exercise_version-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import re\n",
    "\n",
    "data_dir = \"/persistentStorage/SHealth_DataSet/Shealth/\"\n",
    "\n",
    "## Read Exercise Type csv into dataframe\n",
    "file_name = data_dir + \"exercise_type.csv\"\n",
    "\n",
    "pd_exercise_type = pd.read_csv(file_name, header=0, names=[\"exercise_name\", \"exercise_type\"])\n",
    "pd_exercise_type.dropna(inplace=True)\n",
    "#pd_exercise_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Function to extract file name from the full file name maintained by Samsung\n",
    "def get_short_file_name (full_file_name):\n",
    "    \n",
    "    name = re.sub (r\"com.samsung.*health.\", \"\", full_file_name)\n",
    "    end_index = name.find (\".\")\n",
    "    name = name[:end_index]\n",
    "    \n",
    "    return name\n",
    "\n",
    "## Function to extract the full file name, from the list of files.\n",
    "def get_full_file_name (list_to_search, str_to_search):\n",
    "    for f_name, s_name in list_to_search:\n",
    "        if str_to_search in s_name:\n",
    "            return f_name\n",
    "    \n",
    "    return None\n",
    "\n",
    "## Function to get exercise type from the exercise name\n",
    "def get_exercise_name_type (item_to_search, ex_type_df=pd_exercise_type):\n",
    "    \n",
    "    for (ex_name, ex_type) in zip(ex_type_df[\"exercise_name\"], ex_type_df[\"exercise_type\"]):\n",
    "        \n",
    "        if (type(item_to_search) == str):\n",
    "            if (item_to_search.lower() in ex_name.lower()):\n",
    "                return ex_type\n",
    "        else:\n",
    "            if (item_to_search == ex_type):\n",
    "                return ex_name\n",
    "    \n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create file list\n",
    "## 1. Drop the files related to photos and user_profile\n",
    "## 2. Read only .csv files\n",
    "file_list = [(full_file_name, get_short_file_name (full_file_name))\n",
    "             for full_file_name in os.listdir (data_dir)\n",
    "             if ((\".csv\" in full_file_name) and\n",
    "                 (\".photo\" not in full_file_name) and\n",
    "                 (\".user_profile\" not in full_file_name)\n",
    "                )\n",
    "            ]\n",
    "#file_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read Exercise csv into dataframe\n",
    "file_name = data_dir + get_full_file_name(file_list, \"exercise\")\n",
    "pd_exercise = pd.read_csv(file_name, header=1)\n",
    "\n",
    "#pd_exercise[pd_exercise[\"count_type\"] == 30004][\"exercise_type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_remove = [\"max_rpm\", \"mean_rpm\", \"max_caloricburn_rate\", \"additional\", \"deviceuuid\", \"datauuid\", \n",
    "                  \"mean_caloricburn_rate\", \"create_time\", \"time_offset\", \"custom\", \"end_time\", \"max_power\",\n",
    "                  \"pkg_name\", \"mean_power\", \"exercise_custom_type\", \"update_time\"]\n",
    "\n",
    "pd_exercise.drop(columns=cols_to_remove, axis=1, inplace=True)\n",
    "#pd_exercise.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "556598.04538\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      12.652310\n",
       "237    23.251703\n",
       "239    11.550666\n",
       "312    12.670000\n",
       "315    10.110000\n",
       "372    12.148380\n",
       "430    12.610000\n",
       "681    20.000000\n",
       "718    11.462000\n",
       "852    10.020000\n",
       "976    10.600000\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get Walking Data\n",
    "ex_type = get_exercise_name_type(item_to_search=\"walk\")\n",
    "\n",
    "if (ex_type == None):\n",
    "    print (\"Walking Data Not Found!!\")\n",
    "    exit()\n",
    "\n",
    "walk_data = pd_exercise[pd_exercise[\"exercise_type\"] == ex_type].copy()\n",
    "print (walk_data[\"distance\"].sum())\n",
    "walk_data[walk_data[\"distance\"] / 1000 > 10][\"distance\"] / 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>comment</th>\n",
       "      <th>distance</th>\n",
       "      <th>milestone_distance</th>\n",
       "      <th>milestone_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.4</td>\n",
       "      <td>6.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.4</td>\n",
       "      <td>5.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2.85</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>10.9, 10, 3601</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>12, 10, 3608</td>\n",
       "      <td>12.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>5.5</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3.45</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>4.9</td>\n",
       "      <td>4.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2.6</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2.66</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2.66</td>\n",
       "      <td>2.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>11, 10, 3601</td>\n",
