Day_19(17_May_19).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day 19\n",
    "### Date: 17 May 2019, Friday\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 1:\n",
    "### Statement :\n",
    "Define a function to read from CSV file using Pandas library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       GEOID     State     2005     2006     2007     2008     2009     2010  \\\n",
      "0  04000US01   Alabama  37150.0  37952.0  42212.0  44476.0  39980.0  40933.0   \n",
      "1  04000US02    Alaska  55891.0  56418.0  62993.0  63989.0  61604.0  57848.0   \n",
      "2  04000US04   Arizona  45245.0  46657.0  47215.0  46914.0  45739.0  46896.0   \n",
      "3  04000US05  Arkansas  36658.0  37057.0  40795.0  39586.0  36538.0  38587.0   \n",
      "4         fj       NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
      "\n",
      "      2011     2012     2013  \n",
      "0  42590.0  43464.0  41381.0  \n",
      "1  57431.0  63648.0  61137.0  \n",
      "2  48621.0  47044.0  50602.0  \n",
      "3  41302.0  39018.0  39919.0  \n",
      "4      NaN      NaN      NaN  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def readCSV(filename):\n",
    "    df=pd.read_csv(filename)\n",
    "    #for row in df.values:\n",
    "      #  print('GEOID:',row[0],'State:',row[1])\n",
    "    #print(df.head(1))\n",
    "\n",
    "    return df\n",
    "\n",
    "print(readCSV('DataFiles/Income.csv'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 2: \n",
    "## Statement :\n",
    "Define a function to add a new data to .csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150</td>\n",
       "      <td>37952</td>\n",
       "      <td>42212</td>\n",
       "      <td>44476</td>\n",
       "      <td>39980</td>\n",
       "      <td>40933</td>\n",
       "      <td>42590</td>\n",
       "      <td>43464</td>\n",
       "      <td>41381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891</td>\n",
       "      <td>56418</td>\n",
       "      <td>62993</td>\n",
       "      <td>63989</td>\n",
       "      <td>61604</td>\n",
       "      <td>57848</td>\n",
       "      <td>57431</td>\n",
       "      <td>63648</td>\n",
       "      <td>61137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245</td>\n",
       "      <td>46657</td>\n",
       "      <td>47215</td>\n",
       "      <td>46914</td>\n",
       "      <td>45739</td>\n",
       "      <td>46896</td>\n",
       "      <td>48621</td>\n",
       "      <td>47044</td>\n",
       "      <td>50602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658</td>\n",
       "      <td>37057</td>\n",
       "      <td>40795</td>\n",
       "      <td>39586</td>\n",
       "      <td>36538</td>\n",
       "      <td>38587</td>\n",
       "      <td>41302</td>\n",
       "      <td>39018</td>\n",
       "      <td>39919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04000US06</td>\n",
       "      <td>California</td>\n",
       "      <td>51755</td>\n",
       "      <td>55319</td>\n",
       "      <td>55734</td>\n",
       "      <td>57014</td>\n",
       "      <td>56134</td>\n",
       "      <td>54283</td>\n",
       "      <td>53367</td>\n",
       "      <td>57020</td>\n",
       "      <td>57528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>04000US09</td>\n",
       "      <td>State</td>\n",
       "      <td>123</td>\n",
       "      <td>324324</td>\n",
       "      <td>655</td>\n",
       "      <td>4332</td>\n",
       "      <td>231</td>\n",
       "      <td>34</td>\n",
       "      <td>213</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID       State   2005    2006   2007   2008   2009   2010   2011  \\\n",
       "0  04000US01     Alabama  37150   37952  42212  44476  39980  40933  42590   \n",
       "1  04000US02      Alaska  55891   56418  62993  63989  61604  57848  57431   \n",
       "2  04000US04     Arizona  45245   46657  47215  46914  45739  46896  48621   \n",
       "3  04000US05    Arkansas  36658   37057  40795  39586  36538  38587  41302   \n",
       "4  04000US06  California  51755   55319  55734  57014  56134  54283  53367   \n",
       "5  04000US09       State    123  324324    655   4332    231     34    213   \n",
       "\n",
       "    2012   2013  \n",
       "0  43464  41381  \n",
       "1  63648  61137  \n",
       "2  47044  50602  \n",
       "3  39018  39919  \n",
       "4  57020  57528  \n",
       "5     32     34  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def AddRowCSV(filename,rd):\n",
    "    df=pd.read_csv('DataFiles/Income.csv')\n",
    "    df.loc[len(df)]=rd\n",
    "    df.to_csv('DataFiles/Income.csv',index=False)\n",
    "    return df\n",
