(3)_Matplotlib_scatter_plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib - Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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  ",
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     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "# Importing Microsoft 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": 6
    }
   ],
   "source": [
    "# Summary Statistics \n",
    "\n",
    "microsoft_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Series\n",
    "\n",
    "date = microsoft_data.loc[0:15, 'Date']\n",
    "open = microsoft_data.loc[0:15, 'Open']\n",
    "volume = microsoft_data.loc[0:15, 'Volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Scatter PLot - V1 (Colorbar)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(date, open, c=volume, cmap='summer', label='Volume')\n",
    "\n",
    "plt.title('Microsoft Data - Scatter Plot - Colorbar')\n",
    "plt.ylabel('Price (USD)')\n",
    "plt.xlabel('Date')\n",
    "plt.tick_params(axis='x', rotation=70)\n",
    "\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Volume')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "# Scatter Plot - V2 (Scalar)\n",
    "\n",
    "# Normalizing volume\n",
    "volume = volume/volume[0]\n",
    "\n",
    "# Scatter Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(date, open, s=volume*30, label='Volume')\n",
    "\n",
    "plt.title('Microsoft Data - Scatter Plot - Scalar')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price (USD)')\n",
    "plt.tick_params(axis='x', rotation=70)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}