Plotting Images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv(\"./train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m=df.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im=m[0,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "im=im.reshape(28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff79ed30588>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4QeSIvxAUoQfSIrwA0n9P3L2mHPFv4I3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7a0010860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff79ec807f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imshow(im,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH0jq/wEGdtT4efqESQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff79ee6e160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff79ec0fa58>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imshow(255-im,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQTamLS+bfF4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff79ec92780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}