       "      <td>11.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>3.31</td>\n",
       "      <td>3.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>10.1</td>\n",
       "      <td>10.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2.4</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>5.39</td>\n",
       "      <td>5.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>7.5</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>11, 10, 3620</td>\n",
       "      <td>11.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2.9</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2.32</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>15.8, 15, 5700</td>\n",
       "      <td>15.80</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>8.5</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2.39</td>\n",
       "      <td>2.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>6.1</td>\n",
       "      <td>6.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>5.5</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>789</th>\n",
       "      <td>5.44</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>5.7, 5, 1740</td>\n",
       "      <td>5.70</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>6.5</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>3.4</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>4.75</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>2.3</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>3.6</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>3.3</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>2.3</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>2.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>5.5</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>5.3</td>\n",
       "      <td>5.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>3.2</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>10.4, 10, 3612</td>\n",
       "      <td>10.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>2.9</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>3.6</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>5.44</td>\n",
       "      <td>5.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>5.5, 5, 1560</td>\n",
       "      <td>5.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>3</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>8.5</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            comment  distance  milestone_distance  milestone_time\n",
       "8               6.4      6.40                 0.0             0.0\n",
       "15              5.4      5.40                 0.0             0.0\n",
       "24              1.5      1.50                 0.0             0.0\n",
       "29             2.85      2.85                 0.0             0.0\n",
       "39   10.9, 10, 3601     10.90                10.0          3601.0\n",
       "43              3.3      3.30                 0.0             0.0\n",
       "50     12, 10, 3608     12.00                10.0          3608.0\n",
       "54              1.5      1.50                 0.0             0.0\n",
       "55              5.5      5.50                 0.0             0.0\n",
       "66             3.45      3.45                 0.0             0.0\n",
       "67              4.9      4.90                 0.0             0.0\n",
       "89              2.6      2.60                 0.0             0.0\n",
       "95             2.66      2.66                 0.0             0.0\n",
       "97             2.66      2.66                 0.0             0.0\n",
       "99     11, 10, 3601     11.00                10.0          3601.0\n",
       "103            3.31      3.31                 0.0             0.0\n",
       "147            10.1     10.10                 0.0             0.0\n",
       "156             2.4      2.40                 0.0             0.0\n",
       "157            5.39      5.39                 0.0             0.0\n",
       "159             3.3      3.30                 0.0             0.0\n",
       "162             3.3      3.30                 0.0             0.0\n",
       "169             7.5      7.50                 0.0             0.0\n",
       "170    11, 10, 3620     11.00                10.0          3620.0\n",
       "173             2.9      2.90                 0.0             0.0\n",
       "183            2.32      2.32                 0.0             0.0\n",
       "186               1      1.00                 0.0             0.0\n",
       "191             3.3      3.30                 0.0             0.0\n",
       "193  15.8, 15, 5700     15.80                15.0          5700.0\n",
       "195             8.5      8.50                 0.0             0.0\n",
       "202            2.39      2.39                 0.0             0.0\n",
       "..              ...       ...                 ...             ...\n",
       "759             6.1      6.10                 0.0             0.0\n",
