python+intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from random import randint,randrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "7\n",
      "3\n",
      "0\n",
      "9\n",
      "13\n",
      "5\n",
      "3\n",
      "6\n",
      "4\n",
      "14\n",
      "6\n",
      "7\n",
      "0\n",
      "9\n",
      "4\n",
      "8\n",
      "10\n",
      "14\n",
      "1\n",
      "14\n",
      "2\n",
      "7\n",
      "13\n",
      "14\n",
      "10\n",
      "14\n",
      "7\n",
      "0\n",
      "13\n",
      "3\n",
      "6\n",
      "14\n",
      "5\n",
      "13\n",
      "5\n",
      "11\n",
      "2\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for x in range(10,50):\n",
    "    print(randrange(15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mynewfunction(x,y):\n",
    "    z=x**2+3*x**2*y+20*y**2\n",
    "    print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190\n"
     ]
    }
   ],
   "source": [
    "mynewfunction(1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27100\n"
     ]
    }
   ],
   "source": [
    "mynewfunction(10,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mybadfunction(x,y):\n",
    "    z=x**2+3*x**2*y+20*y**2\n",
    "    return(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mybadfunction(1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores=(23,46,69,7,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=(46,45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 46, 69, 7, 5, 46, 45)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores+sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "favourite_movie2={'micky mouse':'steamboat willie','vijay':'slumdog millionaire','john':'passion of christ','donald':'arthur'}\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(favourite_movie2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'slumdog millionaire'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favourite_movie2['vijay']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names=[\"Agung\",\"Deja\", \"Brahm\",\"Nathan\",\"Ratna\",\"Naufal\",\"Scholly\",\"Siska\",\"Bintang\",\"Sandra\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "None\n",
      "None\n",
      "<_sre.SRE_Match object; span=(4, 6), match='an'>\n",
      "None\n",
      "None\n",
      "None\n",
      "None\n",
      "<_sre.SRE_Match object; span=(4, 6), match='an'>\n",
      "<_sre.SRE_Match object; span=(1, 3), match='an'>\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print (re.search(r'(an)',name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n",
      "False\n",
      "True\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print (bool(re.search(r'(an)',name)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numlist=[\"$60000\",\"$80,000\",\"30,000\",70000,\"55000   \"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "enumerate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'60000'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " re.sub(r\"([$,])\",\"\",str(\"$60000\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int('60000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[60000, '$80,000', '30,000', 70000, '55000   ']\n",
      "[60000, 80000, '30,000', 70000, '55000   ']\n",
      "[60000, 80000, 30000, 70000, '55000   ']\n",
      "[60000, 80000, 30000, 70000, '55000   ']\n",
      "[60000, 80000, 30000, 70000, 55000]\n"
     ]
    }
   ],
   "source": [
    "for i,value in enumerate(numlist):\n",
    "    numlist[i]=re.sub(r\"([$,])\",\"\",str(value))\n",
    "    numlist[i]=int(numlist[i])\n",
    "    print(numlist)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[60000, 80000, 30000, 70000, 55000]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59000.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(numlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_object = datetime.strptime('June/17/2016 1:33PM', '%B/%d/%Y %I:%M%p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2016, 6, 17, 13, 33)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_object2=datetime.strptime(\"12dec-2007\",\"%d%b-%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2007, 12, 12, 0, 0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_object2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=date_object2-datetime_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(-3111, 37620)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3111"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-103.7"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.days/30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from dateutil import relativedelta\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r =- relativedelta.relativedelta(date_object2, datetime_object)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(x):return x**3+3*x**2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g=lambda x:x**3+3*x**2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os as os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv(\"C:\\\\Users\\\\KOGENTIX\\\\Desktop\\\\training\\\\BigDiamonds.csv\\\\BigDiamonds.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 598024 entries, 0 to 598023\n",
