Day 5 pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=pd.DataFrame(np.random.rand(15).reshape(5,3), columns=['col1','col2','col3'], index=['a','b','c','d','e'])\n",
    "df2['col4']= np.arange(5)"
   ]
  },
  {
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   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>col2</th>\n",
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       "      <th>col4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.994700</td>\n",
       "      <td>0.374323</td>\n",
       "      <td>0.830171</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.650780</td>\n",
       "      <td>0.924054</td>\n",
       "      <td>0.834534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.825124</td>\n",
       "      <td>0.198440</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.931559</td>\n",
       "      <td>0.076659</td>\n",
       "      <td>0.382428</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.652708</td>\n",
       "      <td>0.995672</td>\n",
       "      <td>0.572294</td>\n",
       "      <td>4</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "       col1      col2      col3  col4\n",
       "a  0.994700  0.374323  0.830171     0\n",
       "b  0.650780  0.924054  0.834534     1\n",
       "c  0.825124  0.198440  0.024221     2\n",
       "d  0.931559  0.076659  0.382428     3\n",
       "e  0.652708  0.995672  0.572294     4"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2"
   ]
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   "execution_count": 7,
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   "outputs": [
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       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
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       "      <td>0.650780</td>\n",
       "      <td>0.924054</td>\n",
       "      <td>0.834534</td>\n",
       "      <td>1</td>\n",
       "      <td>1.485314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.825124</td>\n",
       "      <td>0.198440</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "       col1      col2      col3  col4      col5\n",
       "a  0.994700  0.374323  0.830171     0  1.824871\n",
       "b  0.650780  0.924054  0.834534     1  1.485314\n",
       "c  0.825124  0.198440  0.024221     2  0.849345\n",
       "d  0.931559  0.076659  0.382428     3  1.313987\n",
       "e  0.652708  0.995672  0.572294     4  1.225002"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2['col5']= df2['col1'] + df2['col3']\n",
    "df2"
   ]
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
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       "      <th>a</th>\n",
       "      <td>0.994700</td>\n",
       "      <td>0.374323</td>\n",
       "      <td>0.830171</td>\n",
       "      <td>0</td>\n",
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       "      <th>b</th>\n",
       "      <td>0.650780</td>\n",
       "      <td>0.924054</td>\n",
       "      <td>0.834534</td>\n",
       "      <td>1</td>\n",
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       "      <th>c</th>\n",
       "      <td>0.825124</td>\n",
       "      <td>0.198440</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.652708</td>\n",
       "      <td>0.995672</td>\n",
       "      <td>0.572294</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "       col1      col2      col3  col4\n",
       "a  0.994700  0.374323  0.830171     0\n",
       "b  0.650780  0.924054  0.834534     1\n",
       "c  0.825124  0.198440  0.024221     2\n",
       "d  0.931559  0.076659  0.382428     3\n",
       "e  0.652708  0.995672  0.572294     4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop\n",
    "df2.drop('col5', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th>a</th>\n",
       "      <td>0.994700</td>\n",
       "      <td>0.374323</td>\n",
       "      <td>0.830171</td>\n",
       "      <td>0</td>\n",
       "      <td>1.824871</td>\n",
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       "      <th>b</th>\n",
       "      <td>0.650780</td>\n",
       "      <td>0.924054</td>\n",
       "      <td>0.834534</td>\n",
       "      <td>1</td>\n",
       "      <td>1.485314</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.825124</td>\n",
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      "text/plain": [
       "       col1      col2      col3  col4      col5\n",
       "a  0.994700  0.374323  0.830171     0  1.824871\n",
       "b  0.650780  0.924054  0.834534     1  1.485314\n",
       "c  0.825124  0.198440  0.024221     2  0.849345\n",
       "d  0.931559  0.076659  0.382428     3  1.313987\n",
       "e  0.652708  0.995672  0.572294     4  1.225002"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.drop('col5', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
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       "      <th>a</th>\n",
       "      <td>0.994700</td>\n",
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       "      <td>0.830171</td>\n",
       "      <td>0</td>\n",
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       "      <th>b</th>\n",
       "      <td>0.650780</td>\n",
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       "      <td>0.834534</td>\n",
       "      <td>1</td>\n",
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       "      <th>c</th>\n",
       "      <td>0.825124</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.652708</td>\n",
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       "      <td>4</td>\n",
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      "text/plain": [
       "       col1      col2      col3  col4\n",
       "a  0.994700  0.374323  0.830171     0\n",
       "b  0.650780  0.924054  0.834534     1\n",
       "c  0.825124  0.198440  0.024221     2\n",
       "d  0.931559  0.076659  0.382428     3\n",
       "e  0.652708  0.995672  0.572294     4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
