MLstuff/ISLR/notebooks/.ipynb_checkpoints/ch2-9-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "plt.rcParams[\"figure.figsize\"] = (15,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       Unnamed: 0         mpg   cylinders  displacement  horsepower  \\\ncount  392.000000  392.000000  392.000000    392.000000  392.000000   \nmean   198.520408   23.445918    5.471939    194.411990  104.469388   \nstd    114.438067    7.805007    1.705783    104.644004   38.491160   \nmin      1.000000    9.000000    3.000000     68.000000   46.000000   \n25%     99.750000   17.000000    4.000000    105.000000   75.000000   \n50%    198.500000   22.750000    4.000000    151.000000   93.500000   \n75%    296.250000   29.000000    8.000000    275.750000  126.000000   \nmax    397.000000   46.600000    8.000000    455.000000  230.000000   \n\n            weight  acceleration        year      origin  \ncount   392.000000    392.000000  392.000000  392.000000  \nmean   2977.584184     15.541327   75.979592    1.576531  \nstd     849.402560      2.758864    3.683737    0.805518  \nmin    1613.000000      8.000000   70.000000    1.000000  \n25%    2225.250000     13.775000   73.000000    1.000000  \n50%    2803.500000     15.500000   76.000000    1.000000  \n75%    3614.750000     17.025000   79.000000    2.000000  \nmax    5140.000000     24.800000   82.000000    3.000000  ",
      "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>Unnamed: 0</th>\n      <th>mpg</th>\n      <th>cylinders</th>\n      <th>displacement</th>\n      <th>horsepower</th>\n      <th>weight</th>\n      <th>acceleration</th>\n      <th>year</th>\n      <th>origin</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>392.000000</td>\n      <td>392.000000</td>\n      <td>392.000000</td>\n      <td>392.000000</td>\n      <td>392.000000</td>\n      <td>392.000000</td>\n      <td>392.000000</td>\n      <td>392.000000</td>\n      <td>392.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>198.520408</td>\n      <td>23.445918</td>\n      <td>5.471939</td>\n      <td>194.411990</td>\n      <td>104.469388</td>\n      <td>2977.584184</td>\n      <td>15.541327</td>\n      <td>75.979592</td>\n      <td>1.576531</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>114.438067</td>\n      <td>7.805007</td>\n      <td>1.705783</td>\n      <td>104.644004</td>\n      <td>38.491160</td>\n      <td>849.402560</td>\n      <td>2.758864</td>\n      <td>3.683737</td>\n      <td>0.805518</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>9.000000</td>\n      <td>3.000000</td>\n      <td>68.000000</td>\n      <td>46.000000</td>\n      <td>1613.000000</td>\n      <td>8.000000</td>\n      <td>70.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>99.750000</td>\n      <td>17.000000</td>\n      <td>4.000000</td>\n      <td>105.000000</td>\n      <td>75.000000</td>\n      <td>2225.250000</td>\n      <td>13.775000</td>\n      <td>73.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>198.500000</td>\n      <td>22.750000</td>\n      <td>4.000000</td>\n      <td>151.000000</td>\n      <td>93.500000</td>\n      <td>2803.500000</td>\n      <td>15.500000</td>\n      <td>76.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>296.250000</td>\n      <td>29.000000</td>\n      <td>8.000000</td>\n      <td>275.750000</td>\n      <td>126.000000</td>\n      <td>3614.750000</td>\n      <td>17.025000</td>\n      <td>79.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>397.000000</td>\n      <td>46.600000</td>\n      <td>8.000000</td>\n      <td>455.000000</td>\n      <td>230.000000</td>\n      <td>5140.000000</td>\n      <td>24.800000</td>\n      <td>82.000000</td>\n      <td>3.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "auto = pd.read_csv(\"./../../datasets/Auto.csv\")\n",
