pca.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 主成分分析\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 準備\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### インポート\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ライブラリをインポートする．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scikit-learn の `PCA` モデルをインポートする．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 説明変数の準備\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`univ-power.csv` を pandas の `DataFrame` として読み込む．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num-books</th>\n",
       "      <th>num-rooms</th>\n",
       "      <th>female-ratio</th>\n",
       "      <th>num-faculties</th>\n",
       "      <th>num-students</th>\n",
       "      <th>num-doctors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>77</td>\n",
       "      <td>64</td>\n",
       "      <td>87</td>\n",
       "      <td>71</td>\n",
       "      <td>85</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70</td>\n",
       "      <td>87</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>67</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>73</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>54</td>\n",
       "      <td>70</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>59</td>\n",
       "      <td>25</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>58</td>\n",
       "      <td>17</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>70</td>\n",
       "      <td>56</td>\n",
       "      <td>76</td>\n",
       "      <td>62</td>\n",
       "      <td>67</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num-books  num-rooms  female-ratio  num-faculties  num-students  \\\n",
       "0           29         21            26             30            10   \n",
       "1           32          7            14             18            10   \n",
       "2           77         64            87             71            85   \n",
       "3           91         69           100             83            96   \n",
       "4           70         87            84             88            67   \n",
       "..         ...        ...           ...            ...           ...   \n",
       "145         73         60            59             54            70   \n",
       "146         59         25            45             48            37   \n",
       "147         58         17            31             23            24   \n",
       "148         70         56            76             62            67   \n",
       "149         19         12             9             28            10   \n",
       "\n",
       "     num-doctors  \n",
       "0              6  \n",
       "1              7  \n",
       "2             71  \n",
       "3             91  \n",
       "4             91  \n",
       "..           ...  \n",
       "145           65  \n",
       "146           32  \n",
       "147           25  \n",
       "148           88  \n",
       "149            5  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.read_csv(\"univ-power.csv\")\n",
    "\n",
    "x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "説明変数を標準化する．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num-books</th>\n",
       "      <th>num-rooms</th>\n",
       "      <th>female-ratio</th>\n",
       "      <th>num-faculties</th>\n",
       "      <th>num-students</th>\n",
       "      <th>num-doctors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.089869</td>\n",
       "      <td>-0.886808</td>\n",
       "      <td>-0.804615</td>\n",
