Decision tree  Learning Pandas  Learning Covariance for categorical vars PCA MCA Regular Simplex Method.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#raw CSV data from generatedata.com\n",
    "\n",
    "age,occupation,credit rating,label\n",
    "middle_aged,student,excellent,sell\n",
    "senior,worker,fair,sell\n",
    "youth,worker,excellent,sell\n",
    "middle_aged,worker,fair,dont_sell\n",
    "youth,worker,fair,sell\n",
    "senior,worker,fair,sell\n",
    "youth,worker,fair,sell\n",
    "middle_aged,student,excellent,dont_sell\n",
    "youth,student,fair,sell\n",
    "youth,worker,excellent,sell\n",
    "youth,student,fair,sell\n",
    "senior,student,excellent,dont_sell\n",
    "senior,worker,fair,sell\n",
    "youth,student,excellent,dont_sell\n",
    "senior,student,fair,sell\n",
    "senior,worker,fair,sell\n",
    "middle_aged,student,fair,sell\n",
    "youth,worker,fair,sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "youth,student,fair,dont_sell\n",
    "senior,student,excellent,dont_sell\n",
    "youth,student,fair,dont_sell\n",
    "youth,worker,excellent,dont_sell\n",
    "youth,student,excellent,dont_sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "middle_aged,worker,fair,sell\n",
    "middle_aged,student,excellent,dont_sell\n",
    "senior,student,excellent,dont_sell\n",
    "senior,worker,excellent,sell\n",
    "senior,worker,excellent,sell\n",
    "middle_aged,worker,fair,sell\n",
    "senior,worker,fair,dont_sell\n",
    "youth,worker,fair,sell\n",
    "youth,worker,excellent,sell\n",
    "senior,worker,fair,sell\n",
    "middle_aged,student,fair,sell\n",
    "youth,student,excellent,dont_sell\n",
    "youth,student,fair,dont_sell\n",
    "youth,student,fair,dont_sell\n",
    "senior,student,fair,dont_sell\n",
    "senior,student,fair,dont_sell\n",
    "middle_aged,student,excellent,sell\n",
    "senior,worker,excellent,sell\n",
    "youth,student,excellent,dont_sell\n",
    "youth,student,fair,sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "senior,student,fair,dont_sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "youth,worker,fair,sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "youth,worker,fair,dont_sell\n",
    "youth,student,fair,sell\n",
    "middle_aged,worker,fair,dont_sell\n",
    "youth,worker,fair,dont_sell\n",
    "middle_aged,worker,excellent,dont_sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "senior,student,excellent,sell\n",
    "middle_aged,worker,fair,dont_sell\n",
    "senior,worker,excellent,dont_sell\n",
    "youth,worker,fair,sell\n",
    "senior,student,fair,dont_sell\n",
    "senior,student,fair,dont_sell\n",
    "middle_aged,worker,fair,sell\n",
    "youth,student,excellent,dont_sell\n",
    "middle_aged,worker,fair,sell\n",
    "middle_aged,worker,excellent,sell\n",
    "youth,student,fair,dont_sell\n",
    "youth,student,excellent,sell\n",
    "senior,worker,fair,dont_sell\n",
    "senior,worker,fair,sell\n",
    "youth,student,excellent,dont_sell\n",
    "youth,worker,fair,dont_sell\n",
    "middle_aged,student,excellent,sell\n",
    "middle_aged,student,excellent,sell\n",
    "youth,worker,fair,dont_sell\n",
    "senior,worker,excellent,sell\n",
    "youth,worker,fair,dont_sell\n",
    "senior,worker,fair,sell\n",
    "senior,student,fair,sell\n",
    "middle_aged,worker,excellent,dont_sell\n",
    "senior,student,excellent,dont_sell\n",
    "youth,student,excellent,dont_sell\n",
    "middle_aged,student,fair,sell\n",
    "middle_aged,worker,excellent,sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "youth,student,fair,dont_sell\n",
    "youth,worker,excellent,dont_sell\n",
    "senior,student,excellent,dont_sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "senior,worker,fair,dont_sell\n",
    "middle_aged,student,fair,dont_sell\n",
    "senior,worker,fair,dont_sell\n",
    "senior,worker,excellent,dont_sell\n",
    "senior,worker,fair,sell\n",
    "youth,worker,excellent,sell\n",
    "youth,student,fair,dont_sell\n",
    "senior,student,excellent,sell\n",
    "senior,worker,excellent,dont_sell\n",
    "senior,worker,fair,dont_sell\n",
    "senior,worker,fair,sell\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert raw CSV data into a list of lists\n",
    "import csv\n",
    "data = []\n",
    "with open('TutData.txt', newline='') as inputfile:\n",
    "    for row in csv.reader(inputfile):\n",