    "# auto.set_index(\"name\", inplace=True)\n",
    "auto.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto.drop(range(10,66), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       Unnamed: 0         mpg   cylinders  displacement  horsepower  \\\ncount  336.000000  336.000000  336.000000    336.000000  336.000000   \nmean   225.089286   24.041071    5.407738    189.672619  102.261905   \nstd    101.406281    7.889493    1.686770    101.903056   36.666913   \nmin      1.000000   11.000000    3.000000     68.000000   46.000000   \n25%    142.750000   17.675000    4.000000    100.250000   75.000000   \n50%    226.500000   23.100000    4.000000    145.500000   92.000000   \n75%    310.250000   30.000000    6.000000    258.500000  120.000000   \nmax    397.000000   46.600000    8.000000    455.000000  230.000000   \n\n            weight  acceleration        year      origin  \ncount   336.000000    336.000000  336.000000  336.000000  \nmean   2959.779762     15.656845   76.836310    1.601190  \nstd     824.796492      2.697807    3.256221    0.818735  \nmin    1649.000000      8.500000   70.000000    1.000000  \n25%    2219.750000     14.000000   74.000000    1.000000  \n50%    2803.500000     15.500000   77.000000    1.000000  \n75%    3571.000000     17.200000   80.000000    2.000000  \nmax    4997.000000     24.800000   82.000000    3.000000  ",
      "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>Unnamed: 0</th>\n      <th>mpg</th>\n      <th>cylinders</th>\n      <th>displacement</th>\n      <th>horsepower</th>\n      <th>weight</th>\n      <th>acceleration</th>\n      <th>year</th>\n      <th>origin</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>336.000000</td>\n      <td>336.000000</td>\n      <td>336.000000</td>\n      <td>336.000000</td>\n      <td>336.000000</td>\n      <td>336.000000</td>\n      <td>336.000000</td>\n      <td>336.000000</td>\n      <td>336.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>225.089286</td>\n      <td>24.041071</td>\n      <td>5.407738</td>\n      <td>189.672619</td>\n      <td>102.261905</td>\n      <td>2959.779762</td>\n      <td>15.656845</td>\n      <td>76.836310</td>\n      <td>1.601190</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>101.406281</td>\n      <td>7.889493</td>\n      <td>1.686770</td>\n      <td>101.903056</td>\n      <td>36.666913</td>\n      <td>824.796492</td>\n      <td>2.697807</td>\n      <td>3.256221</td>\n      <td>0.818735</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>11.000000</td>\n      <td>3.000000</td>\n      <td>68.000000</td>\n      <td>46.000000</td>\n      <td>1649.000000</td>\n      <td>8.500000</td>\n      <td>70.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>142.750000</td>\n      <td>17.675000</td>\n      <td>4.000000</td>\n      <td>100.250000</td>\n      <td>75.000000</td>\n      <td>2219.750000</td>\n      <td>14.000000</td>\n      <td>74.