       "      <td>-0.930905</td>\n",
       "      <td>-1.417541</td>\n",
       "      <td>-1.127962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.950102</td>\n",
       "      <td>-1.546029</td>\n",
       "      <td>-1.296734</td>\n",
       "      <td>-1.499300</td>\n",
       "      <td>-1.417541</td>\n",
       "      <td>-1.094359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.146397</td>\n",
       "      <td>1.137940</td>\n",
       "      <td>1.696991</td>\n",
       "      <td>1.011112</td>\n",
       "      <td>1.920118</td>\n",
       "      <td>1.056275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.798641</td>\n",
       "      <td>1.373376</td>\n",
       "      <td>2.230121</td>\n",
       "      <td>1.579507</td>\n",
       "      <td>2.409642</td>\n",
       "      <td>1.728347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.820275</td>\n",
       "      <td>2.220945</td>\n",
       "      <td>1.573962</td>\n",
       "      <td>1.816338</td>\n",
       "      <td>1.119080</td>\n",
       "      <td>1.728347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.960041</td>\n",
       "      <td>0.949591</td>\n",
       "      <td>0.548713</td>\n",
       "      <td>0.205885</td>\n",
       "      <td>1.252587</td>\n",
       "      <td>0.854653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.307797</td>\n",
       "      <td>-0.698460</td>\n",
       "      <td>-0.025426</td>\n",
       "      <td>-0.078312</td>\n",
       "      <td>-0.215984</td>\n",
       "      <td>-0.254268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.261208</td>\n",
       "      <td>-1.075157</td>\n",
       "      <td>-0.599565</td>\n",
       "      <td>-1.262469</td>\n",
       "      <td>-0.794511</td>\n",
       "      <td>-0.489493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.820275</td>\n",
       "      <td>0.761243</td>\n",
       "      <td>1.245882</td>\n",
       "      <td>0.584815</td>\n",
       "      <td>1.119080</td>\n",
       "      <td>1.627536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-1.555757</td>\n",
       "      <td>-1.310593</td>\n",
       "      <td>-1.501784</td>\n",
       "      <td>-1.025637</td>\n",
       "      <td>-1.417541</td>\n",
       "      <td>-1.161566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num-books  num-rooms  female-ratio  num-faculties  num-students  \\\n",
       "0    -1.089869  -0.886808     -0.804615      -0.930905     -1.417541   \n",
       "1    -0.950102  -1.546029     -1.296734      -1.499300     -1.417541   \n",
       "2     1.146397   1.137940      1.696991       1.011112      1.920118   \n",
       "3     1.798641   1.373376      2.230121       1.579507      2.409642   \n",
       "4     0.820275   2.220945      1.573962       1.816338      1.119080   \n",
       "..         ...        ...           ...            ...           ...   \n",
       "145   0.960041   0.949591      0.548713       0.205885      1.252587   \n",
       "146   0.307797  -0.698460     -0.025426      -0.078312     -0.215984   \n",
       "147   0.261208  -1.075157     -0.599565      -1.262469     -0.794511   \n",
       "148   0.820275   0.761243      1.245882       0.584815      1.119080   \n",
       "149  -1.555757  -1.310593     -1.501784      -1.025637     -1.417541   \n",
       "\n",
       "     num-doctors  \n",
       "0      -1.127962  \n",
       "1      -1.094359  \n",
       "2       1.056275  \n",
       "3       1.728347  \n",
       "4       1.728347  \n",
       "..           ...  \n",