    "data=['04000US09','Florida',123,324324,655,4332,231,34,213,32,34]\n",
    "AddRowCSV('DataFiles/Income.csv',data)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 3: \n",
    "## Statement :\n",
    "Define a function to add a new data to .csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150</td>\n",
       "      <td>37952</td>\n",
       "      <td>42212</td>\n",
       "      <td>44476</td>\n",
       "      <td>39980</td>\n",
       "      <td>40933</td>\n",
       "      <td>42590</td>\n",
       "      <td>43464</td>\n",
       "      <td>41381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891</td>\n",
       "      <td>56418</td>\n",
       "      <td>62993</td>\n",
       "      <td>63989</td>\n",
       "      <td>61604</td>\n",
       "      <td>57848</td>\n",
       "      <td>57431</td>\n",
       "      <td>63648</td>\n",
       "      <td>61137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245</td>\n",
       "      <td>46657</td>\n",
       "      <td>47215</td>\n",
       "      <td>46914</td>\n",
       "      <td>45739</td>\n",
       "      <td>46896</td>\n",
       "      <td>48621</td>\n",
       "      <td>47044</td>\n",
       "      <td>50602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658</td>\n",
       "      <td>37057</td>\n",
       "      <td>40795</td>\n",
       "      <td>39586</td>\n",
       "      <td>36538</td>\n",
       "      <td>38587</td>\n",
       "      <td>41302</td>\n",
       "      <td>39018</td>\n",
       "      <td>39919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04000US06</td>\n",
       "      <td>California</td>\n",
       "      <td>51755</td>\n",
       "      <td>55319</td>\n",
       "      <td>55734</td>\n",
       "      <td>57014</td>\n",
       "      <td>56134</td>\n",
       "      <td>54283</td>\n",
       "      <td>53367</td>\n",
       "      <td>57020</td>\n",
       "      <td>57528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>04000US12</td>\n",
       "      <td>State</td>\n",
       "      <td>123</td>\n",
       "      <td>3324</td>\n",
       "      <td>655</td>\n",
       "      <td>4332</td>\n",
       "      <td>231</td>\n",
       "      <td>34</td>\n",
       "      <td>213</td>\n",
       "      <td>382</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID       State   2005   2006   2007   2008   2009   2010   2011  \\\n",
       "0  04000US01     Alabama  37150  37952  42212  44476  39980  40933  42590   \n",
       "1  04000US02      Alaska  55891  56418  62993  63989  61604  57848  57431   \n",
       "2  04000US04     Arizona  45245  46657  47215  46914  45739  46896  48621   \n",
       "3  04000US05    Arkansas  36658  37057  40795  39586  36538  38587  41302   \n",
       "4  04000US06  California  51755  55319  55734  57014  56134  54283  53367   \n",
       "5  04000US12       State    123   3324    655   4332    231     34    213   \n",
       "\n",
       "    2012   2013  \n",
       "0  43464  41381  \n",
       "1  63648  61137  \n",
       "2  47044  50602  \n",
       "3  39018  39919  \n",
       "4  57020  57528  \n",
       "5    382    384  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def updateRowCSV(filename,rowindex,rowdata):\n",
    "    df=readCSV(filename)\n",
    "    df.loc[rowindex]=rowdata\n",
    "    df.to_csv(filename,index=False)\n",
    "    return df\n",
    "\n",
    "#df=df.drop('Unnamed:0',axis=1)\n",
    "#df.to_csv('DataFiles/Income.csv',index=False)\n",
    "data=['04000US12','Florida',123,3324,655,4332,231,34,213,382,384]\n",
    "updateRowCSV('DataFiles/Income.csv',5,data)\n",
    "#readCSV('DataFiles/Income.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 4:\n",
    "## Statement:\n",
    "Delete a row from .csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150.0</td>\n",
       "      <td>37952.0</td>\n",
       "      <td>42212.0</td>\n",
       "      <td>44476.0</td>\n",
       "      <td>39980.0</td>\n",
       "      <td>40933.0</td>\n",
       "      <td>42590.0</td>\n",
       "      <td>43464.0</td>\n",
       "      <td>41381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891.0</td>\n",
       "      <td>56418.0</td>\n",
       "      <td>62993.0</td>\n",
       "      <td>63989.0</td>\n",
       "      <td>61604.0</td>\n",
       "      <td>57848.0</td>\n",
       "      <td>57431.0</td>\n",
       "      <td>63648.0</td>\n",
       "      <td>61137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245.0</td>\n",
       "      <td>46657.0</td>\n",
       "      <td>47215.0</td>\n",
       "      <td>46914.0</td>\n",
       "      <td>45739.0</td>\n",
       "      <td>46896.0</td>\n",
