pandas_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('winequality-red.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1599 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4             0.700         0.00             1.9      0.076   \n",
       "1               7.8             0.880         0.00             2.6      0.098   \n",
       "2               7.8             0.760         0.04             2.3      0.092   \n",
       "3              11.2             0.280         0.56             1.9      0.075   \n",
       "4               7.4             0.700         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1594            6.2             0.600         0.08             2.0      0.090   \n",
       "1595            5.9             0.550         0.10             2.2      0.062   \n",
       "1596            6.3             0.510         0.13             2.3      0.076   \n",
       "1597            5.9             0.645         0.12             2.0      0.075   \n",
       "1598            6.0             0.310         0.47             3.6      0.067   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
       "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
       "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
       "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
       "\n",
       "      alcohol  quality  \n",
       "0         9.4        5  \n",
       "1         9.8        5  \n",
       "2         9.8        5  \n",
       "3         9.8        6  \n",
       "4         9.4        5  \n",
       "...       ...      ...  \n",
       "1594     10.5        5  \n",
       "1595     11.2        6  \n",
       "1596     11.0        6  \n",
       "1597     10.2        5  \n",
       "1598     11.0        6  \n",
       "\n",
       "[1599 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "1594            6.2             0.600         0.08             2.0      0.090   \n",
       "1595            5.9             0.550         0.10             2.2      0.062   \n",
       "1596            6.3             0.510         0.13             2.3      0.076   \n",
       "1597            5.9             0.645         0.12             2.0      0.075   \n",
       "1598            6.0             0.310         0.47             3.6      0.067   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
       "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
       "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
       "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
       "\n",
       "      alcohol  quality  \n",
       "1594     10.5        5  \n",
       "1595     11.2        6  \n",
       "1596     11.0        6  \n",
       "1597     10.2        5  \n",
       "1598     11.0        6  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
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       "      <td>0.9970</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
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       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.069</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.9964</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.3</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.065</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9946</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.073</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.071</td>\n",
       "      <td>17.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "5            7.4              0.66         0.00             1.8      0.075   \n",
       "6            7.9              0.60         0.06             1.6      0.069   \n",
       "7            7.3              0.65         0.00             1.2      0.065   \n",
       "8            7.8              0.58         0.02             2.0      0.073   \n",
       "9            7.5              0.50         0.36             6.1      0.071   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "5                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "6                 15.0                  59.0   0.9964  3.30       0.46   \n",
       "7                 15.0                  21.0   0.9946  3.39       0.47   \n",
       "8                  9.0                  18.0   0.9968  3.36       0.57   \n",
       "9                 17.0                 102.0   0.9978  3.35       0.80   \n",
       "\n",
       "   alcohol  quality  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  \n",
       "5      9.4        5  \n",
       "6      9.4        5  \n",
       "7     10.0        7  \n",
       "8      9.5        7  \n",
       "9     10.5        5  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.99780\n",
       "1       0.99680\n",
       "2       0.99700\n",
       "3       0.99800\n",
       "4       0.99780\n",
       "         ...   \n",
       "1594    0.99490\n",
       "1595    0.99512\n",
       "1596    0.99574\n",
       "1597    0.99547\n",
       "1598    0.99549\n",
       "Name: density, Length: 1599, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.00\n",
       "1       0.00\n",
       "2       0.04\n",
       "3       0.56\n",
       "4       0.00\n",
       "        ... \n",
       "1594    0.08\n",
       "1595    0.10\n",
       "1596    0.13\n",
       "1597    0.12\n",
       "1598    0.47\n",
       "Name: citric acid, Length: 1599, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['citric acid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['citric acid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      citric acid  density\n",
       "0            0.00  0.99780\n",
       "1            0.00  0.99680\n",
       "2            0.04  0.99700\n",
       "3            0.56  0.99800\n",
       "4            0.00  0.99780\n",
       "...           ...      ...\n",
       "1594         0.08  0.99490\n",
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       "1596         0.13  0.99574\n",
       "1597         0.12  0.99547\n",
       "1598         0.47  0.99549\n",
       "\n",
       "[1599 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   "source": [
    "df[['citric acid','density']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=df[['citric acid','density']]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      citric acid  density\n",
       "0            0.00  0.99780\n",
       "1            0.00  0.99680\n",
       "2            0.04  0.99700\n",
       "3            0.56  0.99800\n",
       "4            0.00  0.99780\n",
       "...           ...      ...\n",
       "1594         0.08  0.99490\n",
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       "1596         0.13  0.99574\n",
       "1597         0.12  0.99547\n",
       "1598         0.47  0.99549\n",
       "\n",
       "[1599 rows x 2 columns]"
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     "execution_count": 14,
     "metadata": {},
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   "source": [
    "a"
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.880</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.062</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.99512</td>\n",
       "      <td>3.52</td>\n",
       "      <td>0.76</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.13</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.076</td>\n",
       "      <td>29.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.99574</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.75</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>5.9</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.075</td>\n",
       "      <td>32.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.99547</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.47</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.067</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.99549</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.66</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1599 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4             0.700         0.00             1.9      0.076   \n",
       "1               7.8             0.880         0.00             2.6      0.098   \n",
       "2               7.8             0.760         0.04             2.3      0.092   \n",
       "3              11.2             0.280         0.56             1.9      0.075   \n",
       "4               7.4             0.700         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "1594            6.2             0.600         0.08             2.0      0.090   \n",
       "1595            5.9             0.550         0.10             2.2      0.062   \n",
       "1596            6.3             0.510         0.13             2.3      0.076   \n",
       "1597            5.9             0.645         0.12             2.0      0.075   \n",
       "1598            6.0             0.310         0.47             3.6      0.067   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
       "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
       "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
       "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
       "\n",
       "      alcohol  quality  \n",
       "0         9.4        5  \n",
       "1         9.8        5  \n",
       "2         9.8        5  \n",
       "3         9.8        6  \n",
       "4         9.4        5  \n",
       "...       ...      ...  \n",
       "1594     10.5        5  \n",
       "1595     11.2        6  \n",
       "1596     11.0        6  \n",
       "1597     10.2        5  \n",
       "1598     11.0        6  \n",
       "\n",
       "[1599 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('stock_data.csv', header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers            eps  revenue price         people\n",
       "0   GOOGL          27.82       87   845     larry page\n",
       "1     WMT           4.61      484    65           n.a.\n",
       "2    MSFT             -1       85    64     bill gates\n",
       "3    RIL   not available       50  1023  mukesh ambani\n",
       "4    TATA            5.6       -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('weather_data.csv', parse_dates=['day'])\n",
    "df.set_index('day',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>28</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>32</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed  event\n",
       "day                                      \n",
       "2017-01-01           32          6   Rain\n",
