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Trasnfered the jupyter notebook code to a .py file, no changes made yet. Don't execute, will probably make one unhappy.
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Trasnfered the jupyter notebook code to a .py file, no changes made yet. Don't execute, will probably make one unhappy.
master
1 changed files with 335 additions and 0 deletions
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import os |
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import pandas as pd |
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def process_file_one_hour_no_threshold(file_path, user_label): |
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# Load the dataset |
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df = pd.read_csv(file_path, delimiter=';') |
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# Step 1: Filter for iPhone devices |
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iphone_df = df[df['device'].str.contains('iPhone', na=False)] # Treat NaN as False |
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# Step 2: Select the desired columns |
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result = iphone_df[['startDate', 'endDate', 'value']] |
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# Step 3: Convert startDate to datetime |
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iphone_df['startDate'] = pd.to_datetime(iphone_df['startDate'], format='%Y-%m-%d %H:%M:%S %z') |
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# Step 4: Extract date and hour |
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iphone_df['date'] = iphone_df['startDate'].dt.date |
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iphone_df['hour'] = iphone_df['startDate'].dt.hour |
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# Step 5: Group by date and hour, then sum the values |
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hourly_sum = iphone_df.groupby(['date', 'hour'])['value'].sum().reset_index() |
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# Step 6: Pivot the data to get one row per day with 24 columns for each hour |
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pivot_table = hourly_sum.pivot(index='date', columns='hour', values='value').fillna(0) |
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# Step 7: Rename columns to reflect hours |
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pivot_table.columns = [f'Hour_{i}' for i in pivot_table.columns] |
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# Step 8: Reset index to have 'date' as a column instead of index |
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pivot_table.reset_index(inplace=True) |
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# Step 9: Add day of the week, month, and year columns |
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pivot_table['DayOfWeek'] = pd.to_datetime(pivot_table['date']).dt.day_name() |
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pivot_table['Month'] = pd.to_datetime(pivot_table['date']).dt.month |
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pivot_table['Year'] = pd.to_datetime(pivot_table['date']).dt.year |
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# Step 10: One-hot encode the 'DayOfWeek' column |
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pivot_table = pd.concat([pivot_table, pd.get_dummies(pivot_table['DayOfWeek'], prefix='DayOfWeek')], axis=1) |
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# Step 11: Convert hourly values to binary (True if > 0, else False) |
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for col in pivot_table.columns[1:25]: # Skip the 'date' column and focus on hours |
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pivot_table[col] = pivot_table[col].apply(lambda x: True if x > 0 else False) |
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# Step 12: Add 'user' column with the specified user label |
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pivot_table['user'] = user_label |
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# Print which file is currently being processed |
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print(file_path,user_label) |
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# Step 13: Drop the 'DayOfWeek' column |
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pivot_table.drop(columns=['DayOfWeek'], inplace=True) |
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return pivot_table |
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# List of files to skip |
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files_to_skip = {'StepCount06.csv','StepCount10.csv','StepCount12.csv', 'StepCount13.csv', 'StepCount15.csv', 'StepCount17.csv', |
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'StepCount18.csv', 'StepCount20.csv', 'StepCount24.csv','StepCount27.csv', 'StepCount31.csv','StepCount32.csv', |
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'StepCount42.csv', 'StepCount46.csv'} |
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# Generate file paths, skipping specified files |
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file_paths = [f'/content/drive/My Drive/Data/iOS/StepCount{i:02d}.csv' for i in range(1, 47) |
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if f'StepCount{i:02d}.csv' not in files_to_skip] |
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# Generate user labels based on file index |
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user_labels = list(range(len(file_paths))) |
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# Process each file with its corresponding user label and concatenate the results |
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processed_dfs = [process_file(file_path, user_label) for file_path, user_label in zip(file_paths, user_labels)] |
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combined_df = pd.concat(processed_dfs, ignore_index=True) |
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# Save the combined DataFrame to a new Excel file |
