{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what type the data set is?\n",
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[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",
      "       [5.4, 3.7, 1.5, 0.2],\n",
      "       [4.8, 3.4, 1.6, 0.2],\n",
      "       [4.8, 3. , 1.4, 0.1],\n",
      "       [4.3, 3. , 1.1, 0.1],\n",
      "       [5.8, 4. , 1.2, 0.2],\n",
      "       [5.7, 4.4, 1.5, 0.4],\n",
      "       [5.4, 3.9, 1.3, 0.4],\n",
      "       [5.1, 3.5, 1.4, 0.3],\n",
      "       [5.7, 3.8, 1.7, 0.3],\n",
      "       [5.1, 3.8, 1.5, 0.3],\n",
      "       [5.4, 3.4, 1.7, 0.2],\n",
      "       [5.1, 3.7, 1.5, 0.4],\n",
      "       [4.6, 3.6, 1. , 0.2],\n",
      "       [5.1, 3.3, 1.7, 0.5],\n",
      "       [4.8, 3.4, 1.9, 0.2],\n",
      "       [5. , 3. , 1.6, 0.2],\n",
      "       [5. , 3.4, 1.6, 0.4],\n",
      "       [5.2, 3.5, 1.5, 0.2],\n",
      "       [5.2, 3.4, 1.4, 0.2],\n",
      "       [4.7, 3.2, 1.6, 0.2],\n",
      "       [4.8, 3.1, 1.6, 0.2],\n",
      "       [5.4, 3.4, 1.5, 0.4],\n",
      "       [5.2, 4.1, 1.5, 0.1],\n",
      "       [5.5, 4.2, 1.4, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.1],\n",
      "       [5. , 3.2, 1.2, 0.2],\n",
      "       [5.5, 3.5, 1.3, 0.2],\n",
      "       [4.9, 3.1, 1.5, 0.1],\n",
      "       [4.4, 3. , 1.3, 0.2],\n",
      "       [5.1, 3.4, 1.5, 0.2],\n",
      "       [5. , 3.5, 1.3, 0.3],\n",
      "       [4.5, 2.3, 1.3, 0.3],\n",
      "       [4.4, 3.2, 1.3, 0.2],\n",
      "       [5. , 3.5, 1.6, 0.6],\n",
      "       [5.1, 3.8, 1.9, 0.4],\n",
      "       [4.8, 3. , 1.4, 0.3],\n",
      "       [5.1, 3.8, 1.6, 0.2],\n",
      "       [4.6, 3.2, 1.4, 0.2],\n",
      "       [5.3, 3.7, 1.5, 0.2],\n",
      "       [5. , 3.3, 1.4, 0.2],\n",
      "       [7. , 3.2, 4.7, 1.4],\n",
      "       [6.4, 3.2, 4.5, 1.5],\n",
      "       [6.9, 3.1, 4.9, 1.5],\n",
      "       [5.5, 2.3, 4. , 1.3],\n",
      "       [6.5, 2.8, 4.6, 1.5],\n",
      "       [5.7, 2.8, 4.5, 1.3],\n",
      "       [6.3, 3.3, 4.7, 1.6],\n",
      "       [4.9, 2.4, 3.3, 1. ],\n",
      "       [6.6, 2.9, 4.6, 1.3],\n",
      "       [5.2, 2.7, 3.9, 1.4],\n",
      "       [5. , 2. , 3.5, 1. ],\n",
      "       [5.9, 3. , 4.2, 1.5],\n",
      "       [6. , 2.2, 4. , 1. ],\n",
      "       [6.1, 2.9, 4.7, 1.4],\n",
      "       [5.6, 2.9, 3.6, 1.3],\n",
      "       [6.7, 3.1, 4.4, 1.4],\n",
      "       [5.6, 3. , 4.5, 1.5],\n",
      "       [5.8, 2.7, 4.1, 1. ],\n",
      "       [6.2, 2.2, 4.5, 1.5],\n",
      "       [5.6, 2.5, 3.9, 1.1],\n",
      "       [5.9, 3.2, 4.8, 1.8],\n",
      "       [6.1, 2.8, 4. , 1.3],\n",
      "       [6.3, 2.5, 4.9, 1.5],\n",
      "       [6.1, 2.8, 4.7, 1.2],\n",
      "       [6.4, 2.9, 4.3, 1.3],\n",
      "       [6.6, 3. , 4.4, 1.4],\n",
      "       [6.8, 2.8, 4.8, 1.4],\n",
      "       [6.7, 3. , 5. , 1.7],\n",
