data analysis-boxplot elements-for github.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20fce00f",
   "metadata": {},
   "source": [
    "### Data Analysis\n",
    "#### Boxplot elements\n",
    "<hr>\n",
    "https://github.com/ostad-ai/Machine-Learning\n",
    "<br> Explanation: https://www.pinterest.com/HamedShahHosseini/Machine-Learning/Data-Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eed068e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn import datasets\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a89242e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the dataset\n",
    "iris=datasets.load_iris()\n",
    "data=iris.data\n",
    "feature0=data[:,0] #a column of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "fd9e8f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing elements of boxplot for one column (feature) of dataset\n",
    "maxf,minf,medianf=feature0.max(),feature0.min(),np.median(feature0)\n",
    "mediantf,medianbf=np.median(feature0[np.where(feature0>medianf)]),\\\n",
    "np.median(feature0[np.where(feature0<medianf)])\n",
    "plt.boxplot(feature0)\n",
    "plt.text(1.05,maxf,f'maximum:{maxf}')\n",
    "plt.text(1.05,minf,f'minimum:{minf}')\n",
    "plt.text(1.1,medianf,f'median:{medianf}')\n",
    "plt.text(1.1,mediantf,f'third quartile:{mediantf}')\n",
    "plt.text(1.1,medianbf,f'first quartile:{medianbf}')\n",
    "plt.suptitle(f'Boxplot elements')\n",
    "plt.title(f'IQR=$third\\; quartile-first\\; quartile$={mediantf-medianbf:.2f}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "52b15dd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting all features of the dataset with boxplots\n",
    "# each column of the dataset is a feature\n",
    "# small dots are outliers\n",
    "plt.boxplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a72defc7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}