02 EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 32561 entries, 0 to 32560\n",
      "Data columns (total 11 columns):\n",
      "hours_per_week_bin    32561 non-null object\n",
      "occupation_bin        32561 non-null object\n",
      "msr_bin               32561 non-null object\n",
      "capital_gl_bin        32561 non-null object\n",
      "race_sex_bin          32561 non-null object\n",
      "education_num_bin     32561 non-null object\n",
      "education_bin         32561 non-null object\n",
      "workclass_bin         32561 non-null object\n",
      "age_bin               32561 non-null object\n",
      "flag                  32561 non-null object\n",
      "y                     32561 non-null int64\n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('00 df.csv')\n",
    "df = df[df['flag']=='train']\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18099867f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CIyIiKbDwiIhICiw8IiKSAguPiIikwMIjIiIpsPCIiEgKLDwiIpICC4+IiKTAwiMiIimw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\n/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18099867fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#age_bin\n",
    "x_chart = df.pivot_table(values=['flag'], index=['age_bin'], columns=['y'], aggfunc='count')\n",
    "x_chart = x_chart.apply(lambda c: c / c.sum() * 100, axis=1)\n",
    "x_chart.plot(kind=\"bar\",stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18099b59cf8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18099b59b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#capital_gl_bin\n",
    "x_chart = df.pivot_table(values=['flag'], index=['capital_gl_bin'], columns=['y'], aggfunc='count')\n",
    "x_chart = x_chart.apply(lambda c: c / c.sum() * 100, axis=1)\n",
    "x_chart.plot(kind=\"bar\",stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18099a4e320>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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czp49a+wZmoAp2MODcWbNmqWkpCTl5eWpQoUK8vX1VZUqVbR7927jbi12MyZP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\nZXx7Nee/xgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18099a911d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#education_bin\n",
    "x_chart = df.pivot_table(values=['flag'], index=['education_bin'], columns=['y'], aggfunc='count')\n",
    "x_chart = x_chart.apply(lambda c: c / c.sum() * 100, axis=1)\n",
    "x_chart.plot(kind=\"bar\",stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18099a93cc0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAIzg78lKTqdTs2bN0tGjR1VTU6NJkybpuuuu08yZM2Wz2dSrVy/NmTNHdjt9CgBoHTwq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\nAIARKDwAgBEoPACAESg8AIAR/h+kFb2n2WjcpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x180999950f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hours_per_week_bin\n",
    "x_chart = df.pivot_table(values=['flag'], index=['hours_per_week_bin'], columns=['y'], aggfunc='count')\n",
    "x_chart = x_chart.apply(lambda c: c / c.sum() * 100, axis=1)\n",
    "x_chart.plot(kind=\"bar\",stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18099df72b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18099e02ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#msr_bin\n",
    "x_chart = df.pivot_table(values=['flag'], index=['msr_bin'], columns=['y'], aggfunc='count')\n",
    "x_chart = x_chart.apply(lambda c: c / c.sum() * 100, axis=1)\n",
    "x_chart.plot(kind=\"bar\",stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18099ed32e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FBwAwAoUHADAChQcAMAKFBwAwAoUHADCCuzNPSk1N1ejRo3X69GndvHlT/fr106OPPqpR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18099f0f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#occupation_bin\n",
    "x_chart = df.pivot_table(values=['flag'], index=['occupation_bin'], columns=['y'], aggfunc='count')\n",
    "x_chart = x_chart.apply(lambda c: c / c.sum() * 100, axis=1)\n",
    "x_chart.plot(kind=\"bar\",stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18099a58da0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18099a589b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#race_sex_bin\n",
    "x_chart = df.pivot_table(values=['flag'], index=['race_sex_bin'], columns=['y'], aggfunc='count')\n",
    "x_chart = x_chart.apply(lambda c: c / c.sum() * 100, axis=1)\n",
    "x_chart.plot(kind=\"bar\",stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}