       "762             5.5      5.50                 0.0             0.0\n",
       "763             3.3      3.30                 0.0             0.0\n",
       "787             3.3      3.30                 0.0             0.0\n",
       "789            5.44      5.44                 0.0             0.0\n",
       "809             3.3      3.30                 0.0             0.0\n",
       "834    5.7, 5, 1740      5.70                 5.0          1740.0\n",
       "840             6.5      6.50                 0.0             0.0\n",
       "841             3.4      3.40                 0.0             0.0\n",
       "843             3.3      3.30                 0.0             0.0\n",
       "844            4.75      4.75                 0.0             0.0\n",
       "882             2.3      2.30                 0.0             0.0\n",
       "887             3.6      3.60                 0.0             0.0\n",
       "894             3.3      3.30                 0.0             0.0\n",
       "895             2.3      2.30                 0.0             0.0\n",
       "905               2      2.00                 0.0             0.0\n",
       "908               3      3.00                 0.0             0.0\n",
       "911             2.5      2.50                 0.0             0.0\n",
       "913             5.5      5.50                 0.0             0.0\n",
       "922             5.3      5.30                 0.0             0.0\n",
       "925             3.2      3.20                 0.0             0.0\n",
       "932  10.4, 10, 3612     10.40                10.0          3612.0\n",
       "933             2.9      2.90                 0.0             0.0\n",
       "935             3.6      3.60                 0.0             0.0\n",
       "950            5.44      5.44                 0.0             0.0\n",
       "961             3.5      3.50                 0.0             0.0\n",
       "966    5.5, 5, 1560      5.50                 5.0          1560.0\n",
       "968               3      3.00                 0.0             0.0\n",
       "972               1      1.00                 0.0             0.0\n",
       "983             8.5      8.50                 0.0             0.0\n",
       "\n",
       "[140 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Get Treadmill Data\n",
    "ex_type = get_exercise_name_type(item_to_search=\"tread\")\n",
    "\n",
    "if (ex_type == None):\n",
    "    print (\"Tread Data Not Found!!\")\n",
    "    exit()\n",
    "\n",
    "tread_data = pd_exercise[pd_exercise[\"exercise_type\"] == ex_type].copy()\n",
    "\n",
    "## Update information from comments into treadmill data\n",
    "## Format of data in comments - Tot distance, Milestone distance, Milestone time\n",
    "## For this info, 3 new columns are added to the treadmill dataframe\n",
    "## Tot distance has to be manually entered for treadmill and there is no column as of now to store this info\n",
    "## -- Tot distance value will be copied to the existing distance column.\n",
    "## Milestone distance is the maximum of 2.5, 5, 10, 15, 20....\n",
    "## -- Needs a new column\n",
    "## Milestone time is time taken to complete the milestone distance\n",
    "## -- Needs a new column.\n",
    "\n",
    "if (tread_data[\"comment\"].isnull().any()):\n",
    "    print (\"NULL Comment found in raw treadmill data. This needs to be manually corrected!!\")\n",
    "    exit()\n",
    "\n",
    "## Compile a regular expression for float\n",
    "re_float = re.compile(\"\\d+(\\.\\d+)?\")\n",
    "\n",
    "def extract_dist_time (item_from_df):\n",
    "    items = item_from_df.split(\",\")\n",
    "    #print (\"Raw -- \", items, \"Len -- \", len(items))\n",
    "    \n",
    "    dist = mdist = mtime = 0\n",
    "    \n",
    "    if ((len(items) == 1) and re_float.match(items[0])):\n",
    "        dist = float(items[0])\n",
    "    elif ((len(items) == 3) and [re_float.match(x) for x in items]):\n",
    "        dist  = float(items[0])\n",
    "        mdist = float(items[1])\n",
    "        mtime = float(items[2])\n",
    "    else:\n",
    "        print (\"Incorrect Data format in treadmill!!\")\n",
    "        exit()\n",
    "    \n",
    "    return dist, mdist, mtime\n",
    "\n",
    "tread_data.insert(loc=len(tread_data.columns), column=\"milestone_distance\", value=0)\n",
    "tread_data.insert(loc=len(tread_data.columns), column=\"milestone_time\", value=0)\n",
    "\n",
    "tread_data.loc[:, [\"distance\", \"milestone_distance\", \"milestone_time\"]] = [extract_dist_time(item) \n",
    "                                                                           for item in tread_data[\"comment\"]\n",
    "                                                                           ]        \n",
    "tread_data[[\"comment\", \"distance\", \"milestone_distance\", \"milestone_time\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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