      "       [6. , 2.9, 4.5, 1.5],\n",
      "       [5.7, 2.6, 3.5, 1. ],\n",
      "       [5.5, 2.4, 3.8, 1.1],\n",
      "       [5.5, 2.4, 3.7, 1. ],\n",
      "       [5.8, 2.7, 3.9, 1.2],\n",
      "       [6. , 2.7, 5.1, 1.6],\n",
      "       [5.4, 3. , 4.5, 1.5],\n",
      "       [6. , 3.4, 4.5, 1.6],\n",
      "       [6.7, 3.1, 4.7, 1.5],\n",
      "       [6.3, 2.3, 4.4, 1.3],\n",
      "       [5.6, 3. , 4.1, 1.3],\n",
      "       [5.5, 2.5, 4. , 1.3],\n",
      "       [5.5, 2.6, 4.4, 1.2],\n",
      "       [6.1, 3. , 4.6, 1.4],\n",
      "       [5.8, 2.6, 4. , 1.2],\n",
      "       [5. , 2.3, 3.3, 1. ],\n",
      "       [5.6, 2.7, 4.2, 1.3],\n",
      "       [5.7, 3. , 4.2, 1.2],\n",
      "       [5.7, 2.9, 4.2, 1.3],\n",
      "       [6.2, 2.9, 4.3, 1.3],\n",
      "       [5.1, 2.5, 3. , 1.1],\n",
      "       [5.7, 2.8, 4.1, 1.3],\n",
      "       [6.3, 3.3, 6. , 2.5],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [7.1, 3. , 5.9, 2.1],\n",
      "       [6.3, 2.9, 5.6, 1.8],\n",
      "       [6.5, 3. , 5.8, 2.2],\n",
      "       [7.6, 3. , 6.6, 2.1],\n",
      "       [4.9, 2.5, 4.5, 1.7],\n",
      "       [7.3, 2.9, 6.3, 1.8],\n",
      "       [6.7, 2.5, 5.8, 1.8],\n",
      "       [7.2, 3.6, 6.1, 2.5],\n",
      "       [6.5, 3.2, 5.1, 2. ],\n",
      "       [6.4, 2.7, 5.3, 1.9],\n",
      "       [6.8, 3. , 5.5, 2.1],\n",
      "       [5.7, 2.5, 5. , 2. ],\n",
      "       [5.8, 2.8, 5.1, 2.4],\n",
      "       [6.4, 3.2, 5.3, 2.3],\n",
      "       [6.5, 3. , 5.5, 1.8],\n",
      "       [7.7, 3.8, 6.7, 2.2],\n",
      "       [7.7, 2.6, 6.9, 2.3],\n",
      "       [6. , 2.2, 5. , 1.5],\n",
      "       [6.9, 3.2, 5.7, 2.3],\n",
      "       [5.6, 2.8, 4.9, 2. ],\n",
      "       [7.7, 2.8, 6.7, 2. ],\n",
      "       [6.3, 2.7, 4.9, 1.8],\n",
      "       [6.7, 3.3, 5.7, 2.1],\n",
      "       [7.2, 3.2, 6. , 1.8],\n",
      "       [6.2, 2.8, 4.8, 1.8],\n",
      "       [6.1, 3. , 4.9, 1.8],\n",
      "       [6.4, 2.8, 5.6, 2.1],\n",
      "       [7.2, 3. , 5.8, 1.6],\n",
      "       [7.4, 2.8, 6.1, 1.9],\n",
      "       [7.9, 3.8, 6.4, 2. ],\n",
      "       [6.4, 2.8, 5.6, 2.2],\n",
      "       [6.3, 2.8, 5.1, 1.5],\n",
      "       [6.1, 2.6, 5.6, 1.4],\n",
      "       [7.7, 3. , 6.1, 2.3],\n",
      "       [6.3, 3.4, 5.6, 2.4],\n",
      "       [6.4, 3.1, 5.5, 1.8],\n",
      "       [6. , 3. , 4.8, 1.8],\n",
      "       [6.9, 3.1, 5.4, 2.1],\n",
      "       [6.7, 3.1, 5.6, 2.4],\n",
      "       [6.9, 3.1, 5.1, 2.3],\n",
      "       [5.8, 2.7, 5.1, 1.9],\n",
      "       [6.8, 3.2, 5.9, 2.3],\n",
      "       [6.7, 3.3, 5.7, 2.5],\n",
      "       [6.7, 3. , 5.2, 2.3],\n",
      "       [6.3, 2.5, 5. , 1.9],\n",
      "       [6.5, 3. , 5.2, 2. ],\n",
      "       [6.2, 3.4, 5.4, 2.3],\n",
