04ex_OSEMN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OSEMN Exercises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1\\. Create a random list of number and then save it to a text file named \"simple_data.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "random = np.random.rand(1, 10)\n",
    "np.savetxt('simple_data.txt', random, delimiter=' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2\\. Create a random matrix of 5x5 and then save it to a text file named \"data.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "MatRand = np.random.rand(5, 5)\n",
    "np.savetxt('data.txt', MatRand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3\\. Load the saved txt file of point 2 and convert it to a csv file (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name1 = 'simple_data.txt'\n",
    "file_name2 =        'data.txt'\n",
    "\n",
    "with open(file_name1, mode='r') as f:\n",
    "    with open('simple_data.csv', mode='w') as g:\n",
    "        for line in f:\n",
    "            val = line.split(' ')\n",
    "            for x in val:\n",
    "                g.write(x)\n",
    "                g.write(',')\n",
    "\n",
    "\n",
    "with open(file_name2, mode='r') as f:\n",
    "    with open('data.csv', mode='w') as g:\n",
    "        for line in f:\n",
    "            val = line.split(' ')\n",
    "            val.pop()\n",
    "            for x in val:\n",
    "                g.write(x)\n",
    "                g.write(',')\n",
    "            g.write('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4\\. load the binary file named *credit_card.dat* and convert the data into the real credit-card number.\n",
    "Each line correspond to a credit card number.\n",
    "Each character is composed by 6 bit (even the space) and the last 4 bit are just a padding\n",
    "\n",
    "**hint**: use the `chr()` function to convert a number to a char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7648 5673 3775 2271\n",
      "3257 8247 3354 2266\n",
      "2722 0001 4011 6652\n",
      "0661 3063 3742 3150\n",
      "0432 1608 1462 4742\n",
      "5827 2027 8785 7303\n",
      "5774 8528 2087 1117\n",
      "8140 1210 6352 2845\n",
      "5764 1133 7301 7100\n",
      "6456 1737 4126 6726\n",
      "1228 8631 7382 0000\n",
      "7051 0160 5374 3166\n",
      "0618 3587 1630 6376\n",
      "1545 5454 7444 5636\n",
      "6735 3116 3202 6834\n",
      "7287 5011 1547 8413\n",
      "7033 2607 3328 4200\n",
      "2568 5244 1874 5024\n",
      "1684 2253 7570 7118\n",
      "0672 2576 0575 6631\n",
      "6332 8353 8787 1340\n",
      "1813 3361 1175 4211\n",
      "2477 6450 8840 2368\n",
      "5512 3505 2563 1326\n",
      "3083 7882 0621 0025\n",
      "4521 5148 8045 0334\n",
      "7563 3654 8713 5787\n",
      "8324 2664 0476 5561\n",
      "0565 2504 7168 3510\n",
      "5107 5507 1767 0738\n",
      "2462 1821 2448 1443\n",
      "2788 0638 6861 6554\n",
      "5851 5873 5474 0547\n",
      "0670 1004 4013 2655\n",
      "5874 5506 3048 0806\n",
      "2805 5401 8462 1260\n",
      "5083 8406 6310 1862\n",
      "1076 1445 3013 2266\n",
      "8440 4804 4844 5277\n",
      "4758 6141 0686 1387\n",
      "7586 0675 0315 2568\n",
      "2544 1258 7432 5165\n",
      "3474 5023 4434 5626\n",
      "1410 0270 0434 5086\n",
      "7315 4446 1104 4215\n",
      "0224 7742 8300 0266\n",
      "0170 2700 3145 0640\n",
      "2006 2437 8054 1600\n",
      "8142 4055 1776 0026\n",
      "3026 7380 1241 1084\n",
      "\n"
     ]
    }
   ],
   "source": [
    "char_size = 6\n",
    "\n",
    "with open('credit_card.dat', 'rb') as f:\n",
    "    for line in f:\n",
    "        n = ''\n",
    "        for i in range(0, len(line), char_size):\n",
    "            if len(line[i:]) >= char_size:\n",
    "                c = chr(int(line[i:i+char_size], 2))\n",
    "                n += c\n",
