Code/Introduction/Python/Introduction+to+Python+for+Data+Science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code partially based on: \n",
    "\n",
    "J.R. Johansson (robert@riken.jp) http://dml.riken.jp/~rob/\n",
    "The latest version of this IPython notebook lecture is available at http://github.com/jrjohansson/scientific-python-lectures.\n",
    "The other notebooks in this lecture series are indexed at http://jrjohansson.github.com.\n",
    "\n",
    "Python Machine Learning 2nd Edition by Sebastian Raschka, Packt Publishing Ltd. 2017\n",
    "Code Repository: https://github.com/rasbt/python-machine-learning-book-2nd-edition\n",
    "Code License: MIT License\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Python 3.6.5 :: Anaconda, Inc.\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string = '''x'''\n",
    "len(my_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "squares_list = [1, 4, 9, '16', 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 'x', 25]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 'x']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_list[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'x'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "squares_list[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "str = \"BME\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "M\n"
     ]
    }
   ],
   "source": [
    "print(str[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(len(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BME6938\n"
     ]
    }
   ],
   "source": [
    "print(str + '6938')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raw strings can be used as is, without replacing the escape characters. Python will not change the string.\n",
    "raw_str = r'This is a \\n string'\n",
    "regular_str = 'This is a \\n string'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw: This is a \\n string\n",
      "Regular: This is a \n",
      " string\n"
     ]
    }
   ],
   "source": [
    "print('Raw: ' + raw_str)\n",
    "print ('Regular: ' + regular_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-54ad1d5a68cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m# strings are immutable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'x'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "# strings are immutable\n",
    "str[1] = 'x'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bme'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tple = (0, 1, 4, \"9\", 16, 'x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, 4, '9', 16, 'x')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tple[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-2a4edb909c79>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Tuples are immutable, you cannot change them\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtple\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "# Tuples are immutable, you cannot change them\n",
    "tple[3] = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dict = {'White Blood Cell': 6.9, 'Red Blood Cell': 1.8, 'Hemoglobin': 6.5, 'Hematocrit': 19}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Hematocrit': 19,\n",
       " 'Hemoglobin': 6.5,\n",
       " 'Red Blood Cell': 1.8,\n",
       " 'White Blood Cell': 6.9}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.9"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict['White Blood Cell']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dict['White Blood Cell'] = 6.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'White Blood Cell': 6.4, 'Hemoglobin': 6.5, 'Hematocrit': 19, 'Red Blood Cell': 1.8}\n"
     ]
    }
   ],
   "source": [
    "print(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['White Blood Cell', 'Hemoglobin', 'Hematocrit', 'Red Blood Cell'])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([6.4, 6.5, 19, 1.8])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict.values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "2\n",
      "3\n",
      "3\n",
      "4\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,5):\n",
    "    print(i)\n",
    "    print(i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "4\n",
      "9\n",
      "16\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "for i in squares_list:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "White Blood Cell\n",
      "Hemoglobin\n",
      "Hematocrit\n",
      "Red Blood Cell\n"
     ]
    }
   ],
   "source": [
    "for k in dict.keys():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "4\n",
      "9\n",
      "16\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "for x in tple:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional Statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Abnormal\n"
     ]
    }
   ],
   "source": [
    "dict['White Blood Cell'] = 4.3\n",
    "if (dict['White Blood Cell'] > 10.8) or (dict['White Blood Cell'] < 4.8):\n",
    "    print('Abnormal')\n",
    "else:\n",
    "    print('Normal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5040"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Libraries are key, so we don't have to develop every single function from scratch\n",
    "import math\n",
    "math.factorial(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24b6d13c2e8>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This opens up iPython notebook in pylab environment, which has a few useful libraries already imported. \n",
    "# Also, you will be able to plot your data inline, which makes this a really good environment for \n",
    "# interactive data analysis. \n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "plt.plot(range(20, 30), range(10, 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the dataset\n",
    "url = \"https://gist.githubusercontent.com/ktisha/c21e73a1bd1700294ef790c56c8aec1f/raw/819b69b5736821ccee93d05b51de0510bea00294/pima-indians-diabetes.csv\"\n",
    "\n",
    "\"\"\"\n",
    "Attribute Information:\n",
    "\n",
    "1. Number of times pregnant \n",
    "2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test \n",
    "3. Diastolic blood pressure (mm Hg) \n",
    "4. Triceps skin fold thickness (mm) \n",
    "5. 2-Hour serum insulin (mu U/ml) \n",
    "6. Body mass index (weight in kg/(height in m)^2) \n",
    "7. Diabetes pedigree function \n",
    "8. Age (years) \n",
    "9. Class variable (0 or 1) \"\"\"\n",
    "\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "df = c = pd.read_csv(url, names = names, comment='#')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   preg  plas  pres  skin  test  mass   pedi  age  class\n",
       "0     6   148    72    35     0  33.6  0.627   50      1\n",
       "1     1    85    66    29     0  26.6  0.351   31      0\n",
       "2     8   183    64     0     0  23.3  0.672   32      1\n",
       "3     1    89    66    23    94  28.1  0.167   21      0\n",
       "4     0   137    40    35   168  43.1  2.288   33      1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             preg        plas        pres        skin        test        mass  \\\n",
       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
       "\n",
       "             pedi         age       class  \n",
       "count  768.000000  768.000000  768.000000  \n",
       "mean     0.471876   33.240885    0.348958  \n",
       "std      0.331329   11.760232    0.476951  \n",
       "min      0.078000   21.000000    0.000000  \n",
       "25%      0.243750   24.000000    0.000000  \n",
       "50%      0.372500   29.000000    0.000000  \n",
       "75%      0.626250   41.000000    1.000000  \n",
       "max      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b6d24b860>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['mass'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b6d269dd8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='mass', by='preg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = df['preg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     135\n",
      "0     111\n",
      "2     103\n",
      "3      75\n",
      "4      68\n",
      "5      57\n",
      "6      50\n",
      "7      45\n",
      "8      38\n",
      "9      28\n",
      "10     24\n",
      "11     11\n",
      "13     10\n",
      "12      9\n",
      "14      2\n",
      "15      1\n",
      "17      1\n",
      "Name: preg, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "preg     0\n",
       "plas     0\n",
       "pres     0\n",
       "skin     0\n",
       "test     0\n",
       "mass     0\n",
       "pedi     0\n",
       "age      0\n",
       "class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.apply(lambda x: sum(x.isnull()),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}