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>226.500000</td>\n      <td>23.100000</td>\n      <td>4.000000</td>\n      <td>145.500000</td>\n      <td>92.000000</td>\n      <td>2803.500000</td>\n      <td>15.500000</td>\n      <td>77.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>310.250000</td>\n      <td>30.000000</td>\n      <td>6.000000</td>\n      <td>258.500000</td>\n      <td>120.000000</td>\n      <td>3571.000000</td>\n      <td>17.200000</td>\n      <td>80.000000</td>\n      <td>2.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>397.000000</td>\n      <td>46.600000</td>\n      <td>8.000000</td>\n      <td>455.000000</td>\n      <td>230.000000</td>\n      <td>4997.000000</td>\n      <td>24.800000</td>\n      <td>82.000000</td>\n      <td>3.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "auto.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-7-7f6145cc913f>, line 1)",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-7f6145cc913f>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    range(Auto[,5])\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "range(Auto[,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "source": [
    "axes = pd.plotting.scatter_matrix(auto, alpha=1)\n",
    "# axes = pd.plotting.scatter_matrix(auto.iloc[:,1:5], alpha=1)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.622918</td>\n",
       "      <td>-0.407201</td>\n",
       "      <td>-0.434889</td>\n",
       "      <td>-0.468482</td>\n",
       "      <td>-0.396190</td>\n",
       "      <td>0.305413</td>\n",
       "      <td>0.995918</td>\n",
       "      <td>0.205620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>0.622918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.763358</td>\n",
       "      <td>-0.795919</td>\n",
       "      <td>-0.772484</td>\n",
       "      <td>-0.835404</td>\n",
       "      <td>0.397465</td>\n",
       "      <td>0.618078</td>\n",
       "      <td>0.546191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>-0.407201</td>\n",
       "      <td>-0.763358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947652</td>\n",
       "      <td>0.834880</td>\n",
       "      <td>0.892985</td>\n",
       "      <td>-0.466391</td>\n",
       "      <td>-0.389445</td>\n",
       "      <td>-0.556291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>displacement</th>\n",
       "      <td>-0.434889</td>\n",
       "      <td>-0.795919</td>\n",
       "      <td>0.947652</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898653</td>\n",
       "      <td>0.939702</td>\n",
       "      <td>-0.499334</td>\n",
       "      <td>-0.414558</td>\n",
       "      <td>-0.608198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.468482</td>\n",
       "      <td>-0.772484</td>\n",
       "      <td>0.834880</td>\n",
       "      <td>0.898653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.865636</td>\n",
       "      <td>-0.686725</td>\n",
       "      <td>-0.459743</td>\n",
       "      <td>-0.451026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>-0.396190</td>\n",
       "      <td>-0.835404</td>\n",
       "      <td>0.892985</td>\n",
       "      <td>0.939702</td>\n",