    "        data.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['age', 'occupation', 'credit rating', 'label'],\n",
       " ['middle_aged', 'student', 'excellent', 'sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['youth', 'worker', 'excellent', 'sell'],\n",
       " ['middle_aged', 'worker', 'fair', 'dont_sell'],\n",
       " ['youth', 'worker', 'fair', 'sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['youth', 'worker', 'fair', 'sell'],\n",
       " ['middle_aged', 'student', 'excellent', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'sell'],\n",
       " ['youth', 'worker', 'excellent', 'sell'],\n",
       " ['youth', 'student', 'fair', 'sell'],\n",
       " ['senior', 'student', 'excellent', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['youth', 'student', 'excellent', 'dont_sell'],\n",
       " ['senior', 'student', 'fair', 'sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['middle_aged', 'student', 'fair', 'sell'],\n",
       " ['youth', 'worker', 'fair', 'sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'student', 'excellent', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'worker', 'excellent', 'dont_sell'],\n",
       " ['youth', 'student', 'excellent', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['middle_aged', 'worker', 'fair', 'sell'],\n",
       " ['middle_aged', 'student', 'excellent', 'dont_sell'],\n",
       " ['senior', 'student', 'excellent', 'dont_sell'],\n",
       " ['senior', 'worker', 'excellent', 'sell'],\n",
       " ['senior', 'worker', 'excellent', 'sell'],\n",
       " ['middle_aged', 'worker', 'fair', 'sell'],\n",
       " ['senior', 'worker', 'fair', 'dont_sell'],\n",
       " ['youth', 'worker', 'fair', 'sell'],\n",
       " ['youth', 'worker', 'excellent', 'sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['middle_aged', 'student', 'fair', 'sell'],\n",
       " ['youth', 'student', 'excellent', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'student', 'fair', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'excellent', 'sell'],\n",
       " ['senior', 'worker', 'excellent', 'sell'],\n",
       " ['youth', 'student', 'excellent', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'student', 'fair', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'worker', 'fair', 'sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'worker', 'fair', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'sell'],\n",
       " ['middle_aged', 'worker', 'fair', 'dont_sell'],\n",
       " ['youth', 'worker', 'fair', 'dont_sell'],\n",
       " ['middle_aged', 'worker', 'excellent', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'student', 'excellent', 'sell'],\n",
       " ['middle_aged', 'worker', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'excellent', 'dont_sell'],\n",
       " ['youth', 'worker', 'fair', 'sell'],\n",
       " ['senior', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'student', 'fair', 'dont_sell'],\n",
       " ['middle_aged', 'worker', 'fair', 'sell'],\n",
       " ['youth', 'student', 'excellent', 'dont_sell'],\n",
       " ['middle_aged', 'worker', 'fair', 'sell'],\n",
       " ['middle_aged', 'worker', 'excellent', 'sell'],\n",
       " ['youth', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'student', 'excellent', 'sell'],\n",
       " ['senior', 'worker', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['youth', 'student', 'excellent', 'dont_sell'],\n",
       " ['youth', 'worker', 'fair', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'excellent', 'sell'],\n",
       " ['middle_aged', 'student', 'excellent', 'sell'],\n",
       " ['youth', 'worker', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'excellent', 'sell'],\n",
       " ['youth', 'worker', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['senior', 'student', 'fair', 'sell'],\n",
       " ['middle_aged', 'worker', 'excellent', 'dont_sell'],\n",
       " ['senior', 'student', 'excellent', 'dont_sell'],\n",
       " ['youth', 'student', 'excellent', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'fair', 'sell'],\n",
       " ['middle_aged', 'worker', 'excellent', 'sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'student', 'fair', 'dont_sell'],\n",
       " ['youth', 'worker', 'excellent', 'dont_sell'],\n",
       " ['senior', 'student', 'excellent', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'dont_sell'],\n",
       " ['middle_aged', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'excellent', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'sell'],\n",