      "Data columns (total 13 columns):\n",
      "Unnamed: 0      598024 non-null int64\n",
      "carat           598024 non-null float64\n",
      "cut             598024 non-null object\n",
      "color           598024 non-null object\n",
      "clarity         598024 non-null object\n",
      "table           598024 non-null float64\n",
      "depth           598024 non-null float64\n",
      "cert            598024 non-null object\n",
      "measurements    597978 non-null object\n",
      "price           597311 non-null float64\n",
      "x               596209 non-null float64\n",
      "y               596172 non-null float64\n",
      "z               595480 non-null float64\n",
      "dtypes: float64(7), int64(1), object(5)\n",
      "memory usage: 59.3+ MB\n"
     ]
    }
   ],
   "source": [
    "diamonds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult=pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'this is \\na multiple\\nline comment\\n'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.read_csv?\n",
    "'''this is \n",
    "a multiple\n",
    "line comment\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1       2           3   4                    5   \\\n",
       "0  39          State-gov   77516   Bachelors  13        Never-married   \n",
       "1  50   Self-emp-not-inc   83311   Bachelors  13   Married-civ-spouse   \n",
       "2  38            Private  215646     HS-grad   9             Divorced   \n",
       "3  53            Private  234721        11th   7   Married-civ-spouse   \n",
       "4  28            Private  338409   Bachelors  13   Married-civ-spouse   \n",
       "\n",
       "                   6               7       8        9     10  11  12  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male  2174   0  40   \n",
       "1     Exec-managerial         Husband   White     Male     0   0  13   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male     0   0  40   \n",
       "3   Handlers-cleaners         Husband   Black     Male     0   0  40   \n",
       "4      Prof-specialty            Wife   Black   Female     0   0  40   \n",
       "\n",
       "               13      14  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], dtype='int64')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adult.columns=[\"age \",\n",
    "\"workclass \",\n",
    "\"fnlwgt\",\n",
    "\"education \",\n",
    "\"education-num\",\n",
    "\"marital-status\",\n",
    "\"occupation\",\n",
    "\"relationship\",\n",
    "\"race\",\n",
    "\"sex\",\n",
    "\"capital-gain\",\n",
    "\"capital-loss\",\n",
    "\"hours-per-week\",\n",
    "\"native-country\",\n",
    "\"income\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
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       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   fnlwgt  education   education-num  \\\n",
       "0    39          State-gov   77516   Bachelors             13   \n",
       "1    50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2    38            Private  215646     HS-grad              9   \n",
       "3    53            Private  234721        11th              7   \n",
       "4    28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "wb=pd.read_json(\"C:\\\\Users\\\\KOGENTIX\\\\Desktop\\\\training\\\\world_bank.json\",lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>approvalfy</th>\n",
       "      <th>board_approval_month</th>\n",
       "      <th>boardapprovaldate</th>\n",
       "      <th>borrower</th>\n",
       "      <th>closingdate</th>\n",
       "      <th>country_namecode</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>countryname</th>\n",
       "      <th>countryshortname</th>\n",
       "      <th>...</th>\n",
       "      <th>sectorcode</th>\n",
       "      <th>source</th>\n",
       "      <th>status</th>\n",
       "      <th>supplementprojectflg</th>\n",
       "      <th>theme1</th>\n",
       "      <th>theme_namecode</th>\n",
       "      <th>themecode</th>\n",
       "      <th>totalamt</th>\n",
