LabFitbit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis de un Smartwatch con Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El conjunto de datos que estoy utilizando para el análisis de datos de Smartwatch está disponible públicamente en este repositorio. Este conjunto de datos se recopiló inicialmente de 30 usuarias del reloj inteligente Fitbit. \n",
    "\n",
    "Ahora comenzaré la tarea de Análisis de datos de Smartwatch importando e instalando las bibliotecas de Python necesarias y el conjunto de datos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly==5.8.1 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (5.8.1)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from plotly==5.8.1) (8.0.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install plotly==5.8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (0.13.2)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from statsmodels) (21.3)\n",
      "Requirement already satisfied: scipy>=1.3 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from statsmodels) (1.8.1)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from statsmodels) (1.3.0)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from statsmodels) (1.21.0)\n",
      "Requirement already satisfied: patsy>=0.5.2 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from statsmodels) (0.5.2)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from packaging>=21.3->statsmodels) (2.4.7)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\monsh\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pandas>=0.25->statsmodels) (2021.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\monsh\\appdata\\roaming\\python\\python39\\site-packages (from pandas>=0.25->statsmodels) (2.8.1)\n",
      "Requirement already satisfied: six in c:\\users\\monsh\\appdata\\roaming\\python\\python39\\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importamos la data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Id ActivityDate  TotalSteps  TotalDistance  TrackerDistance  \\\n",
      "0  1503960366    4/12/2016       13162           8.50             8.50   \n",
      "1  1503960366    4/13/2016       10735           6.97             6.97   \n",
      "2  1503960366    4/14/2016       10460           6.74             6.74   \n",
      "3  1503960366    4/15/2016        9762           6.28             6.28   \n",
      "4  1503960366    4/16/2016       12669           8.16             8.16   \n",
      "\n",
      "   LoggedActivitiesDistance  VeryActiveDistance  ModeratelyActiveDistance  \\\n",
      "0                       0.0                1.88                      0.55   \n",
      "1                       0.0                1.57                      0.69   \n",
      "2                       0.0                2.44                      0.40   \n",
      "3                       0.0                2.14                      1.26   \n",
      "4                       0.0                2.71                      0.41   \n",
      "\n",
      "   LightActiveDistance  SedentaryActiveDistance  VeryActiveMinutes  \\\n",
      "0                 6.06                      0.0                 25   \n",
      "1                 4.71                      0.0                 21   \n",
      "2                 3.91                      0.0                 30   \n",
      "3                 2.83                      0.0                 29   \n",
      "4                 5.04                      0.0                 36   \n",
      "\n",
      "   FairlyActiveMinutes  LightlyActiveMinutes  SedentaryMinutes  Calories  \n",
      "0                   13                   328               728      1985  \n",
      "1                   19                   217               776      1797  \n",
      "2                   11                   181              1218      1776  \n",
      "3                   34                   209               726      1745  \n",
      "4                   10                   221               773      1863  \n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('./data/dailyActivity.csv')\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de seguir adelante, revisamos que este conjunto de datos no tenga algún valor nulo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id                          0\n",
      "ActivityDate                0\n",
      "TotalSteps                  0\n",
      "TotalDistance               0\n",
      "TrackerDistance             0\n",
      "LoggedActivitiesDistance    0\n",
      "VeryActiveDistance          0\n",
      "ModeratelyActiveDistance    0\n",
      "LightActiveDistance         0\n",
      "SedentaryActiveDistance     0\n",
      "VeryActiveMinutes           0\n",
      "FairlyActiveMinutes         0\n",
      "LightlyActiveMinutes        0\n",
      "SedentaryMinutes            0\n",
      "Calories                    0\n",
      "TotalMinutes                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por lo tanto, el conjunto de datos no tiene ningún valor nulo. Revisamos la información sobre las columnas del conjunto de datos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 940 entries, 0 to 939\n",
      "Data columns (total 15 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Id                        940 non-null    int64  \n",
      " 1   ActivityDate              940 non-null    object \n",
      " 2   TotalSteps                940 non-null    int64  \n",
      " 3   TotalDistance             940 non-null    float64\n",
      " 4   TrackerDistance           940 non-null    float64\n",
      " 5   LoggedActivitiesDistance  940 non-null    float64\n",
      " 6   VeryActiveDistance        940 non-null    float64\n",
      " 7   ModeratelyActiveDistance  940 non-null    float64\n",
      " 8   LightActiveDistance       940 non-null    float64\n",
      " 9   SedentaryActiveDistance   940 non-null    float64\n",
      " 10  VeryActiveMinutes         940 non-null    int64  \n",