       "      <td>0.865636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378763</td>\n",
       "      <td>-0.381168</td>\n",
       "      <td>-0.585219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acceleration</th>\n",
       "      <td>0.305413</td>\n",
       "      <td>0.397465</td>\n",
       "      <td>-0.466391</td>\n",
       "      <td>-0.499334</td>\n",
       "      <td>-0.686725</td>\n",
       "      <td>-0.378763</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307771</td>\n",
       "      <td>0.189902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>0.995918</td>\n",
       "      <td>0.618078</td>\n",
       "      <td>-0.389445</td>\n",
       "      <td>-0.414558</td>\n",
       "      <td>-0.459743</td>\n",
       "      <td>-0.381168</td>\n",
       "      <td>0.307771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <td>0.205620</td>\n",
       "      <td>0.546191</td>\n",
       "      <td>-0.556291</td>\n",
       "      <td>-0.608198</td>\n",
       "      <td>-0.451026</td>\n",
       "      <td>-0.585219</td>\n",
       "      <td>0.189902</td>\n",
       "      <td>0.182583</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Unnamed: 0       mpg  cylinders  displacement  horsepower  \\\n",
       "Unnamed: 0      1.000000  0.622918  -0.407201     -0.434889   -0.468482   \n",
       "mpg             0.622918  1.000000  -0.763358     -0.795919   -0.772484   \n",
       "cylinders      -0.407201 -0.763358   1.000000      0.947652    0.834880   \n",
       "displacement   -0.434889 -0.795919   0.947652      1.000000    0.898653   \n",
       "horsepower     -0.468482 -0.772484   0.834880      0.898653    1.000000   \n",
       "weight         -0.396190 -0.835404   0.892985      0.939702    0.865636   \n",
       "acceleration    0.305413  0.397465  -0.466391     -0.499334   -0.686725   \n",
       "year            0.995918  0.618078  -0.389445     -0.414558   -0.459743   \n",
       "origin          0.205620  0.546191  -0.556291     -0.608198   -0.451026   \n",
       "\n",
       "                weight  acceleration      year    origin  \n",
       "Unnamed: 0   -0.396190      0.305413  0.995918  0.205620  \n",
       "mpg          -0.835404      0.397465  0.618078  0.546191  \n",
       "cylinders     0.892985     -0.466391 -0.389445 -0.556291  \n",
       "displacement  0.939702     -0.499334 -0.414558 -0.608198  \n",
       "horsepower    0.865636     -0.686725 -0.459743 -0.451026  \n",
       "weight        1.000000     -0.378763 -0.381168 -0.585219  \n",
       "acceleration -0.378763      1.000000  0.307771  0.189902  \n",
       "year         -0.381168      0.307771  1.000000  0.182583  \n",
       "origin       -0.585219      0.189902  0.182583  1.000000  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.violinplot(x=\"Private\", y=\"Outstate\", data=college)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x=\"Private\", y=\"Outstate\", data=college)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "sep =pd.cut(college.Top10perc, pd.interval_range(start=0, end=100, periods=2), labels=[\"Not elite\", \"Elite\"])\n",
