CardioGoodFitness - Descriptive Statistics.ipynb

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   "source": [
    "# Cardio Good Fitness Case Study - Descriptive Statistics\n",
    "The market research team at AdRight is assigned the task to identify the profile of the typical customer for each treadmill product offered by CardioGood Fitness. The market research team decides to investigate whether there are differences across the product lines with respect to customer characteristics. The team decides to collect data on individuals who purchased a treadmill at a CardioGoodFitness retail store during the prior three months. The data are stored in the CardioGoodFitness.csv file.\n",
    "\n",
    "### The team identifies the following customer variables to study: \n",
    "  - product purchased, TM195, TM498, or TM798; \n",
    "  - gender; \n",
    "  - age, in years; \n",
    "  - education, in years; \n",
    "  - relationship status, single or partnered; \n",
    "  - annual household income ; \n",
    "  - average number of times the customer plans to use the treadmill each week; \n",
    "  - average number of miles the customer expects to walk/run each week; \n",
    "  - and self-rated fitness on an 1-to-5 scale, where 1 is poor shape and 5 is excellent shape.\n",
    "\n",
    "### Perform descriptive analytics to create a customer profile for each CardioGood Fitness treadmill product line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "# Load the necessary packages\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PTQiscNQalxi"
   },
   "outputs": [],
   "source": [
    "# Load the Cardio Dataset\n",
    "    \n",
    "mydata = pd.read_csv('CardioGoodFitness-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "id": "6L3dLuhjalxl",
    "outputId": "517ad271-e60d-4665-b785-d4c807052198"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TM195</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>29562</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>Single</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>31836</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30699</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>32973</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TM195</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>35247</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  Miles\n",
       "0   TM195   18    Male         14        Single      3        4   29562    112\n",
       "1   TM195   19    Male         15        Single      2        3   31836     75\n",
       "2   TM195   19  Female         14     Partnered      4        3   30699     66\n",
       "3   TM195   19    Male         12        Single      3        3   32973     85\n",
       "4   TM195   20    Male         13     Partnered      4        2   35247     47"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## top 5 rows\n",
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
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       "      <th>Miles</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>TM798</td>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Single</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>83416</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>TM798</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>89641</td>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>TM798</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>90886</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>TM798</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>104581</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>TM798</td>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>95508</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    Product  Age Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
       "175   TM798   40   Male         21        Single      6        5   83416   \n",
       "176   TM798   42   Male         18        Single      5        4   89641   \n",
       "177   TM798   45   Male         16        Single      5        5   90886   \n",
       "178   TM798   47   Male         18     Partnered      4        5  104581   \n",
       "179   TM798   48   Male         18     Partnered      4        5   95508   \n",
       "\n",
       "     Miles  \n",
       "175    200  \n",
       "176    200  \n",
       "177    160  \n",
       "178    120  \n",
       "179    180  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "      <th>MaritalStatus</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TM195</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>14</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
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       "      <th>1</th>\n",
       "      <td>TM195</td>\n",
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       "      <th>2</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>30699</td>\n",
       "      <td>66</td>\n",
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       "      <th>3</th>\n",
       "      <td>TM195</td>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>12</td>\n",
       "      <td>Single</td>\n",
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       "      <td>85</td>\n",
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       "      <th>4</th>\n",
       "      <td>TM195</td>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>35247</td>\n",
       "      <td>47</td>\n",
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       "      <th>5</th>\n",
       "      <td>TM195</td>\n",
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       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>32973</td>\n",
