pandas_dataframes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(randn(5,4),['A','B','C','D','E'],['W','X','Y','Z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
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      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.706850\n",
       "B    0.651118\n",
       "C   -2.018168\n",
       "D    0.188695\n",
       "E    0.190794\n",
       "Name: W, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['W']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.503826</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
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       "      <th>C</th>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>0.955057</td>\n",
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       "      <th>E</th>\n",
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      ],
      "text/plain": [
       "          W         Z\n",
       "A  2.706850  0.503826\n",
       "B  0.651118  0.605965\n",
       "C -2.018168 -0.589001\n",
       "D  0.188695  0.955057\n",
       "E  0.190794  0.683509"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['W','Z']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['new'] = df['W'] + df['X']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>3.334983</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
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       "      <td>0.605965</td>\n",
       "      <td>0.331800</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
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       "      <td>-0.589001</td>\n",
       "      <td>-1.278046</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
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       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
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      "text/plain": [
       "          W         X         Y         Z       new\n",
       "A  2.706850  0.628133  0.907969  0.503826  3.334983\n",
       "B  0.651118 -0.319318 -0.848077  0.605965  0.331800\n",
       "C -2.018168  0.740122  0.528813 -0.589001 -1.278046\n",
       "D  0.188695 -0.758872 -0.933237  0.955057 -0.570177\n",
       "E  0.190794  1.978757  2.605967  0.683509  2.169552"
      ]
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('new', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
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       "      <th>C</th>\n",
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       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
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       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
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       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
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       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
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     "metadata": {},
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   "source": [
    "\n",
    "df.drop('E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
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       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
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       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
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       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
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       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
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       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
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      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W   -2.018168\n",
       "X    0.740122\n",
       "Y    0.528813\n",
       "Z   -0.589001\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "W   -2.018168\n",
       "X    0.740122\n",
       "Y    0.528813\n",
       "Z   -0.589001\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z\n",
       "A  2.706850  0.628133  0.907969  0.503826\n",
       "B  0.651118 -0.319318 -0.848077  0.605965\n",
       "C -2.018168  0.740122  0.528813 -0.589001\n",
       "D  0.188695 -0.758872 -0.933237  0.955057\n",
       "E  0.190794  1.978757  2.605967  0.683509"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.605965</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.605965</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Z         Z\n",
       "B  0.605965  0.605965\n",
       "B  0.605965  0.605965"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['B','B'],['Z', 'Z']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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