{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter Plot\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# primary data structure library\n",
    "import pandas as pd \n",
    "\n",
    "# primary plotting library\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# using the inline backend\n",
    "%matplotlib inline\n",
    "\n",
    "# optional: for ggplot-like style of plots\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a dummy data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2   3   4   5   6   7   8   9   ...  20  21  22  23  24  25  26  \\\n",
       "x   1   2   3   4   5   6   7   8   9  10  ...  21  22  23  24  25  26  27   \n",
       "y   6   5   6   7   8   7  10   5   2   3  ...   1   5   1   9   8   7   4   \n",
       "\n",
       "   27  28  29  \n",
       "x  28  29  30  \n",
       "y   3   7   6  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        'x':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\n",
    "        'y':[6,5,6,7,8,7,10,5,2,3,4,6,1,4,1,2,4,3,2,2,1,5,1,9,8,7,4,3,7,6]\n",
    "    }\n",
    ")\n",
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting the scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='x', y='y', figsize=(8, 5), alpha=0.8, color='blue')\n",
    "plt.title('Title of the plot')\n",
    "plt.xlabel('Label of x axis')\n",
    "plt.ylabel('Label of y axis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thanks for reading :)\n",
    "Created by [Tarun kamboj](https://www.linkedin.com/in/kambojtarun/)"
   ]
  }
 ],
 "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": "3.7.6"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}