       "      <td>66</td>\n",
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       "      <th>6</th>\n",
       "      <td>TM195</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>14</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>35247</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TM195</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Single</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>32973</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TM195</td>\n",
       "      <td>21</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>35247</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TM195</td>\n",
       "      <td>21</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>37521</td>\n",
       "      <td>85</td>\n",
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      "text/plain": [
       "  Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  Miles\n",
       "0   TM195   18    Male         14        Single      3        4   29562    112\n",
       "1   TM195   19    Male         15        Single      2        3   31836     75\n",
       "2   TM195   19  Female         14     Partnered      4        3   30699     66\n",
       "3   TM195   19    Male         12        Single      3        3   32973     85\n",
       "4   TM195   20    Male         13     Partnered      4        2   35247     47\n",
       "5   TM195   20  Female         14     Partnered      3        3   32973     66\n",
       "6   TM195   21  Female         14     Partnered      3        3   35247     75\n",
       "7   TM195   21    Male         13        Single      3        3   32973     85\n",
       "8   TM195   21    Male         15        Single      5        4   35247    141\n",
       "9   TM195   21  Female         15     Partnered      2        3   37521     85"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>TM798</td>\n",
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       "      <td>16</td>\n",
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       "      <td>5</td>\n",
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       "      <th>174</th>\n",
       "      <td>TM798</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <th>175</th>\n",
       "      <td>TM798</td>\n",
       "      <td>40</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Single</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>83416</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>TM798</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>89641</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>TM798</td>\n",
       "      <td>45</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Single</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>90886</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>TM798</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>104581</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>TM798</td>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>95508</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
       "170   TM798   31    Male         16     Partnered      6        5   89641   \n",
       "171   TM798   33  Female         18     Partnered      4        5   95866   \n",
       "172   TM798   34    Male         16        Single      5        5   92131   \n",
       "173   TM798   35    Male         16     Partnered      4        5   92131   \n",
       "174   TM798   38    Male         18     Partnered      5        5  104581   \n",
       "175   TM798   40    Male         21        Single      6        5   83416   \n",
       "176   TM798   42    Male         18        Single      5        4   89641   \n",
       "177   TM798   45    Male         16        Single      5        5   90886   \n",
       "178   TM798   47    Male         18     Partnered      4        5  104581   \n",
       "179   TM798   48    Male         18     Partnered      4        5   95508   \n",
       "\n",
       "     Miles  \n",
       "170    260  \n",
       "171    200  \n",
       "172    150  \n",
       "173    360  \n",
       "174    150  \n",
       "175    200  \n",
       "176    200  \n",
       "177    160  \n",
       "178    120  \n",
       "179    180  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>TM498</td>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>57987</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Product  Age Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
       "139   TM498   48   Male         16     Partnered      2        3   57987   \n",
       "\n",
       "     Miles  \n",
       "139     64  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.shape # 180 rows and 9 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.788889</td>\n",
       "      <td>15.572222</td>\n",
       "      <td>3.455556</td>\n",
       "      <td>3.311111</td>\n",
       "      <td>53719.577778</td>\n",
       "      <td>103.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.943498</td>\n",
       "      <td>1.617055</td>\n",
       "      <td>1.084797</td>\n",
       "      <td>0.958869</td>\n",
       "      <td>16506.684226</td>\n",
       "      <td>51.863605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44058.750000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50596.500000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>58668.000000</td>\n",
       "      <td>114.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>104581.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   Education       Usage     Fitness         Income  \\\n",
       "count  180.000000  180.000000  180.000000  180.000000     180.000000   \n",