    "        print(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5\\. Load the file \"user_data.json\", filter the data by the \"CreditCardType\" field equals to \"American Express\". Than save the data a to CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using pandas\n",
    "user_data = pd.read_json('user_data.json')\n",
    "user_data_group = user_data.groupby(user_data.CreditCardType)\n",
    "user_data_AMEX  = user_data_group.get_group('American Express')\n",
    "user_data_AMEX.to_csv('user_data_AMEX.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import csv\n",
    "user_data = json.load(open('user_data.json'))\n",
    "\n",
    "def isAmericanExpress(x):\n",
    "    return x['CreditCardType'] == 'American Express'\n",
    "user_data_AMEX = list(filter(isAmericanExpress, user_data))\n",
    "field = [key for key in user_data_AMEX[0]]\n",
    "with open('user_data_AMEX2.csv', mode='w') as f:\n",
    "    csvw = csv.DictWriter(f, fieldnames = field)\n",
    "    csvw.writeheader()\n",
    "    for i in user_data_AMEX:\n",
    "        csvw.writerow(i)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6\\. Load the file from this url: [https://www.dropbox.com/s/7u3lm737ogbqsg8/mushrooms_categorized.csv?dl=1](https://www.dropbox.com/s/7u3lm737ogbqsg8/mushrooms_categorized.csv?dl=1) with Pandas. \n",
    "+ Explore the data (see the info of the data)\n",
    "+ Draw the istogram of the 'class' field. Decribe wath yuou see\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-11 11:34:27--  https://www.dropbox.com/s/7u3lm737ogbqsg8/mushrooms_categorized.csv?dl=1\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 162.125.8.18, 2620:100:6019:18::a27d:412\n",
      "Connecting to www.dropbox.com (www.dropbox.com)|162.125.8.18|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: /s/dl/7u3lm737ogbqsg8/mushrooms_categorized.csv [following]\n",
      "--2021-12-11 11:34:28--  https://www.dropbox.com/s/dl/7u3lm737ogbqsg8/mushrooms_categorized.csv\n",
      "Reusing existing connection to www.dropbox.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://ucb87ebee1ef3fd50af4e333f388.dl.dropboxusercontent.com/cd/0/get/BboLYwj57GVjs4r09eyL-qdVXy-LegrSIqAOoAyPCDZKrIW-DDwx9HUOh52fjp1EhDsAm4uZnDHyyzMjRmLfhZ37z8YmtNRhhqUWRZHBzeV3yfRE0IkVpAWiINLsnNcHlUKr8NNUbR7pTVVPN6XpPQ96/file?dl=1# [following]\n",
      "--2021-12-11 11:34:29--  https://ucb87ebee1ef3fd50af4e333f388.dl.dropboxusercontent.com/cd/0/get/BboLYwj57GVjs4r09eyL-qdVXy-LegrSIqAOoAyPCDZKrIW-DDwx9HUOh52fjp1EhDsAm4uZnDHyyzMjRmLfhZ37z8YmtNRhhqUWRZHBzeV3yfRE0IkVpAWiINLsnNcHlUKr8NNUbR7pTVVPN6XpPQ96/file?dl=1\n",
      "Resolving ucb87ebee1ef3fd50af4e333f388.dl.dropboxusercontent.com (ucb87ebee1ef3fd50af4e333f388.dl.dropboxusercontent.com)... 162.125.8.15, 2620:100:6019:15::a27d:40f\n",
      "Connecting to ucb87ebee1ef3fd50af4e333f388.dl.dropboxusercontent.com (ucb87ebee1ef3fd50af4e333f388.dl.dropboxusercontent.com)|162.125.8.15|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 375292 (366K) [application/binary]\n",
      "Saving to: ‘mushrooms_categorized.csv’\n",
      "\n",
      "mushrooms_categoriz 100%[===================>] 366.50K   490KB/s    in 0.7s    \n",
      "\n",
      "2021-12-11 11:34:32 (490 KB/s) - ‘mushrooms_categorized.csv’ saved [375292/375292]\n",
      "\n"
     ]
    },
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      ],
      "text/plain": [
       "      class  cap-shape  cap-surface  cap-color  bruises  odor  \\\n",
       "0         1          5            2          4        1     6   \n",
       "1         0          5            2          9        1     0   \n",
       "2         0          0            2          8        1     3   \n",
       "3         1          5            3          8        1     6   \n",
       "4         0          5            2          3        0     5   \n",
       "...     ...        ...          ...        ...      ...   ...   \n",
       "8119      0          3            2          4        0     5   \n",
       "8120      0          5            2          4        0     5   \n",
       "8121      0          2            2          4        0     5   \n",
