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import pandas as pd
import datetime as dt

Apple Health Data – Real-World Data Analysis Project

This notebook demonstrates a real-world data analysis workflow using Apple Health export data (walking distance, step counts, and flights climbed). It covers loading multiple CSV files, selecting relevant columns, parsing dates with pd.to_datetime(), applying unit conversion functions (kilometers to meters) with .apply(), computing summary statistics like .mean(), and creating interactive bar charts with Plotly.

Why this matters: Health and fitness data is a perfect example of messy, real-world time-series data that requires cleaning, transformation, and visualization before useful insights emerge. This project illustrates the complete data science pipeline from raw export to actionable visualization.

DistanceWalkingRunning = pd.read_csv("DistanceWalkingRunning.csv",parse_dates=True)
StepCount = pd.read_csv("StepCount.csv",parse_dates=True)
FlightsClimbed = pd.read_csv("FlightsClimbed.csv")

DistanceWalkingRunning

FlightsClimbed

DistanceWalkingRunning.head()

walking = DistanceWalkingRunning[["creationDate","startDate","endDate","value"]]

walking.head()

walking["creationDate"]

walking.head()

walking.head()

pd.to_datetime(walking["creationDate"])

walking["date"]

walking.head()

import plotly
from plotly.graph_objs import Scatter, Layout
import plotly.graph_objs as go


stepdata = [go.Bar(x=walking["date"].dt.date, y=walking["metres"])]


plotly.offline.plot({ 'data': stepdata,
            'layout': {
               'title': 'Walking (metres)',
               'xaxis': {
                 'title': 'Creation Date'},
               'yaxis': {
                'title': 'Value '}
        }})

def km_to_m(km):
    return int(km*1000)

walking["metres"] = walking["value"].apply(km_to_m)

walking["metres"].mean()