       "mean    28.788889   15.572222    3.455556    3.311111   53719.577778   \n",
       "std      6.943498    1.617055    1.084797    0.958869   16506.684226   \n",
       "min     18.000000   12.000000    2.000000    1.000000   29562.000000   \n",
       "25%     24.000000   14.000000    3.000000    3.000000   44058.750000   \n",
       "50%     26.000000   16.000000    3.000000    3.000000   50596.500000   \n",
       "75%     33.000000   16.000000    4.000000    4.000000   58668.000000   \n",
       "max     50.000000   21.000000    7.000000    5.000000  104581.000000   \n",
       "\n",
       "            Miles  \n",
       "count  180.000000  \n",
       "mean   103.194444  \n",
       "std     51.863605  \n",
       "min     21.000000  \n",
       "25%     66.000000  \n",
       "50%     94.000000  \n",
       "75%    114.750000  \n",
       "max    360.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gx7XgVaxalxq",
    "outputId": "5daa8977-f41a-4e35-be8d-ed2468efc4fe"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>TM195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Partnered</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.788889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.572222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.455556</td>\n",
       "      <td>3.311111</td>\n",
       "      <td>53719.577778</td>\n",
       "      <td>103.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.943498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.617055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.084797</td>\n",
       "      <td>0.958869</td>\n",
       "      <td>16506.684226</td>\n",
       "      <td>51.863605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29562.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>44058.750000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50596.500000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>58668.000000</td>\n",
       "      <td>114.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>104581.000000</td>\n",
       "      <td>360.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Product         Age Gender   Education MaritalStatus       Usage  \\\n",
       "count      180  180.000000    180  180.000000           180  180.000000   \n",
       "unique       3         NaN      2         NaN             2         NaN   \n",
       "top      TM195         NaN   Male         NaN     Partnered         NaN   \n",
       "freq        80         NaN    104         NaN           107         NaN   \n",
       "mean       NaN   28.788889    NaN   15.572222           NaN    3.455556   \n",
       "std        NaN    6.943498    NaN    1.617055           NaN    1.084797   \n",
       "min        NaN   18.000000    NaN   12.000000           NaN    2.000000   \n",
       "25%        NaN   24.000000    NaN   14.000000           NaN    3.000000   \n",
       "50%        NaN   26.000000    NaN   16.000000           NaN    3.000000   \n",
       "75%        NaN   33.000000    NaN   16.000000           NaN    4.000000   \n",
       "max        NaN   50.000000    NaN   21.000000           NaN    7.000000   \n",
       "\n",
       "           Fitness         Income       Miles  \n",
       "count   180.000000     180.000000  180.000000  \n",
       "unique         NaN            NaN         NaN  \n",
       "top            NaN            NaN         NaN  \n",
       "freq           NaN            NaN         NaN  \n",
       "mean      3.311111   53719.577778  103.194444  \n",
       "std       0.958869   16506.684226   51.863605  \n",
       "min       1.000000   29562.000000   21.000000  \n",
       "25%       3.000000   44058.750000   66.000000  \n",
       "50%       3.000000   50596.500000   94.000000  \n",
       "75%       4.000000   58668.000000  114.750000  \n",
       "max       5.000000  104581.000000  360.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OpR1RHh-alxt",
    "outputId": "1528037f-982a-410f-8294-1748743bea48",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 180 entries, 0 to 179\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Product        180 non-null    object\n",
      " 1   Age            180 non-null    int64 \n",
      " 2   Gender         180 non-null    object\n",
      " 3   Education      180 non-null    int64 \n",
      " 4   MaritalStatus  180 non-null    object\n",
      " 5   Usage          180 non-null    int64 \n",
      " 6   Fitness        180 non-null    int64 \n",
      " 7   Income         180 non-null    int64 \n",
      " 8   Miles          180 non-null    int64 \n",
      "dtypes: int64(6), object(3)\n",
      "memory usage: 12.8+ KB\n"
     ]
    }
   ],
   "source": [
    "mydata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product          0\n",
       "Age              0\n",
       "Gender           0\n",
       "Education        0\n",
       "MaritalStatus    0\n",
       "Usage            0\n",
       "Fitness          0\n",
       "Income           0\n",
       "Miles            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product          object\n",
       "Age               int64\n",
       "Gender           object\n",
       "Education         int64\n",
       "MaritalStatus    object\n",
       "Usage             int64\n",
       "Fitness           int64\n",
       "Income            int64\n",
       "Miles             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xCWYdGJZalxw",