       " ['youth', 'worker', 'excellent', 'sell'],\n",
       " ['youth', 'student', 'fair', 'dont_sell'],\n",
       " ['senior', 'student', 'excellent', 'sell'],\n",
       " ['senior', 'worker', 'excellent', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'dont_sell'],\n",
       " ['senior', 'worker', 'fair', 'sell']]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Column labels.\n",
    "# These are used only to print the tree.\n",
    "header = [\"age\", \"occupation\", \"credit rating\", \"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique_vals(rows, col):\n",
    "    \"\"\"Find the unique values for a column in a dataset.\"\"\"\n",
    "    return set([row[col] for row in rows])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age', 'middle_aged', 'senior', 'youth'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#######\n",
    "# Demo:\n",
    "unique_vals(data,0)\n",
    "# unique_vals(training_data, 1)\n",
    "#######"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_counts(rows):\n",
    "    \"\"\"Counts the number of each type of example in a dataset.\"\"\"\n",
    "    counts = {}  # a dictionary of label -> count.\n",
    "    rows = rows[1:] # don't cont the column heading as a label type\n",
    "    for row in rows:\n",
    "        # in our dataset format, the label is always the last column\n",
    "        label = row[-1]\n",
    "        if label not in counts:\n",
    "            counts[label] = 0\n",
    "        counts[label] += 1\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dont_sell': 55, 'sell': 45}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#######\n",
    "# Demo:\n",
    "class_counts(data)\n",
    "#######"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this to draw LaTeX http://webdemo.myscript.com/views/math.html#\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\int ^{\\infty }_{0}\\sum ^{n}_{i}=i^{\\infty }_{n}\\gamma ^{i}\\rightarrow \\infty \\dfrac {n!}{\\left( n-r\\right) \\times n!}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       e\n",
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       "29      b\n",
       "       ..\n",
       "7704    b\n",
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       "8095    c\n",
       "8114    c\n",
       "Name: stalk-root, Length: 5644, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import parallel_coordinates\n",
    "\n",
    "data = pd.read_csv('agaricus-lepiota.data', names =[\"class\",\"cap-shape\",\"cap-surface\",\"cap-colour\",\"bruises?\",\n",
    "                                                    \"odor\",\"gill-attachment\",\"gill-spacing\",\"gill-size\",\n",
    "                                                    \"gill-colour\",\"stalk-shape\",\"stalk-root\",\"stalk-surface-above-ring\",\n",
    "                                                    \"stalk-surface-below-ring\",\"stalk-colour-above-ring\",\n",
    "                                                    \"stalk-colour-below-ring\",\"veil-type\",\"veil-colour\",\n",
    "                                                    \"ring-number\",\"ring-type\",\"spore-print-colour\",\"population\",\n",
    "                                                    \"habitat\"],sep=',', na_values=[\"?\"])\n",
    "dframe = pd.DataFrame(data)\n",
    "\n",
    "#dframe\n",
    "\n",
    "#get all unique feature labels and combine them for axis labeling purposes\n",
    "#uq = np.unique(dframe[:].values)\n",
    "\n",
    "#Clean data (remove entries with '?')\n",
    "dframe.dropna(inplace=True)\n",
    "# equivalently:\n",
    "# dframe = dframe.dropna()\n",
    "dframe['stalk-root']\n",
    "# dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
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       "      <th>bruises?</th>\n",
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       "      <th>gill-attachment</th>\n",
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       "      <th>spore-print-colour</th>\n",
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       "      <th>habitat</th>\n",
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       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4208</td>\n",
       "      <td>3656</td>\n",
       "      <td>3244</td>\n",
       "      <td>2284</td>\n",
       "      <td>4748</td>\n",