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   "execution_count": 13,
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       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
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       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.994700</td>\n",
       "      <td>0.374323</td>\n",
       "      <td>0.830171</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.650780</td>\n",
       "      <td>0.924054</td>\n",
       "      <td>0.834534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.825124</td>\n",
       "      <td>0.198440</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.931559</td>\n",
       "      <td>0.076659</td>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "       col1      col2      col3  col4\n",
       "a  0.994700  0.374323  0.830171     0\n",
       "b  0.650780  0.924054  0.834534     1\n",
       "c  0.825124  0.198440  0.024221     2\n",
       "d  0.931559  0.076659  0.382428     3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drooping a row\n",
    "df2.drop('e')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
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       "      <th>b</th>\n",
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       "      <td>0.834534</td>\n",
       "      <td>1</td>\n",
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       "      <th>c</th>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "       col1      col2      col3  col4\n",
       "a  0.994700  0.374323  0.830171     0\n",
       "b  0.650780  0.924054  0.834534     1\n",
       "c  0.825124  0.198440  0.024221     2\n",
       "d  0.931559  0.076659  0.382428     3"
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     "metadata": {},
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    }
   ],
   "source": [
    "# drop rows based on conditions\n",
    "df2.drop(df2[df2['col4'] > 3].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['e'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['col4']>3].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.994700</td>\n",
       "      <td>0.374323</td>\n",
       "      <td>0.830171</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.931559</td>\n",
       "      <td>0.076659</td>\n",
       "      <td>0.382428</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col1      col2      col3  col4\n",
       "a  0.994700  0.374323  0.830171     0\n",
       "d  0.931559  0.076659  0.382428     3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop(df2[(df2['col4']>3) | (df2['col1']<0.9)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['Task1', 'Task2'], ['Lucky', 'Ram']],\n",
       "           labels=[[0, 0, 1, 1], [1, 0, 1, 0]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outer=['Task1','Task1','Task2','Task2']\n",
    "inner=['Ram','Lucky','Ram','Lucky']\n",
    "multiIndex= pd.MultiIndex.from_tuples(list(zip(outer, inner)))\n",
    "multiIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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></th>\n",
       "      <th>level1</th>\n",
       "      <th>level2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Task1</th>\n",
       "      <th>Ram</th>\n",
       "      <td>0.176130</td>\n",
       "      <td>0.080082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucky</th>\n",
       "      <td>0.102994</td>\n",
       "      <td>0.235086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Task2</th>\n",
       "      <th>Ram</th>\n",
       "      <td>0.846569</td>\n",
       "      <td>0.390396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucky</th>\n",
       "      <td>0.464271</td>\n",
       "      <td>0.384739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               level1    level2\n",
       "Task1 Ram    0.176130  0.080082\n",
       "      Lucky  0.102994  0.235086\n",
       "Task2 Ram    0.846569  0.390396\n",
       "      Lucky  0.464271  0.384739"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3= pd.DataFrame(np.random.rand(4,2), index=multiIndex, columns=['level1','level2'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>level1</th>\n",
       "      <th>level2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ram</th>\n",
       "      <td>0.176130</td>\n",
       "      <td>0.080082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lucky</th>\n",
       "      <td>0.102994</td>\n",
       "      <td>0.235086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         level1    level2\n",
       "Ram    0.176130  0.080082\n",
       "Lucky  0.102994  0.235086"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc['Task1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "level1    0.102994\n",
       "level2    0.235086\n",
       "Name: Lucky, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc['Task1'].loc['Lucky']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram      0.080082\n",
       "Lucky    0.235086\n",
       "Name: level2, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc['Task1','level2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Task1  Ram      0.080082\n",
       "       Lucky    0.235086\n",
       "Task2  Ram      0.390396\n",
       "       Lucky    0.384739\n",
       "Name: level2, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[['Task1','Task2'], 'level2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "level1    0.176130\n",
       "level2    0.080082\n",
       "Name: (Task1, Ram), dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[('Task1','Ram')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1761304116915441"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[('Task1','Ram'),'level1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Task1  Ram      0.080082\n",
       "Task2  Lucky    0.384739\n",
       "Name: level2, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[[('Task1','Ram'), ('Task2','Lucky')],'level2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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