Pandas And Reading CSV using various parameters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv ('C:/Users/Shanty/Desktop/Python Start to End/sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8</td>\n",
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      "text/plain": [
       "   column1  column2  column3  column4  column5\n",
       "0        1        2        3        4        5\n",
       "1        6        7        8        9       10\n",
       "2        2        3        4        5        6\n",
       "3        4        5        6        7        8"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv ('C:/Users/Shanty/Desktop/Python Start to End/test1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
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   "outputs": [
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      "text/plain": [
       "   ID  X0 X1  X2 X3 X4 X5 X6 X8  X10  ...  X375  X376  X377  X378  X379  X380  \\\n",
       "0   1  az  v   n  f  d  t  a  w    0  ...     0     0     0     1     0     0   \n",
       "1   2   t  b  ai  a  d  b  g  y    0  ...     0     0     1     0     0     0   \n",
       "2   3  az  v  as  f  d  a  j  j    0  ...     0     0     0     1     0     0   \n",
       "3   4  az  l   n  f  d  z  l  n    0  ...     0     0     0     1     0     0   \n",
       "4   5   w  s  as  c  d  y  i  m    0  ...     1     0     0     0     0     0   \n",
       "\n",
       "   X382  X383  X384  X385  \n",
       "0     0     0     0     0  \n",
       "1     0     0     0     0  \n",
       "2     0     0     0     0  \n",
       "3     0     0     0     0  \n",
       "4     0     0     0     0  \n",
       "\n",
       "[5 rows x 377 columns]"
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     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.head()  # Selects 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4209 entries, 0 to 4208\n",
      "Columns: 377 entries, ID to X385\n",
      "dtypes: int64(369), object(8)\n",
      "memory usage: 12.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>4209.000000</td>\n",
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       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
       "      <td>4209.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4211.039202</td>\n",
       "      <td>0.019007</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.074364</td>\n",
       "      <td>0.061060</td>\n",
       "      <td>0.427893</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.002613</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.010216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.325968</td>\n",
       "      <td>0.049656</td>\n",
       "      <td>0.311951</td>\n",
       "      <td>0.019244</td>\n",
       "      <td>0.011879</td>\n",
       "      <td>0.008078</td>\n",
       "      <td>0.008791</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.000713</td>\n",
       "      <td>0.001663</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2423.078926</td>\n",
       "      <td>0.136565</td>\n",
       "      <td>0.015414</td>\n",
       "      <td>0.262394</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.494832</td>\n",
       "      <td>0.026691</td>\n",
       "      <td>0.051061</td>\n",
       "      <td>0.093357</td>\n",
       "      <td>0.100570</td>\n",
       "      <td>...</td>\n",
       "      <td>0.468791</td>\n",
       "      <td>0.217258</td>\n",
       "      <td>0.463345</td>\n",
       "      <td>0.137399</td>\n",
       "      <td>0.108356</td>\n",
       "      <td>0.089524</td>\n",
       "      <td>0.093357</td>\n",
       "      <td>0.021796</td>\n",
       "      <td>0.026691</td>\n",
       "      <td>0.040752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2115.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6310.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8416.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 369 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID          X10          X11          X12          X13  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000   \n",
       "mean   4211.039202     0.019007     0.000238     0.074364     0.061060   \n",
       "std    2423.078926     0.136565     0.015414     0.262394     0.239468   \n",
       "min       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%    2115.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%    4202.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%    6310.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max    8416.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "               X14          X15          X16          X17          X18  ...  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000  ...   \n",
       "mean      0.427893     0.000713     0.002613     0.008791     0.010216  ...   \n",
       "std       0.494832     0.026691     0.051061     0.093357     0.100570  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       1.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "              X375         X376         X377         X378         X379  \\\n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000   \n",
       "mean      0.325968     0.049656     0.311951     0.019244     0.011879   \n",
       "std       0.468791     0.217258     0.463345     0.137399     0.108356   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "              X380         X382         X383         X384         X385  \n",
       "count  4209.000000  4209.000000  4209.000000  4209.000000  4209.000000  \n",
       "mean      0.008078     0.008791     0.000475     0.000713     0.001663  \n",
       "std       0.089524     0.093357     0.021796     0.026691     0.040752  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 369 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()  #only takes numerical(object) columns into consideration and not categorical or alphabetic features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ak    432\n",
       "y     348\n",
       "z     335\n",
       "x     302\n",
       "ay    299\n",
       "t     293\n",
       "o     246\n",
       "f     213\n",
       "w     198\n",
       "j     171\n",
       "n     167\n",
       "aj    162\n",
       "az    161\n",
       "s     116\n",
       "ap    108\n",
       "al     88\n",
       "h      64\n",
       "d      61\n",
       "e      48\n",
       "v      40\n",
       "ai     38\n",
       "m      34\n",
       "af     34\n",
       "am     28\n",
       "i      25\n",