       "      <td>3528</td>\n",
       "      <td>7914</td>\n",
       "      <td>6812</td>\n",
       "      <td>5612</td>\n",
       "      <td>1728</td>\n",
       "      <td>...</td>\n",
       "      <td>4936</td>\n",
       "      <td>4464</td>\n",
       "      <td>4384</td>\n",
       "      <td>8124</td>\n",
       "      <td>7924</td>\n",
       "      <td>7488</td>\n",
       "      <td>3968</td>\n",
       "      <td>2388</td>\n",
       "      <td>4040</td>\n",
       "      <td>3148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class cap-shape cap-surface cap-colour bruises?  odor gill-attachment  \\\n",
       "count   8124      8124        8124       8124     8124  8124            8124   \n",
       "unique     2         6           4         10        2     9               2   \n",
       "top        e         x           y          n        f     n               f   \n",
       "freq    4208      3656        3244       2284     4748  3528            7914   \n",
       "\n",
       "       gill-spacing gill-size gill-colour   ...   stalk-surface-below-ring  \\\n",
       "count          8124      8124        8124   ...                       8124   \n",
       "unique            2         2          12   ...                          4   \n",
       "top               c         b           b   ...                          s   \n",
       "freq           6812      5612        1728   ...                       4936   \n",
       "\n",
       "       stalk-colour-above-ring stalk-colour-below-ring veil-type veil-colour  \\\n",
       "count                     8124                    8124      8124        8124   \n",
       "unique                       9                       9         1           4   \n",
       "top                          w                       w         p           w   \n",
       "freq                      4464                    4384      8124        7924   \n",
       "\n",
       "       ring-number ring-type spore-print-colour population habitat  \n",
       "count         8124      8124               8124       8124    8124  \n",
       "unique           3         5                  9          6       7  \n",
       "top              o         p                  w          v       d  \n",
       "freq          7488      3968               2388       4040    3148  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "parallel_coordinates(data,'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', '', '', 'd', '']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare arrays and replaceelements not in intersection with ''\n",
    "all_labels = ['a','b','c','d','e']\n",
    "feature_x_labels=['a','d']\n",
    "for index,i in enumerate(all_labels):\n",
    "    if i not in feature_x_labels:\n",
    "        all_labels[index]=''\n",
    "    \n",
    "all_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-5-a7e78b8e7545>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-a7e78b8e7545>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    cont_table = pd.pivot_table(dframe,values=[:],index=['stalk-surface-below-ring'],columns=['class'])\u001b[0m\n\u001b[0m                                               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#contingency tables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DhkoaIukoGhaKV7S/7PKwf/9+1q5dyxVXXJF3KWZmn9DiCEDS/cA4oFJSPXAjME7SGCCAOuA/ACJis6TlNCzu7gPmRMTHWT/XAE8AFcDiiNjc6a+mC3nppZeYNGkSF110EUOHDs27HDOzT2jNWUCXHKT5V4fZ/2bg5oO0PwY81qbqytjw4cN59dVX8y7DzOyQyvKbwBGRdwklk9JrNbPSKrsA6NWrF7t27UrijTEi2LVrF7169cq7FDPrhsruYnCDBg2ivr6enTt35l1KSfTq1YtBgwblXYaZdUNlFwA9evRgyJAheZdhZlb2ym4KyMzMOocDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS1SLASBpsaS3JL1Y0Ha8pJWStma3/bJ2SZovaZukjZJOKzhmRrb/VkkzivNyzMystVozArgbmHhA21xgVUQMBVZljwHOB4ZmP7OBhdAQGMCNwJnAGcCNjaFhZmb5aDEAIuJpYPcBzZOBJdn9JcCUgval0WAt0FfSQGACsDIidkfEO8BKPhkqZmZWQu1dAzgxIt7I7r8JnJjdPwl4rWC/+qztUO2fIGm2pFpJtTt37mxneWZm1pIOLwJHRADRCbU09rcoImoiombAgAGd1a2ZmR2gvQGwI5vaIbt9K2vfDgwu2G9Q1naodjMzy0l7A2AF0HgmzwzgNwXtl2VnA40F9mRTRU8A50nqly3+npe1mZlZTo5saQdJ9wPjgEpJ9TSczXMLsFzSFcBfga9muz8GXABsAz4AvgEQEbsl/QRYl+13U0QcuLBsZa5Xv2/nXYKZtUGLARARlxxi05cOsm8Acw7Rz2JgcZuqMzOzovE3gc3MEuUAMDNLlAPAzCxRDgAzs0Q5AMzMEuUAMDNLlAPAzCxRDgAzs0Q5AMzMEuUAMDNLVIuXgjAzK4Xxqw96FZlOsqWIfZcvjwDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLVoQCQVCdpk6QXJNVmbcdLWilpa3bbL2uXpPmStknaKOm0zngBZmbWPp0xAjg3IsZERE32eC6wKiKGAquyxwDnA0Ozn9nAwk54bjMza6diTAFNBpZk95cAUwral0aDtUBfSQOL8PxmZtYKHQ2AAP5L0npJs7O2EyPijez+m8CJ2f2TgNcKjq3P2pqRNFtSraTanTt3drA8MzM7lCM7ePwXI2K7pBOAlZJeLtwYESEp2tJhRCwCFgHU1NS06VgzM2u9Do0AImJ7dvsW8AhwBrCjcWonu30r2307MLjg8EFZm5mZ5aDdASDpaEnHNt4HzgNeBFYAM7LdZgC/ye6vAC7LzgYaC+wpmCoyM7MS68gU0InAI5Ia+7kvIh6XtA5YLukK4K/AV7P9HwMuALYBHwDf6MBzm5lZB7U7ACLiVWD0Qdp3AV86SHsAc9r7fGZm1rn8TWAzs0R19CygLuXWaZOK2v/1y35b1P4tX/79ydewi1/Pu4TkeARgZpaobjUCMLPyVbX3vqL1XVe0nsubRwBmZonqViOAXv2+nXcJVsb8+2Op8QjAzCxRDgAzs0R1qymg8auL/T2zLUXu3/Lk3x9LjUcAZmaJcgCYmSXKAWBmlqhutQZgZpaHBVc9WdT+59w5vij9egRgZpYoB4CZWaIcAGZmiXIAmJklyovAXUi5LiSZWXnyCMDMLFEOADOzRHkKyDqNr6VjVl48AjAzS1S3GgF89QfFfTmbitq75c2/P9Ze5Tr69QjAzCxR3WoEUO7K9VOEmZUnjwDMzBLlADAzS5QDwMwsUSVfA5A0EbgDqADuiohbSl2DWbc0r0+R+99T3P6t5Eo6ApBUASwAzgeGA5dIGl7KGszMrEGpp4DOALZFxKsR8Q/gAWByiWswMzNAEVG6J5O+AkyMiCuzx9OBMyPimoJ9ZgOzs4enAK8UsaRK4O0i9l9srj9frj9f5Vx/sWv/TEQMaGmnLvc9gIhYBCwqxXNJqo2ImlI8VzG4/ny5/nyVc/1dpfZSTwFtBwYXPB6UtZmZWYmVOgDWAUMlDZF0FHAxsKLENZiZGSWeAoqIfZKuAZ6g4TTQxRGxuZQ1HKAkU01F5Prz5frzVc71d4naS7oIbGZmXYe/CWxmligHgJlZohwAZmaJ6nLfAygFSf2AoUCvxraIeDq/iro3SfdExHRJ10bEHXnXY5YHSb2Aq4EvAgE8CyyMiL251ZTaIrCkK4FrafgOwgvAWOC5iBifa2GtJGkJcG1E/L/scT/g1oiYmW9lhybpJeDfgN8B4wAVbo+I3TmUlSRJNcANwGdo+AAoICJiVK6FtYKknsCXgSoKPrxGxE151dQWkpYD7wH/mTV9DegbEVPzqinFEcC1wOeBtRFxrqRTgZ/lXFNbjGp88weIiHckVedZUCvcCawCPgusp3kARNbepUl6j4ZaP7GJhjfQ40pcUnvdC3yXhj9RvD/nWtrqN8AeGn6HPsy5lvb4XEQUXvzyD9mHo9ykGAB7I2KvJCT1jIiXJZ2Sd1FtcISkfhHxDoCk4+ni/x0jYj4wX9LCiPgfedfTHhFxbN41dJKdEVGuX74cFBET8y6iAzZIGhsRawEknQnU5llQl37jKJJ6SX2B/wOslPQO8Neca2qLW4HnJD2YPZ4K3JxjPa1Wrm/+3cyNku6iYUTW9Ck6In6dX0mttkbSyIjYlHch7XQ6Da/hb9njTwOvSNpETtNwya0BFJJ0DtAHeDy7PHVZyP6GQuOaxZMRkesw0sqHpP8ETgU2888poOjKa0iNsumS/wb8hYbwKpv1CwBJnznc9ogo+QfRpAPALDWSXomIcprybHKoN9A83ji7ixSngMxStkbS8HIcNfqNvvN5BGCWEElbgH+lTKdRrHM5AMwS4mkUK+QAMDNLlK8FZGaWKAeAmVmiHABmbSRpnqTv5F2HWUc5AMyKTJJPt7YuyQFgdgBJ35b0YvZzXdZ2g6T/K+lZ4JSCfcdIWitpo6RHsquzImm1pF9IqqXhAoRmXY4/mZgVkHQ68A3gTBrOkf+jpGeAi4ExNPw/s4GGK1ICLAX+Z0Q8Jekm4EbgumzbURFRU8r6zdrCAWDW3BeBRyLifQBJvwYuzNo+yNpWZLd9aLie+1PZsUuABwv6Wlayqs3awVNAZsXzft4FmB2OA8CsuWeAKZJ6SzoauAh4NGv7F0nHAv8dICL2AO9IOjs7djrw1ME6NeuKPAVkViAiNki6G3g+a7orItZLWgb8GXgLWFdwyAzgTkm9gVdpWD8wKwu+FISZWaI8BWRmligHgJlZohwAZmaJcgCYmSXKAWBmligHgJlZohwAZmaJ+v/pEbjFfyfkIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def bar_plot_feature(feature1,feature2):\n",
    "    cont_tab=pd.crosstab(index=dframe[feature1],  # Make a crosstab\n",
    "                          columns=dframe[feature2], margins=True)      \n",
    "#     cont_tab\n",
    "    plot_cont_tab = pd.crosstab(index=dframe[feature2],  # Make a crosstab\n",
    "                          columns=dframe[feature1], margins=False)      \n",
    "#     plot_cont_tab\n",
    "# make bargraph from contingency table\n",
    "    plot_cont_tab.plot.bar(stacked=True)\n",
    "    \n",
    "bar_plot_feature(\"odor\",\"population\")\n",
    "bar_plot_feature(\"population\",\"odor\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[None, None, None], [None, None, None], [None, None, None]]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XJpkJzALuBu4BZiWZmeQl9F4oXjX8tiVJB2vAI4Akfwe8HZicpAe4HHh7krlAAQ8CHwGoqs1JbqH34u5zwIVV9Xwzz0XA7cAEYGVVbR7xvZEkDdpg7gJ6Tz/l6w4w/irgqn7qq4HVQ+pOkjRq/CawJLWUASBJLWUASFJLGQCS1FIGgCS1lAEgSS1lAEhSSxkAktRSBoAktZQBIEktZQBIUksZAJLUUgaAJLWUASBJLWUASFJLGQCS1FIGgCS1lAEgSS1lAEhSSxkAktRSBoAktdSAAZBkZZKdSTb1qR2RZE2Src3vw5t6kixPsi3JxiRv7LPN4mb81iSLR2d3JEmDNZgjgK8BC/epXQKsrapZwNpmGeB0YFbzswS4BnoDA7gcmA/MAy7fGxqSpM4YMACq6nvAnn3Ki4Drm9fXA+/sU7+hev0YOCzJFOA0YE1V7amqR4E1/GGoSJLG0HCvARxVVQ83r38JHNW8ngps7zOup6ntr/4HkixJsi7Jul27dg2zPUnSQA76InBVFVAj0Mve+VZUVXdVdXd1dY3UtJKkfQw3AB5pTu3Q/N7Z1HcA0/uMm9bU9leXJHXIcANgFbD3Tp7FwHf61N/f3A10EvB4c6roduDUJIc3F39PbWqSpA45ZKABSf4OeDswOUkPvXfzfAG4JcmHgIeAs5vhq4EzgG3AU8AHAKpqT5IrgXuacUurat8Ly5KkMTRgAFTVe/azakE/Ywu4cD/zrARWDqk7SdKo8ZvAktRSBoAktZQBIEktZQBIUksZAJLUUgaAJLWUASBJLWUASFJLGQCS1FIGgCS1lAEgSS1lAEhSSxkAktRSBoAktZQBIEktZQBIUksZAJLUUgaAJLWUASBJLWUASFJLHVQAJHkwyb1JNiRZ19SOSLImydbm9+FNPUmWJ9mWZGOSN47EDkiShmckjgD+S1XNraruZvkSYG1VzQLWNssApwOzmp8lwDUj8N6SpGEajVNAi4Drm9fXA+/sU7+hev0YOCzJlFF4f0nSIBxsABTwj0nWJ1nS1I6qqoeb178EjmpeTwW299m2p6lJkjrgkIPc/i1VtSPJq4A1Sf6578qqqiQ1lAmbIFkCcPTRRx9ke5Kk/TmoI4Cq2tH83gl8G5gHPLL31E7ze2czfAcwvc/m05ravnOuqKruquru6uo6mPYkSQcw7ABI8u+SHLr3NXAqsAlYBSxuhi0GvtO8XgW8v7kb6CTg8T6niiRJY+xgTgEdBXw7yd55vlFV/5DkHuCWJB8CHgLObsavBs4AtgFPAR84iPeWJB2kYQdAVT0AzOmnvhtY0E+9gAuH+36SpJHlN4ElqaUMAElqKQNAklrKAJCkljIAJKmlDABJaikDQJJaygCQpJYyACSppQwASWopA0CSWsoAkKSWMgAkqaUMAElqKQNAklrKAJCkljIAJKmlDABJaikDQJJaygCQpJYyACSppcY8AJIsTHJ/km1JLhnr95ck9RrTAEgyAbgaOB2YDbwnyeyx7EGS1GusjwDmAduq6oGq+g1wM7BojHuQJDH2ATAV2N5nuaepSZLG2CGdbmBfSZYAS5rFJ5Pc38l+XmQmA7/qdBMDyRc73YE6ZPz/+/xcOt3BYL1mMIPGOgB2ANP7LE9rar9TVSuAFWPZVFskWVdV3Z3uQ+qP/z7H3lifAroHmJVkZpKXAOcCq8a4B0kSY3wEUFXPJbkIuB2YAKysqs1j2YMkqdeYXwOoqtXA6rF+XwGeWtP45r/PMZaq6nQPkqQO8FEQktRSBoAktdS4+x6ARlaSAOcBr62qpUmOBl5dVXd3uDW1XJJJwMeAtwAFfB+4pqqe7mhjLeI1gBe5JNcAvwVOqapjkhwO/GNVndjh1tRySW4BngBubErvBQ6rqrM611W7eATw4je/qt6Y5J8AqurR5jsYUqcdV1V9HwZ5Z5L7OtZNC3kN4MXv2eYprAWQpIveIwKp036S5KS9C0nmA+s62E/reATw4rcc+DbwqiRXAe8GLutsSxIAbwJ+mOQXzfLRwP1J7gWqqo7vXGvt4DWAFkjyBmABEGBtVW3pcEsSSQ74wLKqemisemkrA0CSWsprAJLUUgaAJLWUASAdQJIrklzc6T6k0WAASFJLGQBSH0nen2Rjkp8m+fo+6z6c5J5m3beS/NumflaSTU39e03t2CR3J9nQzDerE/sjHYh3AUmNJMfS+52J/1xVv0pyBPAJ4Mmq+qskR1bV7mbsMuCRqvrb5r71hVW1I8lhVfVYkr8FflxVNzXfvJ5QVb/u1L5J/fEIQPpXpwC3VtWvAKpqzz7rj0vy/5oP/POAY5v6D4CvJfkwvX/pDuBHwKVJPg28xg9/jUcGgDR4XwMuqqr/BHwOmARQVX9K77erpwPrmyOFbwB/DPwaWJ3klM60LO2fASD9qzuAs5IcCdCcAurrUODhJBPpPQKgGfe6qrqrqv4bsAuYnuS1wANVtRz4DuBjDTTu+CwgqVFVm5vnJf3fJM8D/wQ82GfIfwXuovdD/i56AwHgvzcXeQOsBX4KfBo4P8mzwC+BvxyTnZCGwIvAktRSngKSpJYyACSppQwASWopA0CSWsoAkKSWMgAkqaUMAElqKQNAklrq/wNhjx3HHoWs5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RfBYC13Zh3ZKkUdoXh4DmAzc1wzcBb+5ov7kGfQd4XpIj98H6JUnDsLcBUMDXk9ybZGHTdkRVPdgM/ww4ohk+CtjSMe9A0/YMSRYmWZlk5bZt2/ayPEnSrhywl/O/qqq2Jnk+cHeSH3VOrKpKUiNZYFVdD1wPMHfu3BHNK0kavr3aA6iqrc33Q8DfAScCPx86tNN8P9R03wpM7Zh9StMmSeqDUQdAkt9NcvDQMHAqsBZYAixoui0AvtoMLwHOba4GOgl4pONQkSSpx/bmENARwN8lGVrO7VX1D0lWAHckeS+wGXh7038pcDqwEXgMePderFuStJdGHQBV9QAweyft24FTdtJewAWjXZ8kqbu8E1iSWsoAkKSWMgAkqaUMAElqKQNAklrKAJCkljIAJKmlDABJaikDQJJaam+fBrp/WvTcHq/vkd6uT5K6wD0ASWopA0CSWmpcHgKa9sTtPV3fpp6uTZK6wz0ASWopA0CSWsoAkKSWMgAkqaUMAElqKQNAklrKAJCkljIAJKmlDABJaikDQJJaygCQpJYyACSppQwASWopA0CSWqrnAZDktCT3J9mY5KJer1+SNKin7wNIMgG4Bvh9YABYkWRJVf2wl3VIfTXOX1k67aKv9XR9mz7xpp6ubzzp9QthTgQ2VtUDAEm+AMwHDAC1hi8s0v6i1wFwFLClY3wAmNfjGsa+cf4vSEm9karq3cqStwGnVdUfNePnAPOq6sKOPguBhc3oS4D7e1YgHA78oofr6zW3b2xz+8auXm/b0VU1eU+der0HsBWY2jE+pWl7WlVdD1zfy6KGJFlZVXP7se5ecPvGNrdv7Npft63XVwGtAGYkmZ7k2cBZwJIe1yBJosd7AFX16yQXAncBE4DFVbWulzVIkgb1+hAQVbUUWNrr9Q5TXw499ZDbN7a5fWPXfrltPT0JLEnaf/goCElqKQNAklqq9QGQZFKS/5jkK0m+nOQ/JJnU77q0a0luab4/2O9a9rUkr9hJ2x/0o5ZuS3JGkoOb4T9v/g6+vN91dUuSWTtpe10fStml1gcAcDNwDPAZ4GpgFnBLXyvqoiQ3JXlex/ghSRb3s6YueEWSFwLvabbn0M5Pv4vrshuSvGxoJMnZwF/0sZ5u+ouqejTJq4DfA24Eru1zTd10R5L/nEEHJvkM8PF+F9Wp51cB7YdeVlWdSf2NJOPp2UTHVdX/GRqpql8mOb6fBXXBdcAy4EXAvUA6plXTPl68DfhSkncArwbOBU7tb0ld81Tz/Sbg+qr6WpLL+1lQl80DrgT+F3AwcBtwcl8r2oF7APD9JCcNjSSZB6zsYz3d9qwkhwyNNP9CHtPBX1WfrqqZDN5H8qKqmt7xGU8//jQPTjwL+ArwVuDUqhovD2famuS/AWcCS5P8DuPrN+lJ4HHgQGAS8JOq+k1/S3qm1l4GmuQ+Bv+1OJHBZw79tBk/GvjRDnsFY1aSc4GLgTubpjOAK6pq3BzmGo86/nwOeT7wCPArgKo6rh91dVOSfwGcBtxXVRuSHAkcW1Vf73NpXZFkNfBV4DIGnwV0HfD/quqMvhbWoc0BcPTuplfV5l7Vsq81J6Ne34wu9/0L+782/fkcr5LMraqVO7Sdsz/946u1ASBJbTeejrdJkkbAAJCkljIApL2Q5LwkV/e7Dmk0DABJaikDQONWknOTrEmyOsktSf5dku8m+UGSf0xyRNNvUTP920k2JPnjXSzvjCRrm+V9s2PSC5P8QzPvX3f0vzbJyiTrkvxVR/umJH+d5L4k30vyr5r2yc3jSFY0n/3qpiGNP2P6hiBpV5IcA/w58G+q6hfNDXAFnFRVleSPgP8EfKiZ5TjgJOB3gR8k+VpV/e8dFvuXwBuqamvn4zWAOcDxDF6jf3+Sz1TVFuCjVfVwkgnAsiTHVdWaZp5HqurY5j6N/wr8AXAV8KmquifJv2TwxUkzu/ofRupgAGi8ej1wZ1X9AqD5IT4W+GJzw9GzgZ909P9qVT0OPJ7kG8CJwP/YYZnfAj6X5A4G78wdsmzo7tzmMSJHA1uAtydZyODfsyMZfM7UUAB8vuP7U83w7wGzkqefbPGcJAdV1T+P9j+CtDseAlKbfAa4uqqOBc5n8Pb8ITveEFNJrkiyKskqgKr6Ewb3KqYC9yY5rOn7q475ngIOSDId+DBwSnPX7td2s76h4WcxuIcyp/kc5Y+/9iUDQOPVcuCMoR/p5hDQc4GtzfQFO/Sf3zwa/DDgdcCKqvro0I9xs4wXV9V3q+ovgW0MBsGuPAf4v8AjzbmGN+4w/cyO7283w18H/v1QhyRzhr210ih4CEjjUlWtS3IF8D+TPAX8AFgE3JnklwwGxPSOWdYA32DwmS2X7eT4P8B/STKDwaePLgNWM3j8f2frX53kB8CPGDwc9K0duhySZA2Dew9nN20fAK5p2g8Avgn8yYg2XBoBHwWh1kuyCPjnqvqbHq1vEzB36PyE1C8eApKklnIPQJJayj0ASWopA0CSWsoAkKSWMgAkqaUMAElqKQNAklrq/wNXP92MsLxgewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KAWBmlikHgJlZphwAZmaZcgCYdbIZM2YwbNgwJk2aVHQrZn+m3dtAzfYlo2aPqur6Xm58ud0xd955JwsWLKC+vr6q2zbbWz4CMOtEV1xxBa+//jpnnnkmt912W9HtmP0ZHwGYdaK7776bX/7ylzz77LM7vw3UrKvwEYCZWaYcAGZmmXIAmJllygFgZpYpXwS2rFRy22a1NTc313ybZpXwEYCZWaYcAGZmmXIAmJllygFgZpYpB4CZWaYcAGZmmXIAmHWygw46qOgWzFrV7nMAkmYBZwPvRsTIVOsHPAwMBpqBr0TEJkkCbgfOArYAl0bEb9MyjcDfp9VeHxGzq7srZu1bcfSwqq5v2MoVVV2fWS1VcgRwPzBhl9o04OmIGAo8nd4DnAkMTa/JwF2wMzCuAU4AxgLXSOq7t82bmdmeazcAIuLXwMZdyucAO/4FPxs4t6z+QJQsAvpIOhw4A5gfERsjYhMwn78MFTMzq6E9vQZwWESsS9NvA4el6YHAmrJxLanWVv0vSJosqUlS0/r16/ewPTMza89eXwSOiACiCr3sWN/MiGiIiIa6urpqrdbMzHaxpwHwTjq1Q/r7bqqvBQaVjatPtbbqZmZWkD0NgLlAY5puBJ4oq1+iknHA5nSq6CngdEl908Xf01PNzMwKUsltoP8MnAoMkNRC6W6eG4FHJF0GvAl8JQ2fR+kW0NWUbgP9GkBEbJT0D8DiNO6HEbHrhWWzTlfEbZsffvhhzbdpVol2AyAiLmpj1mmtjA1gShvrmQXM6lB3ZmbWafwksJlZphwAZmaZcgCYmWXKAWBmlikHgJlZphwAZmaZavc2UGvfWS/9e9EtWIXuuOKZqq5vyt3jq7o+s1ryEYBZJ2tububoo4/m0ksv5aijjmLSpEksWLCAE088kaFDh/Liiy8W3aJlygFgVgOrV69m6tSprFy5kpUrV/LQQw+xcOFCbr75Zm644Yai27NM+RSQWQ0MGTKEUaNGATBixAhOO+00JDFq1Ciam5uLbc6y5SMAsxo48MADd07vt99+O9/vt99+bN++vai2LHMOADOzTDkAzMwy5WsAlpUibtscPHgwr7zyys73999/f5vzzGrJRwBmZplyAJiZZcoBYGaWKQeAmVmmHABmZplyAJiZZWqvAkBSs6SXJS2V1JRq/STNl/Ra+ts31SVphqTVkpZJOrYaO2BmZnumGs8B/LeIeK/s/TTg6Yi4UdK09P57wJnA0PQ6Abgr/TWrmVsuOLuq65v68JNVXZ9ZLXXGKaBzgNlpejZwbln9gShZBPSRdHgnbN+sS2lubmbYsGFcfvnljBgxgtNPP52tW7cW3ZbZXgdAAP8maYmkyal2WESsS9NvA4el6YHAmrJlW1LNbJ/32muvMWXKFJYvX06fPn149NFHi27JbK9PAZ0UEWslHQrMl7SyfGZEhKToyApTkEwGOOKII/ayPbOuYciQIYwZMwaA4447zl8BbV3CXh0BRMTa9Pdd4DFgLPDOjlM76e+7afhaYFDZ4vWptus6Z0ZEQ0Q01NXV7U17Zl1G+ddB9+jRw18BbV3CHgeApE9JOnjHNHA68AowF2hMwxqBJ9L0XOCSdDfQOGBz2akiMzOrsb05BXQY8JikHet5KCJ+KWkx8Iiky4A3ga+k8fOAs4DVwBbga3uxbTMz20t7HAAR8TowupX6BuC0VuoBTNnT7ZlVQxG3be76lc/f/e53a96DWWv8JLCZWab8gzBVcOa5Nxfdwj6luegGzDLhIwAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0z5LiDLSsu056q6vvobT67q+sxqyUcAZp2submZo48+mkmTJjFs2DDOO+88tmzZUnRbZg4As1pYtWoV3/rWt1ixYgWf/vSnufPOO4tuycwBYFYLgwYN4sQTTwTgq1/9KgsXLiy4IzMHgFlNpC9NbPO9WREcAGY18NZbb/H8888D8NBDD3HSSScV3JGZ7wIyq4nPfvaz3HHHHXz9619n+PDhfPOb36zZtl9+462abcu6FweAZaWo2zb3339/fvzjHxeybbO2+BSQmVmmHABmnWzXH4Qx6yocAGZmmXIA2D6v9Guk3Ud369e6LweA7dN69erFhg0bus2HakSwYcMGevXqVXQrlgHfBWT7tPr6elpaWli/fn3RrVSsV69e1NfXF92GZaDmASBpAnA70AP4p4i4sdY9WD569uzJkCFDim7DrEuq6SkgST2AO4AzgeHARZKG17IHMzMrqfU1gLHA6oh4PSL+AMwBzqlxD2ZmRu0DYCCwpux9S6qZmVmNdbmLwJImA5PT2w8lrSqyn33MAOC9optoj24qugMrSNf/7/O6bvMtrn9dyaBaB8BaYFDZ+/pU2ykiZgIza9lULiQ1RURD0X2Ytcb/fdZerU8BLQaGShoi6QDgQmBujXswMzNqfAQQEdslXQk8Rek20FkRsbyWPZiZWUnNrwFExDxgXq23a4BPrVnX5v8+a0zd5RF5MzOrLn8XkJlZphwAZmaZ6nLPAVh1SRIwCTgyIn4o6QjgP0XEiwW3ZpmT1Av4FnASEMBC4K6I2FZoYxnxNYB9nKS7gD8C4yNimKS+wL9FxPEFt2aZk/QI8AGw48eS/zvQJyLOL66rvPgIYN93QkQcK+l3ABGxKT2DYVa0kRFR/mWQz0p6tbBuMuRrAPu+j9O3sAaApDpKRwRmRfutpHE73kg6AWgqsJ/s+Ahg3zcDeAw4VNJ04Dzg74ttyQyA44DfSHorvT8CWCXpZSAi4pjiWsuDrwFkQNLRwGmAgKcjYkXBLZkhabdfWBYRb9aql1w5AMzMMuVrAGZmmXIAmJllygFgthuSrpX03aL7MOsMDgAzs0w5AMzKSLpE0jJJL0l6cJd5l0tanOY9Kql3qp8v6ZVU/3WqjZD0oqSlaX1Di9gfs93xXUBmiaQRlJ6Z+K8R8Z6kfsDfAR9GxM2S+kfEhjT2euCdiPjHdN/6hIhYK6lPRLwv6R+BRRHxk/TkdY+I2FrUvpm1xkcAZn8yHvhpRLwHEBEbd5k/UtJz6QN/EjAi1f8fcL+kyyn90h3A88D3JX0P+Gt/+FtX5AAwq9z9wJURMQq4DugFEBFXUHq6ehCwJB0pPAR8EdgKzJM0vpiWzdrmADD7k2eA8yX1B0ingModDKyT1JPSEQBp3Gci4oWI+F/AemCQpCOB1yNiBvAE4K81sC7H3wVklkTE8vR9Sf9X0ifA74DmsiH/E3iB0of8C5QCAeB/p4u8Ap4GXgK+B1ws6WPgbeCGmuyEWQf4IrCZWaZ8CsjMLFMOADOzTDkAzMwy5QAwM8uUA8DMLFMOADOzTDkAzMwy5QAwM8vU/wex3riYwfwhHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1DTQitta8lnQfUDMvcjPQv2DVflkb+2gvutaetjlv3jw6d+7MJZdcwueff85Xv/pVlixZwsiRrX9bajOz+uxXAEg6PCK2ZG+/BtRMcVkIPCTpJ1RfBB4AvAYIGCDpSKoP/BcBlzSn8Lbs8ssv5/LLLwegU6dOvPrqqzlXZNb2de35L3mXkJwGA0DSw8BpQG9JFcDtwGmShgIBbAKuBIiINZIepfri7i5gckR8nvVzDfAs0Al4ICLWtPinMTOzRmvMLKCL99L8i32sPx2Yvpf2p4Gnm1SdmZkVTbu8FUREICnvMoouIvIuwazVjHxhchF7X1fEvtuvdhcAXbt2paqqikMPPbRDh0BEUFVVRdeuXfMuxaxVXPC94h2OVhWt5/at3QVAv379qKiooD1/R6CxunbtSr9+/fIuw8w6qHYXAJ07d+bII4/Muwwzs3bPdwM1M0uUA8DMLFEOADOzRDkAzMwS5QAwM0uUA8DMLFEOADOzRDkAzMwS5QAwM0uUA8DMLFEOADOzRDkAzMwS5QAwM0uUA8DMLFEOADOzRDkAzMwS5QAwM0uUA8DMLFEOADOzRDkAzMwS5QAwM0uUA8DMLFEOADOzRDkAzMwS5QAwM0tUgwEg6QFJH0haXdDWS9IiSW9lzz2zdkmaIWmDpJWSji3YZmK2/luSJhbn45iZWWM15gxgDjBmt7ZbgcURMQBYnL0HOBsYkD0mATOhOjCA24ERwPHA7TWhYWZm+WgwACLiN8C23ZrHAXOz13OB8wva50W1pUAPSYcDo4FFEbEtIj4CFrFnqJiZWSva32sAh0XEluz1H4DDstd9gfcK1qvI2upr34OkSZKWS1peWVm5n+WZmVlDmn0ROCICiBaopaa/2RExLCKG9enTp6W6NTOz3exvAGzNhnbInj/I2jcD/QvW65e11dduZmY52d8AWAjUzOSZCDxR0H55NhvoBGB7NlT0LHCWpJ7Zxd+zsjYzM8vJFxpaQdLDwGlAb0kVVM/m+SHwqKR/At4BLshWfxo4B9gAfAZ8GyAitkm6A1iWrTctIna/sGxmZq2owQCIiIvrWTRqL+sGMLmefh4AHmhSdWZmVjT+JrCZWaIcAGZmiXIAmJklygFgZpYoB4CZWaIcAGZmiXIAmJklygFgZpYoB4CZWaIa/CawWWPNOeedovZ/Y1F7N0uPzwDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS5QDwMwsUQ4AM7NEOQDMzBLlADAzS1SzAkDSJkmrJK2QtDxr6yVpkaS3sueeWbskzZC0QdJKSce2xAcwM7P90xJnAKdHxNCIGJa9vxVYHBEDgMXZe4CzgQHZYxIwswX2bWZm+6kYQ0DjgLnZ67nA+QXt86LaUqCHpMOLsH8zM2uE5gZAAP9X0uuSJmVth0XEluz1H4DDstd9gfcKtq3I2uqQNEnScknLKysrm1memZnV5wvN3P7kiNgs6b8AiyS9WbgwIkJSNKXDiJgNzAYYNmxYk7Y1M7PGa9YZQERszp4/AB4Hjge21gztZM