       "2017-01-02       -99999          7  Sunny\n",
       "2017-01-03           28     -99999   Snow\n",
       "2017-01-04       -99999          7      0\n",
       "2017-01-05           32     -99999   Rain\n",
       "2017-01-06           31          2  Sunny\n",
       "2017-01-06           34          5      0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
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       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2017-01-01</th>\n",
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       "      <th>2017-01-02</th>\n",
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       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>28</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>-99999</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>32</td>\n",
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       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>34</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed  event\n",
       "day                                      \n",
       "2017-01-01           32          6   Rain\n",
       "2017-01-02       -99999          7  Sunny\n",
       "2017-01-03           28     -99999   Snow\n",
       "2017-01-04       -99999          7      0\n",
       "2017-01-05           32     -99999   Rain\n",
       "2017-01-06           31          2  Sunny\n",
       "2017-01-06           34          5      0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df=df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-01-04</th>\n",
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       "      <th>2017-01-05</th>\n",
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       "      <th>2017-01-06</th>\n",
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       "      <th>2017-01-06</th>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed  event\n",
       "day                                      \n",
       "2017-01-01           32          6   Rain\n",
       "2017-01-02       -99999          7  Sunny\n",
       "2017-01-03           28     -99999   Snow\n",
       "2017-01-04       -99999          7      0\n",
       "2017-01-05           32     -99999   Rain\n",
       "2017-01-06           31          2  Sunny\n",
       "2017-01-06           34          5      0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df=df.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed  event\n",
       "day                                      \n",
       "2017-01-01           32          6   Rain\n",
       "2017-01-02       -99999          7  Sunny\n",
       "2017-01-03           28     -99999   Snow\n",
       "2017-01-04       -99999          7      0\n",
       "2017-01-05           32     -99999   Rain\n",
       "2017-01-06           31          2  Sunny\n",
       "2017-01-06           34          5      0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('weather_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017       -99999          7  Sunny\n",
       "2  1/3/2017           28     -99999   Snow\n",
       "3  1/4/2017       -99999          7      0\n",
       "4  1/5/2017           32     -99999   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/6/2017           34          5      0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.replace(-99999,np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017          NaN        7.0  Sunny\n",
       "2  1/3/2017         28.0        NaN   Snow\n",
       "3  1/4/2017          NaN        7.0      0\n",
       "4  1/5/2017         32.0        NaN   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0      0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "':' expected after dictionary key (1245065004.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[30], line 3\u001b[1;36m\u001b[0m\n\u001b[1;33m    {'event':{0:'sunny'}}\u001b[0m\n\u001b[1;37m                        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m ':' expected after dictionary key\n"
     ]
    }
   ],
   "source": [
    "new_df=df.replace({\n",
    "    'NaN': '0',\n",
    "    {'event':{0:'sunny'}}\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "new_df = df.replace({\n",
    "    -99999:np.NaN,\n",
    "    'event':'0',\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
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       "      <td>Sunny</td>\n",
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       "      <th>6</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017         32.0        6.0   Rain\n",
       "1  1/2/2017          NaN        7.0  Sunny\n",
       "2  1/3/2017         28.0        NaN   Snow\n",
       "3  1/4/2017          NaN        7.0      0\n",
       "4  1/5/2017         32.0        NaN   Rain\n",
       "5  1/6/2017         31.0        2.0  Sunny\n",
       "6  1/6/2017         34.0        5.0      0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "hf=pd.read_csv('weather_by_cities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
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       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "g=hf.groupby('city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mumbai\n",
      "        day    city  temperature  windspeed  event\n",
      "4  1/1/2017  mumbai           90          5  Sunny\n",
      "5  1/2/2017  mumbai           85         12    Fog\n",
      "6  1/3/2017  mumbai           87         15    Fog\n",
      "7  1/4/2017  mumbai           92          5   Rain\n",
      "new york\n",
      "        day      city  temperature  windspeed  event\n",
      "0  1/1/2017  new york           32          6   Rain\n",
      "1  1/2/2017  new york           36          7  Sunny\n",
      "2  1/3/2017  new york           28         12   Snow\n",
      "3  1/4/2017  new york           33          7  Sunny\n",
      "paris\n",
      "         day   city  temperature  windspeed   event\n",
      "8   1/1/2017  paris           45         20   Sunny\n",
      "9   1/2/2017  paris           50         13  Cloudy\n",
      "10  1/3/2017  paris           54          8  Cloudy\n",
      "11  1/4/2017  paris           42         10  Cloudy\n"
     ]
    }
   ],
   "source": [
    "for city, city_hf in g:\n",
    "    print(city)\n",
    "    print(city_hf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>92</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paris</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>54</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               day  temperature  windspeed  event\n",
       "city                                             \n",
       "mumbai    1/4/2017           92         15  Sunny\n",
       "new york  1/4/2017           36         12  Sunny\n",
       "paris     1/4/2017           54         20  Sunny"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_18304\\2978112660.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  g.mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>88.50</td>\n",
       "      <td>9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>32.25</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paris</th>\n",
       "      <td>47.75</td>\n",
       "      <td>12.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          temperature  windspeed\n",
       "city                            \n",
       "mumbai          88.50       9.25\n",
       "new york        32.25       8.00\n",
       "paris           47.75      12.75"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:last-of-type th {\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">temperature</th>\n",
       "      <th colspan=\"8\" halign=\"left\">windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>4.0</td>\n",
       "      <td>88.50</td>\n",
       "      <td>3.109126</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.50</td>\n",
       "      <td>88.5</td>\n",
       "      <td>90.50</td>\n",
       "      <td>92.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.25</td>\n",
       "      <td>5.057997</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.75</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.25</td>\n",
       "      <td>3.304038</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>32.5</td>\n",
       "      <td>33.75</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.708013</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.75</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.25</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paris</th>\n",
       "      <td>4.0</td>\n",
       "      <td>47.75</td>\n",
       "      <td>5.315073</td>\n",
       "      <td>42.0</td>\n",
       "      <td>44.25</td>\n",
       "      <td>47.5</td>\n",
       "      <td>51.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.75</td>\n",
       "      <td>5.251984</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>11.5</td>\n",
       "      <td>14.75</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         temperature                                                   \\\n",
       "               count   mean       std   min    25%   50%    75%   max   \n",
       "city                                                                    \n",
       "mumbai           4.0  88.50  3.109126  85.0  86.50  88.5  90.50  92.0   \n",
       "new york         4.0  32.25  3.304038  28.0  31.00  32.5  33.75  36.0   \n",
       "paris            4.0  47.75  5.315073  42.0  44.25  47.5  51.00  54.0   \n",
       "\n",
       "         windspeed                                                 \n",
       "             count   mean       std  min   25%   50%    75%   max  \n",
       "city                                                               \n",
       "mumbai         4.0   9.25  5.057997  5.0  5.00   8.5  12.75  15.0  \n",
       "new york       4.0   8.00  2.708013  6.0  6.75   7.0   8.25  12.0  \n",
       "paris          4.0  12.75  5.251984  8.0  9.50  11.5  14.75  20.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.groupby.GroupByPlot object at 0x000001CE83BA22D0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "mumbai      Axes(0.125,0.11;0.775x0.77)\n",
       "new york    Axes(0.125,0.11;0.775x0.77)\n",
       "paris       Axes(0.125,0.11;0.775x0.77)\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "india_weather =pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "    \"humidity\": [80, 60, 78]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " india_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_weather = pd.DataFrame({\n",