       "at     21\n",
       "u      20\n",
       "ba     19\n",
       "a      18\n",
       "b      13\n",
       "ad     12\n",
       "k      12\n",
       "aw     11\n",
       "aq     11\n",
       "r      10\n",
       "ax      8\n",
       "as      6\n",
       "l       6\n",
       "c       6\n",
       "bc      6\n",
       "ao      5\n",
       "au      5\n",
       "g       3\n",
       "p       1\n",
       "ag      1\n",
       "an      1\n",
       "av      1\n",
       "ae      1\n",
       "bb      1\n",
       "Name: X0, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['X0'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import StringIO,BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data=('column1,column2,column3,column4,column5\\n'\n",
    "'r0,1,2,3,4,5\\n'\n",
    "'r1,6,7,8,9,10\\n'\n",
    "'r2,2,3,4,5,6\\n'\n",
    "'r3,4,5,6,7,8\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type('Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column1</th>\n",
       "      <th>column2</th>\n",
       "      <th>column3</th>\n",
       "      <th>column4</th>\n",
       "      <th>column5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    column1  column2  column3  column4  column5\n",
       "r0        1        2        3        4        5\n",
       "r1        6        7        8        9       10\n",
       "r2        2        3        4        5        6\n",
       "r3        4        5        6        7        8"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(Data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(StringIO(Data),usecols=['column1','column4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('jupyterCSV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data=('a,b,c,d\\n'\n",
    "     '1,2,3,4\\n'\n",
    "     '5,6,7,8\\n'\n",
    "      '9,10,11,12\\n')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(StringIO(Data),dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b   c   d\n",
       "0  1   2   3   4\n",
       "1  5   6   7   8\n",
       "2  9  10  11  12"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    5\n",
       "2    9\n",
       "Name: a, dtype: int32"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(StringIO(Data),dtype={'a':int,'b':'float','c':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a     b   c   d\n",
       "0  1   2.0   3   4\n",
       "1  5   6.0   7   8\n",
       "2  9  10.0  11  12"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df1['b'][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.0\n",
       "1     6.0\n",
       "2    10.0\n",
       "Name: b, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['b']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Providing Index Column to Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from io import StringIO,BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data1=('a,b,c\\n'\n",
    "      '4,apple,cat\\n'\n",
    "      '5,mango,dog\\n'\n",
    "      '6,chiku,rat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a,b,c\n",
      "4,apple,cat\n",
      "5,mango,dog\n",
      "6,chiku,rat\n"
     ]
    }
   ],
   "source": [
    "print(Data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.read_csv(StringIO(Data1),dtype={'a':int,'b':str,'c':str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>apple</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>mango</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>chiku</td>\n",
       "      <td>rat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a      b    c\n",
       "0  4  apple  cat\n",
       "1  5  mango  dog\n",
       "2  6  chiku  rat"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=pd.read_csv(StringIO(Data1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a      b    c\n",
      "0  4  apple  cat\n",
      "1  5  mango  dog\n",
      "2  6  chiku  rat\n"
     ]
    }
   ],
   "source": [
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data2=('a,b,c\\n'\n",
    "      '4,apple,cat\\n'\n",
    "      '5,mango,dog\\n'\n",
    "      '6,chiku,rat\\n'\n",
    "      '7,peru,parrot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a,b,c\n",
      "4,apple,cat\n",
      "5,mango,dog\n",
      "6,chiku,rat\n",
      "7,peru,parrot\n"
     ]
    }
   ],
   "source": [
    "print(Data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a      b       c\n",
      "0  4  apple     cat\n",
      "1  5  mango     dog\n",
      "2  6  chiku     rat\n",
      "3  7   peru  parrot\n"
     ]
    }
   ],
   "source": [
    "df4=pd.read_csv(StringIO(Data2))\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       b       c\n",
      "0  apple     cat\n",
      "1  mango     dog\n",
      "2  chiku     rat\n",
      "3   peru  parrot\n"
     ]
    }
   ],
   "source": [
    "df4=pd.read_csv(StringIO(Data2),usecols=['b','c']) #displayes only column b & c along with index column\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            b\n",
      "c            \n",
      "cat     apple\n",
      "dog     mango\n",
      "rat     chiku\n",
      "parrot   peru\n"
     ]
    }
   ],
   "source": [
    "df4=pd.read_csv(StringIO(Data2),usecols=['b','c'],index_col='c') # uses column 'C' As an index(1st) column)\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quoting and Escape Charecters,Usefull for NLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data='a,b\\n\"hello,\\\\\"bob\\\\\",nice to see you\",6'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5=pd.read_csv(StringIO(Data),escapechar='\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hello,\"bob\",nice to see you</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             a  b\n",
       "0  hello,\"bob\",nice to see you  6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### URL To CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "parser_f() got an unexpected keyword argument 'SEP'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-464fa4962ab1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf6\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[1;34m'https://www.myonlinetraininghub.com/excel-tabular-data-format'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mSEP\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'/t'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: parser_f() got an unexpected keyword argument 'SEP'"
     ]
    }
   ],
   "source": [
    "df6=pd.read_csv('https://www.myonlinetraininghub.com/excel-tabular-data-format',SEP='/t')"
   ]
  }
 ],
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