    "outputId": "c27ab41e-25a5-47d0-9d50-372df8cb06c0",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000246F2712BB0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000246F275D160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000246F2797550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000246F27C49A0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000246F27F1DF0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000246F28291C0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata.hist(figsize=(20,30)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lLAY-ph-alxz",
    "outputId": "791a3af9-22e6-4cc1-a6be-ddde9cbe2063",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2468aef9730>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.boxplot(x=\"Gender\", y=\"Age\", data=mydata) ## Boxplot-One should be numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2468af56d00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"MaritalStatus\", y=\"Age\", data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WQD1evkUalx2",
    "outputId": "1ae66f2a-92fc-4688-d8ba-adca174425c1"
   },
   "outputs": [
    {
     "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>Gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TM195</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TM498</th>\n",
       "      <td>29</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TM798</th>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender   Female  Male\n",
       "Product              \n",
       "TM195        40    40\n",
       "TM498        29    31\n",
       "TM798         7    33"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata['Product'],mydata['Gender'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cDxYJ64nalx5",
    "outputId": "d8a984c9-43fd-454c-a790-2a8eaa2dbc4e"
   },
   "outputs": [
    {
     "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>MaritalStatus</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TM195</th>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TM498</th>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TM798</th>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "MaritalStatus  Partnered  Single\n",
       "Product                         \n",
       "TM195                 48      32\n",
       "TM498                 36      24\n",
       "TM798                 23      17"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(mydata['Product'],mydata['MaritalStatus'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NOLByyabalx8",
    "outputId": "a7bd5799-7dac-4ac6-92e8-c506530430d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2468afec6d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Product\", hue=\"Gender\", data=mydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e5v_4VlValx-",
    "outputId": "8deaa184-1d4a-436e-8262-42c88d3bf270"
   },
   "outputs": [
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Age</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Education</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Fitness</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Income</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Miles</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Usage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TM195</th>\n",
       "      <th>Female</th>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TM498</th>\n",
       "      <th>Female</th>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TM798</th>\n",
       "      <th>Female</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Age        Education          Fitness           Income  \\\n",
       "MaritalStatus  Partnered Single Partnered Single Partnered Single Partnered   \n",
       "Product Gender                                                                \n",
       "TM195   Female        27     13        27     13        27     13        27   \n",
       "        Male          21     19        21     19        21     19        21   \n",
       "TM498   Female        15     14        15     14        15     14        15   \n",
       "        Male          21     10        21     10        21     10        21   \n",
       "TM798   Female         4      3         4      3         4      3         4   \n",
       "        Male          19     14        19     14        19     14        19   \n",
       "\n",
       "                          Miles            Usage         \n",
       "MaritalStatus  Single Partnered Single Partnered Single  \n",
       "Product Gender                                           \n",
       "TM195   Female     13        27     13        27     13  \n",
       "        Male       19        21     19        21     19  \n",
       "TM498   Female     14        15     14        15     14  \n",
       "        Male       10        21     10        21     10  \n",
       "TM798   Female      3         4      3         4      3  \n",
       "        Male       14        19     14        19     14  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(mydata, index=['Product', 'Gender'],\n",
    "                     columns=[ 'MaritalStatus'], aggfunc=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UCjf0j-galyB",
    "outputId": "cccb35aa-319e-4784-d75c-8f73eb6e19b3"
   },
   "outputs": [
    {
     "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>MaritalStatus</th>\n",
       "      <th>Partnered</th>\n",
       "      <th>Single</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TM195</th>\n",
       "      <th>Female</th>\n",
       "      <td>46153.777778</td>\n",
       "      <td>45742.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>50028.000000</td>\n",
       "      <td>43265.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TM498</th>\n",
       "      <th>Female</th>\n",
       "      <td>49724.800000</td>\n",
