(7)_Matplotlib_TimeSeries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib - TimeSeries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.dates as mpl_dates\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nDatetime object: 2019-10-26\nType: <class 'datetime.date'>\n\nToday's date: 2020-07-22\nType: <class 'datetime.date'>\n\nDelta (days): 270\n\nInterval (1000 sec): 0:16:40\n"
    }
   ],
   "source": [
    "# Datetime basics - Part 1\n",
    "\n",
    "# Creating a datetime object\n",
    "\n",
    "d = datetime.date(2019, 10, 26)\n",
    "print('\\nDatetime object: {}'.format(d))\n",
    "print('Type: {}'.format(type(d)))\n",
    "\n",
    "# Todays date\n",
    "\n",
    "t = datetime.date.today()\n",
    "print(\"\\nToday's date: {}\".format(t))\n",
    "print('Type: {}'.format(type(t)))\n",
    "\n",
    "# Calculating a time interval\n",
    "\n",
    "delta_t = t-d\n",
    "delta_t_minutes = delta_t\n",
    "delta_t_days = delta_t.days\n",
    "\n",
    "print(\"\\nDelta (days): {}\".format(delta_t_days))\n",
    "\n",
    "# Specific time interval\n",
    "\n",
    "delta_t_1 = datetime.timedelta(seconds=1000)\n",
    "print('\\nInterval (1000 sec): {}'.format(delta_t_1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Today (datetime obejct): 2020-07-22\nToday (string): July 22, 2020\n\nToday (datetime): 2020-07-22 00:00:00\n"
    }
   ],
   "source": [
    "# Datetime basics - Part 2 \n",
    "\n",
    "# Converting datetime objects to strings\n",
    "\n",
    "t = datetime.date.today()\n",
    "t_str = t.strftime('%B %d, %Y')\n",
    "print(\"Today (datetime obejct): {}\".format(t))\n",
    "print(\"Today (string): {}\".format(t_str))\n",
    "\n",
    "string_i = t_str\n",
    "dt = datetime.datetime.strptime(string_i, '%B %d, %Y')\n",
    "\n",
    "print('\\nToday (datetime): {}'.format(dt))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "         Date        Open        High         Low       Close   Adj Close  \\\n0  2019-04-08  119.809998  120.019997  118.639999  119.930000  118.363968   \n1  2019-04-09  118.629997  119.540001  118.580002  119.279999  117.722450   \n2  2019-04-11  120.540001  120.849998  119.919998  120.330002  118.758751   \n3  2019-04-12  120.639999  120.980003  120.370003  120.949997  119.370651   \n4  2019-04-15  120.940002  121.580002  120.570000  121.050003  119.469353   \n\n     Volume  \n0  15116200  \n1  17612000  \n2  14209100  \n3  19745100  \n4  15792600  ",
      "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>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2019-04-08</td>\n      <td>119.809998</td>\n      <td>120.019997</td>\n      <td>118.639999</td>\n      <td>119.930000</td>\n      <td>118.363968</td>\n      <td>15116200</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2019-04-09</td>\n      <td>118.629997</td>\n      <td>119.540001</td>\n      <td>118.580002</td>\n      <td>119.279999</td>\n      <td>117.722450</td>\n      <td>17612000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2019-04-11</td>\n      <td>120.540001</td>\n      <td>120.849998</td>\n      <td>119.919998</td>\n      <td>120.330002</td>\n      <td>118.758751</td>\n      <td>14209100</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2019-04-12</td>\n      <td>120.639999</td>\n      <td>120.980003</td>\n      <td>120.370003</td>\n      <td>120.949997</td>\n      <td>119.370651</td>\n      <td>19745100</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2019-04-15</td>\n      <td>120.940002</td>\n      <td>121.580002</td>\n      <td>120.570000</td>\n      <td>121.050003</td>\n      <td>119.469353</td>\n      <td>15792600</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 527
    }
   ],
   "source": [
    "# Importing Data\n",
    "\n",
    "csv_path = ('/Users/antonioelias/Desktop/MOOCs/Python Coding Bootcamp/Data Science Libs/microsoft.csv')\n",
    "microsoft_data = pd.read_csv(csv_path)\n",
    "microsoft_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 252 entries, 0 to 251\nData columns (total 7 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Date       252 non-null    object \n 1   Open       252 non-null    float64\n 2   High       252 non-null    float64\n 3   Low        252 non-null    float64\n 4   Close      252 non-null    float64\n 5   Adj Close  252 non-null    float64\n 6   Volume     252 non-null    int64  \ndtypes: float64(5), int64(1), object(1)\nmemory usage: 13.9+ KB\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             Open        High         Low       Close   Adj Close  \\\ncount  252.000000  252.000000  252.000000  252.000000  252.000000   \nmean   144.473770  145.972778  142.936905  144.547103  143.725583   \nstd     15.571227   15.998560   15.167923   15.644981   16.018049   \nmin    118.629997  119.540001  118.580002  119.279999  117.722450   \n25%    135.760002  136.557499  133.652500  135.532498  134.488182   \n50%    139.724998  140.530006  138.114998  139.120002  138.270546   \n75%    152.417504  155.802502  151.290001  154.409996  153.989399   \nmax    190.649994  190.699997  186.470001  188.699997  188.185989   \n\n             Volume  \ncount  2.520000e+02  \nmean   3.013386e+07  \nstd    1.785521e+07  \nmin    8.989200e+06  \n25%    1.886612e+07  \n50%    2.413585e+07  \n75%    3.306405e+07  \nmax    9.707360e+07  ",