    "    \"city\": [\"new york\",\"chicago\",\"orlando\"],\n",
    "    \"temperature\": [21,14,35],\n",
    "    \"humidity\": [68, 65, 75]\n",
    "})\n",
    "us_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.concat([india_weather,us_weather], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78\n",
       "3  new york           21        68\n",
       "4   chicago           14        65\n",
       "5   orlando           35        75"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([india_weather, us_weather], keys=[\"india\", \"us\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">india</th>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">us</th>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city  temperature  humidity\n",
       "india 0    mumbai           32        80\n",
       "      1     delhi           45        60\n",
       "      2  banglore           30        78\n",
       "us    0  new york           21        68\n",
       "      1   chicago           14        65\n",
       "      2   orlando           35        75"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['india']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([\"Humid\",\"Dry\",\"Rain\"], name=\"event\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.concat([india_weather,s,us_weather,s], axis=1)"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>event</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "      <td>Humid</td>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "      <td>Humid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>Dry</td>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "      <td>Dry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "      <td>Rain</td>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity  event      city  temperature  humidity  \\\n",
       "0    mumbai           32        80  Humid  new york           21        68   \n",
       "1     delhi           45        60    Dry   chicago           14        65   \n",
       "2  banglore           30        78   Rain   orlando           35        75   \n",
       "\n",
       "   event  \n",
       "0  Humid  \n",
       "1    Dry  \n",
       "2   Rain  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature\n",
       "0  new york           21\n",
       "1   chicago           14\n",
       "2   orlando           35"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.DataFrame({\n",
    "    \"city\": [\"new york\",\"chicago\",\"orlando\"],\n",
    "    \"temperature\": [21,14,35],\n",
    "})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>city</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity\n",
       "0   chicago        65\n",
       "1  new york        68\n",
       "2   orlando        75"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({\n",
    "    \"city\": [\"chicago\",\"new york\",\"orlando\"],\n",
    "    \"humidity\": [65,68,75],\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df1, df2, on=\"city\")\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "hf=pd.read_csv('weather_by_cities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>day</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         temperature                windspeed                 event           \\\n",
       "city          mumbai new york paris    mumbai new york paris mumbai new york   \n",
       "day                                                                            \n",
       "1/1/2017          90       32    45         5        6    20  Sunny     Rain   \n",
       "1/2/2017          85       36    50        12        7    13    Fog    Sunny   \n",
       "1/3/2017          87       28    54        15       12     8    Fog     Snow   \n",
       "1/4/2017          92       33    42         5        7    10   Rain    Sunny   \n",
       "\n",
       "                  \n",
       "city       paris  \n",
       "day               \n",
       "1/1/2017   Sunny  \n",
       "1/2/2017  Cloudy  \n",
       "1/3/2017  Cloudy  \n",
       "1/4/2017  Cloudy  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf.pivot(index='day', columns='city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         temperature                windspeed                 event           \\\n",
       "city          mumbai new york paris    mumbai new york paris mumbai new york   \n",
       "day                                                                            \n",
       "1/1/2017          90       32    45         5        6    20  Sunny     Rain   \n",
       "1/2/2017          85       36    50        12        7    13    Fog    Sunny   \n",
       "1/3/2017          87       28    54        15       12     8    Fog     Snow   \n",
       "1/4/2017          92       33    42         5        7    10   Rain    Sunny   \n",
       "\n",
       "                  \n",
       "city       paris  \n",
       "day               \n",
       "1/1/2017   Sunny  \n",
       "1/2/2017  Cloudy  \n",
       "1/3/2017  Cloudy  \n",
       "1/4/2017  Cloudy  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf.pivot(index='day', columns='city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "         day  chicago  chennai  berlin\n",
       "0     Monday       32       75      41\n",
       "1    Tuesday       30       77      43\n",
       "2  Wednesday       28       75      45\n",
       "3   Thursday       22       82      38\n",
       "4     Friday       30       83      30\n",
       "5   Saturday       20       81      45\n",
       "6     Sunday       25       77      47"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
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       "      <td>berlin</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Friday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>berlin</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
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       "      <td>berlin</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day     city  temperature\n",
       "0      Monday  chicago           32\n",
       "1     Tuesday  chicago           30\n",
       "2   Wednesday  chicago           28\n",
       "3    Thursday  chicago           22\n",
       "4      Friday  chicago           30\n",
       "5    Saturday  chicago           20\n",
       "6      Sunday  chicago           25\n",
       "7      Monday  chennai           75\n",
       "8     Tuesday  chennai           77\n",
       "9   Wednesday  chennai           75\n",
       "10   Thursday  chennai           82\n",
       "11     Friday  chennai           83\n",
       "12   Saturday  chennai           81\n",
       "13     Sunday  chennai           77\n",
       "14     Monday   berlin           41\n",
       "15    Tuesday   berlin           43\n",
       "16  Wednesday   berlin           45\n",
       "17   Thursday   berlin           38\n",
       "18     Friday   berlin           30\n",
       "19   Saturday   berlin           45\n",
       "20     Sunday   berlin           47"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.melt(df , id_vars=['day'],var_name='city',value_name='temperature')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>chicago</td>\n",
       "      <td>28</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>chicago</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
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       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>chicago</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day     city  temperature\n",
       "0     Monday  chicago           32\n",
       "1    Tuesday  chicago           30\n",
       "2  Wednesday  chicago           28\n",
       "3   Thursday  chicago           22\n",
       "4     Friday  chicago           30\n",
       "5   Saturday  chicago           20\n",
       "6     Sunday  chicago           25"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['city']=='chicago']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>31.7</td>\n",
       "      <td>30.46</td>\n",
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       "      <th>3</th>\n",
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       "      <td>61</td>\n",
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       "      <td>31.2</td>\n",
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       "      <th>4</th>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>156</td>\n",
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       "      <td>66</td>\n",
       "      <td>37.07</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    Price                  Price to earnings ratio (P/E)         \\\n",
       "     Company Facebook Google Microsoft                      Facebook Google   \n",
       "0 2017-06-05      155    955        66                         37.10   32.0   \n",
       "1 2017-06-06      150    987        69                         36.98   31.3   \n",
       "2 2017-06-07      153    963        62                         36.78   31.7   \n",
       "3 2017-06-08      155   1000        61                         36.11   31.2   \n",
       "4 2017-06-09      156   1012        66                         37.07   30.0   \n",
       "\n",
       "             \n",
       "  Microsoft  \n",
       "0     30.31  \n",
       "1     30.56  \n",
       "2     30.46  \n",
       "3     30.11  \n",
       "4     31.00  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock = pd.read_excel('stocks.xlsx' ,header=[0,1])\n",
    "stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>Google</th>\n",
       "      <td>963.0</td>\n",
       "      <td>31.70</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>Google</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>31.20</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>Microsoft</th>\n",
       "      <td>61.0</td>\n",
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       "      <td>NaT</td>\n",
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      ],
      "text/plain": [
       "              Price  Price to earnings ratio (P/E)       date\n",
       "0 Company       NaN                            NaN 2017-06-05\n",
       "  Facebook    155.0                          37.10        NaT\n",
       "  Google      955.0                          32.00        NaT\n",
       "  Microsoft    66.0                          30.31        NaT\n",
       "1 Company       NaN                            NaN 2017-06-06\n",