      " 11  FairlyActiveMinutes       940 non-null    int64  \n",
      " 12  LightlyActiveMinutes      940 non-null    int64  \n",
      " 13  SedentaryMinutes          940 non-null    int64  \n",
      " 14  Calories                  940 non-null    int64  \n",
      "dtypes: float64(7), int64(7), object(1)\n",
      "memory usage: 110.3+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La columna que contiene la fecha del registro es un objeto. Es posible que necesitemos usar fechas en nuestro análisis, así que convirtamos esta columna en una columna de fecha y hora:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 940 entries, 0 to 939\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   Id                        940 non-null    int64         \n",
      " 1   ActivityDate              940 non-null    datetime64[ns]\n",
      " 2   TotalSteps                940 non-null    int64         \n",
      " 3   TotalDistance             940 non-null    float64       \n",
      " 4   TrackerDistance           940 non-null    float64       \n",
      " 5   LoggedActivitiesDistance  940 non-null    float64       \n",
      " 6   VeryActiveDistance        940 non-null    float64       \n",
      " 7   ModeratelyActiveDistance  940 non-null    float64       \n",
      " 8   LightActiveDistance       940 non-null    float64       \n",
      " 9   SedentaryActiveDistance   940 non-null    float64       \n",
      " 10  VeryActiveMinutes         940 non-null    int64         \n",
      " 11  FairlyActiveMinutes       940 non-null    int64         \n",
      " 12  LightlyActiveMinutes      940 non-null    int64         \n",
      " 13  SedentaryMinutes          940 non-null    int64         \n",
      " 14  Calories                  940 non-null    int64         \n",
      " 15  TotalMinutes              940 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(7), int64(8)\n",
      "memory usage: 117.6 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Changing datatype of ActivityDate\n",
    "data[\"ActivityDate\"] = pd.to_datetime(data[\"ActivityDate\"], \n",
    "                                      format=\"%m/%d/%Y\")\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observando todas las columnas; verás información sobre minutos muy activos, bastante activos, ligeramente activos y sedentarios en el conjunto de datos. Combinemos todas estas columnas como minutos totales antes de avanzar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "670    1440\n",
      "168    1440\n",
      "376     742\n",
      "156    1440\n",
      "258    1018\n",
      "Name: TotalMinutes, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data[\"TotalMinutes\"] = data[\"VeryActiveMinutes\"] + data[\"FairlyActiveMinutes\"] + data[\"LightlyActiveMinutes\"] + data[\"SedentaryMinutes\"]\n",
    "print(data[\"TotalMinutes\"].sample(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora revisamos las estadísticas descriptivas del conjunto de datos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Id    TotalSteps  TotalDistance  TrackerDistance  \\\n",
      "count  9.400000e+02    940.000000     940.000000       940.000000   \n",
      "mean   4.855407e+09   7637.910638       5.489702         5.475351   \n",
      "std    2.424805e+09   5087.150742       3.924606         3.907276   \n",
      "min    1.503960e+09      0.000000       0.000000         0.000000   \n",
      "25%    2.320127e+09   3789.750000       2.620000         2.620000   \n",
      "50%    4.445115e+09   7405.500000       5.245000         5.245000   \n",
      "75%    6.962181e+09  10727.000000       7.712500         7.710000   \n",
      "max    8.877689e+09  36019.000000      28.030001        28.030001   \n",
      "\n",
      "       LoggedActivitiesDistance  VeryActiveDistance  ModeratelyActiveDistance  \\\n",
      "count                940.000000          940.000000                940.000000   \n",
      "mean                   0.108171            1.502681                  0.567543   \n",
      "std                    0.619897            2.658941                  0.883580   \n",
      "min                    0.000000            0.000000                  0.000000   \n",
      "25%                    0.000000            0.000000                  0.000000   \n",
      "50%                    0.000000            0.210000                  0.240000   \n",
      "75%                    0.000000            2.052500                  0.800000   \n",
      "max                    4.942142           21.920000                  6.480000   \n",
      "\n",
      "       LightActiveDistance  SedentaryActiveDistance  VeryActiveMinutes  \\\n",
      "count           940.000000               940.000000         940.000000   \n",
      "mean              3.340819                 0.001606          21.164894   \n",
      "std               2.040655                 0.007346          32.844803   \n",
      "min               0.000000                 0.000000           0.000000   \n",
      "25%               1.945000                 0.000000           0.000000   \n",
      "50%               3.365000                 0.000000           4.000000   \n",
      "75%               4.782500                 0.000000          32.000000   \n",
      "max              10.710000                 0.110000         210.000000   \n",
      "\n",
      "       FairlyActiveMinutes  LightlyActiveMinutes  SedentaryMinutes  \\\n",
      "count           940.000000            940.000000        940.000000   \n",
      "mean             13.564894            192.812766        991.210638   \n",
      "std              19.987404            109.174700        301.267437   \n",
      "min               0.000000              0.000000          0.000000   \n",
      "25%               0.000000            127.000000        729.750000   \n",
      "50%               6.000000            199.000000       1057.500000   \n",