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updated_file_path = '/content/combined_aggregated_data.xlsx' |
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combined_df.to_excel(updated_file_path, index=False) |
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# Print the final DataFrame |
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print(combined_df) |
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def process_file_15_min_no_threshold(file_path, user_label): |
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# Load the dataset |
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df = pd.read_csv(file_path, delimiter=';') |
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# Filter for iPhone devices |
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iphone_df = df[df['device'].str.contains('iPhone', na=False)] |
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# Convert startDate to datetime |
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iphone_df['startDate'] = pd.to_datetime(iphone_df['startDate'], format='%Y-%m-%d %H:%M:%S %z') |
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# Round down the startDate to the nearest 15-minute interval |
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iphone_df['15min_interval'] = iphone_df['startDate'].dt.floor('15T') |
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# Extract date, time, year, and month for 15-minute intervals |
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iphone_df['date'] = iphone_df['15min_interval'].dt.date |
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iphone_df['time'] = iphone_df['15min_interval'].dt.time |
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iphone_df['Year'] = iphone_df['15min_interval'].dt.year |
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iphone_df['Month'] = iphone_df['15min_interval'].dt.month |
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# Group by date, time, year, and month, then sum the values |
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interval_sum = iphone_df.groupby(['date', 'time', 'Year', 'Month'])['value'].sum().reset_index() |
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# Create a full range of 15-minute intervals (00:00:00 to 23:45:00) |
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full_time_range = pd.date_range('00:00', '23:45', freq='15T').time |
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# Pivot the data to get one row per day with columns for each 15-minute interval |
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pivot_table = interval_sum.pivot(index=['date', 'Year', 'Month'], columns='time', values='value').fillna(0) |
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# Reindex to include all possible 15-minute intervals |
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pivot_table = pivot_table.reindex(columns=full_time_range, fill_value=0) |
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# Rename columns to reflect 15-minute intervals |
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pivot_table.columns = [f'{str(col)}' for col in pivot_table.columns] |
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# Convert interval values to boolean (True if > 0, else False) |
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pivot_table = pivot_table.apply(lambda col: col != 0, axis=0) |
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# Reset index to have 'date', 'Year', and 'Month' as columns instead of index |
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pivot_table.reset_index(inplace=True) |
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# Add day of the week |
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pivot_table['DayOfWeek'] = pd.to_datetime(pivot_table['date']).dt.day_name() |
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# One-hot encode the 'DayOfWeek' column |
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pivot_table = pd.concat([pivot_table, pd.get_dummies(pivot_table['DayOfWeek'], prefix='DayOfWeek')], axis=1) |
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# Add a user column with the specified user label |
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pivot_table['user'] = user_label |
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# Print which file is currently being processed |
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print(f"Processing file: {file_path}, User label: {user_label}") |
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return pivot_table |
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# List of files to skip |
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files_to_skip = {'StepCount06.csv','StepCount10.csv','StepCount12.csv', 'StepCount13.csv', 'StepCount15.csv', 'StepCount17.csv', |
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'StepCount18.csv', 'StepCount20.csv', 'StepCount24.csv', 'StepCount27.csv','StepCount31.csv','StepCount32.csv', |
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'StepCount42.csv', 'StepCount46.csv'} |
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# Generate file paths, skipping specified files |
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file_paths = [f'/content/drive/My Drive/Data/iOS/StepCount{i:02d}.csv' for i in range(1, 47) |
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if f'StepCount{i:02d}.csv' not in files_to_skip] |
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# Generate user labels based on file index |
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user_labels = list(range(len(file_paths))) |
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# Process each file with its corresponding user label and concatenate the results |
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processed_dfs = [process_file(file_path, user_label) for file_path, user_label in zip(file_paths, user_labels)] |
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combined_df = pd.concat(processed_dfs, ignore_index=True) |