       "      <th>totalcommamt</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'$oid': '52b213b38594d8a2be17c780'}</td>\n",
       "      <td>1999</td>\n",
       "      <td>November</td>\n",
       "      <td>2013-11-12T00:00:00Z</td>\n",
       "      <td>FEDERAL DEMOCRATIC REPUBLIC OF ETHIOPIA</td>\n",
       "      <td>2018-07-07T00:00:00Z</td>\n",
       "      <td>Federal Democratic Republic of Ethiopia!$!ET</td>\n",
       "      <td>ET</td>\n",
       "      <td>Federal Democratic Republic of Ethiopia</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>...</td>\n",
       "      <td>ET,BS,ES,EP</td>\n",
       "      <td>IBRD</td>\n",
       "      <td>Active</td>\n",
       "      <td>N</td>\n",
       "      <td>{'Name': 'Education for all', 'Percent': 100}</td>\n",
       "      <td>[{'name': 'Education for all', 'code': '65'}]</td>\n",
       "      <td>65</td>\n",
       "      <td>130000000</td>\n",
       "      <td>130000000</td>\n",
       "      <td>http://www.worldbank.org/projects/P129828/ethi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'$oid': '52b213b38594d8a2be17c781'}</td>\n",
       "      <td>2015</td>\n",
       "      <td>November</td>\n",
       "      <td>2013-11-04T00:00:00Z</td>\n",
       "      <td>GOVERNMENT OF TUNISIA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Republic of Tunisia!$!TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>Republic of Tunisia</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>...</td>\n",
       "      <td>BZ,BS</td>\n",
       "      <td>IBRD</td>\n",
       "      <td>Active</td>\n",
       "      <td>N</td>\n",
       "      <td>{'Name': 'Other economic management', 'Percent...</td>\n",
       "      <td>[{'name': 'Other economic management', 'code':...</td>\n",
       "      <td>54,24</td>\n",
       "      <td>0</td>\n",
       "      <td>4700000</td>\n",
       "      <td>http://www.worldbank.org/projects/P144674?lang=en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'$oid': '52b213b38594d8a2be17c782'}</td>\n",
       "      <td>2014</td>\n",
       "      <td>November</td>\n",
       "      <td>2013-11-01T00:00:00Z</td>\n",
       "      <td>MINISTRY OF FINANCE AND ECONOMIC DEVEL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuvalu!$!TV</td>\n",
       "      <td>TV</td>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>...</td>\n",
       "      <td>TI</td>\n",
       "      <td>IBRD</td>\n",
       "      <td>Active</td>\n",
       "      <td>Y</td>\n",
       "      <td>{'Name': 'Regional integration', 'Percent': 46}</td>\n",
       "      <td>[{'name': 'Regional integration', 'code': '47'...</td>\n",
       "      <td>52,81,25,47</td>\n",
       "      <td>6060000</td>\n",
       "      <td>6060000</td>\n",
       "      <td>http://www.worldbank.org/projects/P145310?lang=en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'$oid': '52b213b38594d8a2be17c783'}</td>\n",
       "      <td>2014</td>\n",
       "      <td>October</td>\n",
       "      <td>2013-10-31T00:00:00Z</td>\n",
       "      <td>MIN. OF PLANNING AND INT'L COOPERATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Republic of Yemen!$!RY</td>\n",
       "      <td>RY</td>\n",
       "      <td>Republic of Yemen</td>\n",
       "      <td>Yemen, Republic of</td>\n",
       "      <td>...</td>\n",
       "      <td>JB</td>\n",
       "      <td>IBRD</td>\n",
       "      <td>Active</td>\n",
       "      <td>N</td>\n",
       "      <td>{'Name': 'Participation and civic engagement',...</td>\n",
       "      <td>[{'name': 'Participation and civic engagement'...</td>\n",
       "      <td>59,57</td>\n",
       "      <td>0</td>\n",
       "      <td>1500000</td>\n",
       "      <td>http://www.worldbank.org/projects/P144665?lang=en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'$oid': '52b213b38594d8a2be17c784'}</td>\n",
       "      <td>2014</td>\n",
       "      <td>October</td>\n",
       "      <td>2013-10-31T00:00:00Z</td>\n",
       "      <td>MINISTRY OF FINANCE</td>\n",
       "      <td>2019-04-30T00:00:00Z</td>\n",
       "      <td>Kingdom of Lesotho!$!LS</td>\n",
       "      <td>LS</td>\n",
       "      <td>Kingdom of Lesotho</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>...</td>\n",
       "      <td>FH,YW,YZ</td>\n",
       "      <td>IBRD</td>\n",
       "      <td>Active</td>\n",
       "      <td>N</td>\n",
       "      <td>{'Name': 'Export development and competitivene...</td>\n",
       "      <td>[{'name': 'Export development and competitiven...</td>\n",
       "      <td>41,45</td>\n",
       "      <td>13100000</td>\n",
       "      <td>13100000</td>\n",