       "8122      1          3            3          4        0     8   \n",
       "8123      0          5            2          4        0     5   \n",
       "\n",
       "      gill-attachment  gill-spacing  gill-size  gill-color  ...  \\\n",
       "0                   1             0          1           4  ...   \n",
       "1                   1             0          0           4  ...   \n",
       "2                   1             0          0           5  ...   \n",
       "3                   1             0          1           5  ...   \n",
       "4                   1             1          0           4  ...   \n",
       "...               ...           ...        ...         ...  ...   \n",
       "8119                0             0          0          11  ...   \n",
       "8120                0             0          0          11  ...   \n",
       "8121                0             0          0           5  ...   \n",
       "8122                1             0          1           0  ...   \n",
       "8123                0             0          0          11  ...   \n",
       "\n",
       "      stalk-surface-below-ring  stalk-color-above-ring  \\\n",
       "0                            2                       7   \n",
       "1                            2                       7   \n",
       "2                            2                       7   \n",
       "3                            2                       7   \n",
       "4                            2                       7   \n",
       "...                        ...                     ...   \n",
       "8119                         2                       5   \n",
       "8120                         2                       5   \n",
       "8121                         2                       5   \n",
       "8122                         1                       7   \n",
       "8123                         2                       5   \n",
       "\n",
       "      stalk-color-below-ring  veil-type  veil-color  ring-number  ring-type  \\\n",
       "0                          7          0           2            1          4   \n",
       "1                          7          0           2            1          4   \n",
       "2                          7          0           2            1          4   \n",
       "3                          7          0           2            1          4   \n",
       "4                          7          0           2            1          0   \n",
       "...                      ...        ...         ...          ...        ...   \n",
       "8119                       5          0           1            1          4   \n",
       "8120                       5          0           0            1          4   \n",
       "8121                       5          0           1            1          4   \n",
       "8122                       7          0           2            1          0   \n",
       "8123                       5          0           1            1          4   \n",
       "\n",
       "      spore-print-color  population  habitat  \n",
       "0                     2           3        5  \n",
       "1                     3           2        1  \n",
       "2                     3           2        3  \n",
       "3                     2           3        5  \n",
       "4                     3           0        1  \n",
       "...                 ...         ...      ...  \n",
       "8119                  0           1        2  \n",
       "8120                  0           4        2  \n",
       "8121                  0           1        2  \n",
       "8122                  7           4        2  \n",
       "8123                  4           1        2  \n",
       "\n",
       "[8124 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!wget -O mushrooms_categorized.csv https://www.dropbox.com/s/7u3lm737ogbqsg8/mushrooms_categorized.csv?dl=1\n",