       "145     0.854653  \n",
       "146    -0.254268  \n",
       "147    -0.489493  \n",
       "148     1.627536  \n",
       "149    -1.161566  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "x_standardized = pd.DataFrame(\n",
    "    StandardScaler().fit_transform(x), columns=x.columns\n",
    ")\n",
    "\n",
    "x_standardized\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 次元削減\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "保持する成分の数を 6 として，モデルを定義する．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc_names = [f\"PC{i+1}\" for i in range(pca.n_components)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "モデルを説明変数に適合させ，説明変数に次元削減を適用する．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.546952</td>\n",
       "      <td>0.014808</td>\n",
       "      <td>0.328814</td>\n",
       "      <td>-0.331392</td>\n",
       "      <td>0.247082</td>\n",
       "      <td>-0.061482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.192309</td>\n",
       "      <td>-0.325995</td>\n",
       "      <td>0.135591</td>\n",
       "      <td>0.289910</td>\n",
       "      <td>0.096612</td>\n",
       "      <td>0.143128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.245310</td>\n",
       "      <td>0.451240</td>\n",
       "      <td>-0.381275</td>\n",
       "      <td>0.508205</td>\n",
       "      <td>0.210692</td>\n",
       "      <td>-0.360747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.527843</td>\n",
       "      <td>0.393265</td>\n",
       "      <td>-0.318516</td>\n",
       "      <td>0.742538</td>\n",
       "      <td>0.255266</td>\n",
       "      <td>-0.095458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.808062</td>\n",
       "      <td>0.275880</td>\n",
       "      <td>0.557937</td>\n",
       "      <td>-0.695622</td>\n",
       "      <td>-0.385472</td>\n",
       "      <td>-0.316531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.936989</td>\n",
       "      <td>-0.291028</td>\n",
       "      <td>-0.418691</td>\n",
       "      <td>0.427678</td>\n",
       "      <td>-0.286610</td>\n",
       "      <td>-0.435086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-0.402250</td>\n",
       "      <td>-0.154525</td>\n",
       "      <td>-0.091104</td>\n",
       "      <td>0.317355</td>\n",
       "      <td>0.449089</td>\n",
       "      <td>0.451982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>-1.634274</td>\n",
       "      <td>-0.832479</td>\n",
       "      <td>-0.015162</td>\n",
       "      <td>0.711467</td>\n",
       "      <td>0.410630</td>\n",
       "      <td>0.097183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2.508633</td>\n",
       "      <td>0.068520</td>\n",
       "      <td>0.409679</td>\n",
       "      <td>0.706319</td>\n",
       "      <td>-0.242591</td>\n",
       "      <td>-0.155822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-3.248678</td>\n",
       "      <td>0.159673</td>\n",
       "      <td>0.175113</td>\n",
       "      <td>-0.265337</td>\n",
       "      <td>-0.275777</td>\n",
       "      <td>0.217837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PC1       PC2       PC3       PC4       PC5       PC6\n",
       "0   -2.546952  0.014808  0.328814 -0.331392  0.247082 -0.061482\n",
       "1   -3.192309 -0.325995  0.135591  0.289910  0.096612  0.143128\n",
       "2    3.245310  0.451240 -0.381275  0.508205  0.210692 -0.360747\n",
       "3    4.527843  0.393265 -0.318516  0.742538  0.255266 -0.095458\n",
       "4    3.808062  0.275880  0.557937 -0.695622 -0.385472 -0.316531\n",