8fZKtvBvoXbN4vazMzsxzsdwBIOljSITWvgbOA1cBCYGK22kTgiez1QuDybDbQCcD2gqEiMzNrZc0ZAjoMeFxSTT8PRcQzkpYBj0r6J+Ad4IJs/aeBc4ANwGfAt5uxbzMza6b9DoCI2AiU7aW9Chi1l/YAJu/v/szMrGX5m8BmZolq7iygNuWuC88tav83zn+yqP1bvvzzY6nxGYCZWaIcAGZmiXIAmJklygFgZpYoB4CZWaIcAGZmiXIAmJklygFgZpYoB4CZWaIcAGZmiepQt4IwM8tDe72NiM8AzMwS5QAwM0uUA8DMLFEd6hrAnHPeKWr/Nxa1d8ubf34sNR0qANq79nohyczaJw8BmZklygFgZpYoB4CZWaIcAGZmiepQF4E/WffDvEuwdsw/P7a/2usMMp8BmJklygFgZpYoB4CZWaIcAGZmiXIAmJklqtVnAUkaA/wb0Am4PyI89cKsBbT3W4l4Flbra9UAkNQJuAc4E6gAlklaGBFrW7MOs47onP/8fd4lWDvT2mcAxwMbImIjgKRHgHGAA8CsmS74XnH/d15V1N4tD4qI1tuZ9E1gTET8c/Z+AjAiIq4pWGcSMCl7exSwvogl9QY+LGL/xeb68+X689We6y927V+OiD4NrdTmvgkcEbOB2a2xL0nLI2JYa+yrGFx/vlx/vtpz/W2l9taeBbQZ6F/wvl/WZmZmray1A2AZMEDSkZIOBC4CFrZyDWZmRisPAUXELknXAM9SPQ30gYhY05o17KZVhpqKyPXny/Xnqz3X3yZqb9WLwGZm1nb4m8BmZolyAJiZJcoBYGaWqDb3PYDWIKknMADoWtMWEb/Jr6KOTdL/jogJkq6LiH/Lux6zPEjqClwNnAwE8BIwMyJ25lZTaheBJf0zcB3V30FYAZwAvBIRI3MtrJEkzQWui4g/Zu97AndFxBX5VlY/SWuBM4BfAacBKlweEdtyKCtJkoYBU4AvU/0LoICIiNJcC2sESV2AbwAlFPzyGhHT8qqpKSQ9CnwC/HvWdAnQIyLG51VTimcA1wHDgaURcbqko4F/zbmmpiitOfgDRMRHksrzLKgRZgGLgf8KvE7dAIisvU2T9AnVte6xiOoD6BdbuaT99SBwE9W39vlbzrU01RPAdqp/hv6ccy37Y3BEDCx4/3z2y1FuUgyAnRGxUxKSukTEm5KOyruoJjhAUs+I+AhAUi/a+H/HiJgBzJA0MyKuyrue/RERh+RdQwupjIj2+uXLfhExJu8imuENSSdExFIASSOA5XkW1KYPHEVSIakH8H+ARZI+At7JuaamuAt4RdKC7P14YHqO9TRaez34dzC3S7qf6jOy2t+iI+Kx/EpqtJclDYmI9npj0uOo/gzvZu+/BKyXtIqchuGSuwZQSNKpQHfgmYj4S971NJakgUDNNYsl/nsK1liS/h04GljD34eAoi1fQ6qRDZf8N+BtqsOr3Vy/AJD05X0tj4hW/0U06QAwS42k9RHRnoY8a9V3AM3jwNlRpDgEZJaylyUNbI9njT7QtzyfAZglRNI64B9pp8Mo1rIcAGYJ8TCKFXIAmJklyvcCMjNLlAPAzCxRDgCzJpI0VdJ3867DrLkcAGZFJsnTra1NcgCY7UbSv0hanT2uz9qmSPp/kl4CjipYd6ikpZJWSno8uzsrkl6Q9L8kLaf6BoRmbY5/MzErIOk44NvACKrnyL8q6UXgImAo1f/PvEH1HSkB5gHXRsSvJU0Dbgeuz5YdGBHDWrN+s6ZwAJjVdTLweETsAJD0GDA2a/ssa1uYPXen+n7uv862nQssKOhrfqtVbbYfPARkVjw78i7AbF8cAGZ1vQicL+kfJB0MfA14Kms7SNIhwP8AiIjtwEeS/nu27QTg13vr1Kwt8hCQWYGIeEPSHOC1rOn+iHhd0nzgP4EPgGUFm0wEZkn6B2Aj1dcPzNoF3wrCzCxRHgIyM0uUA8DMLFEOADOzRDkAzMwS5QAwM0uUA8DMLFEOADOzRP1/bAbfXvUvsx8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make a contingency matrix\n",
    "# feature_names = [\"class\",\"cap-shape\",\"cap-surface\",\"cap-colour\",\"bruises?\",\"odor\",\"gill-attachment\",\"gill-spacing\",\"gill-size\",\"gill-colour\",\"stalk-shape\",\"stalk-root\",\"stalk-surface-above-ring\",\"stalk-surface-below-ring\",\"stalk-colour-above-ring\",\"stalk-colour-below-ring\",\"veil-type\",\"veil-colour\",\"ring-number\",\"ring-type\",\"spore-print-colour\",\"population\",\"habitat\"]\n",
    "feature_names=[\"class\",\"odor\",\"cap-shape\"]\n",
    "matrix = [[0 for x in range(3)] for y in range(3)]\n",
    "for ind1,f1 in enumerate(feature_names):\n",
    "    for ind2,f2 in enumerate(feature_names):\n",
    "        matrix[ind1][ind2] = bar_plot_feature(f1,f2)\n",
    "#         bar_plot_feature(f1,f2)\n",
    "\n",
    "matrix\n",
    "# fig, ax = plt.subplots(10, 10, sharex='col', sharey='row',figsize=(20,20))\n",
    "# fig.subplots_adjust(hspace=2, wspace=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map symbols to names\n",
    "def feature_val_names(feature,symbol):\n",
    "    feature_names = [\"class\",\"cap-shape\",\"cap-surface\",\"cap-colour\",\"bruises?\",\"odor\",\"gill-attachment\",\"gill-spacing\",\"gill-size\",\"gill-colour\",\"stalk-shape\",\"stalk-root\",\"stalk-surface-above-ring\",\"stalk-surface-below-ring\",\"stalk-colour-above-ring\",\"stalk-colour-below-ring\",\"veil-type\",\"veil-colour\",\"ring-number\",\"ring-type\",\"spore-print-colour\",\"population\",\"habitat\"]\n",
    "    all_feature_vals = ['x','s','n','t','p','f','c','k','e','w','o','u','y','a','b','g','l','m','v','d','r','h','?']\n",
    "    #maybe later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# did MCA on a contingency table - it gave something, not sure what\n",
    "import prince\n",
    "mca = prince.MCA(n_components=4, n_iter=3, copy=True, engine='auto', random_state=42)\n",
    "mca = mca.fit(cont_tab)\n",
    "\n",
    "ax = mca.plot_principal_coordinates(X=cont_tab, ax=None, figsize=(10, 10), show_row_points=True, row_points_size=10, show_row_labels=False, show_column_points=True, column_points_size=30, show_column_labels=False, legend_n_cols=2)\n",
    "\n",
    "ax.legend().set_visible(False)\n",
    "ax.legend(bbox_to_anchor=(1, 0.9))\n",
    "ax.get_figure().savefig('mca_principal_coordinates.png')"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}