    "mushrooms_categorized = pd.read_csv('mushrooms_categorized.csv')\n",
    "mushrooms_categorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'class'}>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mushrooms_categorized.hist('class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7\\. Load the remote file [https://www.dropbox.com/s/vkl89yce7xjdq4n/regression_generated.csv?dl=1](https://www.dropbox.com/s/vkl89yce7xjdq4n/regression_generated.csv?dl=1) with Pandas and plot a scatter plot all possible combination of the following fields:\n",
    "    \n",
    "  + features_1\n",
    "  + features_2\n",
    "  + features_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-12-11 11:34:33--  https://www.dropbox.com/s/vkl89yce7xjdq4n/regression_generated.csv?dl=1\n",
      "Resolving www.dropbox.com (www.dropbox.com)... 162.125.8.18, 2620:100:6019:18::a27d:412\n",
      "Connecting to www.dropbox.com (www.dropbox.com)|162.125.8.18|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: /s/dl/vkl89yce7xjdq4n/regression_generated.csv [following]\n",
      "--2021-12-11 11:34:34--  https://www.dropbox.com/s/dl/vkl89yce7xjdq4n/regression_generated.csv\n",
      "Reusing existing connection to www.dropbox.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://uc84a7755d301f069a52296f8e6c.dl.dropboxusercontent.com/cd/0/get/Bbob3kQvqL_CMmCciloOyP7D6I79VznfaqJXVClWBStrlgzUw-MJC6pf4e8rbBkl1MZtS9adLbS1xbHr2WorNj11rOFH5qo46MbHHQXLmtueA6xeHSQnz3TBQeedgBImICIXcyfN-qWnIUmCtyPwMLb4/file?dl=1# [following]\n",
      "--2021-12-11 11:34:35--  https://uc84a7755d301f069a52296f8e6c.dl.dropboxusercontent.com/cd/0/get/Bbob3kQvqL_CMmCciloOyP7D6I79VznfaqJXVClWBStrlgzUw-MJC6pf4e8rbBkl1MZtS9adLbS1xbHr2WorNj11rOFH5qo46MbHHQXLmtueA6xeHSQnz3TBQeedgBImICIXcyfN-qWnIUmCtyPwMLb4/file?dl=1\n",
      "Resolving uc84a7755d301f069a52296f8e6c.dl.dropboxusercontent.com (uc84a7755d301f069a52296f8e6c.dl.dropboxusercontent.com)... 162.125.8.15, 2620:100:6019:15::a27d:40f\n",
      "Connecting to uc84a7755d301f069a52296f8e6c.dl.dropboxusercontent.com (uc84a7755d301f069a52296f8e6c.dl.dropboxusercontent.com)|162.125.8.15|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 823374 (804K) [application/binary]\n",
      "Saving to: ‘regression_generated.csv’\n",
      "\n",
      "regression_generate 100%[===================>] 804.08K   817KB/s    in 1.0s    \n",
      "\n",
      "2021-12-11 11:34:37 (817 KB/s) - ‘regression_generated.csv’ saved [823374/823374]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>features_1</th>\n",
       "      <th>features_2</th>\n",
       "      <th>features_3</th>\n",
       "      <th>features_4</th>\n",
       "      <th>features_5</th>\n",
       "      <th>features_6</th>\n",
       "      <th>features_7</th>\n",
       "      <th>features_8</th>\n",
       "      <th>features_9</th>\n",
       "      <th>...</th>\n",
       "      <th>features_11</th>\n",
       "      <th>features_12</th>\n",
       "      <th>features_13</th>\n",
       "      <th>features_14</th>\n",
       "      <th>features_15</th>\n",
       "      <th>features_16</th>\n",
       "      <th>features_17</th>\n",
       "      <th>features_18</th>\n",
       "      <th>features_19</th>\n",
       "      <th>features_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-89.243497</td>\n",
       "      <td>2.175170</td>\n",
       "      <td>-0.285786</td>\n",
       "      <td>-0.603396</td>\n",
       "      <td>-0.627453</td>\n",
       "      <td>-0.686474</td>\n",
       "      <td>0.381067</td>\n",
       "      <td>0.306205</td>\n",
       "      <td>-0.637447</td>\n",
       "      <td>-1.332087</td>\n",
       "      <td>...</td>\n",
       "      <td>1.290725</td>\n",
       "      <td>1.047483</td>\n",
       "      <td>-1.055467</td>\n",
       "      <td>0.853204</td>\n",
       "      <td>0.038665</td>\n",
       "      <td>-0.752959</td>\n",
       "      <td>0.577920</td>\n",
       "      <td>-0.657400</td>\n",
       "      <td>1.367308</td>\n",