       "      <td>http://www.worldbank.org/projects/P144933/seco...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    _id  approvalfy board_approval_month  \\\n",
       "0  {'$oid': '52b213b38594d8a2be17c780'}        1999             November   \n",
       "1  {'$oid': '52b213b38594d8a2be17c781'}        2015             November   \n",
       "2  {'$oid': '52b213b38594d8a2be17c782'}        2014             November   \n",
       "3  {'$oid': '52b213b38594d8a2be17c783'}        2014              October   \n",
       "4  {'$oid': '52b213b38594d8a2be17c784'}        2014              October   \n",
       "\n",
       "      boardapprovaldate                                 borrower  \\\n",
       "0  2013-11-12T00:00:00Z  FEDERAL DEMOCRATIC REPUBLIC OF ETHIOPIA   \n",
       "1  2013-11-04T00:00:00Z                    GOVERNMENT OF TUNISIA   \n",
       "2  2013-11-01T00:00:00Z   MINISTRY OF FINANCE AND ECONOMIC DEVEL   \n",
       "3  2013-10-31T00:00:00Z   MIN. OF PLANNING AND INT'L COOPERATION   \n",
       "4  2013-10-31T00:00:00Z                      MINISTRY OF FINANCE   \n",
       "\n",
       "            closingdate                              country_namecode  \\\n",
       "0  2018-07-07T00:00:00Z  Federal Democratic Republic of Ethiopia!$!ET   \n",
       "1                   NaN                      Republic of Tunisia!$!TN   \n",
       "2                   NaN                                   Tuvalu!$!TV   \n",
       "3                   NaN                        Republic of Yemen!$!RY   \n",
       "4  2019-04-30T00:00:00Z                       Kingdom of Lesotho!$!LS   \n",
       "\n",
       "  countrycode                              countryname    countryshortname  \\\n",
       "0          ET  Federal Democratic Republic of Ethiopia            Ethiopia   \n",
       "1          TN                      Republic of Tunisia             Tunisia   \n",
       "2          TV                                   Tuvalu              Tuvalu   \n",
       "3          RY                        Republic of Yemen  Yemen, Republic of   \n",
       "4          LS                       Kingdom of Lesotho             Lesotho   \n",
       "\n",
       "                         ...                           sectorcode source  \\\n",
       "0                        ...                          ET,BS,ES,EP   IBRD   \n",
       "1                        ...                                BZ,BS   IBRD   \n",
       "2                        ...                                   TI   IBRD   \n",
       "3                        ...                                   JB   IBRD   \n",
       "4                        ...                             FH,YW,YZ   IBRD   \n",
       "\n",
       "   status  supplementprojectflg  \\\n",
       "0  Active                     N   \n",
       "1  Active                     N   \n",
       "2  Active                     Y   \n",
       "3  Active                     N   \n",
       "4  Active                     N   \n",
       "\n",
       "                                              theme1  \\\n",
       "0      {'Name': 'Education for all', 'Percent': 100}   \n",
       "1  {'Name': 'Other economic management', 'Percent...   \n",
       "2    {'Name': 'Regional integration', 'Percent': 46}   \n",
       "3  {'Name': 'Participation and civic engagement',...   \n",
       "4  {'Name': 'Export development and competitivene...   \n",
       "\n",
       "                                      theme_namecode    themecode   totalamt  \\\n",
       "0      [{'name': 'Education for all', 'code': '65'}]           65  130000000   \n",
       "1  [{'name': 'Other economic management', 'code':...        54,24          0   \n",
       "2  [{'name': 'Regional integration', 'code': '47'...  52,81,25,47    6060000   \n",
       "3  [{'name': 'Participation and civic engagement'...        59,57          0   \n",
       "4  [{'name': 'Export development and competitiven...        41,45   13100000   \n",
       "\n",
       "  totalcommamt                                                url  \n",
       "0    130000000  http://www.worldbank.org/projects/P129828/ethi...  \n",
       "1      4700000  http://www.worldbank.org/projects/P144674?lang=en  \n",
       "2      6060000  http://www.worldbank.org/projects/P145310?lang=en  \n",
       "3      1500000  http://www.worldbank.org/projects/P144665?lang=en  \n",
       "4     13100000  http://www.worldbank.org/projects/P144933/seco...  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['_id', 'approvalfy', 'board_approval_month', 'boardapprovaldate',\n",
       "       'borrower', 'closingdate', 'country_namecode', 'countrycode',\n",