       "  Facebook    150.0                          36.98        NaT\n",
       "  Google      987.0                          31.30        NaT\n",
       "  Microsoft    69.0                          30.56        NaT\n",
       "2 Company       NaN                            NaN 2017-06-07\n",
       "  Facebook    153.0                          36.78        NaT\n",
       "  Google      963.0                          31.70        NaT\n",
       "  Microsoft    62.0                          30.46        NaT\n",
       "3 Company       NaN                            NaN 2017-06-08\n",
       "  Facebook    155.0                          36.11        NaT\n",
       "  Google     1000.0                          31.20        NaT\n",
       "  Microsoft    61.0                          30.11        NaT\n",
       "4 Company       NaN                            NaN 2017-06-09\n",
       "  Facebook    156.0                          37.07        NaT\n",
       "  Google     1012.0                          30.00        NaT\n",
       "  Microsoft    66.0                          31.00        NaT"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>66.00</td>\n",
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       "      <td>69.00</td>\n",
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       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaT</td>\n",
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       "      <td>30.56</td>\n",
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       "      <td>2017-06-06</td>\n",
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       "      <td>NaT</td>\n",
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       "      <td>963.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaT</td>\n",
       "      <td>36.78</td>\n",
       "      <td>31.7</td>\n",
       "      <td>30.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>2017-06-07</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
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       "      <td>NaT</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>61.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaT</td>\n",
       "      <td>36.11</td>\n",
       "      <td>31.2</td>\n",
       "      <td>30.11</td>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>2017-06-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>Price</th>\n",
       "      <td>NaT</td>\n",
       "      <td>156.00</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>66.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price to earnings ratio (P/E)</th>\n",
       "      <td>NaT</td>\n",
       "      <td>37.07</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.00</td>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>2017-06-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                   Company  Facebook  Google  Microsoft\n",
       "0 Price                                NaT    155.00   955.0      66.00\n",
       "  Price to earnings ratio (P/E)        NaT     37.10    32.0      30.31\n",
       "  date                          2017-06-05       NaN     NaN        NaN\n",
       "1 Price                                NaT    150.00   987.0      69.00\n",
       "  Price to earnings ratio (P/E)        NaT     36.98    31.3      30.56\n",
       "  date                          2017-06-06       NaN     NaN        NaN\n",
       "2 Price                                NaT    153.00   963.0      62.00\n",
       "  Price to earnings ratio (P/E)        NaT     36.78    31.7      30.46\n",
       "  date                          2017-06-07       NaN     NaN        NaN\n",
       "3 Price                                NaT    155.00  1000.0      61.00\n",
       "  Price to earnings ratio (P/E)        NaT     36.11    31.2      30.11\n",
       "  date                          2017-06-08       NaN     NaN        NaN\n",
       "4 Price                                NaT    156.00  1012.0      66.00\n",
       "  Price to earnings ratio (P/E)        NaT     37.07    30.0      31.00\n",
       "  date                          2017-06-09       NaN     NaN        NaN"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.stack(level=0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                           Company    0    2017-06-05 00:00:00\n",
       "                                          1    2017-06-06 00:00:00\n",
       "                                          2    2017-06-07 00:00:00\n",
       "                                          3    2017-06-08 00:00:00\n",
       "                                          4    2017-06-09 00:00:00\n",
       "Price                          Facebook   0                    155\n",
       "                                          1                    150\n",
       "                                          2                    153\n",
       "                                          3                    155\n",
       "                                          4                    156\n",
       "                               Google     0                    955\n",
       "                                          1                    987\n",
       "                                          2                    963\n",
       "                                          3                   1000\n",
       "                                          4                   1012\n",
       "                               Microsoft  0                     66\n",
       "                                          1                     69\n",
       "                                          2                     62\n",
       "                                          3                     61\n",
       "                                          4                     66\n",
       "Price to earnings ratio (P/E)  Facebook   0                   37.1\n",
       "                                          1                  36.98\n",
       "                                          2                  36.78\n",
       "                                          3                  36.11\n",
       "                                          4                  37.07\n",
       "                               Google     0                   32.0\n",
       "                                          1                   31.3\n",
       "                                          2                   31.7\n",
       "                                          3                   31.2\n",
       "                                          4                   30.0\n",
       "                               Microsoft  0                  30.31\n",
       "                                          1                  30.56\n",
       "                                          2                  30.46\n",
       "                                          3                  30.11\n",
       "                                          4                   31.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "st1 = pd.read_excel('stocks_3_levels.xlsx', header=[0,1,2] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
       "      <th colspan=\"6\" halign=\"left\">Price Ratios</th>\n",
       "      <th colspan=\"6\" halign=\"left\">Income Statement</th>\n",
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       "      <th colspan=\"3\" halign=\"left\">Net Profit</th>\n",
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       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Facebook</th>\n",
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       "      <th>Microsoft</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>Google</th>\n",
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       "      <td>Q1 2016</td>\n",
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       "      <td>66</td>\n",
       "      <td>37.10</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.31</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q2 2016</td>\n",
       "      <td>150</td>\n",
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       "      <td>69</td>\n",
       "      <td>36.98</td>\n",
       "      <td>31.3</td>\n",
       "      <td>30.56</td>\n",
       "      <td>3.1</td>\n",
       "      <td>22</td>\n",
       "      <td>21.30</td>\n",
       "      <td>0.97</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.10</td>\n",
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       "      <th>2</th>\n",
       "      <td>Q3 2016</td>\n",
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       "      <td>36.11</td>\n",
       "      <td>31.2</td>\n",
       "      <td>30.11</td>\n",
       "      <td>6.7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.88</td>\n",
       "      <td>1.67</td>\n",
       "      <td>6.50</td>\n",
       "      <td>5.89</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q1 2017</td>\n",
       "      <td>156</td>\n",
       "      <td>1012</td>\n",
       "      <td>66</td>\n",
       "      <td>37.07</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>8.1</td>\n",
       "      <td>31</td>\n",
       "      <td>22.34</td>\n",
       "      <td>2.03</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.09</td>\n",
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      ],
      "text/plain": [
       "  Unnamed: 0_level_0 Price Ratios                   \\\n",
       "  Unnamed: 0_level_1        Price                    \n",
       "             Company     Facebook Google Microsoft   \n",
       "0            Q1 2016          155    955        66   \n",
       "1            Q2 2016          150    987        69   \n",
       "2            Q3 2016          153    963        62   \n",
       "3            Q4 2016          155   1000        61   \n",
       "4            Q1 2017          156   1012        66   \n",
       "\n",
       "                                                 Income Statement         \\\n",
       "  Price to earnings ratio (P/E)                         Net Sales          \n",
       "                       Facebook Google Microsoft         Facebook Google   \n",
       "0                         37.10   32.0     30.31              2.6     20   \n",
       "1                         36.98   31.3     30.56              3.1     22   \n",
       "2                         36.78   31.7     30.46              4.3     24   \n",
       "3                         36.11   31.2     30.11              6.7     26   \n",
       "4                         37.07   30.0     31.00              8.1     31   \n",