       "      <td>0.570199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>230.050125</td>\n",
       "      <td>1.481941</td>\n",
       "      <td>-1.327870</td>\n",
       "      <td>-0.543583</td>\n",
       "      <td>-0.303578</td>\n",
       "      <td>1.552964</td>\n",
       "      <td>0.549738</td>\n",
       "      <td>-0.763094</td>\n",
       "      <td>-0.455796</td>\n",
       "      <td>2.053388</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.761306</td>\n",
       "      <td>-0.934284</td>\n",
       "      <td>-1.050999</td>\n",
       "      <td>0.444026</td>\n",
       "      <td>-0.037959</td>\n",
       "      <td>1.061624</td>\n",
       "      <td>-1.569870</td>\n",
       "      <td>2.410696</td>\n",
       "      <td>1.113594</td>\n",
       "      <td>2.329479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-286.844411</td>\n",
       "      <td>-1.154394</td>\n",
       "      <td>-0.178649</td>\n",
       "      <td>-1.636646</td>\n",
       "      <td>0.239353</td>\n",
       "      <td>-0.684994</td>\n",
       "      <td>0.587201</td>\n",
       "      <td>-0.209564</td>\n",
       "      <td>-0.428956</td>\n",
       "      <td>-0.757998</td>\n",
       "      <td>...</td>\n",
       "      <td>0.374645</td>\n",
       "      <td>-1.702189</td>\n",
       "      <td>-0.014514</td>\n",
       "      <td>-0.711557</td>\n",
       "      <td>-0.558523</td>\n",
       "      <td>-1.204526</td>\n",
       "      <td>0.234989</td>\n",
       "      <td>0.398384</td>\n",
       "      <td>-0.236555</td>\n",
       "      <td>0.642003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>364.552862</td>\n",
       "      <td>0.197665</td>\n",
       "      <td>1.455707</td>\n",
       "      <td>1.562205</td>\n",
       "      <td>2.168207</td>\n",
       "      <td>0.053335</td>\n",
       "      <td>0.790492</td>\n",
       "      <td>-0.212023</td>\n",
       "      <td>-1.142483</td>\n",
       "      <td>-1.124906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.712160</td>\n",
       "      <td>-2.844936</td>\n",
       "      <td>0.483994</td>\n",
       "      <td>-0.694294</td>\n",
       "      <td>1.349605</td>\n",
       "      <td>-1.303414</td>\n",
       "      <td>0.161987</td>\n",
       "      <td>0.754084</td>\n",
       "      <td>1.248258</td>\n",
       "      <td>-1.466045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>515.460006</td>\n",
       "      <td>0.596676</td>\n",
       "      <td>0.969860</td>\n",
       "      <td>1.294158</td>\n",
       "      <td>-0.404728</td>\n",
       "      <td>2.145297</td>\n",
       "      <td>0.997481</td>\n",
       "      <td>-0.541670</td>\n",
       "      <td>-0.952850</td>\n",
       "      <td>-0.592084</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.347072</td>\n",
       "      <td>0.243422</td>\n",
       "      <td>0.290336</td>\n",
       "      <td>0.798331</td>\n",
       "      <td>0.876428</td>\n",
       "      <td>-0.366807</td>\n",
       "      <td>-0.119534</td>\n",
       "      <td>0.892320</td>\n",
       "      <td>-0.806912</td>\n",
       "      <td>0.736080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-328.100239</td>\n",
       "      <td>-1.820929</td>\n",
       "      <td>1.358539</td>\n",
       "      <td>-2.494838</td>\n",
       "      <td>0.828299</td>\n",
       "      <td>0.199255</td>\n",
       "      <td>0.142303</td>\n",
       "      <td>-0.582563</td>\n",
       "      <td>0.472356</td>\n",
       "      <td>-1.411304</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.062502</td>\n",
       "      <td>2.299985</td>\n",
       "      <td>-0.085065</td>\n",
       "      <td>0.357165</td>\n",
       "      <td>-1.400150</td>\n",
       "      <td>-0.976176</td>\n",
       "      <td>-0.796009</td>\n",
       "      <td>-0.148662</td>\n",
       "      <td>0.978054</td>\n",
       "      <td>-0.804272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-62.960036</td>\n",
       "      <td>-0.883712</td>\n",