       "      <td>48920.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>49378.285714</td>\n",
       "      <td>47071.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TM798</th>\n",
       "      <th>Female</th>\n",
       "      <td>84972.250000</td>\n",
       "      <td>58516.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>81431.368421</td>\n",
       "      <td>68216.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "MaritalStatus      Partnered        Single\n",
       "Product Gender                            \n",
       "TM195   Female  46153.777778  45742.384615\n",
       "        Male    50028.000000  43265.842105\n",
       "TM498   Female  49724.800000  48920.357143\n",
       "        Male    49378.285714  47071.800000\n",
       "TM798   Female  84972.250000  58516.000000\n",
       "        Male    81431.368421  68216.428571"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(mydata,'Income', index=['Product', 'Gender'],\n",
    "                     columns=[ 'MaritalStatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bGbFJOc-alyE",
    "outputId": "5a8d24d3-d93b-474b-9790-7466084b8fa5"
   },
   "outputs": [],
   "source": [
    "pd.pivot_table(mydata,'Miles', index=['Product', 'Gender'],\n",
    "                     columns=[ 'MaritalStatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iK2KSu7qalyI",
    "outputId": "eb7fad0f-dae6-4234-8eb9-deadc82729a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2468b07cb80>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(mydata,diag_kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C2_8GA9balyM",
    "outputId": "e705f4a7-8dd1-430e-a465-ca58f6faced5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.943498135399795"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata['Age'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LZYVrJgQalyQ",
    "outputId": "f585f5ca-0344-4f64-8a57-e17c3a9de046"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.788888888888888"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gcol88gJalyX",
    "outputId": "81407f07-9cd8-42b8-93e5-2e9427a6f549"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000247012F89D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000002470138FD60>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata.hist(by='Gender',column = 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rEPATncaalyd",
    "outputId": "64e8abef-01ad-4ae8-80f6-94cb03415ae0"
   },
   "outputs": [],
   "source": [
    "mydata.hist(by='Gender',column = 'Income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4sK-v64Aalyg",
    "outputId": "32ca72c4-c876-4c40-b5c8-955cf2835a0d"
   },
   "outputs": [],
   "source": [
    "mydata.hist(by='Gender',column = 'Miles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DE8kTfUmalyi",
    "outputId": "af6f6fb6-575d-4939-ee25-4ff3d2a39a1b"
   },
   "outputs": [],
   "source": [
    "mydata.hist(by='Product',column = 'Miles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6zeD93f_alym",
    "outputId": "7331fe3c-14d7-4f29-fae6-c898a3863afe"
   },
   "outputs": [
    {
     "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>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Fitness</th>\n",
       "      <th>Income</th>\n",
       "      <th>Miles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.280496</td>\n",
       "      <td>0.015064</td>\n",
       "      <td>0.061105</td>\n",
       "      <td>0.513414</td>\n",
       "      <td>0.036618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>0.280496</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.395155</td>\n",
       "      <td>0.410581</td>\n",
       "      <td>0.625827</td>\n",
       "      <td>0.307284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usage</th>\n",
       "      <td>0.015064</td>\n",
       "      <td>0.395155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.668606</td>\n",
       "      <td>0.519537</td>\n",
       "      <td>0.759130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fitness</th>\n",
       "      <td>0.061105</td>\n",
       "      <td>0.410581</td>\n",
       "      <td>0.668606</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.535005</td>\n",
       "      <td>0.785702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>0.513414</td>\n",
       "      <td>0.625827</td>\n",
       "      <td>0.519537</td>\n",
       "      <td>0.535005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.543473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miles</th>\n",
       "      <td>0.036618</td>\n",
       "      <td>0.307284</td>\n",
       "      <td>0.759130</td>\n",
       "      <td>0.785702</td>\n",
       "      <td>0.543473</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age  Education     Usage   Fitness    Income     Miles\n",
       "Age        1.000000   0.280496  0.015064  0.061105  0.513414  0.036618\n",
       "Education  0.280496   1.000000  0.395155  0.410581  0.625827  0.307284\n",
       "Usage      0.015064   0.395155  1.000000  0.668606  0.519537  0.759130\n",
       "Fitness    0.061105   0.410581  0.668606  1.000000  0.535005  0.785702\n",
       "Income     0.513414   0.625827  0.519537  0.535005  1.000000  0.543473\n",
       "Miles      0.036618   0.307284  0.759130  0.785702  0.543473  1.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = mydata.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fCB5MQo3alyo",
    "outputId": "8c1c70cf-05e2-4ee0-b9ca-7a724edc66eb"
   },
   "outputs": [],
   "source": [
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "CardioGoodFitness - Descriptive Statistics.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}