       "      <td>48621.0</td>\n",
       "      <td>47044.0</td>\n",
       "      <td>50602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>37057.0</td>\n",
       "      <td>40795.0</td>\n",
       "      <td>39586.0</td>\n",
       "      <td>36538.0</td>\n",
       "      <td>38587.0</td>\n",
       "      <td>41302.0</td>\n",
       "      <td>39018.0</td>\n",
       "      <td>39919.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID     State     2005     2006     2007     2008     2009     2010  \\\n",
       "0  04000US01   Alabama  37150.0  37952.0  42212.0  44476.0  39980.0  40933.0   \n",
       "1  04000US02    Alaska  55891.0  56418.0  62993.0  63989.0  61604.0  57848.0   \n",
       "2  04000US04   Arizona  45245.0  46657.0  47215.0  46914.0  45739.0  46896.0   \n",
       "3  04000US05  Arkansas  36658.0  37057.0  40795.0  39586.0  36538.0  38587.0   \n",
       "\n",
       "      2011     2012     2013  \n",
       "0  42590.0  43464.0  41381.0  \n",
       "1  57431.0  63648.0  61137.0  \n",
       "2  48621.0  47044.0  50602.0  \n",
       "3  41302.0  39018.0  39919.0  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def deleteRowCSV(filename,rowindex):\n",
    "    df=readCSV(filename)\n",
    "    df=df.drop(rowindex)\n",
    "    df.to_csv(filename,index=False)\n",
    "    return df\n",
    "\n",
    "deleteRowCSV('DataFiles/Income.csv',4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 5:\n",
    "## Statement:\n",
    "Generate marks of 2000  students in a .csv file with columns ,RollNo,Marks\n",
    "\n",
    "2210410001\n",
    "...\n",
    "2210412000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'DataFiles/StudentsData.csv' does not exist: b'DataFiles/StudentsData.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-173-b9ef186dc436>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'student'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rollnumber'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mupdateRowCSV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'DataFiles/StudentsData.csv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;31m#readCSV('DataFiles/Income.csv')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-142-3d7875029d20>\u001b[0m in \u001b[0;36mupdateRowCSV\u001b[1;34m(filename, rowindex, rowdata)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mupdateRowCSV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrowindex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrowdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreadCSV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrowindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrowdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-167-7c0eda298fdb>\u001b[0m in \u001b[0;36mreadCSV\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreadCSV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[1;31m#for row in df.values:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m       \u001b[1;31m#  print('GEOID:',row[0],'State:',row[1])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    428\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 429\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    430\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1120\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1124\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1851\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'usecols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1852\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1853\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1854\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1855\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File b'DataFiles/StudentsData.csv' does not exist: b'DataFiles/StudentsData.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "def Sheet(filename,nr,rollnumber):\n",
    "    df=pd.Sheet(filename)\n",
    "    df.loc[nr]=rollnumber\n",
    "    df.to_csv(filename,index=False)\n",
    "    return df\n",
    "\n",
    "data=['student','rollnumber']\n",
    "updateRowCSV('DataFiles/StudentsData.csv',1,data)\n",
    "#readCSV('DataFiles/Income.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-176-2abe99a001ac>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'file_{}.csv'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "for i in range(3):\n",
    "    data = pd.DataFrame(np.random.randn(10, 4))\n",
    "    data.to_csv('file_{}.csv'.format(i))\n",
    "\n",
    "files = ['file_0.csv', 'file_1.csv', 'file_2.csv']\n",
    "\n",
    "result = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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