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# Save the combined DataFrame to a new Excel file |
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updated_file_path = '/content/combined_aggregated_data_15min_without_threshold.xlsx' |
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combined_df.to_excel(updated_file_path, index=False) |
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# Print the final DataFrame |
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print(combined_df) |
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user_counts = combined_df['user'].value_counts() |
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# Display the count of each user |
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print(user_counts.sort_index()) |
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def process_file_15_min_with_threshold(file_path, user_label): |
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# Load the dataset |
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df = pd.read_csv(file_path, delimiter=';') |
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# Step 1: Filter for iPhone devices |
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iphone_df = df[df['device'].str.contains('iPhone', na=False)] # Treat NaN as False |
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# Step 2: Select the desired columns |
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result = iphone_df[['startDate', 'endDate', 'value']] |
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# Step 3: Convert startDate to datetime |
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iphone_df['startDate'] = pd.to_datetime(iphone_df['startDate'], format='%Y-%m-%d %H:%M:%S %z') |
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# Step 4: Round down the startDate to the nearest 15-minute interval |
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iphone_df['15min_interval'] = iphone_df['startDate'].dt.floor('15T') |
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# Step 5: Extract date and time |
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iphone_df['date'] = iphone_df['15min_interval'].dt.date |
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iphone_df['time'] = iphone_df['15min_interval'].dt.time |
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# Step 6: Group by date and time, then sum the values for 15-minute intervals |
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iphone_df_filtered = iphone_df[iphone_df['value'] > 25].dropna(subset=['value']) |
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interval_sum = iphone_df.groupby(['date', 'time'])['value'].sum().reset_index() |
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# Step 7: Pivot the data to get one row per day with columns for each 15-minute interval |
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pivot_table = interval_sum.pivot(index='date', columns='time', values='value').fillna(0) |
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# Step 8: Create a full range of 15-minute intervals (00:00:00 to 23:45:00) |
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full_time_range = pd.date_range('00:00', '23:45', freq='15T').time |
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# Step 9: Reindex to include all possible 15-minute intervals and fill missing values with 0 |
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pivot_table = pivot_table.reindex(columns=full_time_range, fill_value=0) |
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# Step 10: Rename columns to reflect 15-minute intervals |
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pivot_table.columns = [f'{str(col)}' for col in pivot_table.columns] |
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# Step 11: Reset index to have 'date' as a column instead of an index |
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pivot_table.reset_index(inplace=True) |
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# Step 12: Add day of the week, month, and year columns |
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pivot_table['DayOfWeek'] = pd.to_datetime(pivot_table['date']).dt.day_name() |
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pivot_table['Month'] = pd.to_datetime(pivot_table['date']).dt.month |
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pivot_table['Year'] = pd.to_datetime(pivot_table['date']).dt.year |
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# Step 13: One-hot encode the 'DayOfWeek' column |
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pivot_table = pd.concat([pivot_table, pd.get_dummies(pivot_table['DayOfWeek'], prefix='DayOfWeek')], axis=1) |
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# Step 14: Convert 15-minute interval values to binary (True if > 0, else False) |
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for col in pivot_table.columns[1:97]: # Skip the 'date' column and focus on 15-minute intervals |
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pivot_table[col] = pivot_table[col].apply(lambda x: True if x > 0 else False) |
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# Step 15: Add 'user' column with the specified user label |
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pivot_table['user'] = user_label |
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# Print which file is currently being processed |
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print(f"Processing file: {file_path}, User label: {user_label}") |
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# Step 16: Drop the 'DayOfWeek' column as it has been one-hot encoded |
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pivot_table.drop(columns=['DayOfWeek'], inplace=True) |
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return pivot_table |
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# List of files to skip |
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files_to_skip = {'StepCount06.csv','StepCount10.csv','StepCount12.csv', 'StepCount13.csv', 'StepCount15.csv', 'StepCount17.csv', |
