prepare.py

# import packages and alias
import numpy as np # data arrays
import pandas as pd # data structure and data analysis
import matplotlib as plt # data visualization
import datetime as dt # date time

# read_csv function to read the required CSV file
daily_activity = pd.read_csv("../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv")

# preview first 10 rows with all columns
daily_activity.head(10)

# obtain the # of missing data points per column
missing_values_count = daily_activity.isnull().sum()

# look at the # of missing points in all columns
missing_values_count[:]
# show basic information of data
daily_activity.info()
# count distinct value of "Id"
unique_id = len(pd.unique(daily_activity["Id"]))
  
print("# of unique Id: " + str(unique_id))

# convert "ActivityDate" to datatime64 dtype and format to yyyy-mm-dd
daily_activity["ActivityDate"] = pd.to_datetime(daily_activity["ActivityDate"], format="%m/%d/%Y")

# re-print information to confirm
daily_activity.info()

# print the first 5 rows of "ActivityDate" to confirm
daily_activity["ActivityDate"].head()

#r create new list of rearranged columns
new_cols = ['Id', 'ActivityDate', 'DayOfTheWeek', 'TotalSteps', 'TotalDistance', 'TrackerDistance', 'LoggedActivitiesDistance', 'VeryActiveDistance', 'ModeratelyActiveDistance', 'LightActiveDistance', 'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'TotalExerciseMinutes', 'TotalExerciseHours', 'Calories']

# reindex function to rearrange columns based on "new_cols"
df_activity = daily_activity.reindex(columns=new_cols)

# print 1st 5 rows to confirm
df_activity.head(5)

# create new column "day_of_the_week" to represent day of the week 
df_activity["DayOfTheWeek"] = df_activity["ActivityDate"].dt.day_name()

# print 1st 5 rows to confirm
df_activity["DayOfTheWeek"].head(5)

# rename columns
df_activity.rename(columns = {"Id":"id", "ActivityDate":"date", "DayOfTheWeek":"day_of_the_week", "TotalSteps":"total_steps", "TotalDistance":"total_dist", "TrackerDistance":"track_dist", "LoggedActivitiesDistance":"logged_dist", "VeryActiveDistance":"very_active_dist", "ModeratelyActiveDistance":"moderate_active_dist", "LightActiveDistance":"light_active_dist", "SedentaryActiveDistance":"sedentary_active_dist", "VeryActiveMinutes":"very_active_mins", "FairlyActiveMinutes":"fairly_active_mins", "LightlyActiveMinutes":"lightly_active_mins", "SedentaryMinutes":"sedentary_mins", "TotalExerciseMinutes":"total_mins","TotalExerciseHours":"total_hours","Calories":"calories"}, inplace = True)

# print column names to confirm
print(df_activity.columns.values)
df_activity.head(5)

# create new column "total_mins" containing sum of total minutes.
df_activity["total_mins"] = df_activity["very_active_mins"] + df_activity["fairly_active_mins"] + df_activity["lightly_active_mins"] + df_activity["sedentary_mins"]
df_activity["total_mins"].head(5)

# create new column *total_hours* by converting to hour and round float to two decimal places
df_activity["total_hours"] = round(df_activity["total_mins"] / 60)

# print 1st 5 rows to confirm
df_activity["total_hours"].head(5)