       "\n",
       "                                         \n",
       "            Net Profit                   \n",
       "  Microsoft   Facebook Google Microsoft  \n",
       "0     18.70       0.80   5.43      4.56  \n",
       "1     21.30       0.97   5.89      5.10  \n",
       "2     21.45       1.20   6.10      5.43  \n",
       "3     21.88       1.67   6.50      5.89  \n",
       "4     22.34       2.03   6.40      6.09  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1000.0</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
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       "      <th>Price Ratios</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1012.0</td>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q1 2017</td>\n",
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      ],
      "text/plain": [
       "                     Net Profit                  Net Sales                   \\\n",
       "                       Facebook Google Microsoft  Facebook Google Microsoft   \n",
       "0 Income Statement         0.80   5.43      4.56       2.6   20.0     18.70   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "1 Income Statement         0.97   5.89      5.10       3.1   22.0     21.30   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "2 Income Statement         1.20   6.10      5.43       4.3   24.0     21.45   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "3 Income Statement         1.67   6.50      5.89       6.7   26.0     21.88   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "4 Income Statement         2.03   6.40      6.09       8.1   31.0     22.34   \n",
       "  Price Ratios              NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "  Unnamed: 0_level_0        NaN    NaN       NaN       NaN    NaN       NaN   \n",
       "\n",
       "                        Price                   Price to earnings ratio (P/E)  \\\n",
       "                     Facebook  Google Microsoft                      Facebook   \n",
       "0 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          155.0   955.0      66.0                         37.10   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "1 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          150.0   987.0      69.0                         36.98   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "2 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          153.0   963.0      62.0                         36.78   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "3 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          155.0  1000.0      61.0                         36.11   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "4 Income Statement        NaN     NaN       NaN                           NaN   \n",
       "  Price Ratios          156.0  1012.0      66.0                         37.07   \n",
       "  Unnamed: 0_level_0      NaN     NaN       NaN                           NaN   \n",
       "\n",
       "                                      Unnamed: 0_level_1  \n",
       "                     Google Microsoft            Company  \n",
       "0 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         32.0     30.31                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q1 2016  \n",
       "1 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         31.3     30.56                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q2 2016  \n",
       "2 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         31.7     30.46                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q3 2016  \n",
       "3 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         31.2     30.11                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q4 2016  \n",
       "4 Income Statement      NaN       NaN                NaN  \n",
       "  Price Ratios         30.0     31.00                NaN  \n",
       "  Unnamed: 0_level_0    NaN       NaN            Q1 2017  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st1.stack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_excel('survey.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Handedness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kathy</td>\n",
       "      <td>USA</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linda</td>\n",
       "      <td>USA</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peter</td>\n",
       "      <td>USA</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>John</td>\n",
       "      <td>USA</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fatima</td>\n",
       "      <td>Bangadesh</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kadir</td>\n",
       "      <td>Bangadesh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dhaval</td>\n",
       "      <td>India</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sudhir</td>\n",
       "      <td>India</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Parvir</td>\n",
       "      <td>India</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yan</td>\n",
       "      <td>China</td>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>Right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Juan</td>\n",
       "      <td>China</td>\n",
       "      <td>Female</td>\n",
       "      <td>58</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Liang</td>\n",
       "      <td>China</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>Left</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name Nationality     Sex  Age Handedness\n",
       "0    Kathy         USA  Female   23      Right\n",
       "1    Linda         USA  Female   18      Right\n",
       "2    Peter         USA    Male   19      Right\n",
       "3     John         USA    Male   22       Left\n",
       "4   Fatima   Bangadesh  Female   31       Left\n",
       "5    Kadir   Bangadesh    Male   25       Left\n",
       "6   Dhaval       India    Male   35       Left\n",
       "7   Sudhir       India    Male   31       Left\n",
       "8   Parvir       India    Male   37      Right\n",
       "9      Yan       China  Female   52      Right\n",
       "10    Juan       China  Female   58       Left\n",
       "11   Liang       China    Male   43       Left"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Handedness</th>\n",
       "      <th colspan=\"4\" halign=\"left\">Left</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nationality</th>\n",
       "      <th>Bangadesh</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "      <th>USA</th>\n",
       "      <th>China</th>\n",
       "      <th>India</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>31.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>25.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Handedness       Left                   Right            \n",
       "Nationality Bangadesh China India   USA China India   USA\n",
       "Sex                                                      \n",
       "Female           31.0  58.0   NaN   NaN  52.0   NaN  20.5\n",
       "Male             25.0  43.0  33.0  22.0   NaN  37.0  19.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "pd.crosstab(df.Sex, [df.Handedness, df.Nationality], values=df.Age,aggfunc=np.average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlalchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>142.90</td>\n",
       "      <td>144.75</td>\n",
       "      <td>142.90</td>\n",
       "      <td>144.18</td>\n",
       "      <td>19201712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>143.02</td>\n",
       "      <td>143.50</td>\n",
       "      <td>142.41</td>\n",
       "      <td>142.73</td>\n",
       "      <td>24128782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-05</th>\n",
       "      <td>143.69</td>\n",
       "      <td>144.79</td>\n",
       "      <td>142.72</td>\n",
       "      <td>144.09</td>\n",
       "      <td>21569557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03</th>\n",
       "      <td>144.88</td>\n",
       "      <td>145.30</td>\n",
       "      <td>143.10</td>\n",
       "      <td>143.50</td>\n",
       "      <td>14277848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>144.45</td>\n",
       "      <td>144.96</td>\n",
       "      <td>143.78</td>\n",
       "      <td>144.02</td>\n",
       "      <td>23024107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-07-07  142.90  144.75  142.90  144.18  19201712\n",
       "2017-07-06  143.02  143.50  142.41  142.73  24128782\n",
       "2017-07-05  143.69  144.79  142.72  144.09  21569557\n",
       "2017-07-03  144.88  145.30  143.10  143.50  14277848\n",
       "2017-06-30  144.45  144.96  143.78  144.02  23024107"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('aapl.csv', parse_dates=['Date'], index_col='Date')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.Date[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_18304\\1372776413.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df['2017-01']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>121.15</td>\n",
       "      <td>121.39</td>\n",
       "      <td>120.62</td>\n",
       "      <td>121.35</td>\n",
       "      <td>49200993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-30</th>\n",
       "      <td>120.93</td>\n",
       "      <td>121.63</td>\n",
       "      <td>120.66</td>\n",
       "      <td>121.63</td>\n",
       "      <td>30377503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-27</th>\n",
       "      <td>122.14</td>\n",
       "      <td>122.35</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.95</td>\n",
       "      <td>20562944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-26</th>\n",
       "      <td>121.67</td>\n",
       "      <td>122.44</td>\n",
       "      <td>121.60</td>\n",
       "      <td>121.94</td>\n",
       "      <td>26337576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-25</th>\n",
       "      <td>120.42</td>\n",
       "      <td>122.10</td>\n",
       "      <td>120.28</td>\n",
       "      <td>121.88</td>\n",
       "      <td>32586673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-24</th>\n",
       "      <td>119.55</td>\n",
       "      <td>120.10</td>\n",
       "      <td>119.50</td>\n",
       "      <td>119.97</td>\n",
       "      <td>23211038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-23</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.81</td>\n",
       "      <td>119.77</td>\n",
