, 1, 10, 100, 1000, 10000,\n",
    "                 100_000, 1_000_000, 10_000_000])\n",
    "\n",
    "labels = pd.cut(fec_mrbo[\"contb_receipt_amt\"], bins)\n",
    "\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "840744ab",
   "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>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>493</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>40070</td>\n",
       "      <td>3681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>372280</td>\n",
       "      <td>31853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>153991</td>\n",
       "      <td>43357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>22284</td>\n",
       "      <td>26186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                         493            77\n",
       "(1, 10]                      40070          3681\n",
       "(10, 100]                   372280         31853\n",
       "(100, 1000]                 153991         43357\n",
       "(1000, 10000]                22284         26186\n",
       "(10000, 100000]                  2             1\n",
       "(100000, 1000000]                3             0\n",
       "(1000000, 10000000]              4             0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby([\"cand_nm\", labels])\n",
    "\n",
    "grouped.size().unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3557575",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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