Descption of IrisDataSet (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the iris dataset\n",
    "iris = load_iris() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "# print keys of dataset\n",
    "print(\"Keys:\", iris.keys()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape (150, 4)\n",
      "Target shape (150,)\n"
     ]
    }
   ],
   "source": [
    "# print shape of the data and the target\n",
    "print(\"Data shape\", iris.data.shape) \n",
    "print(\"Target shape\", iris.target.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: [[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]]\n"
     ]
    }
   ],
   "source": [
    "# print 10 first elements \n",
    "print(\"data:\", iris.data[:10]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique targets: [0 1 2]\n"
     ]
    }
   ],
   "source": [
    "# print the unique targets\n",
    "print(\"Unique targets:\", np.unique(iris.target)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bin counts for targets: [50 50 50]\n"
     ]
    }
   ],
   "source": [
    "# print the size of each target\n",
    "print(\"Bin counts for targets:\", np.bincount(iris.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "Target names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "# print feature and target names\n",
    "print(\"Feature names:\", iris.feature_names)\n",
    "print(\"Target names:\", iris.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DESCR gives us details of the data set:\n",
    "print(irisData[\"DESCR\"])"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}