       "      <td>120.08</td>\n",
       "      <td>22050218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-20</th>\n",
       "      <td>120.45</td>\n",
       "      <td>120.45</td>\n",
       "      <td>119.73</td>\n",
       "      <td>120.00</td>\n",
       "      <td>32597892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-19</th>\n",
       "      <td>119.40</td>\n",
       "      <td>120.09</td>\n",
       "      <td>119.37</td>\n",
       "      <td>119.78</td>\n",
       "      <td>25597291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-18</th>\n",
       "      <td>120.00</td>\n",
       "      <td>120.50</td>\n",
       "      <td>119.71</td>\n",
       "      <td>119.99</td>\n",
       "      <td>23712961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>118.34</td>\n",
       "      <td>120.24</td>\n",
       "      <td>118.22</td>\n",
       "      <td>120.00</td>\n",
       "      <td>34439843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>119.11</td>\n",
       "      <td>119.62</td>\n",
       "      <td>118.81</td>\n",
       "      <td>119.04</td>\n",
       "      <td>26111948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>118.90</td>\n",
       "      <td>119.30</td>\n",
       "      <td>118.21</td>\n",
       "      <td>119.25</td>\n",
       "      <td>27086220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>118.74</td>\n",
       "      <td>119.93</td>\n",
       "      <td>118.60</td>\n",
       "      <td>119.75</td>\n",
       "      <td>27588593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>118.77</td>\n",
       "      <td>119.38</td>\n",
       "      <td>118.30</td>\n",
       "      <td>119.11</td>\n",
       "      <td>24462051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>117.95</td>\n",
       "      <td>119.43</td>\n",
       "      <td>117.94</td>\n",
       "      <td>118.99</td>\n",
       "      <td>33561948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>116.78</td>\n",
       "      <td>118.16</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>115.92</td>\n",
       "      <td>116.86</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
       "      <td>22193587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>115.85</td>\n",
       "      <td>116.51</td>\n",
       "      <td>115.75</td>\n",
       "      <td>116.02</td>\n",
       "      <td>21118116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>115.80</td>\n",
       "      <td>116.33</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
       "      <td>28781865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "Date                                                \n",
       "2017-01-31  121.15  121.39  120.62  121.35  49200993\n",
       "2017-01-30  120.93  121.63  120.66  121.63  30377503\n",
       "2017-01-27  122.14  122.35  121.60  121.95  20562944\n",
       "2017-01-26  121.67  122.44  121.60  121.94  26337576\n",
       "2017-01-25  120.42  122.10  120.28  121.88  32586673\n",
       "2017-01-24  119.55  120.10  119.50  119.97  23211038\n",
       "2017-01-23  120.00  120.81  119.77  120.08  22050218\n",
       "2017-01-20  120.45  120.45  119.73  120.00  32597892\n",
       "2017-01-19  119.40  120.09  119.37  119.78  25597291\n",
       "2017-01-18  120.00  120.50  119.71  119.99  23712961\n",
       "2017-01-17  118.34  120.24  118.22  120.00  34439843\n",
       "2017-01-13  119.11  119.62  118.81  119.04  26111948\n",
       "2017-01-12  118.90  119.30  118.21  119.25  27086220\n",
       "2017-01-11  118.74  119.93  118.60  119.75  27588593\n",
       "2017-01-10  118.77  119.38  118.30  119.11  24462051\n",
       "2017-01-09  117.95  119.43  117.94  118.99  33561948\n",
       "2017-01-06  116.78  118.16  116.47  117.91  31751900\n",
       "2017-01-05  115.92  116.86  115.81  116.61  22193587\n",
       "2017-01-04  115.85  116.51  115.75  116.02  21118116\n",
       "2017-01-03  115.80  116.33  114.76  116.15  28781865"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_18304\\1481154476.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  df['2017-01'].Close.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "119.57000000000001"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2017-01'].Close.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kpESTJ09WIBCQFDqSkpmZGX5cdXX1GUdVTktKSlJSUlK0RwUAAEZF/QjKiRMnFBcX+bTx8fHhtxnn5OQoEAiorKwsvN7U1KSNGzdq6NCh0R4HAABcgKJ+BGXs2LF6/PHH1b17d11++eV65513tGDBAn33u9+VFHppp7CwUMXFxerdu7d69+6t4uJipaSkaMKECdEeBwAAXICiXlAWLlyohx9+WDNmzFB1dbWysrI0bdo0/eAHPwjvM2vWLDU0NGjGjBmqqanR4MGDtWbNGqWlpUV7HAAAcAGKekFJS0tTaWmpSktLz7mPz+dTUVGRioqKov3lAQBAG8Bn8QAAAHMoKAAAwBwKCgAAMIeCAgAAzKGgAAAAcygoAADAHAoKAAAwh4ICAADMoaAAAABzKCgAAMAcCgoAADCHggIAAMyhoAAAAHMoKAAAwBwKCgAAMIeCAgAAzKGgAAAAcygoAADAHAoKAAAwh4ICAADMoaAAAABzKCgAAMAcCgoAADCHggIAAMyhoAAAAHMoKAAAwBwKCgAAMIeCAgAAzKGgAAAAcygoAADAHAoKAAAwh4ICAADMoaAAAABzKCgAAMAcCgoAADCHggIAAMxplYJy+PBhfec731Hnzp2VkpKiK664QhUVFeF1x3FUVFSkrKwsJScna/jw4dq9e3drjAIAAC5AUS8oNTU1GjZsmNq1a6fVq1drz549+vGPf6wOHTqE95k/f74WLFigRYsWaevWrQoEAsrPz9exY8eiPQ4AALgAJUT7CefNm6fs7GwtXrw4vK1nz57h/+04jkpLSzVnzhyNGzdOkrR06VL5/X4tX75c06ZNi/ZIAADgAhP1Iygvv/yyrr76an3729/WJZdcoiuvvFK//OUvw+uVlZUKBoMqKCgIb0tKSlJeXp62bNly1udsbGxUXV1dxA0AALRdUS8of/7zn/Xkk0+qd+/eeu211zR9+nTdeeed+vWvfy1JCgaDkiS/3x/xOL/fH177WyUlJcrIyAjfsrOzoz02AAAwJOoFpaWlRVdddZWKi4t15ZVXatq0abr11lv15JNPRuzn8/ki7juOc8a202bPnq3a2trwraqqKtpjAwAAQ6JeUDIzM9WvX7+IbX379tXBgwclSYFAQJLOOFpSXV19xlGV05KSkpSenh5xAwAAbVfUC8qwYcO0d+/eiG0ffPCBevToIUnKyclRIBBQWVlZeL2pqUkbN27U0KFDoz0OAAC4AEX9XTx33323hg4dquLiYt1www363//9Xz311FN66qmnJIVe2iksLFRxcbF69+6t3r17q7i4WCkpKZowYUK0xwEAABegqBeUL37xi1q5cqVmz56txx57TDk5OSotLdXEiRPD+8yaNUsNDQ2aMWOGampqNHjwYK1Zs0ZpaWnRHgcAAFyAol5QJGnMmDEaM2bMOdd9Pp+KiopUVFTUGl8eAABc4PgsHgAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYE6rF5SSkhL5fD4VFhaGtzmOo6KiImVlZSk5OVnDhw/X7t27W3sUAABwgWjVgrJ161Y99dRT+sIXvhCxff78+VqwYIEWLVqkrVu3KhAIKD8/X8eOHWvNcQAAwAWi1QpKfX29Jk6cqF/+8pfq2LFjeLvjOCotLdWcOXM0btw49e/fX0uXLtWJEye0fPny1hoHAABcQFqtoMycOVPXXXedRo0aFbG9srJSwWBQBQUF4W1JSUnKy8vTli1bzvpcjY2Nqquri7gBAIC2K6E1nnTFihXatm2btm7desZaMBiUJPn9/ojtfr9fBw4cOOvzlZSU6NFHH43+oAAAwKSoH0GpqqrSXXfdpWXLlql9+/bn3M/n80XcdxznjG2nzZ49W7W1teFbVVVVVGcGAAC2RP0ISkVFhaqrq5Wbmxve1tzcrE2bNmnRokXau3evpNCRlMzMzPA+1dXVZxxVOS0pKUlJSUnRHhUAABgV9SMoI0eO1M6dO7V9+/bw7eqrr9bEiRO1fft29erVS4FAQGVlZeHHNDU1aePGjRo6dGi0xwEAABegqB9BSUtLU//+/SO2paamqnPnzuHthYWFKi4uVu/evdW7d28VFxcrJSVFEyZMiPY4AADgAtQqJ8n+PbNmzVJDQ4NmzJihmpoaDR48WGvWrFFaWpob4wAAAGNiUlBef/31iPs+n09FRUUqKiqKxZcHAAAXGD6LBwAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYE7UC0pJSYm++MUvKi0tTZdccom+8Y1vaO/evRH7OI6joqIiZWVlKTk5WcOHD9fu3bujPQoAALhARb2gbNy4UTNnztRbb72lsrIynTp1SgUFBTp+/Hh4n/nz52vBggVatGiRtm7dqkAgoPz8fB07diza4wAAgAtQQrSf8NVXX424v3jxYl1yySWqqKjQV77yFTmOo9LSUs2ZM0fjxo2TJC1dulR+v1/Lly/XtGnToj0SAAC4wLT6OSi1tbWSpE6dOkmSKisrFQwGVVBQEN4nKSlJeXl52rJly1mfo7GxUXV1dRE3AADQdrVqQXEcR/fcc4++9KUvqX///pKkYDAoSfL7/RH7+v3+8NrfKikpUUZGRviWnZ3dmmMDAACXtWpBuf3227Vjxw4999xzZ6z5fL6I+47jnLHttNmzZ6u2tjZ8q6qqapV5AQCADVE/B+W0O+64Qy+//LI2bdqkbt26hbcHAgFJoSMpmZmZ4e3V1dVnHFU5LSkpSUlJSa01KgAAMCbqR1Acx9Htt9+uF198UevXr1dOTk7Eek5OjgKBgMrKysLbmpqatHHjRg0dOjTa4wAAgAtQ1I+gzJw5U8uXL9dLL72ktLS08HklGRkZSk5Ols/nU2FhoYqLi9W7d2/17t1bxcXFSklJ0YQJE6I9DgAAuABFvaA8+eSTkqThw4dHbF+8eLGmTJkiSZo1a5YaGho0Y8YM1dTUaPDgwVqzZo3S0tKiPQ4AALgARb2gOI7zd/fx+XwqKipSUVFRtL88AABoA/gsHgAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYA4FBQAAmENBAQAA5lBQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYI6rBeXnP/+5cnJy1L59e+Xm5uqPf/yjm+MAAAAjXCsozz//vAoLCzVnzhy98847+vKXv6zRo0fr4MGDbo0EAACMcK2gLFiwQFOnTtX3vvc99e3bV6WlpcrOztaTTz7p1kgAAMCIBDe+aFNTkyoqKvTAAw9EbC8oKNCWLVvO2L+xsVGNjY3h+7W1tZKkurq6Vp2zpfFEqz5/rLT2v1MskIUtbSEPsrCDLGxpzTxOP7fjOH93X1cKyscff6zm5mb5/f6I7X6/X8Fg8Iz9S0pK9Oijj56xPTs7u9VmbEsySt2eAKeRhR1kYQdZ2BKLPI4dO6aMjIzP3ceVgnKaz+eLuO84zhnbJGn27Nm65557wvdbWlr017/+VZ07dz7r/heKuro6ZWdnq6qqSunp6W6P42lkYQdZ2EIedrSFLBzH0bFjx5SVlfV393WloHTp0kXx8fFnHC2prq4+46iKJCUlJSkpKSliW4cOHVpzxJhKT0+/YP9ja2vIwg6ysIU87LjQs/h7R05Oc+Uk2cTEROXm5qqsrCxie1lZmYYOHerGSAAAwBDXXuK55557NGnSJF199dUaMmSInnrqKR08eFDTp093ayQAAGCEawXlxhtv1F/+8hc99thjOnr0qPr376//+Z//UY8ePdwaKeaSkpL0yCOPnPHyFWKPLOwgC1vIww6vZeFz/pH3+gAAAMQQn8UDAADMoaAAAABzKCgAAMAcCgoAADCHggIAAMxx9VL3XuI4jtauXastW7YoGAzK5/PJ7/dr2LBhGjly5AV9yf4LDVnYQRa2kIcdZMHbjGPi8OHDGjNmjHbu3Kn+/fvL7/fLcRxVV1dr165dGjhwoF5++WV17drV7VHbPLKwgyxsIQ87yCKEghIDX//611VfX69ly5YpMzMzYu3o0aP6zne+o7S0NK1atcqdAT2ELOwgC1vIww6yCKGgxMBFF12kN954QwMHDjzr+jvvvKMvf/nLqq+vj/Fk3kMWdpCFLeRhB1mEcJJsDCQnJ+uvf/3rOddramqUnJwcw4m8iyzsIAtbyMMOsgihoMTATTfdpMmTJ+uFF15QbW1teHttba1eeOEF3XLLLZowYYKLE3oHWdhBFraQhx1k8RkHra6xsdGZPn26k5iY6MTFxTnt27d32rdv78TFxTmJiYnObbfd5jQ2Nro9pieQhR1kYQt52EEWIZyDEkN1dXUqLy/XRx99JEkKBALKzc1Venq6y5N5D1nYQRa2kIcdXs+CggIAAMzhQm0xcvz4cS1fvvysF90ZP368UlNT3R7RM8jCDrKwhTzsIAuOoMTEnj17lJ+frxMnTigvLy/iojsbN25Uamqq1qxZo379+rk9aptHFnaQhS3kYQdZhFBQYmDEiBEKBAJaunSpEhMTI9aampo0ZcoUHT16VBs2bHBpQu8gCzvIwhbysIMsQigoMZCSkqLy8vJztt1du3Zp0KBBOnHiRIwn8x6ysIMsbCEPO8gihOugxEDHjh21b9++c67v379fHTt2jOFE3kUWdpCFLeRhB1mEcJJsDNx6662aPHmyHnroIeXn58vv98vn8ykYDKqsrEzFxcUqLCx0e0xPIAs7yMIW8rCDLD7jwrVXPGnu3LlOZmam4/P5nLi4OCcuLs7x+XxOZmamM2/ePLfH8xSysIMsbCEPO8iCC7XFXGVlpYLBoKTQRXdycnJcnsi7yMIOsrCFPOzwchYUFAAAYA4nycbYpk2bVF5eHrGtvLxcmzZtcmki7yILO8jCFvKww8tZcAQlxuLi4nTZZZdpz5494W19+/bVBx98oObmZhcn8x6ysIMsbCEPO7ycBe/iibHKykq1a9cuYtu6det08uRJlybyLrKwgyxsIQ87vJwFR1AAAIA5HEGJsQMHDkR88FOPHj3cHsmzyMIOsrCFPOzwdBbuvcPZWxYsWOB069Yt/F720+9t79atm/PEE0+4PZ6nkIUdZGELedhBFo5DQYmBxx57zElPT3fmzp3rvPPOO86RI0ecw4cPO++8844zd+5cJyMjw/nhD3/o9pieQBZ2kIUt5GEHWYRQUGKgW7duzsqVK8+5/uKLLzpZWVmxG8jDyMIOsrCFPOwgixCugxIDf/nLX3TppZeec71Pnz6qqamJ4UTeRRZ2kIUt5GEHWYRQUGJg0KBBevzxx3Xq1Kkz1k6dOqXi4mINGjTIhcm8hyzsIAtbyMMOsgjhbcYxsHPnThUUFKixsVF5eXkRn0y5adMmJSUlqaysTJdffrnbo7Z5ZGEHWdhCHnaQRQgFJUaOHTumZcuW6a233or44KchQ4ZowoQJSk9Pd3lC7yALO8jCFvKwgywoKAAAwCAu1BZD9fX1qqioCF90JxAI6KqrrtJFF13k9mieQxZ2kIUt5GGH57Nw8y1EXnHy5EnnzjvvdJKTkx2fz+ckJSU5iYmJjs/nc5KTk5277rrLaWpqcntMTyALO8jCFvKwgyxCKCgxcOeddzpdu3Z1VqxY4dTU1IS319TUOCtWrHCys7Odu+66y7X5vIQs7CALW8jDDrIIoaDEQJcuXZx169adc33t2rVOly5dYjiRd5GFHWRhC3nYQRYhXAclBhoaGtSlS5dzrnfu3FkNDQ0xnMi7yMIOsrCFPOwgixDexRMDY8eOVUNDg37zm9/I7/dHrH300UeaNGmS2rdvr5dfftmlCb2DLOwgC1vIww6yCKGgxEBVVZWuvfZavf/+++rfv3/ERXd27dqlfv366ZVXXlG3bt3cHrXNIws7yMIW8rCDLEIoKDHS0tKi11577awX3SkoKFBcHK+2xQpZ2EEWtpCHHWRBQQEAAAa1/QoGAAAuOBSUGMvJyVF+fn7EtlGjRqlXr14uTeRdZGEHWdhCHnZ4OQsudR9jkydP1sUXXxyx7Zvf/KY+/vhjlybyLrKwgyxsIQ87vJwF56AAAABzeIkHAACYw0s8MXLo0CE9+eST2rJlS/iTKf1+v4YOHarp06crOzvb7RE9gyzsIAtbyMMOsuAlnpjYvHmzRo8erezsbBUUFMjv98txHFVXV6usrExVVVVavXq1hg0b5vaobR5Z2EEWtpCHHWQRQkGJgS9+8Yv60pe+pCeeeOKs63fffbc2b96srVu3xngy7yELO8jCFvKwgyxCKCgxkJycrO3bt+vSSy896/r777+vK6+80hMf/uQ2srCDLGwhDzvIIoSTZGMgMzNTW7ZsOef6m2++qczMzBhO5F1kYQdZ2EIedpBFCCfJxsB9992n6dOnq6KiQvn5+REf/FRWVqann35apaWlbo/pCWRhB1nYQh52kMVnHMTEihUrnMGDBzsJCQmOz+dzfD6fk5CQ4AwePNh5/vnn3R7PU8jCDrKwhTzsIAvH4RyUGDt58mT4CoBdunRRu3btXJ7Iu8jCDrKwhTzs8HIWFBQAAGAOJ8nGyNatWzVx4kTl5OQoOTlZKSkpysnJ0cSJE1VeXu72eJ5CFnaQhS3kYQdZcAQlJlatWqUbbrhBI0eO1DXXXBNx0Z01a9Zo3bp1+u1vf6uvf/3rbo/a5pGFHWRhC3nYQRafcencF0+5/PLLnZKSknOuz5071+nXr18MJ/IusrCDLGwhDzvIIoQjKDHQvn177dixQ3369Dnr+t69ezVw4EB9+umnMZ7Me8jCDrKwhTzsIIsQzkGJgX/7t3/TqlWrzrn+0ksvqVevXrEbyMPIwg6ysIU87CCLEC7UFgOPPfaYbrrpJm3cuDH8wU//96I7a9as0YoVK9we0xPIwg6ysIU87CCLEF7iiZE333xTP/nJT/Tmm28qGAxKkgKBgIYMGaK77rpLQ4YMcXlC7yALO8jCFvKwgywoKAAAwCDOQQEAAOZQUAAAgDkUFAAAYA4FBQAAmENBAQAA5nAdlBg5fvy4li9fri1btigYDMrn88nv92vYsGEaP368UlNT3R4Rkj766CP913/9l37wgx+4PYrnkUXs8XvKDrLgbcYxsWfPHuXn5+vEiRPKy8uL+OCnjRs3KjU1VWvWrFG/fv3cHtXz3n33XV111VVqbm52exTPI4vY4veUHWQRQkGJgREjRigQCGjp0qVKTEyMWGtqatKUKVN09OhRbdiwwaUJvWPHjh2fu/7+++9r/Pjx/FGMAbKwhd9TdpBFCAUlBlJSUlReXn7Otrtr1y4NGjRIJ06ciPFk3hMXFyefz6ez/Wd/ervP5+OPYgyQhS38nrKDLEI4ByUGOnbsqH379p3zP7b9+/erY8eOMZ7Kmzp37qx58+Zp5MiRZ13fvXu3xo4dG+OpvIksbOH3lB1kEUJBiYFbb71VkydP1kMPPaT8/PwzPvipuLhYhYWFbo/pCbm5uTpy5Ih69Ohx1vVPPvnkrP+PHtFHFrbwe8oOsviMg5iYO3euk5mZ6fh8PicuLs6Ji4tzfD6fk5mZ6cybN8/t8TzjxRdfdJ599tlzrv/1r391lixZEsOJvIss7OH3lB1k4TicgxJjlZWVEZ9MmZOT4/JEABCJ31N2eDkLCgoAADCHK8nGyHvvvafFixfr/ffflxR6C+Vtt92m7373u1q/fr3L03kLWdhBFraQhx1kIc5BiYXVq1c7iYmJTqdOnZz27ds7q1evdi6++GJn1KhRzsiRI52EhARn3bp1bo/pCWRhB1nYQh52kEUIBSUGhgwZ4syZM8dxHMd57rnnnI4dOzoPPvhgeP3BBx908vPz3RrPU8jCDrKwhTzsIIsQCkoMpKenO/v27XMcx3Gam5udhIQEp6KiIry+c+dOx+/3uzWep5CFHWRhC3nYQRYhnIMSY3FxcWrfvr06dOgQ3paWlqba2lr3hvIosrCDLGwhDzu8nAUFJQZ69uyp/fv3h++/+eab6t69e/h+VVWVMjMz3RjNc8jCDrKwhTzsIIsQriQbA7fddlvE54n0798/Yn316tX66le/GuuxPIks7CALW8jDDrII4TooAADAHF7icclzzz2n48ePuz0GRBaWkIUt5GGHF7PgCIpL0tPTtX37dvXq1cvtUTyPLOwgC1vIww4vZsERFJfQC+0gCzvIwhbysMOLWVBQAACAORQUl6xevVpZWVlujwGFsujatavbY0D8XFjDz4YdXvzZ4ByUGGtubtbHH3+s+Ph4denSxe1xAOCcGhsbdejQIXXr1k1JSUluj+NJXv6bwRGUGHnllVf0la98RampqcrKypLf71eHDh00adIkHTx40O3xPOXdd9/Vf/zHf+jnP/+5Pv7444i1uro6ffe733VpMu95+umnNXnyZC1evFiS9Pzzz6tv377q1auXHnnkEZen85YlS5borbfekiR9+umn+t73vqfU1FT16dNHF110kaZPn67GxkaXp/QO/mZQUGLi2Wef1fjx45Wbm6u7775bF198sWbNmqW5c+eqqqpKubm52rdvn9tjesKaNWs0aNAgrVixQvPmzVPfvn21YcOG8HpDQ4OWLl3q4oTeUVpaqsLCQtXX12vOnDl6/PHHNXPmTH3nO9/RLbfcop/85Cd66qmn3B7TMx5//HElJISu3fnwww9r3bp1+t3vfqfdu3frhRde0IYNG/Twww+7PKU38DfjMy59BpCnXHbZZc6KFSvC97du3ep069bNaWlpcRzHcW688Ubnm9/8plvjecqQIUPCnwra0tLizJ8/37nooouc1atXO47jOMFg0ImLi3NzRM+47LLLnN/85jeO4zjOtm3bnISEBOfpp58Or//qV79ycnNz3RrPc5KSkpwDBw44juM4ffr0Cf9MnLZx40ane/fubozmOfzNCKGgxEBycrJTWVkZsS0hIcE5fPiw4ziO8/bbbzsdOnRwYTLvSU9Pd/bv3x+xbfny5U5qaqrz8ssvU1BiKDk5OfwH0XFCfyB37doVvr9v3z5+LmKoR48ezvr16x3HcZyuXbs6W7dujVjfs2ePk5qa6sZonsPfjBBe4omBnj17qry8PHx/27ZtiouLk9/vlyR16tRJJ0+edGs8T0lKStInn3wSsW38+PF65plndNNNN2nlypXuDOZBKSkpEVfGvPjii3XRRRdF7HPq1KlYj+VZEydO1Jw5c/TJJ59o0qRJeuyxx1RfXy9JOnHihIqKijRs2DCXp/QG/maE8GGBMTBz5kx973vf09atW9W+fXs9/fTTmjRpkuLj4yVJb7/9tvr06ePylN5wxRVXaMOGDcrNzY3YfuONN6qlpUWTJ092aTLvueyyy7Rjxw717dtXUugTWv+v999/Xz179nRhMm965JFHtGvXLvXq1UtXX321/vjHP8rv96tr1646cuSIOnfurLKyMrfH9AT+ZoRQUGJg5syZiouL07Jly9TY2KgpU6ZEnGw2aNAgLV++3MUJveO2227Tpk2bzro2fvx4SeLEzBiZN2+eUlNTz7l+8OBBTZs2LYYTeVtiYqJeeuklvfrqq/r973+v+Ph4tbS0KDMzU8OGDdOECRM+Ny9ED38zQrgOCgAAMIcjKDF24MABBYNB+Xw++f1+9ejRw+2RANfxc2ELedjh5Sw4STZGnnjiCWVnZ6tXr14aMmSI/t//+3/q1auXsrOzVVpa6vZ4+My7774bfp0XrY+fC1vIww6y4AhKTPzwhz/Uj370Iz344IO65ppr5Pf75TiOqqur9dprr6moqEj19fV66KGH3B4V8uanhrqBnwtbyMMOsgjhHJQYyM7O1sKFC/WNb3zjrOsrV67U7bffrsOHD8d2MA8aN27c567X1tbq9ddfV3Nzc4wm8i5+LmwhDzvIIoQjKDHwl7/8RZdeeuk51/v06aOampoYTuRdv//975Wfnx++nsDfopjEDj8XtpCHHWQRwhGUGBg+fLi6deumJUuWhD/r4rRTp05p8uTJOnz4sF5//XV3BvSQL3zhC7rrrrs0derUs65v375dubm5FJUY4OfCFvKwgyxCKCgxsHPnThUUFKixsVF5eXny+/3y+XwKBoPatGmTkpKSVFZWpssvv9ztUdu8W265RSkpKfrZz3521vX33ntP1157rSorK2M8mffwc2ELedhBFiEUlBg5duyYli1bprfeekvBYFCSFAgENGTIEE2YMEHp6ekuT+gNjY2Nam5uVkpKitujQPxcWEMedpAFBQUAABjEdVBcct111+no0aNujwGRhSVkYQt52OHFLCgoLtm0aZMaGhrcHgMiC0vIwhbysMOLWVBQAACAORQUl/To0UPt2rVzewyILCwhC1vIww4vZsFJsgAAwByuJOuCTz75RL/73e908OBB9ejRQ9/+9reVkZHh9lieRBbuqaioUG5urttj4DPkYQdZhHAEJQa+9a1vacKECRo3bpz27NmjvLw8+Xw+9erVSx9++KF8Pp/Wr1+vvn37uj1qm0cWdsTFxSknJ0dTp07V5MmT1bVrV7dH8jTysIMsQjgHJQY2btyoAQMGSJLuu+8+FRQU6NChQ3rrrbdUVVWl6667ToWFhe4O6RFkYcvIkSP105/+VD179tSYMWO0atUqPmbAReRhB1lIctDqkpOTnf379zuO4ziZmZnOtm3bItb37t3rZGRkuDCZ95CFHT6fz/noo4+ckydPOi+88IJz7bXXOvHx8Y7f73dmzZrlvP/++26P6CnkYQdZhHAEJQa+8IUvaP369ZJClyo+cOBAxPqBAweUnJzsxmieQxb2JCQk6N///d/1yiuv6MCBA5o5c6ZeeOEF9evXT1/5ylfcHs9zyMMOz2fhdkPygj/84Q9Op06dnMWLFzuLFy92evbs6Tz99NPOG2+84fzqV79ysrOznfvvv9/tMT2BLOyIi4tzPvroo3Our1271pkwYUIMJ/I28rCDLEI4STZG/vu//1uFhYU6cuSI/u8/eVJSkqZPn64f/ehHio+Pd3FC7yALG+Li4hQMBnXJJZe4PQpEHpaQRQgFJYaam5u1bds2/fnPf1ZLS4syMzOVm5urtLQ0t0fzHLJw38aNGzVs2DAlJHC1AwvIww6yCKGgAAAAczhJ1oCamhr9+te/dnsMiCwsIQtbyMMOr2TBERQD3n33XV111VXee4+7QWRhB1nYQh52eCULb7/AFSN1dXWfu37s2LEYTQKysIMsbCEPO8gihCMoMRAXFyefz3fOdcdx5PP52nwbtoAs7CALW8jDDrII4QhKDKSlpWnOnDkaPHjwWdf37dunadOmxXgqbyILO8jCFvKwgyxCKCgxcNVVV0mS8vLyzrreoUMHcSArNsjCDrKwhTzsIIsQ3sUTAxMmTFD79u3PuR4IBPTII4/EcCLvIgs7yMIW8rCDLEI4BwUAAJjDERQAAGAO56DEyPHjx7V8+XJt2bJFwWBQPp9Pfr9fw4YN0/jx45Wamur2iJ5BFnaQhS3kYQdZ8BJPTOzZs0f5+fk6ceKE8vLy5Pf75TiOqqurtXHjRqWmpmrNmjXq16+f26O2eWRhB1nYQh52kEUIBSUGRowYoUAgoKVLlyoxMTFirampSVOmTNHRo0e1YcMGlyb0DrKwgyxsIQ87yCKEghIDKSkpKi8vP2fb3bVrlwYNGqQTJ07EeDLvIQs7yMIW8rCDLEI4STYGOnbsqH379p1zff/+/erYsWMMJ/IusrCDLGwhDzvIIoSTZGPg1ltv1eTJk/XQQw8pPz9ffr9fPp9PwWBQZWVlKi4uVmFhodtjegJZ2EEWtpCHHWTxGQcxMXfuXCczM9Px+XxOXFycExcX5/h8PiczM9OZN2+e2+N5ClnYQRa2kIcdZOE4nIMSY5WVlQoGg5JCVwPMyclxeSLvIgs7yMIW8rDDy1lQUAAAgDmcJBsjDQ0N2rx5s/bs2XPG2qeffqpf//rXLkzlTWRhB1nYQh52kIU4ByUW9u7d6/To0SP8WmJeXp5z5MiR8HowGHTi4uJcnNA7yMIOsrCFPOwgixCOoMTA97//fQ0YMEDV1dXau3ev0tPTNWzYMB08eNDt0TyHLOwgC1vIww6y+IzbDckLLrnkEmfHjh0R22bMmOF0797d+dOf/uSZNmwBWdhBFraQhx1kEcJ1UGKgoaFBCQmR/9Q/+9nPFBcXp7y8PC1fvtylybyHLOwgC1vIww6yCKGgxMBll12m8vJy9e3bN2L7woUL5TiOrr/+epcm8x6ysIMsbCEPO8gihHNQYuCb3/ymnnvuubOuLVq0SOPHj5fDu71jgizsIAtbyMMOsgjhOigAAMAcjqAAAABzKCgAAMAcCgoAADCHggIAAMyhoAAAAHMoKAAAwBwKCoBWMWXKFPl8Pvl8PrVr105+v1/5+fn61a9+pZaWln/4eZYsWaIOHTq03qAATKKgAGg1X/va13T06FF9+OGHWr16tUaMGKG77rpLY8aM0alTp9weD4BhFBQArSYpKUmBQEBdu3bVVVddpQcffFAvvfSSVq9erSVLlkiSFixYoAEDBig1NVXZ2dmaMWOG6uvrJUmvv/66brnlFtXW1oaPxhQVFUmSmpqaNGvWLHXt2lWpqakaPHiwXn/9dXe+UQBRR0EBEFNf/epXNXDgQL344ouSpLi4OP30pz/Vrl27tHTpUq1fv16zZs2SJA0dOlSlpaVKT0/X0aNHdfToUd13332SpFtuuUVvvPGGVqxYoR07dujb3/62vva1r2nfvn2ufW8AoodL3QNoFVOmTNEnn3yiVatWnbF20003aceOHdqzZ88Za7/73e9022236eOPP5YUOgelsLBQn3zySXifP/3pT+rdu7cOHTqkrKys8PZRo0Zp0KBBKi4ujvr3AyC2+DRjADHnOI58Pp8kacOGDSouLtaePXtUV1enU6dO6dNPP9Xx48eVmpp61sdv27ZNjuOoT58+EdsbGxvVuXPnVp8fQOujoACIuffee085OTk6cOCArr32Wk2fPl0//OEP1alTJ23evFlTp07VyZMnz/n4lpYWxcfHq6KiQvHx8RFrF110UWuPDyAGKCgAYmr9+vXauXOn7r77bpWXl+vUqVP68Y9/rLi40Clxv/3tbyP2T0xMVHNzc8S2K6+8Us3NzaqurtaXv/zlmM0OIHYoKABaTWNjo4LBoJqbm/XRRx/p1VdfVUlJicaMGaObb75ZO3fu1KlTp7Rw4UKNHTtWb7zxhn7xi19EPEfPnj1VX1+vdevWaeDAgUpJSVGfPn00ceJE3Xzzzfrxj3+sK6+8Uh9//LHWr1+vAQMG6Nprr3XpOwYQLbyLB0CrefXVV5WZmamePXvqa1/7mjZs2KCf/vSneumllxQfH68rrrhCCxYs0Lx589S/f3/95je/UUlJScRzDB06VNOnT9eNN96oiy++WPPnz5ckLV68WDfffLPuvfdeXXrppbr++uv19ttvKzs7241vFUCU8S4eAABgDkdQAACAORQUAABgDgUFAACYQ0EBAADmUFAAAIA5FBQAAGAOBQUAAJhDQQEAAOZQUAAAgDkUFAAAYA4FBQAAmPP/AVAw4E17tjGLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.resample('Q').mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153.58</td>\n",
       "      <td>155.45</td>\n",
       "      <td>152.89</td>\n",
       "      <td>155.45</td>\n",
       "      <td>27770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153.90</td>\n",
       "      <td>155.81</td>\n",
       "      <td>153.78</td>\n",
       "      <td>154.45</td>\n",
       "      <td>26624926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155.02</td>\n",
       "      <td>155.98</td>\n",
       "      <td>154.48</td>\n",
       "      <td>155.37</td>\n",
       "      <td>21069647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Open    High     Low   Close    Volume\n",
       "0  153.17  153.33  152.22  153.18  16404088\n",
       "1  153.58  155.45  152.89  155.45  27770715\n",
       "2  154.34  154.45  153.46  153.93  25331662\n",
       "3  153.90  155.81  153.78  154.45  26624926\n",
       "4  155.02  155.98  154.48  155.37  21069647"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"aapl_no_dates.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-06-01', '2016-06-02', '2016-06-03', '2016-06-06',\n",
       "               '2016-06-07', '2016-06-08', '2016-06-09', '2016-06-10',\n",
       "               '2016-06-13', '2016-06-14', '2016-06-15', '2016-06-16',\n",
       "               '2016-06-17', '2016-06-20', '2016-06-21', '2016-06-22',\n",
       "               '2016-06-23', '2016-06-24', '2016-06-27', '2016-06-28',\n",
       "               '2016-06-29', '2016-06-30'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range(start=\"6/1/2016\",end=\"6/30/2016\",freq='B')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-02</th>\n",
       "      <td>153.58</td>\n",
       "      <td>155.45</td>\n",
       "      <td>152.89</td>\n",
       "      <td>155.45</td>\n",
       "      <td>27770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-03</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-06</th>\n",
       "      <td>153.90</td>\n",
       "      <td>155.81</td>\n",
       "      <td>153.78</td>\n",
       "      <td>154.45</td>\n",
       "      <td>26624926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-07</th>\n",
       "      <td>155.02</td>\n",
       "      <td>155.98</td>\n",
       "      <td>154.48</td>\n",
       "      <td>155.37</td>\n",
       "      <td>21069647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "2016-06-01  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-02  153.58  155.45  152.89  155.45  27770715\n",
       "2016-06-03  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-06  153.90  155.81  153.78  154.45  26624926\n",
       "2016-06-07  155.02  155.98  154.48  155.37  21069647"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(rng, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>153.17</td>\n",
       "      <td>153.33</td>\n",
       "      <td>152.22</td>\n",
       "      <td>153.18</td>\n",
       "      <td>16404088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-02</th>\n",
       "      <td>153.58</td>\n",
       "      <td>155.45</td>\n",
       "      <td>152.89</td>\n",
       "      <td>155.45</td>\n",
       "      <td>27770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-03</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-04</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-05</th>\n",