       "      <td>-0.063687</td>\n",
       "      <td>-0.418704</td>\n",
       "      <td>-0.872601</td>\n",
       "      <td>0.363769</td>\n",
       "      <td>-0.368570</td>\n",
       "      <td>-0.104321</td>\n",
       "      <td>-2.493615</td>\n",
       "      <td>-1.694770</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197659</td>\n",
       "      <td>-0.533637</td>\n",
       "      <td>-0.598431</td>\n",
       "      <td>-0.039737</td>\n",
       "      <td>1.882953</td>\n",
       "      <td>-1.837356</td>\n",
       "      <td>0.583887</td>\n",
       "      <td>0.603931</td>\n",
       "      <td>-0.356482</td>\n",
       "      <td>0.029355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-90.262252</td>\n",
       "      <td>-0.021394</td>\n",
       "      <td>-0.312864</td>\n",
       "      <td>-0.730848</td>\n",
       "      <td>-1.341482</td>\n",
       "      <td>-0.720862</td>\n",
       "      <td>-0.683872</td>\n",
       "      <td>-0.805370</td>\n",
       "      <td>-0.521334</td>\n",
       "      <td>-0.074645</td>\n",
       "      <td>...</td>\n",
       "      <td>1.899276</td>\n",
       "      <td>0.417949</td>\n",
       "      <td>0.324229</td>\n",
       "      <td>0.448252</td>\n",
       "      <td>-0.282110</td>\n",
       "      <td>-2.320763</td>\n",
       "      <td>1.484126</td>\n",
       "      <td>0.392324</td>\n",
       "      <td>-1.116838</td>\n",
       "      <td>-0.459027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-74.333901</td>\n",
       "      <td>-0.041909</td>\n",
       "      <td>-0.862731</td>\n",
       "      <td>-1.232011</td>\n",
       "      <td>1.268843</td>\n",
       "      <td>0.093043</td>\n",
       "      <td>1.684069</td>\n",
       "      <td>0.753851</td>\n",
       "      <td>-0.537096</td>\n",
       "      <td>-0.971790</td>\n",
       "      <td>...</td>\n",
       "      <td>1.401321</td>\n",
       "      <td>-0.933149</td>\n",
       "      <td>-0.166864</td>\n",
       "      <td>1.152448</td>\n",
       "      <td>1.605114</td>\n",
       "      <td>-1.339651</td>\n",
       "      <td>0.105628</td>\n",
       "      <td>0.676177</td>\n",
       "      <td>-0.035598</td>\n",
       "      <td>-1.329490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>8.321454</td>\n",
       "      <td>-1.056364</td>\n",
       "      <td>0.259247</td>\n",
       "      <td>0.307835</td>\n",
       "      <td>1.094903</td>\n",
       "      <td>0.472395</td>\n",
       "      <td>1.702894</td>\n",
       "      <td>-1.981785</td>\n",
       "      <td>0.032232</td>\n",
       "      <td>-1.142503</td>\n",
       "      <td>...</td>\n",
       "      <td>0.370107</td>\n",
       "      <td>-1.574455</td>\n",
       "      <td>0.092401</td>\n",
       "      <td>0.249970</td>\n",
       "      <td>0.048932</td>\n",
       "      <td>-0.376273</td>\n",
       "      <td>1.285961</td>\n",
       "      <td>-0.054588</td>\n",
       "      <td>-1.183828</td>\n",
       "      <td>-0.356557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           label  features_1  features_2  features_3  features_4  features_5  \\\n",
       "0     -89.243497    2.175170   -0.285786   -0.603396   -0.627453   -0.686474   \n",
       "1     230.050125    1.481941   -1.327870   -0.543583   -0.303578    1.552964   \n",
       "2    -286.844411   -1.154394   -0.178649   -1.636646    0.239353   -0.684994   \n",
       "3     364.552862    0.197665    1.455707    1.562205    2.168207    0.053335   \n",
       "4     515.460006    0.596676    0.969860    1.294158   -0.404728    2.145297   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "1995 -328.100239   -1.820929    1.358539   -2.494838    0.828299    0.199255   \n",
       "1996  -62.960036   -0.883712   -0.063687   -0.418704   -0.872601    0.363769   \n",
       "1997  -90.262252   -0.021394   -0.312864   -0.730848   -1.341482   -0.720862   \n",
       "1998  -74.333901   -0.041909   -0.862731   -1.232011    1.268843    0.093043   \n",