      "75%              19.000000            264.000000       1229.500000   \n",
      "max             143.000000            518.000000       1440.000000   \n",
      "\n",
      "          Calories  TotalMinutes  \n",
      "count   940.000000    940.000000  \n",
      "mean   2303.609574   1218.753191  \n",
      "std     718.166862    265.931767  \n",
      "min       0.000000      2.000000  \n",
      "25%    1828.500000    989.750000  \n",
      "50%    2134.000000   1440.000000  \n",
      "75%    2793.250000   1440.000000  \n",
      "max    4900.000000   1440.000000  \n"
     ]
    }
   ],
   "source": [
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analicemos los datos⌚️ del smartwatch\n",
    "El conjunto de datos tiene una columna \"Calorías\"; contiene los datos sobre el número de calorías quemadas en un día.  \n",
    "Verificamos la relación entre las calorías quemadas y el total de pasos caminados en un día:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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    "figure = px.scatter(data_frame = data, x=\"Calories\",\n",
    "                    y=\"TotalSteps\", size=\"VeryActiveMinutes\", \n",
    "                    trendline=\"ols\", \n",
    "                    title=\"Relationship between Calories & Total Steps\")\n",
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    "Puedes ver que existe una relación lineal entre el número total de pasos y el número de calorías quemadas en un día. Ahora veamos el número total promedio de minutos activos en un día:"
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    "         \"Lightly Active Minutes\", \"Inactive Minutes\"]\n",
    "counts = data[[\"VeryActiveMinutes\", \"FairlyActiveMinutes\", \n",
    "               \"LightlyActiveMinutes\", \"SedentaryMinutes\"]].mean()\n",
    "colors = ['gold','lightgreen', \"pink\", \"blue\"]\n",
    "\n",
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    "fig.update_layout(title_text='Total Active Minutes')\n",
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    "Observaciones:  \n",
    "* 81,3% del total de minutos inactivos en un día\n",
    "* 15.8% de minutos ligeramente activos en un día\n",
    "* En promedio, solo 21 minutos (1,74%) fueron muy activos.\n",
    "* y 1,11% (13 minutos) de minutos bastante activos en un día  \n",
    "\n",
    "Transformamos el tipo de datos de la columna ActivityDate en la columna datetime anterior. Usémoslo para encontrar los días de la semana de los registros y agregar una nueva columna a este conjunto de datos como \"Día\":"
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      "0      Tuesday\n",
      "1    Wednesday\n",
      "2     Thursday\n",
      "3       Friday\n",
      "4     Saturday\n",
      "Name: Day, dtype: object\n"
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    "data[\"Day\"] = data[\"ActivityDate\"].dt.day_name()\n",
    "print(data[\"Day\"].head())"
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   "source": [
    "Ahora listemos los minutos muy activos, bastante activos y ligeramente activos en cada día de la semana:"
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   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(\n",
    "    x=data[\"Day\"],\n",
    "    y=data[\"VeryActiveMinutes\"],\n",
    "    name='Very Active',\n",
    "    marker_color='purple'\n",
    "))\n",
    "fig.add_trace(go.Bar(\n",
    "    x=data[\"Day\"],\n",
    "    y=data[\"FairlyActiveMinutes\"],\n",
    "    name='Fairly Active',\n",
    "    marker_color='green'\n",
    "))\n",
    "fig.add_trace(go.Bar(\n",
    "    x=data[\"Day\"],\n",
    "    y=data[\"LightlyActiveMinutes\"],\n",
    "    name='Lightly Active',\n",
    "    marker_color='pink'\n",
    "))\n",
    "fig.update_layout(barmode='group', xaxis_tickangle=-45)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora a la cantidad de minutos inactivos en cada día de la semana:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "day = data[\"Day\"].value_counts()\n",
    "label = day.index\n",
    "counts = data[\"SedentaryMinutes\"]\n",
    "colors = ['gold','lightgreen', \"pink\", \"blue\", \"skyblue\", \"cyan\", \"orange\"]\n",
    "\n",
    "fig = go.Figure(data=[go.Pie(labels=label, values=counts)])\n",
    "fig.update_layout(title_text='Inactive Minutes Daily')\n",
    "fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=30,\n",
    "                  marker=dict(colors=colors, line=dict(color='black', width=3)))\n",
    "fig.show()"
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   "source": [
    "Por lo tanto, el jueves es el día más inactivo de acuerdo con el estilo de vida de todos los individuos en el conjunto de datos. Ahora revisamos la cantidad de calorías quemadas en cada día de la semana:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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    "El martes es, por lo tanto, uno de los días más activos para todas las personas en el conjunto de datos, ya que el mayor número de calorías se quemó los martes.\n",
    "\n",
    "Así es como puede analizar los datos del reloj inteligente utilizando el lenguaje de programación Python. Hay mucho más que puede hacer con este conjunto de datos. También puede usarlo para predecir la cantidad de calorías quemadas en un día."
   ]
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  {
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   "source": [
    "Así es como puede analizar los datos recopilados por un reloj inteligente sobre el estado físico utilizando Python. Los relojes inteligentes son los preferidos por las personas a las que les gusta cuidar su estado físico. El análisis de los datos recopilados sobre su estado físico es uno de los casos de uso de la ciencia de datos en la atención médica. Espero que te haya gustado este artículo sobre análisis de datos de Smartwatch usando Python. "
   ]
  }
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