       "      <td>154.34</td>\n",
       "      <td>154.45</td>\n",
       "      <td>153.46</td>\n",
       "      <td>153.93</td>\n",
       "      <td>25331662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-06</th>\n",
       "      <td>153.90</td>\n",
       "      <td>155.81</td>\n",
       "      <td>153.78</td>\n",
       "      <td>154.45</td>\n",
       "      <td>26624926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-07</th>\n",
       "      <td>155.02</td>\n",
       "      <td>155.98</td>\n",
       "      <td>154.48</td>\n",
       "      <td>155.37</td>\n",
       "      <td>21069647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-08</th>\n",
       "      <td>155.25</td>\n",
       "      <td>155.54</td>\n",
       "      <td>154.40</td>\n",
       "      <td>154.99</td>\n",
       "      <td>21250798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-09</th>\n",
       "      <td>155.19</td>\n",
       "      <td>155.19</td>\n",
       "      <td>146.02</td>\n",
       "      <td>148.98</td>\n",
       "      <td>64882657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-10</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-11</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-12</th>\n",
       "      <td>145.74</td>\n",
       "      <td>146.09</td>\n",
       "      <td>142.51</td>\n",
       "      <td>145.42</td>\n",
       "      <td>72307330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-13</th>\n",
       "      <td>147.16</td>\n",
       "      <td>147.45</td>\n",
       "      <td>145.15</td>\n",
       "      <td>146.59</td>\n",
       "      <td>34165445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-14</th>\n",
       "      <td>147.50</td>\n",
       "      <td>147.50</td>\n",
       "      <td>143.84</td>\n",
       "      <td>145.16</td>\n",
       "      <td>31531232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-15</th>\n",
       "      <td>143.32</td>\n",
       "      <td>144.48</td>\n",
       "      <td>142.21</td>\n",
       "      <td>144.29</td>\n",
       "      <td>32165373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-16</th>\n",
       "      <td>143.78</td>\n",
       "      <td>144.50</td>\n",
       "      <td>142.20</td>\n",
       "      <td>142.27</td>\n",
       "      <td>50361093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-17</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-18</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-19</th>\n",
       "      <td>143.66</td>\n",
       "      <td>146.74</td>\n",
       "      <td>143.66</td>\n",
       "      <td>146.34</td>\n",
       "      <td>32541404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-20</th>\n",
       "      <td>146.87</td>\n",
       "      <td>146.87</td>\n",
       "      <td>144.94</td>\n",
       "      <td>145.01</td>\n",
       "      <td>24900073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-21</th>\n",
       "      <td>145.52</td>\n",
       "      <td>146.07</td>\n",
       "      <td>144.61</td>\n",
       "      <td>145.87</td>\n",
       "      <td>21265751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-22</th>\n",
       "      <td>145.77</td>\n",
       "      <td>146.70</td>\n",
       "      <td>145.12</td>\n",
       "      <td>145.63</td>\n",
       "      <td>19106294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-23</th>\n",
       "      <td>145.13</td>\n",
       "      <td>147.16</td>\n",
       "      <td>145.11</td>\n",
       "      <td>146.28</td>\n",
       "      <td>35439389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-24</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-25</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-26</th>\n",
       "      <td>147.17</td>\n",
       "      <td>148.28</td>\n",
       "      <td>145.38</td>\n",
       "      <td>145.82</td>\n",
       "      <td>25692361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>145.01</td>\n",
       "      <td>146.16</td>\n",
       "      <td>143.62</td>\n",
       "      <td>143.73</td>\n",
       "      <td>24761891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-28</th>\n",
       "      <td>144.49</td>\n",
       "      <td>146.11</td>\n",
       "      <td>143.16</td>\n",
       "      <td>145.83</td>\n",
       "      <td>22082432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-29</th>\n",
       "      <td>144.71</td>\n",
       "      <td>145.13</td>\n",
       "      <td>142.28</td>\n",
       "      <td>143.68</td>\n",
       "      <td>31499368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>144.45</td>\n",
       "      <td>144.96</td>\n",
       "      <td>143.78</td>\n",
       "      <td>144.02</td>\n",
       "      <td>23024107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume\n",
       "2016-06-01  153.17  153.33  152.22  153.18  16404088\n",
       "2016-06-02  153.58  155.45  152.89  155.45  27770715\n",
       "2016-06-03  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-04  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-05  154.34  154.45  153.46  153.93  25331662\n",
       "2016-06-06  153.90  155.81  153.78  154.45  26624926\n",
       "2016-06-07  155.02  155.98  154.48  155.37  21069647\n",
       "2016-06-08  155.25  155.54  154.40  154.99  21250798\n",
       "2016-06-09  155.19  155.19  146.02  148.98  64882657\n",
       "2016-06-10  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-11  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-12  145.74  146.09  142.51  145.42  72307330\n",
       "2016-06-13  147.16  147.45  145.15  146.59  34165445\n",
       "2016-06-14  147.50  147.50  143.84  145.16  31531232\n",
       "2016-06-15  143.32  144.48  142.21  144.29  32165373\n",
       "2016-06-16  143.78  144.50  142.20  142.27  50361093\n",
       "2016-06-17  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-18  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-19  143.66  146.74  143.66  146.34  32541404\n",
       "2016-06-20  146.87  146.87  144.94  145.01  24900073\n",
       "2016-06-21  145.52  146.07  144.61  145.87  21265751\n",
       "2016-06-22  145.77  146.70  145.12  145.63  19106294\n",
       "2016-06-23  145.13  147.16  145.11  146.28  35439389\n",
       "2016-06-24  147.17  148.28  145.38  145.82  25692361\n",
       "2016-06-25  147.17  148.28  145.38  145.82  25692361\n",
       "2016-06-26  147.17  148.28  145.38  145.82  25692361\n",
       "2016-06-27  145.01  146.16  143.62  143.73  24761891\n",
       "2016-06-28  144.49  146.11  143.16  145.83  22082432\n",
       "2016-06-29  144.71  145.13  142.28  143.68  31499368\n",
       "2016-06-30  144.45  144.96  143.78  144.02  23024107"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.asfreq('D',method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 01:00:00',\n",
       "               '2011-01-01 02:00:00', '2011-01-01 03:00:00',\n",
       "               '2011-01-01 04:00:00', '2011-01-01 05:00:00',\n",
       "               '2011-01-01 06:00:00', '2011-01-01 07:00:00',\n",
       "               '2011-01-01 08:00:00', '2011-01-01 09:00:00',\n",
       "               '2011-01-01 10:00:00', '2011-01-01 11:00:00',\n",
       "               '2011-01-01 12:00:00', '2011-01-01 13:00:00',\n",
       "               '2011-01-01 14:00:00', '2011-01-01 15:00:00',\n",
       "               '2011-01-01 16:00:00', '2011-01-01 17:00:00',\n",
       "               '2011-01-01 18:00:00', '2011-01-01 19:00:00',\n",
       "               '2011-01-01 20:00:00', '2011-01-01 21:00:00',\n",
       "               '2011-01-01 22:00:00', '2011-01-01 23:00:00',\n",
       "               '2011-01-02 00:00:00', '2011-01-02 01:00:00',\n",
       "               '2011-01-02 02:00:00', '2011-01-02 03:00:00',\n",
       "               '2011-01-02 04:00:00', '2011-01-02 05:00:00',\n",
       "               '2011-01-02 06:00:00', '2011-01-02 07:00:00',\n",
       "               '2011-01-02 08:00:00', '2011-01-02 09:00:00',\n",
       "               '2011-01-02 10:00:00', '2011-01-02 11:00:00',\n",
       "               '2011-01-02 12:00:00', '2011-01-02 13:00:00',\n",
       "               '2011-01-02 14:00:00', '2011-01-02 15:00:00',\n",
       "               '2011-01-02 16:00:00', '2011-01-02 17:00:00',\n",
       "               '2011-01-02 18:00:00', '2011-01-02 19:00:00',\n",
       "               '2011-01-02 20:00:00', '2011-01-02 21:00:00',\n",
       "               '2011-01-02 22:00:00', '2011-01-02 23:00:00',\n",
       "               '2011-01-03 00:00:00', '2011-01-03 01:00:00',\n",
       "               '2011-01-03 02:00:00', '2011-01-03 03:00:00',\n",
       "               '2011-01-03 04:00:00', '2011-01-03 05:00:00',\n",
       "               '2011-01-03 06:00:00', '2011-01-03 07:00:00',\n",
       "               '2011-01-03 08:00:00', '2011-01-03 09:00:00',\n",
       "               '2011-01-03 10:00:00', '2011-01-03 11:00:00',\n",
       "               '2011-01-03 12:00:00', '2011-01-03 13:00:00',\n",
       "               '2011-01-03 14:00:00', '2011-01-03 15:00:00',\n",
       "               '2011-01-03 16:00:00', '2011-01-03 17:00:00',\n",
       "               '2011-01-03 18:00:00', '2011-01-03 19:00:00',\n",
       "               '2011-01-03 20:00:00', '2011-01-03 21:00:00',\n",
       "               '2011-01-03 22:00:00', '2011-01-03 23:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2011', periods=72, freq='H')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-01 00:00:00    6\n",
       "2011-01-01 01:00:00    7\n",
       "2011-01-01 02:00:00    4\n",
       "2011-01-01 03:00:00    1\n",
       "2011-01-01 04:00:00    5\n",
       "2011-01-01 05:00:00    2\n",
       "2011-01-01 06:00:00    5\n",
       "2011-01-01 07:00:00    3\n",
       "2011-01-01 08:00:00    5\n",
       "2011-01-01 09:00:00    2\n",
       "2011-01-01 10:00:00    5\n",
       "2011-01-01 11:00:00    8\n",
       "2011-01-01 12:00:00    1\n",
       "2011-01-01 13:00:00    2\n",
       "2011-01-01 14:00:00    1\n",
       "2011-01-01 15:00:00    1\n",
       "2011-01-01 16:00:00    9\n",
       "2011-01-01 17:00:00    7\n",
       "2011-01-01 18:00:00    2\n",
       "2011-01-01 19:00:00    7\n",
       "Freq: H, dtype: int32"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "ts = pd.Series(np.random.randint(0,10,len(rng)), index=rng)\n",
    "ts.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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