      "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>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>252.000000</td>\n      <td>252.000000</td>\n      <td>252.000000</td>\n      <td>252.000000</td>\n      <td>252.000000</td>\n      <td>2.520000e+02</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>144.473770</td>\n      <td>145.972778</td>\n      <td>142.936905</td>\n      <td>144.547103</td>\n      <td>143.725583</td>\n      <td>3.013386e+07</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>15.571227</td>\n      <td>15.998560</td>\n      <td>15.167923</td>\n      <td>15.644981</td>\n      <td>16.018049</td>\n      <td>1.785521e+07</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>118.629997</td>\n      <td>119.540001</td>\n      <td>118.580002</td>\n      <td>119.279999</td>\n      <td>117.722450</td>\n      <td>8.989200e+06</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>135.760002</td>\n      <td>136.557499</td>\n      <td>133.652500</td>\n      <td>135.532498</td>\n      <td>134.488182</td>\n      <td>1.886612e+07</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>139.724998</td>\n      <td>140.530006</td>\n      <td>138.114998</td>\n      <td>139.120002</td>\n      <td>138.270546</td>\n      <td>2.413585e+07</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>152.417504</td>\n      <td>155.802502</td>\n      <td>151.290001</td>\n      <td>154.409996</td>\n      <td>153.989399</td>\n      <td>3.306405e+07</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>190.649994</td>\n      <td>190.699997</td>\n      <td>186.470001</td>\n      <td>188.699997</td>\n      <td>188.185989</td>\n      <td>9.707360e+07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 528
    }
   ],
   "source": [
    "# DataFrame Infos + Summary Statistics\n",
    "\n",
    "microsoft_data.info()\n",
    "microsoft_data.describe()\n",
    "\n",
    "# Note that the 'Date' column has Dtype == object and not datetime64[ns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0    2019-04-08\n1    2019-04-09\n2    2019-04-11\n3    2019-04-12\n4    2019-04-15\nName: Date, dtype: object"
     },
     "metadata": {},
     "execution_count": 529
    }
   ],
   "source": [
    "# Creating Series \n",
    "\n",
    "open = microsoft_data.loc[0:50, 'Open']\n",
    "date = microsoft_data.loc[0:50, 'Date']\n",
    "volume = microsoft_data.loc[0:50, 'Volume']\n",
    "\n",
    "date.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nStart date: April 08, 2019\n\nEnd date: June 19, 2019\n"
    }
   ],
   "source": [
    "# Converting Series to datetime\n",
    "\n",
    "date = pd.to_datetime(date)\n",
    "date.sort_values(inplace=True) # Note that date was already sorted\n",
    "\n",
    "date.head(), date.tail()\n",
    "\n",
    "start_date = date.iloc[0]\n",
    "start_date = start_date.strftime('%B %d, %Y')\n",
    "\n",
    "end_date = date.iloc[-1]\n",
    "end_date = end_date.strftime('%B %d, %Y')\n",
    "\n",
    "print('\\nStart date: {}'.format(start_date))\n",
    "print('\\nEnd date: {}'.format(end_date))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Plotting Microsoft Data\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "formato = mpl_dates.DateFormatter('%B %d, %Y')\n",
    "plt.gca().xaxis.set_major_formatter(formato)\n",
    "\n",
    "plt.plot(date, open)\n",
    "\n",
    "plt.title('Microsoft: {} - {}'.format(start_date, end_date))\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price (USD)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.tick_params(axis='x', rotation=30)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x432 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Another approach\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "formato = mpl_dates.DateFormatter('%B %d, %Y')\n",
    "plt.gca().xaxis.set_major_formatter(formato)\n",
    "\n",
    "plt.plot_date(date, open)\n",
    "\n",
    "plt.title('Microsoft: {} - {}'.format(start_date, end_date))\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price (USD)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.tick_params(axis='x', rotation=30)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "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
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python_defaultSpec_1595423222734",
   "display_name": "Python 3.8.2 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}