Seaborn Visualization Package.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Relating variables with scatter plots</h2>\n",
    "from https://seaborn.pydata.org/tutorial/relational.html\n",
    "<p>The scatter plot is a mainstay of statistical visualization. It depicts the joint distribution of two variables using a cloud of points, where each point represents an observation in the dataset. This depiction allows the eye to infer a substantial amount of information about whether there is any meaningful relationship between them.</p>\n",
    "\n",
    "<p>There are several ways to draw a scatter plot in seaborn. The most basic, which should be used when both variables are numeric, is the scatterplot() function. In the categorical visualization tutorial, we will see specialized tools for using scatterplots to visualize categorical data. The scatterplot() is the default kind in relplot() (it can also be forced by setting kind=\"scatter\"):</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     total_bill   tip     sex smoker   day    time  size\n",
      "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
      "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
      "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
      "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
      "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
      "5         25.29  4.71    Male     No   Sun  Dinner     4\n",
      "6          8.77  2.00    Male     No   Sun  Dinner     2\n",
      "7         26.88  3.12    Male     No   Sun  Dinner     4\n",
      "8         15.04  1.96    Male     No   Sun  Dinner     2\n",
      "9         14.78  3.23    Male     No   Sun  Dinner     2\n",
      "10        10.27  1.71    Male     No   Sun  Dinner     2\n",
      "11        35.26  5.00  Female     No   Sun  Dinner     4\n",
      "12        15.42  1.57    Male     No   Sun  Dinner     2\n",
      "13        18.43  3.00    Male     No   Sun  Dinner     4\n",
      "14        14.83  3.02  Female     No   Sun  Dinner     2\n",
      "15        21.58  3.92    Male     No   Sun  Dinner     2\n",
      "16        10.33  1.67  Female     No   Sun  Dinner     3\n",
      "17        16.29  3.71    Male     No   Sun  Dinner     3\n",
      "18        16.97  3.50  Female     No   Sun  Dinner     3\n",
      "19        20.65  3.35    Male     No   Sat  Dinner     3\n",
      "20        17.92  4.08    Male     No   Sat  Dinner     2\n",
      "21        20.29  2.75  Female     No   Sat  Dinner     2\n",
      "22        15.77  2.23  Female     No   Sat  Dinner     2\n",
      "23        39.42  7.58    Male     No   Sat  Dinner     4\n",
      "24        19.82  3.18    Male     No   Sat  Dinner     2\n",
      "25        17.81  2.34    Male     No   Sat  Dinner     4\n",
      "26        13.37  2.00    Male     No   Sat  Dinner     2\n",
      "27        12.69  2.00    Male     No   Sat  Dinner     2\n",
      "28        21.70  4.30    Male     No   Sat  Dinner     2\n",
      "29        19.65  3.00  Female     No   Sat  Dinner     2\n",
      "..          ...   ...     ...    ...   ...     ...   ...\n",
      "214       28.17  6.50  Female    Yes   Sat  Dinner     3\n",
      "215       12.90  1.10  Female    Yes   Sat  Dinner     2\n",
      "216       28.15  3.00    Male    Yes   Sat  Dinner     5\n",
      "217       11.59  1.50    Male    Yes   Sat  Dinner     2\n",
      "218        7.74  1.44    Male    Yes   Sat  Dinner     2\n",
      "219       30.14  3.09  Female    Yes   Sat  Dinner     4\n",
      "220       12.16  2.20    Male    Yes   Fri   Lunch     2\n",
      "221       13.42  3.48  Female    Yes   Fri   Lunch     2\n",
      "222        8.58  1.92    Male    Yes   Fri   Lunch     1\n",
      "223       15.98  3.00  Female     No   Fri   Lunch     3\n",
      "224       13.42  1.58    Male    Yes   Fri   Lunch     2\n",
      "225       16.27  2.50  Female    Yes   Fri   Lunch     2\n",
      "226       10.09  2.00  Female    Yes   Fri   Lunch     2\n",
      "227       20.45  3.00    Male     No   Sat  Dinner     4\n",
      "228       13.28  2.72    Male     No   Sat  Dinner     2\n",
      "229       22.12  2.88  Female    Yes   Sat  Dinner     2\n",
      "230       24.01  2.00    Male    Yes   Sat  Dinner     4\n",
      "231       15.69  3.00    Male    Yes   Sat  Dinner     3\n",
      "232       11.61  3.39    Male     No   Sat  Dinner     2\n",
      "233       10.77  1.47    Male     No   Sat  Dinner     2\n",
      "234       15.53  3.00    Male    Yes   Sat  Dinner     2\n",
      "235       10.07  1.25    Male     No   Sat  Dinner     2\n",
      "236       12.60  1.00    Male    Yes   Sat  Dinner     2\n",
      "237       32.83  1.17    Male    Yes   Sat  Dinner     2\n",
      "238       35.83  4.67  Female     No   Sat  Dinner     3\n",
      "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
      "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
      "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
      "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
      "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
      "\n",
      "[244 rows x 7 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print (tips)\n",
    "print (type(tips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"total_bill\", y=\"tip\", data=tips);"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}