    "college[\"Elite\"] = sep "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.violinplot(x=\"Elite\", y=\"Outstate\", data=college)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEMCAYAAADj8ECOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de3SU5aHv8e/M5AaEJEzMZUIiCNUYTfASL00tXVUCSWtwIss0nhTbXRVWN7Ss0tZl2p7Npepu417Hta1b9jpl23py1r64KEfYxJiNSN0CrRdSRWJAFAMRMkkgk0AC5Dbznj9iRqeBZJK8cyH+PkvWyrzP8848z0zMb57nvTwWwzAMREREJska7gaIiMjUoEARERFTKFBERMQUChQRETGFAkVEREwRFe4GhIPX6+XcuXNER0djsVjC3RwRkcuCYRgMDAwwY8YMrNaR45GAAqWpqYnKykq6urpISkqiqqqKuXPn+tXxeDw8/vjj7NmzB4vFwsqVKykrK5tU2bCPP/6Ye++9l4qKCh599FEALly4wM9+9jPef/99bDYbjz76KHfeeWdAb8q5c+c4cuRIQHVFRMTfNddcw8yZM0dsDyhQ1q9fT0VFBU6nk+3bt7Nu3Tqqq6v96uzYsYPm5mZ27txJV1cXpaWlFBQUkJmZOeEyGAqc9evXU1hY6Pd6zz33HPHx8bzyyiscO3aMb3/72+zcuZMZM2aM2Z/o6GjfmxITExPIW2C6hoYGcnNzw/LaoaD+Xf6meh/Vv/Hr7+/nyJEjvr+hf23MQOno6KCxsZHf//73AJSUlPDYY4/hdrux2+2+erW1tZSVlWG1WrHb7RQWFlJXV8fDDz884TKA3/72t3z961/n/PnznD9/3vd6L7/8Mr/+9a8BmDt3Lrm5ubz++ut84xvfGPNNGZ7miomJITY2dsz6wRLO1w4F9e/yN9X7qP5NzKUOFYwZKC6Xi7S0NGw2GwA2m43U1FRcLpdfoLhcLjIyMnyPHQ4Hra2tkyo7fPgwe/fupbq6mk2bNvm1q6WlhdmzZ190v0A1NDSMq77Z6uvrw/r6wab+Xf6meh/VP3NF7EH5gYEB/u7v/o5f/epXvjAzW25ubti+odTX15Ofnx+W1w4F9e/yN9X7qP6NX19f36hfxMcMFIfDQVtbGx6PB5vNhsfjob29HYfDMaJeS0sLCxYsAPxHHhMpO3XqFM3NzaxcuRKAs2fPYhgGPT09PPbYY2RkZHDy5EnfKMnlcnH77beP680RERHzjHkdSnJyMjk5OdTU1ABQU1NDTk6O33QXQHFxMVu2bMHr9eJ2u9m1axdFRUUTLsvIyODNN99k9+7d7N69m+9+97t861vf4rHHHvPt98ILLwBw7NgxDh48yMKFC817Z0REZFwCmvLasGEDlZWVbNq0iYSEBKqqqgBYsWIFa9asIS8vD6fTyYEDB1iyZAkAq1evJisrC2DCZaN56KGHqKysZPHixVitVn75y18SHx8/zu6LiIhZLF/E29cPzwPqGErwqH+Xv6neR/Vv/Mb626lbr4iIiCki9iyvL4Lu8/1c6B302zYtLoqZ08NzsaWIyGQoUMLoQu8gf/mg3W/bzdmpChQRuSxpyktEREyhQBEREVMoUERExBQKFBERMYUCRURETKFAERERUyhQRETEFAoUERExhQJFRERMoUARERFTKFBERMQUChQRETGFAkVEREyhQBEREVMoUERExBQBrYfS1NREZWUlXV1dJCUlUVVVxdy5c/3qeDweHn/8cfbs2YPFYmHlypWUlZVNqmzr1q08//zzWK1WvF4vZWVlfOc73wHgmWee4d/+7d9ITU0F4Oabb2b9+vWmvCkiIjJ+AQXK+vXrqaiowOl0sn37dtatW0d1dbVfnR07dtDc3MzOnTvp6uqitLSUgoICMjMzJ1xWVFTEsmXLsFgs9PT0sHTpUm677TauvfZaAEpLS3n00UfNf1dERGTcxpzy6ujooLGxkZKSEgBKSkpobGzE7Xb71autraWsrAyr1YrdbqewsJC6urpJlcXHx2OxWADo7e1lYGDA91hERCLLmCMUl8tFWloaNpsNAJvNRmpqKi6XC7vd7lcvIyPD99jhcNDa2jqpMoBXX32Vp556iubmZn7yk5+QnZ3tK3vppZfYu3cvKSkp/PCHP+Smm24aV+cbGhrGVd9srlYXx5tP+G1zJHr4pKkrTC0yV319fbibEFRTvX8w9fuo/pkr4teUX7RoEYsWLaKlpYXVq1fzta99jXnz5nH//ffz/e9/n+joaPbt28eqVauora1l1qxZAT93bm4usbGxQWz9pdXX1+NIdzDnjM1vuyM9lVT7/LC0yUz19fXk5+eHuxlBM9X7B1O/j+rf+PX19Y36RXzMKS+Hw0FbWxsejwcYOoje3t6Ow+EYUa+lpcX32OVykZ6ePqmyz8vIyCAvL4/XXnsNgJSUFKKjowG44447cDgcfPjhh2N1R0REgmTMQElOTiYnJ4eamhoAampqyMnJ8ZvuAiguLmbLli14vV7cbje7du2iqKhoUmVHjx71Pb/b7ebNN9/kmmuuAaCtrc1XdujQIU6ePMlVV101mfdCREQmIaAprw0bNlBZWcmmTZtISEigqqoKgBUrVrBmzRry8vJwOp0cOHCAJUuWALB69WqysrIAJlz2wgsvsG/fPqKiojAMg+XLl/PVr34VgKeeeor3338fq9VKdHQ0Tz75JCkpKWa9LyIiMk4BBcr8+fPZsmXLiO2bN2/2/Wyz2di4ceNF959o2c9//vNLtmk41EREJDLoSnkRETGFAkVEREyhQBEREVMoUERExBQKFBERMYUCRURETKFAERERUyhQRETEFAoUERExhQJFRERMoUARERFTKFBERMQUChQRETGFAkVEREyhQBEREVMoUERExBQKFBERMYUCRURETBFQoDQ1NVFeXk5RURHl5eUcO3ZsRB2Px8PGjRspLCxk8eLFfksGT7Rs69atLF26FKfTydKlS6murg5oPxERCb2A1pRfv349FRUVOJ1Otm/fzrp16/z+uAPs2LGD5uZmdu7cSVdXF6WlpRQUFJCZmTnhsqKiIpYtW4bFYqGnp4elS5dy2223ce211466n4iIhN6YI5SOjg4aGxspKSkBoKSkhMbGRtxut1+92tpaysrKsFqt2O12CgsLqaurm1RZfHw8FosFgN7eXgYGBnyPR9tPRERCb8wRisvlIi0tDZvNBoDNZiM1NRWXy4Xdbverl5GR4XvscDhobW2dVBnAq6++ylNPPUVzczM/+clPyM7ODmi/QDQ0NIyrvtlcrS6ON5/w2+ZI9PBJU1eYWmSu+vr6cDchqKZ6/2Dq91H9M1dAU17htGjRIhYtWkRLSwurV6/ma1/7GvPmzTPluXNzc4mNjTXlucarvr4eR7qDOWdsftsd6amk2ueHpU1mqq+vJz8/P9zNCJqp3j+Y+n1U/8avr69v1C/iY055ORwO2tra8Hg8wNDB8Pb2dhwOx4h6LS0tvscul4v09PRJlX1eRkYGeXl5vPbaa+PaT0REQmPMQElOTiYnJ4eamhoAampqyMnJ8ZvuAiguLmbLli14vV7cbje7du2iqKhoUmVHjx71Pb/b7ebNN9/kmmuuGXM/EREJvYCmvDZs2EBlZSWbNm0iISGBqqoqAFasWMGaNWvIy8vD6XRy4MABlixZAsDq1avJysoCmHDZCy+8wL59+4iKisIwDJYvX85Xv/rVMfcTEZHQCyhQ5s+ff9HrPDZv3uz72WazsXHjxovuP9Gyn//855ds02j7iYhI6OlKeRERMYUCRURETKFAERERUyhQRETEFAoUERExhQJFRERMoUARERFTKFBERMQUChQRETGFAkVEREyhQBEREVMoUERExBQKFBERMYUCRURETKFAERERUyhQRETEFAoUERExRUArNjY1NVFZWUlXVxdJSUlUVVUxd+5cvzoej4fHH3+cPXv2YLFYWLlyJWVlZZMqe/bZZ6mtrcVqtRIdHc3atWtZuHAhAJWVlfzpT39i1qxZwNAa83/7t39rypsiIiL
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Index(['Private', 'Apps', 'Accept', 'Enroll', 'Top10perc', 'Top25perc',\n",
    "#        'F.Undergrad', 'P.Undergrad', 'Outstate', 'Room.Board', 'Books',\n",
    "#        'Personal', 'PhD', 'Terminal', 'S.F.Ratio', 'perc.alumni', 'Expend',\n",
    "#        'Grad.Rate'],\n",
    "#       dtype='object')\n",
    "ax = sns.distplot(college.Apps)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f62cedb3130>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Index(['Private', 'Apps', 'Accept', 'Enroll', 'Top10perc', 'Top25perc',\n",
    "#        'F.Undergrad', 'P.Undergrad', 'Outstate', 'Room.Board', 'Books',\n",
    "#        'Personal', 'PhD', 'Terminal', 'S.F.Ratio', 'perc.alumni', 'Expend',\n",
    "#        'Grad.Rate'],\n",
    "sns.distplot(college.Accept)\n",
    "sns.distplot(college.Enroll)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEMCAYAAADAqxFbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deXSUVZ74/3fVU5V9rayVnQQJwRCFIIoCjhIIPQaTpodON63T31HhdEvLuEx/pftMA2n1dOP4taddcEHb/jFOL5O2RyTQiLgCikCk0RB2spGVpFKEbJWk6vn9ESkpCKkCktSSz+ucnFPJc++tz00q9ann3ufeR6OqqooQQgjxNa27AxBCCOFZJDEIIYRwIIlBCCGEA0kMQgghHEhiEEII4UDn7gCuhc1mo6urC71ej0ajcXc4QgjhFVRVpb+/n+DgYLTaS88PvDoxdHV1cezYMXeHIYQQXmnSpEmEhoZe8nOvTgx6vR4Y7Jyfn5+bo7lyFRUVZGdnuzuMa+YL/fCFPoBv9MMX+gCe3Y++vj6OHTtmfw+9mFcnhvPDR35+fvj7+7s5mqvjrXFfzBf64Qt9AN/ohy/0ATy/H5cbgpfJZyGEEA4kMQghhHAgiUEIIYQDl+YYqqqqWLVqFWazmYiICNatW0daWppDGavVypNPPsnOnTvRaDQsX76cJUuWAPDWW2/x+9//Hq1Wi81mY8mSJfzzP/8zAM8//zx/+MMfiI2NBWD69OmsWbNmBLsohBDiSriUGNasWcPSpUspLCxk06ZNrF69mo0bNzqU2bx5M7W1tWzfvh2z2UxRURGzZs0iKSmJ/Px8Fi9ejEajobOzk0WLFjFz5kwmT54MQFFREY8//vjI904IIcQVczqU1NbWRmVlJQUFBQAUFBRQWVmJyWRyKLd161aWLFmCVqvFYDCQl5fHtm3bAAgJCbHPfvf29tLf3y8L0oQQwkM5PWNobGwkLi4ORVEAUBSF2NhYGhsbMRgMDuUSEhLs3xuNRpqamuzfv//++zz77LPU1tby2GOPkZmZaT+2ZcsWdu3aRUxMDA899BDTpk27ok5UVFRcUXlPUl5e7u4QRoQv9MMX+gC+0Q9f6AN4bz/GbB3DvHnzmDdvHg0NDaxYsYK5c+eSnp7O9773PX70ox+h1+vZvXs3Dz74IFu3biUyMtLltrOzsz3+euGhlJeXk5ub6+4wADjX3UdP74BLZQMDdIQGfbOg0JP6cbV8oQ/gG/3whT6AZ/fDYrEM+4HaaWIwGo00NzdjtVpRFAWr1UpLSwtGo/GScg0NDeTk5ACXnkGcl5CQwNSpU/noo49IT08nJibGfuy2227DaDRy/PhxZs6c6XInxbXr6R3gi6MtLpWdnhnrkBiEEL7F6RxDVFQUWVlZlJWVAVBWVkZWVpbDMBLAwoULKS0txWazYTKZ2LFjB/n5+QCcPHnSXs5kMvH5558zadIkAJqbm+3HDh8+TH19PRMmTLj2ngkhhLgqLg0lrV27llWrVrF+/XrCwsJYt24dAMuWLWPlypVMnTqVwsJCDh48yIIFCwBYsWIFycnJAPz5z39m9+7d6HQ6VFXlnnvuYfbs2QA8++yzHDp0CK1Wi16v5+mnn3Y4ixBCCDG2XEoMGRkZlJaWXvLzDRs22B8rikJJScmQ9X/+859ftu3zSUYIIYRnkJXPQgghHEhiEEII4UASgxBCCAeSGIQQQjiQxCCEEMKBJAYhhBAOJDEIIYRwIIlBCCGEA0kMQgghHEhiEEII4UASgxBCCAeSGIQQQjiQxCCEEMKBJAYhhBAOJDEIIYRwIIlBCCGEA0kMQgghHEhiEEII4UASgxBCCAeSGIQQQjhwKTFUVVVRXFxMfn4+xcXFVFdXX1LGarVSUlJCXl4e8+fPp7S01H7srbfeYtGiRRQWFrJo0SI2btzoUj0hhBBjT+dKoTVr1rB06VIKCwvZtGkTq1evdnhzB9i8eTO1tbVs374ds9lMUVERs2bNIikpifz8fBYvXoxGo6Gzs5NFixYxc+ZMJk+ePGw9IYQQY8/pGUNbWxuVlZUUFBQAUFBQQGVlJSaTyaHc1q1bWbJkCVqtFoPBQF5eHtu2bQMgJCQEjUYDQG9vL/39/fbvh6snhBBi7DlNDI2NjcTFxaEoCgCKohAbG0tjY+Ml5RISEuzfG41Gmpqa7N+///773HXXXdxxxx088MADZGZmulRPCCHE2HJpKGkkzJs3j3nz5tHQ0MCKFSuYO3cu6enpI9J2RUXFiLTjDuXl5e4OAQBdYAQ1taddKmsMt1JXZXb4maf041r4Qh/AN/rhC30A7+2H08RgNBppbm7GarWiKApWq5WWlhaMRuMl5RoaGsjJyQEuPRM4LyEhgalTp/LRRx+Rnp7ucr3hZGdn4+/vf0V1PEF5eTm5ubnuDgOAFlM3qWcVl8oa42OJNWTYv/ekflwtX+gD+EY/fKEP4Nn9sFgsw36gdjqUFBUVRVZWFmVlZQCUlZWRlZWFwWBwKLdw4UJKS0ux2WyYTCZ27NhBfn4+ACdPnrSXM5lMfP7550yaNMlpPSGEEGPPpaGktWvXsmrVKtavX09YWBjr1q0DYNmyZaxcuZKpU6dSWFjIwYMHWbBgAQArVqwgOTkZgD//+c/s3r0bnU6Hqqrcc889zJ49G2DYesJz2GwqFadaqWk8x/bPa1BVlZAgP759+0RUVXV3eEKIEeRSYsjIyBhyfcGGDRvsjxVFoaSkZMj6P//5zy/b9nD1hGdoauvioy9O03a2l6jwAIICdIQE+VPT1MGaDZ+RGOWHNrSF6Zmx7g5VCDECxmzyWXinvZVN7KtsJiRQz8JZqaQnhHPDdTHoFC0DVhu7Dzbwzs4TrHn1M+5dOJl/yP3mbC8wQEdokJ8boxdCXA1JDOKyjtW2s6+ymUkpkdw+PRE/3eDktKXfypcnWgEIDfZj1uQgDter/Ne2I9S2dJKdHgXA9MxYSQxCeCHZK0kM6Ux7Nx/sryMhOpg7ZyTbk8JQFK2GhbekkmYM4+MvTlNxqm0MIxVCjDRJDOIS3b39bP2smkB/Hfm3pKJoNU7rKIqWhbekkhofysdfnOZ0y7nRD1QIMSokMYhLfLC/jp7eAb51axpBAXqX6ymKlvxb0ggP8RtswzIwilEKIUaLJAbhoOFMJzVN55h5fTyxkUFXXF+v0zJvRgrnuvspff/YKEQohBhtkhiEnaqq7DnURFCAjqkZ0VfdjjE6mBsnxfDxgXq+ONoyghEKIcaCJAZhV9fcSWNrFzMmx6HXXdtL4+br4zFGBfP8//yd7t7+EYpQCDEWJDEIYPBs4fNDjYQE6ZkyweC8ghM6Rcv/KZhCq7mHd3aeGoEIhRBjRdYxCAAOHDtDS3sPd+QmoSgj83khzRjGtEkx/PXDE8ycEk9I4OUnsmUxnBCeQxKDDzvX3UdPr2tXBr2z8xThIX5MTr32s4XzLP1WMlMjOXDsDBu3VnJLtvGyZWUxnBCeQxKDD+vpHXBp8veMuYe65nPMuTERrQtrFq5EVHgg1yVHcPB4KzdcF0Ogv7zkhPB0MscgOFJtQlE0TEqOGJX2b5oSh9Vq44sjcoWSEN5AEsM4Z7XZOFbbztSMaAJG6dN8ZGgAmamRfHWyla4euUJJCE8niWGcq2k8R2+flZuvjx/V55mRFYfVpvLVydZRfR4hxLWTxDDOHakxERSgY3Ja5Kg+T3iIPxMSwqisMjFgtY3qcwkhro0khnGsu7efmsYOMlMiUbSj/1KYmhFNj2WAE3XmUX8uIcTVk8Qwjh2rNWNTYXLayF2iOpyk2BAiw/w5eKJVbgcqhAeTxDCOHakxERsZhCEsYEyeT6PRkDMxhlZzD01t3WPynEKIKyeJYZzq6Oqj7Wwv143SJaqXk5kSgb9esd8BTgjheSQxjFM1TR3A4LYVY0mvU8hKM3Cy3kxnd9+YPrcQwjUuJYaqqiqKi4vJz8+nuLiY6urqS8pYrVZKSkrIy8tj/vz5lJaW2o+9+OKL3HXXXSxatIjFixezc+dO+7FVq1Yxd+5cCgsLKSws5KWXXrr2Xgm
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(college.Top10perc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEMCAYAAADj8ECOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de1hU5733//fMcD4zyGEQgpFEpEFMxJqaxLQmIOwGA83elNQmu62N+WVrY2N2u6Xp3iiNeRrT/TNt8tPmirtNHveV3eTxMYkVCSWYpFHTxEiMJ6JG5BBlAGEA5TysWb8/kKkjIMOwYAb9vq6LS2bd91rz5c6ED+t0L52qqipCCCHEOOndXYAQQohrgwSKEEIITUigCCGE0IQEihBCCE1IoAghhNCEl7sLcAebzUZnZyfe3t7odDp3lyOEEFOCqqpYrVYCAwPR64fuj1yXgdLZ2cmpU6fcXYYQQkxJs2bNIjg4eMjy6zJQvL29gYFB8fHxsS8/duwYKSkp7irrqqQ210htY+epdYHU5iqtauvr6+PUqVP236FXui4DZfAwl4+PD76+vg5tV772JFKba6S2sfPUukBqc5WWtY10qkBOygshhNCEBIoQQghNSKAIIYTQhASKEEIITUigCCGE0IQEihBCCE1IoAghhNDEdXkfyrXkYlcf3T39Dsv8/bwIDvAZYQ0hhJgYEihTXHdPP5+dbHJYNi8pSgJFCDHp5JCXEEIITUigCCGE0IQEihBCCE1IoAghhNCEU4FSXV1Nfn4+mZmZ5OfnU1NTM6SPoigUFRWRnp5ORkYG27dvH3fboDNnzjB37lw2btxoX9bd3c0TTzxBRkYGWVlZvP/++2P5uYUQQmjMqau81q1bx7Jly8jJyWHnzp0UFhaybds2hz67du2irq6OsrIy2trayM3NZeHChcTFxbncBgOBs27dOtLT0x3e7w9/+ANBQUG8++671NTU8P3vf5+ysjICAwM1GhohhBBjMeoeSktLC5WVlWRnZwOQnZ1NZWUlFovFoV9JSQl5eXno9XqMRiPp6emUlpaOqw3g5Zdf5lvf+hYzZsxweL933nmH/Px8AGbMmEFKSgoffvih6yMhhBBiXEbdQzGbzURHR2MwGAAwGAxERUVhNpsxGo0O/WJjY+2vTSYTDQ0N42o7ceIE+/btY9u2bWzZssWhrvr6eqZPnz7ses46duzYkGUVFRVj2sZkGq42L/8wauvOOiwzhSp8Vd02WWUBU2/cPIWn1uapdYHU5qrJqM1jb2y0Wq38x3/8B7/+9a/tYaa1lJQUh6eYVVRUkJaWNiHvNV4j1dZk6SKh3XF8TDFRRBkTJ6u0KTlunsBTa/PUukBqc5VWtfX29g77h/igUQPFZDLR2NiIoigYDAYURaGpqQmTyTSkX319PampqYDjnocrbefPn6euro5HH30UgAsXLqCqKh0dHTz99NPExsZy7tw5+16S2Wzm9ttvH9PgCCGE0M6o51AiIiJITk6muLgYgOLiYpKTkx0OdwFkZWWxfft2bDYbFouF8vJyMjMzXW6LjY3lk08+4b333uO9997jBz/4Ad/97nd5+umn7eu98cYbANTU1HD06FEWLVqk3cgIIYQYE6cOea1fv56CggK2bNlCSEiI/fLdFStWsHr1aubMmUNOTg6HDx9myZIlAKxatYr4+HgAl9uu5sc//jEFBQVkZGSg1+v51a9+RVBQ0Bh/fCGEEFpxKlASExOHvT9k69at9u8NBgNFRUXDru9q2+Uef/xxh9cBAQG88MILo64nhBBicsid8kIIITQhgSKEEEITEihCCCE0IYEihBBCExIoQgghNCGBIoQQQhMSKEIIITQhgSKEEEITEihCCCE0IYEihBBCExIoQgghNCGBIoQQQhMSKEIIITQhgSKEEEITEihCCCE04dTzUKqrqykoKKCtrY2wsDA2btzIjBkzHPooisKGDRvYu3cvOp2ORx99lLy8vHG17dixg1dffRW9Xo/NZiMvL49//ud/BuDFF1/kf/7nf4iKigJg3rx5rFu3TpNBEUIIMXZOBcq6detYtmwZOTk57Ny5k8LCQrZt2+bQZ9euXdTV1VFWVkZbWxu5ubksXLiQuLg4l9syMzN54IEH0Ol0dHR0sHTpUhYsWMDs2bMByM3NZe3atdqPihBCiDEb9ZBXS0sLlZWVZGdnA5CdnU1lZSUWi8WhX0lJCXl5eej1eoxGI+np6ZSWlo6rLSgoCJ1OB0BPTw9Wq9X+WgghhGcZNVDMZjPR0dEYDAZg4JG9UVFRmM3mIf1iY2Ptr00mEw0NDeNqA9izZw/33Xcfixcv5pFHHiEpKcnetnv3bpYuXcry5cs5dOjQmH5wIYQQ2nLqkJc73Xvvvdx7773U19ezatUq7r77bmbOnMmDDz7IY489hre3N/v372flypWUlJQQHh7u9LaPHTs2ZFlFRYWW5WtquNq8/MOorTvrsMwUqvBVddtklQVMvXHzFJ5am6fWBVKbqyajtlEDxWQy0djYiKIoGAwGFEWhqakJk8k0pF99fT2pqamA456Hq22Xi42NZc6cOXzwwQfMnDmTyMhIe9udd96JyWTiyy+/ZMGCBU7/8CkpKfj6+tpfV1RUkJaW5vT6k2mk2posXSS0GxyWmWKiiDImTlZpU3LcPIGn1uapdYHU5iqtauvt7R32D/FBox7yioiIIDk5meLiYgCKi4tJTk7GaDQ69MvKymL79u3YbDYsFgvl5eVkZmaOq62qqsq+fYvFwieffMKsWbMAaGxstLd98cUXnDt3jhtvvNGpQRFCCKE9pw55rV+/noKCArZs2UJISAgbN24EYMWKFaxevZo5c+aQk5PD4cOHWbJkCQCrVq0iPj4ewOW2N954g/379+Pl5YWqqjz00EPcddddAGzatInjx4+j1+vx9vbmueeec9hrEUIIMbmcCpTExES2b98+ZPnWrVvt3xsMBoqKioZd39W2p556asSaBkNNCCGEZ5A75YUQQmhCAkUIIYQmJFCEEEJoQgJFCCGEJiRQhBBCaEICRQghhCYkUIQQQmhCAkUIIYQmJFCEEEJoQgJFCCGEJiRQhBBCaEICRQghhCYkUIQQQmhCAkUIIYQmJFCEEEJoQgJFCCGEJpwKlOrqavLz88nMzCQ/P5+ampohfRRFoaioiPT0dDIyMhweyOVq244dO1i6dCk5OTksXbqUbdu2ObWeEEKIyefUExvXrVvHsmXLyMnJYefOnRQWFjr8cgfYtWsXdXV1lJWV0dbWRm5uLgsXLiQuLs7ltszMTB544AF0Oh0dHR0sXbqUBQsWMHv27KuuJ4QQYvKNuofS0tJCZWUl2dnZAGRnZ1NZWYnFYnHoV1JSQl5eHnq9HqPRSHp6OqWlpeNqCwoKQqfTAdDT04PVarW/vtp6QgghJt+ogWI2m4mOjsZgMAADz4CPiorCbDYP6RcbG2t/bTKZaGhoGFcbwJ49e7jvvvtYvHgxjzzyCElJSU6tJ4QQYnI5dcjLne69917uvfde6uvrWbVqFXfffTczZ87UZNvHjh0bsqyiokKTbU+E4Wrz8g+jtu6swzJTqMJX1W2TVRYw9cbNU3hqbZ5aF0htrpqM2kYNFJPJRGNjI4qiYDAYUBSFpqYmTCbTkH719fWkpqYCjnsQrrZdLjY2ljlz5vDBBx8wc+ZMp9e7mpSUFHx9fe2vKyoqSEtLG9M2JstItTVZukhoNzgsM8VEEWVMnKzSpuS4eQJPrc1T6wKpzVVa1dbb2zvsH+KDRj3kFRERQXJyMsXFxQAUFxeTnJyM0Wh06JeVlcX27dux2WxYLBbKy8vJzMwcV1tVVZV9+xaLhU8++YRZs2aNup4QQojJ59Qhr/Xr11NQUMCWLVsICQlh48aNAKxYsYLVq1czZ84ccnJyOHz4MEuWLAFg1apVxMfHA7jc9sYbb7B//368vLxQVZWHHnqIu+66a9T1hBBCTD6nAiUxMXHY+zy2bt1q/95gMFBUVDTs+q62PfXUUyPWdLX1hBBCTD65U14IIYQmJFCEEEJoQgJFCCGEJiR
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(college[\"F.Undergrad\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(college[\"P.Undergrad\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(college.Books)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.distplot(college.Expend)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}