      "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': 'Iris Plants Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']}\n"
     ]
    }
   ],
   "source": [
    "print(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(iris['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,\n",
       "       4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,\n",
       "       5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,\n",
       "       5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,\n",
       "       6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,\n",
       "       6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,\n",
       "       6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,\n",
       "       6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,\n",
       "       6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,\n",
       "       7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,\n",
       "       7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,\n",
       "       6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.9, 3. , 1.4, 0.2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = pl.hist(iris.data[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = pl.hist(iris.data[iris.target==0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qauAfgVcDB4CDwJv7j/o0vRw+miDzLY35Dt8sZwPzLVU/h86rnnZoXpL0Y853+EpSgyx/SWrQTJZ/km8kuSPJ7vlOazrU5SSSvDHJfw5vb5xSvjcMc+1JcluSM7quu0z5XpHku8PHdye5cs5jFyb5yvC1/eMpZHvXnFxfSvJEkuO7rNtTvjVJNiX58vCSJi8deXzac29cvmnPvXH5pjb3Ouab2vxL8oI5z707yYNJLh0Z09/8q6qZuwHfAE44xOOvBm5i8P6Cs4Htw+XHA18b/nnc8P5xU8j3y089L4NLX2zvuu4y5XsFcOM8y1cAXwV+BngmcDvwC8uZbWTsa4DPLfNr93HgrcP7zwTWzNjcG5dv2nNvXL6pzb0u+aY9/0Zej/8Gnj+p+TeTe/4dLHQ5iV8DtlbVd6rqfmArcOFyh6uq24bPD7CNwfse/j84CzhQVV+rqu8Dn2TwWk/LbwJ/s1xPluRY4FwGpzZTVd+vqgdGhk1t7nXJN8251/H1W8jE595h5FvW+TfifOCrVfVfI8t7m3+zWv4F/HOSnRlcEmLUQpeTWGj5cueb6y0MvlMfzrqTzPfSJLcnuSnJLw6XLcfr1+nvn2QVg8l7/WLXXYKfAf4X+KskX0xyTZKjR8ZMc+51yTfXcs+9rvmmNfc6v35Tmn9zvZ75v/H0Nv9mtfzPqaoXM/ix9e1Jzh15fKHLSXS6zEQPxuUDIMmvMvgPePli151wvl0Mfpw8A/gL4O+eijzPtvp+/br+/V8D/FtVfecw1j1cRwAvBj5aVWuBR4DRY8/TnHtd8gFTm3td8k1z7nV+/ZjO/AMgyTOBi4BPz/fwPMsOa/7NZPlX1T3DP+8DtjD4kXCuhS4nsSyXmeiQjyQvAq4BXltV317MupPOV1UPVtXDw/v/CKxMcgLL8Pot4u//tD2fZXjt7gburqqnLly4iUFZjI6Z1tzrkm+ac29svmnOvS755pjG/HvKq4BdVfU/8zzW2/ybufJPcnQGnxvA8EeyVwJfGhm20OUkPgO8MslxSY4brvuZ5c6X5FRgM3BJVd25yL/bcuR7TpIM75/FYB58G/gP4GeT/PRw7+P1DF7rZcs2fOzZwMuBv1/suktRVf8N3JXkqasmng/sGxk2tbnXJd80517HfFOZe13zDXNNZf7NcajfNfQ3/yb12+rDvTE4Lnf78LYXePdw+duAtw3vB/gwg7MD7gDWzVn/dxlcZuIA8OYp5bsGuB/YPbztONS6U8j3e8PHbmfwS8FfnrP+q4E7h69tr/m6ZBt+/Sbgk13WncDrdyawA9jD4JDEcbMy9zrmm9rc65hvKnOva74ZmH+rGHwzfPacZROZf17eQZIaNHOHfSRJk2f5S1KDLH9JapDlL0kNsvwlqUGWvyQ1yPKXpAb9HzW5ySLUC9xHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2 variable:\n",
    "_ = pl.hist2d(iris.data[iris.target==1,0], iris.data[iris.target==1,1])"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}