Pandas Exercises-10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Exercise: Time Series (Apple Stock)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Time_Series/Apple_Stock/appl_1980_2014.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Assign it to a variable apple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-07-07</td>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-07-03</td>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-07-02</td>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close    Volume  Adj Close\n",
       "0  2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "1  2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2  2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "3  2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "4  2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple = pd.read_csv(\"C:/Users/Mayur Maheshwari/Desktop/assisgment homework of data analytix/2. Pandas Basic Exercises/Pandas Exercise10 files/appl_1980_2014.csv\", encoding=\"ISO-8859-1\")\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4.  Check out the type of the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date          object\n",
       "Open         float64\n",
       "High         float64\n",
       "Low          float64\n",
       "Close        float64\n",
       "Volume         int64\n",
       "Adj Close    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Transform the Date column as a datetime type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2014-07-08\n",
       "1   2014-07-07\n",
       "2   2014-07-03\n",
       "3   2014-07-02\n",
       "4   2014-07-01\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.Date = pd.to_datetime(apple.Date)\n",
    "apple['Date'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6.  Set the date as the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
       "      <th>Adj Close</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-07-08</th>\n",
       "      <td>96.27</td>\n",
       "      <td>96.80</td>\n",
       "      <td>93.92</td>\n",
       "      <td>95.35</td>\n",
       "      <td>65130000</td>\n",
       "      <td>95.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-07</th>\n",
       "      <td>94.14</td>\n",
       "      <td>95.99</td>\n",
       "      <td>94.10</td>\n",
       "      <td>95.97</td>\n",
       "      <td>56305400</td>\n",
       "      <td>95.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-03</th>\n",
       "      <td>93.67</td>\n",
       "      <td>94.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>94.03</td>\n",
       "      <td>22891800</td>\n",
       "      <td>94.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-02</th>\n",
       "      <td>93.87</td>\n",
       "      <td>94.06</td>\n",
       "      <td>93.09</td>\n",
       "      <td>93.48</td>\n",
       "      <td>28420900</td>\n",
       "      <td>93.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-01</th>\n",
       "      <td>93.52</td>\n",
       "      <td>94.07</td>\n",
       "      <td>93.13</td>\n",
       "      <td>93.52</td>\n",
       "      <td>38170200</td>\n",
       "      <td>93.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume  Adj Close\n",
       "Date                                                       \n",
       "2014-07-08  96.27  96.80  93.92  95.35  65130000      95.35\n",
       "2014-07-07  94.14  95.99  94.10  95.97  56305400      95.97\n",
       "2014-07-03  93.67  94.10  93.20  94.03  22891800      94.03\n",
       "2014-07-02  93.87  94.06  93.09  93.48  28420900      93.48\n",
       "2014-07-01  93.52  94.07  93.13  93.52  38170200      93.52"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple = apple.set_index('Date')\n",
    "\n",
    "apple.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7.  Is there any duplicate dates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.index.is_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8.  Ops...it seems the index is from the most recent date. Make the first entry the oldest date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>28.75</td>\n",
       "      <td>28.87</td>\n",
       "      <td>28.75</td>\n",
       "      <td>28.75</td>\n",
       "      <td>117258400</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>27.38</td>\n",
       "      <td>27.38</td>\n",
       "      <td>27.25</td>\n",
       "      <td>27.25</td>\n",
       "      <td>43971200</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>25.37</td>\n",
       "      <td>25.37</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.25</td>\n",
       "      <td>26432000</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>25.87</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.87</td>\n",
       "      <td>25.87</td>\n",
       "      <td>21610400</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>26.63</td>\n",
       "      <td>26.75</td>\n",
       "      <td>26.63</td>\n",
       "      <td>26.63</td>\n",
       "      <td>18362400</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close     Volume  Adj Close\n",
       "Date                                                        \n",
       "1980-12-12  28.75  28.87  28.75  28.75  117258400       0.45\n",
       "1980-12-15  27.38  27.38  27.25  27.25   43971200       0.42\n",
       "1980-12-16  25.37  25.37  25.25  25.25   26432000       0.39\n",
       "1980-12-17  25.87  26.00  25.87  25.87   21610400       0.40\n",
       "1980-12-18  26.63  26.75  26.63  26.63   18362400       0.41"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple.sort_index(ascending = True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Get the last business day of each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "      <th>Adj Close</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-31</th>\n",
       "      <td>30.481538</td>\n",
       "      <td>30.567692</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>30.443077</td>\n",
       "      <td>2.586252e+07</td>\n",
       "      <td>0.473077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>31.754762</td>\n",
       "      <td>31.826667</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>31.654762</td>\n",
       "      <td>7.249867e+06</td>\n",
       "      <td>0.493810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-27</th>\n",
       "      <td>26.480000</td>\n",
       "      <td>26.572105</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>26.407895</td>\n",
       "      <td>4.231832e+06</td>\n",
       "      <td>0.411053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-31</th>\n",
       "      <td>24.937727</td>\n",
       "      <td>25.016818</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>24.836364</td>\n",
       "      <td>7.962691e+06</td>\n",
       "      <td>0.387727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-30</th>\n",
       "      <td>27.286667</td>\n",
       "      <td>27.368095</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>27.227143</td>\n",
       "      <td>6.392000e+06</td>\n",
       "      <td>0.423333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close        Volume  \\\n",
       "Date                                                                   \n",
       "1980-12-31  30.481538  30.567692  30.443077  30.443077  2.586252e+07   \n",
       "1981-01-30  31.754762  31.826667  31.654762  31.654762  7.249867e+06   \n",
       "1981-02-27  26.480000  26.572105  26.407895  26.407895  4.231832e+06   \n",
       "1981-03-31  24.937727  25.016818  24.836364  24.836364  7.962691e+06   \n",
       "1981-04-30  27.286667  27.368095  27.227143  27.227143  6.392000e+06   \n",
       "\n",
       "            Adj Close  \n",
       "Date                   \n",
       "1980-12-31   0.473077  \n",
       "1981-01-30   0.493810  \n",
       "1981-02-27   0.411053  \n",
       "1981-03-31   0.387727  \n",
       "1981-04-30   0.423333  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_month = apple.resample('BM').mean()\n",
    "\n",
    "apple_month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10.  What is the difference in days between the first day and the oldest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12261"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(apple.index.max() - apple.index.min()).days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11.  How many months in the data we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "404"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_months = apple.resample('BM').mean()\n",
    "\n",
    "len(apple_months.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "appl_open = apple['Adj Close'].plot(title = \"Apple Stock\")\n",
    "fig = appl_open.get_figure()\n",
    "fig.set_size_inches(13.5, 9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BONUS: Create your own question and answer it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}