       "       'countryname', 'countryshortname', 'docty', 'envassesmentcategorycode',\n",
       "       'grantamt', 'ibrdcommamt', 'id', 'idacommamt', 'impagency',\n",
       "       'lendinginstr', 'lendinginstrtype', 'lendprojectcost',\n",
       "       'majorsector_percent', 'mjsector_namecode', 'mjtheme',\n",
       "       'mjtheme_namecode', 'mjthemecode', 'prodline', 'prodlinetext',\n",
       "       'productlinetype', 'project_abstract', 'project_name', 'projectdocs',\n",
       "       'projectfinancialtype', 'projectstatusdisplay', 'regionname', 'sector',\n",
       "       'sector1', 'sector2', 'sector3', 'sector4', 'sector_namecode',\n",
       "       'sectorcode', 'source', 'status', 'supplementprojectflg', 'theme1',\n",
       "       'theme_namecode', 'themecode', 'totalamt', 'totalcommamt', 'url'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wb.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(adult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, ' State-gov', 77516, ..., 40, ' United-States', ' <=50K'],\n",
       "       [50, ' Self-emp-not-inc', 83311, ..., 13, ' United-States', ' <=50K'],\n",
       "       [38, ' Private', 215646, ..., 40, ' United-States', ' <=50K'],\n",
       "       ..., \n",
       "       [58, ' Private', 151910, ..., 40, ' United-States', ' <=50K'],\n",
       "       [22, ' Private', 201490, ..., 20, ' United-States', ' <=50K'],\n",
       "       [52, ' Self-emp-inc', 287927, ..., 40, ' United-States', ' >50K']], dtype=object)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b=adult.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32561"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2, ..., 32558, 32559, 32560])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(len(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c=[\"age \",\n",
    "\"workclass \",\n",
    "\"fnlwgt\",\n",
    "\"education \",\n",
    "\"education-num\",\n",
    "\"marital-status\",\n",
    "\"occupation\",\n",
    "\"relationship\",\n",
    "\"race\",\n",
    "\"sex\",\n",
    "\"capital-gain\",\n",
    "\"capital-loss\",\n",
    "\"hours-per-week\",\n",
    "\"native-country\",\n",
    "\"income\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, ' State-gov', 77516, ..., 40, ' United-States', ' <=50K'],\n",
       "       [50, ' Self-emp-not-inc', 83311, ..., 13, ' United-States', ' <=50K'],\n",
       "       [38, ' Private', 215646, ..., 40, ' United-States', ' <=50K'],\n",
       "       ..., \n",
       "       [58, ' Private', 151910, ..., 40, ' United-States', ' <=50K'],\n",
       "       [22, ' Private', 201490, ..., 20, ' United-States', ' <=50K'],\n",
       "       [52, ' Self-emp-inc', 287927, ..., 40, ' United-States', ' >50K']], dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age ',\n",
       " 'workclass ',\n",
       " 'fnlwgt',\n",
       " 'education ',\n",
       " 'education-num',\n",
       " 'marital-status',\n",
       " 'occupation',\n",
       " 'relationship',\n",
       " 'race',\n",
       " 'sex',\n",
       " 'capital-gain',\n",
       " 'capital-loss',\n",
       " 'hours-per-week',\n",
       " 'native-country',\n",
       " 'income']"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=np.arange(len(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2, ..., 32558, 32559, 32560])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adult3=pd.DataFrame(data=b,    # values\n",
    "             index=d,    # 1st column as index\n",
    "              columns=c)  # 1st row as the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age          workclass   fnlwgt  education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors            13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors            13   \n",
       "2   38            Private  215646     HS-grad             9   \n",
       "3   53            Private  234721        11th             7   \n",
       "4   28            Private  338409   Bachelors            13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "  capital-gain capital-loss hours-per-week  native-country  income  \n",
       "0         2174            0             40   United-States   <=50K  \n",
       "1            0            0             13   United-States   <=50K  \n",
       "2            0            0             40   United-States   <=50K  \n",
       "3            0            0             40   United-States   <=50K  \n",
       "4            0            0             40            Cuba   <=50K  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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