       "1999    8.321454   -1.056364    0.259247    0.307835    1.094903    0.472395   \n",
       "\n",
       "      features_6  features_7  features_8  features_9  ...  features_11  \\\n",
       "0       0.381067    0.306205   -0.637447   -1.332087  ...     1.290725   \n",
       "1       0.549738   -0.763094   -0.455796    2.053388  ...    -1.761306   \n",
       "2       0.587201   -0.209564   -0.428956   -0.757998  ...     0.374645   \n",
       "3       0.790492   -0.212023   -1.142483   -1.124906  ...     0.712160   \n",
       "4       0.997481   -0.541670   -0.952850   -0.592084  ...    -1.347072   \n",
       "...          ...         ...         ...         ...  ...          ...   \n",
       "1995    0.142303   -0.582563    0.472356   -1.411304  ...    -1.062502   \n",
       "1996   -0.368570   -0.104321   -2.493615   -1.694770  ...    -0.197659   \n",
       "1997   -0.683872   -0.805370   -0.521334   -0.074645  ...     1.899276   \n",
       "1998    1.684069    0.753851   -0.537096   -0.971790  ...     1.401321   \n",
       "1999    1.702894   -1.981785    0.032232   -1.142503  ...     0.370107   \n",
       "\n",
       "      features_12  features_13  features_14  features_15  features_16  \\\n",
       "0        1.047483    -1.055467     0.853204     0.038665    -0.752959   \n",
       "1       -0.934284    -1.050999     0.444026    -0.037959     1.061624   \n",
       "2       -1.702189    -0.014514    -0.711557    -0.558523    -1.204526   \n",
       "3       -2.844936     0.483994    -0.694294     1.349605    -1.303414   \n",
       "4        0.243422     0.290336     0.798331     0.876428    -0.366807   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "1995     2.299985    -0.085065     0.357165    -1.400150    -0.976176   \n",
       "1996    -0.533637    -0.598431    -0.039737     1.882953    -1.837356   \n",
       "1997     0.417949     0.324229     0.448252    -0.282110    -2.320763   \n",
       "1998    -0.933149    -0.166864     1.152448     1.605114    -1.339651   \n",
       "1999    -1.574455     0.092401     0.249970     0.048932    -0.376273   \n",
       "\n",
       "      features_17  features_18  features_19  features_20  \n",
       "0        0.577920    -0.657400     1.367308     0.570199  \n",
       "1       -1.569870     2.410696     1.113594     2.329479  \n",
       "2        0.234989     0.398384    -0.236555     0.642003  \n",
       "3        0.161987     0.754084     1.248258    -1.466045  \n",
       "4       -0.119534     0.892320    -0.806912     0.736080  \n",
       "...           ...          ...          ...          ...  \n",
       "1995    -0.796009    -0.148662     0.978054    -0.804272  \n",
       "1996     0.583887     0.603931    -0.356482     0.029355  \n",
       "1997     1.484126     0.392324    -1.116838    -0.459027  \n",
       "1998     0.105628     0.676177    -0.035598    -1.329490  \n",
       "1999     1.285961    -0.054588    -1.183828    -0.356557  \n",
       "\n",
       "[2000 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!wget -O regression_generated.csv https://www.dropbox.com/s/vkl89yce7xjdq4n/regression_generated.csv?dl=1\n",
    "regression = pd.read_csv(\"regression_generated.csv\")\n",
    "regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='features_2', ylabel='features_3'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "regression.plot(x='features_1', y='features_2', kind='scatter' )\n",
    "regression.plot(x='features_1', y='features_3', kind='scatter' )\n",
    "regression.plot(x='features_2', y='features_3', kind='scatter' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8\\. Load the same file of point 6, and convert the file to json with Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv('mushrooms_categorized.csv').to_json('mushrooms_categorized.json', orient='index')"
   ]
  }
 ],
 "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",
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