       "..        ...       ...       ...       ...       ...       ...\n",
       "145  1.936989 -0.291028 -0.418691  0.427678 -0.286610 -0.435086\n",
       "146 -0.402250 -0.154525 -0.091104  0.317355  0.449089  0.451982\n",
       "147 -1.634274 -0.832479 -0.015162  0.711467  0.410630  0.097183\n",
       "148  2.508633  0.068520  0.409679  0.706319 -0.242591 -0.155822\n",
       "149 -3.248678  0.159673  0.175113 -0.265337 -0.275777  0.217837\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_transformed = pd.DataFrame(\n",
    "    pca.fit_transform(x_standardized),\n",
    "    columns=pc_names,\n",
    ")\n",
    "\n",
    "x_transformed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 結果\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第 1, 2 主成分のプロット\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第 1 主成分を $x$ 軸に，第 2 主成分を $y$ 軸にして，散布図をプロットする．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_transformed.plot.scatter(x=\"PC1\", y=\"PC2\").plot()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 寄与率と累積寄与率\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第 6 主成分までのそれぞれの寄与率を表示する．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>0.804057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>0.054051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>0.049957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>0.039154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC5</th>\n",
       "      <td>0.030101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC6</th>\n",
       "      <td>0.022680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "PC1  0.804057\n",
       "PC2  0.054051\n",
       "PC3  0.049957\n",
       "PC4  0.039154\n",
       "PC5  0.030101\n",
       "PC6  0.022680"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    pca.explained_variance_ratio_,\n",
    "    index=pc_names,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "累積寄与率を NumPy の `cumsum` 関数で計算し，折れ線グラフとしてプロットする．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "plt.gca().get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True))\n",
    "plt.plot([0] + list(np.cumsum(pca.explained_variance_ratio_)), \"-o\")\n",
    "plt.xlabel(\"Number of principal components\")\n",
    "plt.ylabel(\"Cumulative contribution rate\")\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主成分負荷量\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第 6 主成分までのそれぞれの主成分負荷量を表示する．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>num-books</th>\n",
       "      <th>num-rooms</th>\n",
       "      <th>female-ratio</th>\n",
       "      <th>num-faculties</th>\n",
       "      <th>num-students</th>\n",
       "      <th>num-doctors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>0.397546</td>\n",
       "      <td>0.417847</td>\n",
       "      <td>0.410562</td>\n",
       "      <td>0.417156</td>\n",
       "      <td>0.398580</td>\n",
       "      <td>0.407327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>-0.732606</td>\n",
       "      <td>-0.213198</td>\n",
       "      <td>0.373410</td>\n",
       "      <td>0.334262</td>\n",
       "      <td>0.377811</td>\n",
       "      <td>-0.154685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>-0.279346</td>\n",
       "      <td>-0.003814</td>\n",
       "      <td>0.385340</td>\n",
       "      <td>-0.024319</td>\n",
       "      <td>-0.670241</td>\n",
       "      <td>0.568902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>0.164974</td>\n",
       "      <td>-0.593188</td>\n",
       "      <td>0.208070</td>\n",
