Scatterplot_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f9584e02b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "data = 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Galton.csv'\n",
    "df = pd.read_csv(data, index_col=0)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.scatterplot(x='parent', y='child', data=df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(64, 73, 'r=0.46, p=0.0')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "corr = pearsonr(df['parent'], df['child'])\n",
    "corr = [np.round(c, 2) for c in corr]\n",
    "\n",
    "\n",
    "\n",
    "text = 'r=%s, p=%s' % (corr[0], corr[1])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.regplot(x='parent', y='child', data=df)\n",
    "ax.text(64, 73, text, fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}