       "      <td>-0.522054</td>\n",
       "      <td>0.398138</td>\n",
       "      <td>0.382837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC5</th>\n",
       "      <td>0.381840</td>\n",
       "      <td>-0.213542</td>\n",
       "      <td>0.650397</td>\n",
       "      <td>0.009895</td>\n",
       "      <td>-0.262098</td>\n",
       "      <td>-0.562840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC6</th>\n",
       "      <td>0.232795</td>\n",
       "      <td>-0.618437</td>\n",
       "      <td>-0.277862</td>\n",
       "      <td>0.664093</td>\n",
       "      <td>-0.148194</td>\n",
       "      <td>0.152166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     num-books  num-rooms  female-ratio  num-faculties  num-students  \\\n",
       "PC1   0.397546   0.417847      0.410562       0.417156      0.398580   \n",
       "PC2  -0.732606  -0.213198      0.373410       0.334262      0.377811   \n",
       "PC3  -0.279346  -0.003814      0.385340      -0.024319     -0.670241   \n",
       "PC4   0.164974  -0.593188      0.208070      -0.522054      0.398138   \n",
       "PC5   0.381840  -0.213542      0.650397       0.009895     -0.262098   \n",
       "PC6   0.232795  -0.618437     -0.277862       0.664093     -0.148194   \n",
       "\n",
       "     num-doctors  \n",
       "PC1     0.407327  \n",
       "PC2    -0.154685  \n",
       "PC3     0.568902  \n",
       "PC4     0.382837  \n",
       "PC5    -0.562840  \n",
       "PC6     0.152166  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components_loading = pd.DataFrame(\n",
    "    pca.components_,\n",
    "    index=pc_names,\n",
    "    columns=x.columns,\n",
    ")\n",
    "\n",
    "components_loading\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第 6 主成分までのそれぞれの主成分負荷量を棒グラフとしてプロットする．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nKeXfSyn/UUo5qqcPKqWcWkpZWEpZeP/99/fD1PrfihUrMm7cuMyePTv77LNPjjjiiKxcuTKHHnpo1j5E5YEHHkhHR0eSNWdzZ8yYkenTp6ejoyMXXnhhLrjggkyePDkHHHBAHnrooWccY86cOfnRj36UN7zhDfnIRz6Sm2++OQceeGAmT56c17zmNbnjjjuSJE8++WTOOOOMjB8/PhMmTMgnPvGJJElHR0ceeOCBJMnChQtz6KGHPu3zv/e97+Xqq6/OmWeemUmTJuWuu+7KrFmz1j0We9GiRTnkkEMyZcqUHHnkkbnvvvuSrDnbvffee2fChAl5y1ve0u8/WwCArc1APe56RJJXJjk0ye5JbiildNZaf7n+oFrr3CRzkzVP8BuguW225cuX5/Of/3w+/elP58QTT8wVV1yx0fFLly7NrbfemlWrVuUVr3hFzj///Nx666354z/+41x66aV5z3ve87TxF198cb72ta/l+uuvz84775z/+Z//yY033pgRI0bkuuuuy5//+Z/niiuuyNy5c7NixYp0dXVlxIgRPYZ3T17zmtfkuOOOyzHHHJMTTjjhafueeOKJ/OEf/mG+/OUvZ8yYMbn88stz9tln5x//8R9z3nnn5cc//nG23377/PKXv9ycHxkAwJDUH7H8syR7rPd+9+5t67snyfdrrU8k+XEp5YdZE88L+uH4A27s2LGZNGlSkmTKlClZsWLFRsdPmzYtI0eOzMiRIzNq1Kgce+yxSZLOzs7cdtttmzzeww8/nJkzZ2b58uUppeSJJ55Iklx33XWZM2dORoxYs4wvetGLnv031e2OO+7I0qVLM3369CRrzl7vuuuuSZIJEybkpJNOyowZMzJjxow+HwsAYGvXH5dhLEjyylLK2FLK85K8JcnVG4y5KmvOKqeUsnPWXJbxo3449qDYfvvt170ePnx4Vq9enREjRuSpp55Kkqxatao5ftiwYeveDxs2LKtXr87dd9+dSZMmZdKkSbn44oufcbwPfOADmTZtWpYuXZp//dd/fcbnb2hjc9mUWmv22WefdHV1paurK0uWLMm1116bJPnKV76S008/Pbfcckv222+/rF69erM+GwBgqOlzLNdaVyd5V5KvJ1mW5F9qrbeXUs4tpRzXPezrSR4spfxnkuuTnFlrfbCvx96adHR0ZNGiRUmy7trf3tpjjz3WxemcOXOesf/hhx/OS1+65jLwefPmrds+ffr0/P3f//26aF17Gcb6c2ldIjJy5Mg88sgjz9i+11575f77789NN92UZM1lGbfffnueeuqp3H333Zk2bVrOP//8PPzww3n00Uc36/sEABhq+uWa5VrrV5N8dYNtH1zvdU3yJ93/9Jut6VZvZ5xxRk488cTMnTs3Rx99dL9+9p/+6Z9m5syZ+eu//uunffYpp5ySH/7wh5kwYUK22267zJ49O+9617vyF3/xF3nHO96RD3zgA8/4y31rveUtb8ns2bPz8Y9//Glx/7znPS/z58/Pu9/97jz88MNZvXp13vOe92TPPffM2972tjz88MOptebd7353Ro8e3a/fJwDA1qas6ditz9SpU+vau0ustWzZsowb17/3Vqb/WSeA55bOSzp7NW7JzCVbeCZsKb19tsXWdCJzc5RSFtVap/a0z+OuAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADT0y32WB8tFc77Vr593+sWH9evn9bePfvSjOfXUU7Pjjjtu1tfttNNOz/oBIvPmzcsRRxyR3Xbb7Vl9PWzL/u7Nx2xyzHsvv2YAZgLAluLM8hDy0Y9+NI8//viAHnPevHm59957B/SYAABbC7G8mVasWJFx48Zl9uzZ2WeffXLEEUdk5cqVOfTQQ7P2ISoPPPBAOjo6kqyJzRkzZmT69Onp6OjIhRdemAsuuCCTJ0/OAQccsO4R1et77LHHcvTRR2fixIkZP358Lr/88nz84x/Pvffem2nTpmXatGlJ1pwxXmv+/PmZNWtWkuTHP/5xDjzwwHR2dub973//0z77wx/+cPbbb79MmDAhf/EXf7HR72n+/PlZuHBhTjrppEyaNCkrV67MWWedlb333jsTJkzIGWec0d8/XgCArYpYfhaWL1+e008/PbfffntGjx6dK664YqPjly5dmiuvvDILFizI2WefnR133DG33nprDjzwwFx66aXPGP+1r30tu+22WxYvXpylS5fmqKOOyrvf/e7stttuuf7663P99ddv9Hh/9Ed/lHe+851ZsmRJdt1113Xbr7322ixfvjw333xzurq6smjRotxwww3N7+mEE07I1KlTc9lll6WrqyuPP/54vvSlL+X222/Pbbfd9owQBwDY1ojlZ2Hs2LGZNGlSkmTKlClZsWLFRsdPmzYtI0eOzJgxYzJq1Kgce+yxSZLOzs4ev7azszPf+MY38r73vS833nhjRo0atVnz+/d///e89a1vTZL83u/93rrt1157ba699tpMnjw5++67b37wgx9k+fLlvf6eRo0alR122CHveMc7cuWVV272tdMAAEONWH4Wtt9++3Wvhw8fntWrV2fEiBF56qmnkiSrVq1qjh82bNi698OGDcvq1atz9913Z9KkSZk0aVIuvvji7LnnnrnlllvWXUZx7rnn9jiPUsq61xsec/19a9Va82d/9mfp6upKV1dX7rzzzrzjHe9ofk8bGjFiRG6++eaccMIJueaaa3LUUUf1/AMCANhGiOV+0tHRkUWLFiVZc/3w5thjjz3WBeycOXNy7733Zscdd8zb3va2nHnmmbnllluSJCNHjswjjzyy7ut22WWXLFu2LE899VS+9KUvrdv+2te+Nl/4wheSJJdddtm67UceeWT+8R//cd2dMX72s5/l5z//+Ubntv4xH3300Tz88MP5rd/6rXzkIx/J4sWLN+v7BAAYaob0reO2plu9nXHGGTnxxBMzd+7cHH300X36rCVLluTMM8/MsGHDst122+VTn/pUkuTUU0/NUUcdte7a5fPOOy/HHHNMxowZk6lTp66L4I997GP53d/93Zx//vk5/vjj133uEUcckWXLluXAAw9MsuYvCP7zP/9zhg8f3pzLrFmzMmfOnDz/+c/Pv/3bv+X444/PqlWrUmvNBRdc0KfvEwBga1dqrYM9hx5NnTq1rr27xFrLli3LuHHjBmlG9JZ14rnCfZZhjc5LOns1bsnMJVt4Jmwpy17Vu/+uj/vBsi08ky2jlLKo1jq1p30uwwAAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQMKTvs9yb2zZtjq31Fk/nnHNOdtppp5xxxhmb9XUrVqzI9773vfzu7/7uFpoZAMC2zZnlbdiKFSvyuc99brO+pqfHXAMAPFeJ5c20YsWKjBs3LrNnz84+++yTI444IitXrsyhhx6atQ9ReeCBB9LR0ZEkmTdvXmbMmJHp06eno6MjF154YS644IJMnjw5BxxwQB566KEej/M3f/M32XPPPfO6170ud9xxx7rtXV1dOeCAAzJhwoS88Y1vzC9+8YskyZ133pnDDz88EydOzL777pu77rorZ511Vm688cZMmjQpH/nIR7Jq1ar8/u//fjo7OzN58uRcf/316+Z43HHH5bDDDsvrX//63HfffTn44IMzadKkjB8/PjfeeOMW/IkCAGy9xPKzsHz58px++um5/fbbM3r06FxxxRUbHb906dJceeWVWbBgQc4+++zsuOOOufXWW3PggQfm0ksvfcb4RYsW5Qtf+EK6urry1a9+NQsWLFi37+1vf3vOP//83Hbbbens7Mxf/uVfJklOOumknH766Vm8eHG+973vZdddd815552Xgw46KF1dXfnjP/7jXHTRRSmlZMmSJfn85z+fmTNnZtWqVUmSW265JfPnz893vvOdfO5zn8uRRx6Zrq6uLF68OJMmTeq/Hx4AwBAypK9ZHixjx45dF5BTpkzJihUrNjp+2rRpGTlyZEaOHJlRo0bl2GOPTZJ0dnbmtttue8b4G2+8MW984xuz4447JkmOO+64JMnDDz+cX/7ylznkkEOSJDNnzsyb3vSmPPLII/nZz36WN77xjUmSHXbYocd5fPe7380f/uEfJkle9apX5eUvf3l++MMfJkmmT5+eF73oRUmS/fbbLyeffHKeeOKJzJgxQywDAM9Zziw/C9tvv/2618OHD8/q1aszYsSIPPXUU0my7mxtT+OHDRu27v2wYcOyevXq3H333Zk0aVImTZqUiy++eAC+g2f6tV/7tXWvDz744Nxwww156UtfmlmzZvV49hsA4LlALPeTjo6OLFq0KEkyf/78zfraPfbYI11dXenq6sqcOXNy8MEH56qrrsrKlSvzyCOP5F//9V+TJKNGjcoLX/jCddcQf/azn80hhxySkSNHZvfdd89VV12VJPnVr36Vxx9/PCNHjswjjzyy7jgHHXRQLrvssiTJD3/4w/z0pz/NXnvt9Yz5/OQnP8kuu+yS2bNn55RTTsktt9yy2T8PAIBtwZC+DGNrutXbGWeckRNPPDFz587N0Ucf3afP2nffffPmN785EydOzK//+q9nv/32W7fvkksuyZw5c/L444/nN37jN/JP//RPSdaE8x/8wR/kgx/8YLbbbrt88YtfzIQJEzJ8+PBMnDgxs2bNymmnnZZ3vvOd6ezszIgRIzJv3rynnfVe69vf/nY+/OEPZ7vttstOO+3kzDIA8JxVaq2DPYceTZ06ta69u8Ray5Yty7hx4wZpRvSWdeK5ojf3et+a/k89bCmdl3T2atySmUu28EzYUpa9qnf/XR/3g2VbeCZbRillUa11ak/7XIYBAAANYhkAABqGXCxvrZeNsIb1AQC2JUMqlnfYYYc8+OCDgmwrVWvNgw8+2LzPMwDAUDOk7oax++6755577sn9998/2FOhYYcddsjuu+8+2NMAAOgXQyqWt9tuu4wdO3awpwEAwHPEkLoMAwAABpJYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAICGEYM9gSHnnFG9GPPwlp8HAABbnFgeJMteNa5X48b9YNkWngkAAC0uwwAAgAaxDAAADS7D2AI6L+nc5Jh/GYB5AADQN/0Sy6WUo5J8LMnwJJ+ptZ7XGPc7SeYn2a/WurA/jk3yd28+ZpNj3nv5NQMwEwCAbUufL8MopQxPclGSNyTZO8lbSyl79zBuZJI/SvL9vh4TAAAGQn9cs7x/kjtrrT+qtf5vki8kOb6HcX+V5Pwkq/rhmAAAsMX1Ryy/NMnd672/p3vbOqWUfZPsUWv9ysY+qJRyaillYSll4f33398PUwMAgGdvi/8Fv1LKsCQXJJm1qbG11rlJ5ibJ1KlT65ad2dN1nLXRjl9nxQ5beCIbuGjOtwb2gABDXG9/3zz94sO28EyAbUF/nFn+WZI91nu/e/e2tUYmGZ/k26WUFUkOSHJ1KWVqPxwbAAC2mP6I5QVJXllKGVtKeV6StyS5eu3OWuvDtdada60dtdaOJP+R5Dh3wwAAYGvX51iuta5O8q4kX0+yLMm/1FpvL6WcW0o5rq+fDwAAg6VfrlmutX41yVc32PbBxthD++OYAACwpXmCH7DN6M3TM5fMXDIAMwFgWyGWAXhO8vRToDfEMgA8R/Xmtqkrzjt6AGYCWy+xDFtIb+716j6vALB1649bxwEAwDbJmWXYgL8kBgCs5cwyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABpGDPYEYCha9qpxmx506EVbfiIAwBblzDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgYMdgTAABg83Sc9ZVejVtx3tFbeCbbPmeWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQEO/PO66lHJUko8lGZ7kM7XW8zbY/ydJTkmyOsn9SU6utf6kP44NwLah85LOTY5ZMnPJAMwE4P/0+cxyKWV4kouSvCHJ3kneWkrZe4NhtyaZWmudkGR+kg/19bgAALCl9cdlGPsnubPW+qNa6/8m+UKS49cfUGu9vtb6ePfb/0iyez8cFwAAtqj+iOWXJrl7vff3dG9reUeSf+tpRynl1FLKwlLKwvvvv78fpgYAAM/egP4Fv1LK25JMTfLhnvbXWufWWqfWWqeOGTNmIKcGAADP0B9/we9nSfZY7/3u3dueppRyeJKzkxxSa/1VPxwXAAC2qP44s7wgyStLKWNLKc9L8pYkV68/oJQyOcnfJzmu1vrzfjgmAABscX2O5Vrr6iTvSvL1JMuS/Eut9fZSyrmllOO6h304yU5JvlhK6SqlXN34OAAA2Gr0y32Wa61fTfLVDbZ9cL3Xh/fHcQAAYCB5gh8AADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANPTLE/wAeG7qOOsrmxyz4ryjB2AmAFuGM8sAANAglgEAoEEsAwBAg1gGAIAGf8EPAIAB83dvPmaTY957+TUDMJPecWYZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAwYrAnAAA8Nyx71bhNjhn3g2UDMBPovX45s1xKOaqUckcp5c5Sylk97N++lHJ59/7vl1I6+uO4AACwJfU5lkspw5NclOQNSfZO8tZSyt4bDHtHkl/UWl+R5CNJzu/rcQEAYEvrjzPL+ye5s9b6o1rr/yb5QpLjNxhzfJJLul/PT/L6Ukrph2MDAMAWU2qtffuAUk5IclSt9ZTu97+X5NW11netN2Zp95h7ut/f1T3mgQ0+69QkpybJy172sik/+clP+jQ3njs6zvrKJses2OF3e/dh5zzcx9n03t+9+ZhejXvv5dds4ZkMrn5bv16sXW+umUySbx160SbHnH7xYb36rOe8c0b1clz/rJ9rXvtZb9avH3/fvGjOtzY5ZtUvLujVZ23rv3f2Si/Wr3PsyzY5ZsnMJb06XG/Wb2v8vbOUsqjWOrWnfVvVX/Crtc5NMjdJpk6d2reKB7YtA/h/YgBgrf64DONnSfZY7/3u3dt6HFNKGZFkVJIH++HYAACwxfRHLC9I8spSythSyvOSvCXJ1RuMuTrJzO7XJyT5Vu3r9R8AALCF9fkyjFrr6lLKu5J8PcnwJP9Ya729lHJukoW11quT/EOSz5ZS7kzyUNYENQAAbNX65ZrlWutXk3x1g20fXO/1qiRv6o9jAQDAQPG4awAAaBDLAADQIJYBAKBBLAMAQMNW9VASgC2tt093+1YvnkIFwLbPmWUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAEDDiMGeAAAA24bTLz5ssKfQ75xZBgCABrEMAAANLsOAQfTey68Z7CkAABvhzDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA19iuVSyotKKd8opSzv/vcLexgzqZRyUynl9lLKbaWUN/flmAAAMFD6emb5rCTfrLW+Msk3u99v6PEkb6+17pPkqCQfLaWM7uNxAQBgi+trLB+f5JLu15ckmbHhgFrrD2uty7tf35vk50nG9PG4AACwxfU1lneptd7X/fq/kuyyscGllP2TPC/JXY39p5ZSFpZSFt5///19nBoAAPTNiE0NKKVcl+QlPew6e/03tdZaSqkb+Zxdk3w2ycxa61M9jam1zk0yN0mmTp3a/CwAABgIm4zlWuvhrX2llP8upexaa72vO4Z/3hj3giRfSXJ2rfU/nvVsAQBgAPX1Moyrk8zsfj0zyZc3HFBKeV6SLyW5tNY6v4/HAwCAAdPXWD4vyfRSyvIkh3e/TyllainlM91jTkxycJJZpZSu7n8m9fG4AACwxW3yMoyNqbU+mOT1PWxfmOSU7tf/nOSf+3Ic6BfnPDzYMwAAhpg+xTIAsI1zooHnOI+7BgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaRgz2BAAA1jr94sM2Oebv3nzBAMwE1nBmGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDW8cBg27FeUcP9hQAnrOWzFwy2FPYqjmzDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgIY+xXIp5UWllG+UUpZ3//uFGxn7glLKPaWUC/tyTAAAGCh9PbN8VpJv1lpfmeSb3e9b/irJDX08HgAADJi+xvLxSS7pfn1Jkhk9DSqlTEmyS5Jr+3g8AAAYMH2N5V1qrfd1v/6vrAnipymlDEvyd0nO2NSHlVJOLaUsLKUsvP/++/s4NQAA6JsRmxpQSrkuyUt62HX2+m9qrbWUUnsYd1qSr9Za7ymlbPRYtda5SeYmydSpU3v6LAAAGDCbjOVa6+GtfaWU/y6l7Fprva+UsmuSn/cw7MAkB5VSTkuyU5LnlVIerbVu7PpmAAAYdJuM5U24OsnMJOd1//vLGw6otZ609nUpZVaSqUIZAIChoK/XLJ+XZHopZXmSw7vfp5QytZTymb5ODgAABlOfzizXWh9M8voeti9MckoP2+clmdeXYwIAwEDxBD8AAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA0jBnsCANBb436wbLCnADzHOLMMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgIYRgz0BAIDN8d7LrxnsKfAc4swyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBhRF++uJTyoiSXJ+lIsiLJibXWX/Qw7mVJPpNkjyQ1yW/VWlf05dgADBHnPDzYMwB41vp6ZvmsJN+stb4yyTe73/fk0iQfrrWOS7J/kp/38bgAALDF9TWWj09ySffrS5LM2HBAKWXvJCNqrd9Iklrro7XWx/t4XAAA2OL6Gsu71Frv6379X0l26WHMnkl+WUq5spRyaynlw6WU4T19WCnl1FLKwlLKwvvvv7+PUwMAgL7Z5DXLpZTrkrykh11nr/+m1lpLKbVxjIOSTE7y06y5xnlWkn/YcGCtdW6SuUkyderUnj4LAAAGzCZjudZ6eGtfKeW/Sym71lrvK6Xsmp6vRb4nSVet9UfdX3NVkgPSQywDAMDWpK+XYVydZGb365lJvtzDmAVJRpdSxnS/PyzJf/bxuAAAsMX1NZbPSzK9lLI8yeHd71NKmVpK+UyS1FqfTHJGkm+WUpYkKUk+3cfjAgDAFten+yzXWh9M8voeti9Mcsp677+RZEJfjgUAAAPNE/wAAKChT2eWAQDYinmCZp85swwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAwYrAnALA1Ov3iwwZ7CgBsBZxZBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgAaxDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANIwZ7AtAfVpx39GBPAQDYBjmzDAAADWIZAAAaxDIAADSIZQAAaBDLAADQIJYBAKBBLAMAQINYBgCABrEMAAANYhkAABrEMgAANIhlAABoEMsAANAglgEAoEEsAwBAg1gGAIAGsQwAAA1iGQAAGsQyAAA0iGUAAGgQywAA0CCWAQCgQSwDAECDWAYAgIY+xXIp5UWllG+UUpZ3//uFjXEfKqXcXkpZVkr5eCml9OW4AAAwEPp6ZvmsJN+stb4yyTe73z9NKeU1SV6bZEKS8Un2S3JIH48LAABbXF9j+fgkl3S/viTJjB7G1CQ7JHleku2TbJfkv/t4XAAA2OL6Gsu71Frv6379X0l22XBArfWmJNcnua/7n6/XWpf18bgAALDFjdjUgFLKdUle0sOus9d/U2utpZTaw9e/Ism4JLt3b/pGKeWgWuuNPYw9NcmpSfKyl71s07MHAIAtaJOxXGs9vLWvlPLfpZRda633lVJ2TfLzHoa9Mcl/1Fof7f6af0tyYJJnxHKtdW6Sud3j7i+l/KR338aQtHOSBwZ7Ejxr1m/osnZDm/Ub2qzf0LWtr93LWzs2GcubcHWSmUnO6/73l3sY89Mks0sp/y9JyZq/3PfRTX1wrXVMH+e2VSulLKy1Th3sefDsWL+hy9oNbdZvaLN+Q9dzee36es3yeUmml1KWJzm8+31KKVNLKZ/pHjM/yV1JliRZnGRxrfVf+3hcAADY4vp0ZrnW+mCS1/ewfWGSU7pfP5nkD/pyHAAAGAye4Dd45g72BOgT6zd0WbuhzfoNbdZv6HrOrl2p9Rk3sAAAAOLMMgAANIllAABoEMsAANAglgEAoEEsD7JSyvTBngObVkp5QSnlN3vYPmEw5kPvlVJeUkp5SffrMaWU3y6l7DPY8+LZKaX8f4M9B56dUsrY7l9/rxrsubBxpZSXlVJ26H5dSim/X0r5RCnlnaWUvj7QbshxN4xBVkr5aa31ZYM9D9pKKSdmzVMnf55kuySzaq0LuvfdUmvddxCnx0aUUv4gyVlZ8/TQ85PMSrI0yeuSfKjW+g+DNzs2pZTy8Q03Jfm9JJcmSa313QM+KXqtlHJVrXVG9+vjs+b30W8neU2S/1drnTdYc2PjSilLk+xfa328lHJ+kt9MclWSw5Kk1nryIE5vwD3n/t/BYCilXN3aleTFAzkXnpU/TzKl1npfKWX/JJ8tpfxZrfVLWbOGbL3elWSfJM9P8pMkr6i1/lcp5YVJrk8ilrdub0zynSTX5v9+rb0lyaJBmxGb4+XrvX5fksNqrT8upeyc5JtJ5g3KrOiNYbXWx7tfH55kv1rrU0n+uZSyeBDnNSjE8sA4KMnbkjy6wfaSZP+Bnw6baXit9b4kqbXeXEqZluSaUsoeSfzRzNbtie7f8B8vpdxVa/2vJKm1/qKUYu22fnsn+askRyU5o9Z6bynlL2qtlwzyvOid9X+Njai1/jhJaq0PlFKeGqQ50Tt3l1IOq7V+K8mKJHsk+Ukp5Tl5gk8sD4z/SPJ4rfU7G+4opdwxCPNh8zxSSvnNWutdSdJ9hvnQrPkjKde+bt1qKWW7WusTSY5eu7H7Wjx/Z2MrV2t9JMl7SilTklxWSvlKrNtQMrGU8j9Zc2Jo+1LKrt2/fz4vyfBBnhsbd0qSS0sp5yR5OElXKaUryegk7x28aQ0O1yzDJpRSJiZ5rNZ65wbbt0tyYq31ssGZGZtSSnlZkntrras32P7SJONqrdcNzszYXKWUkuS0JAfWWt822PPh2SuljM6aX383DfZc2LhSyrgke2bNydV7kizovhzjOUUsD4BSyiuS7FJr/fcNtr82yX+tPWPJ1sn6DV0bWbvXJbnP2m3d/Nob2vz6G7r82ns6f5w1MD6a5H962P4/3fvYun001m+o+mh6XruHY+2Ggo/Gr72h7KPx62+o+mj82ltHLA+MXWqtSzbc2L2tY+Cnw2ayfkOXtRvarN/QZv2GLmu3HrE8MEZvZN/zB2oSPGujN7LP+m3dRm9kn7Xb+o3eyD7rt/UbvZF91m/rNnoj+55zayeWB8bCUsrsDTeWUk6J+4UOBdZv6LJ2Q5v1G9qs39Bl7dbjL/gNgFLKLkm+lOR/83//I5ua5HlJ3rj23q9snazf0GXthjbrN7RZv6HL2j2dWB5A3Q+zGN/99vbum30zRFi/ocvaDW3Wb2izfkOXtVvDQ0kGQPcDEOYkeUWSJUn+YcP7vrL1sn5Dl7Ub2qzf0Gb9hi5r93TOLA+AUsrlSZ5IcmOSNyRZUWt9z6BOil6zfkOXtRvarN/QZv2GLmv3dGJ5AJRSltRaO7tfj0hyc61130GeFr1k/YYuaze0Wb+hzfoNXdbu6dwNY2A8sfbFc/mPMYYw6zd0WbuhzfoNbdZv6LJ263FmeQCUUp5M8tjat1lzj8LHu1/XWusLBmtubJr1G7qs3dBm/YY26zd0WbunE8sAANDgMgwAAGgQywAA0CCWAQCgQSwDAEDD/w8Mltjm8jkN+AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "components_loading.plot.bar(figsize=(12,12))\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a9e25344b8a403a44a7f45bc450256bac66c51cdc3f974f040b265f33bb4dd09"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}