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'StepCount18.csv', 'StepCount20.csv', 'StepCount24.csv', 'StepCount27.csv','StepCount31.csv','StepCount32.csv', |
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'StepCount42.csv', 'StepCount46.csv'} |
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# Generate file paths, skipping specified files |
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file_paths = [f'/content/drive/My Drive/Data/iOS/StepCount{i:02d}.csv' for i in range(1, 47) |
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if f'StepCount{i:02d}.csv' not in files_to_skip] |
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# Generate user labels based on file index |
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user_labels = list(range(len(file_paths))) |
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# Process each file with its corresponding user label and concatenate the results |
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processed_dfs = [process_file(file_path, user_label) for file_path, user_label in zip(file_paths, user_labels)] |
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combined_df = pd.concat(processed_dfs, ignore_index=True) |
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# Save the combined DataFrame to a new Excel file |
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updated_file_path = '/content/combined_aggregated_data_15min_with_threshold.xlsx' |
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combined_df.to_excel(updated_file_path, index=False) |
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# Print the final DataFrame |
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print(combined_df) |
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def process_file_1_hour_with_threshold(file_path, user_label): |
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# Load the dataset |
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df = pd.read_csv(file_path, delimiter=';') |
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# Step 1: Filter for iPhone devices |
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iphone_df = df[df['device'].str.contains('iPhone', na=False)] # Treat NaN as False |
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# Step 2: Select the desired columns |
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result = iphone_df[['startDate', 'endDate', 'value']] |
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# Step 3: Convert startDate to datetime |
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iphone_df['startDate'] = pd.to_datetime(iphone_df['startDate'], format='%Y-%m-%d %H:%M:%S %z') |
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# Step 4: Round down the startDate to the nearest 1-hour interval |
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iphone_df['1hr_interval'] = iphone_df['startDate'].dt.floor('H') |
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# Step 5: Extract date and time |
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iphone_df['date'] = iphone_df['1hr_interval'].dt.date |
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iphone_df['time'] = iphone_df['1hr_interval'].dt.time |
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# Step 6: Group by date and time, then sum the values for 1-hour intervals |
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iphone_df_filtered = iphone_df[iphone_df['value'] > 25].dropna(subset=['value']) |
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interval_sum = iphone_df.groupby(['date', 'time'])['value'].sum().reset_index() |
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# Step 7: Pivot the data to get one row per day with columns for each 1-hour interval |
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pivot_table = interval_sum.pivot(index='date', columns='time', values='value').fillna(0) |
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# Step 8: Create a full range of 1-hour intervals (00:00:00 to 23:00:00) |
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full_time_range = pd.date_range('00:00', '23:00', freq='H').time |
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# Step 9: Reindex to include all possible 1-hour intervals and fill missing values with 0 |
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pivot_table = pivot_table.reindex(columns=full_time_range, fill_value=0) |
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# Step 10: Rename columns to reflect 1-hour intervals |
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pivot_table.columns = [f'{str(col)}' for col in pivot_table.columns] |
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# Step 11: Reset index to have 'date' as a column instead of an index |
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pivot_table.reset_index(inplace=True) |
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# Step 12: Add day of the week, month, and year columns |
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pivot_table['DayOfWeek'] = pd.to_datetime(pivot_table['date']).dt.day_name() |
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pivot_table['Month'] = pd.to_datetime(pivot_table['date']).dt.month |
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pivot_table['Year'] = pd.to_datetime(pivot_table['date']).dt.year |
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# Step 13: One-hot encode the 'DayOfWeek' column |
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pivot_table = pd.concat([pivot_table, pd.get_dummies(pivot_table['DayOfWeek'], prefix='DayOfWeek')], axis=1) |
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# Step 14: Convert 1-hour interval values to binary (True if > 0, else False) |
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for col in pivot_table.columns[1:25]: # Skip the 'date' column and focus on 1-hour intervals |
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pivot_table[col] = pivot_table[col].apply(lambda x: True if x > 0 else False) |
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# Step 15: Add 'user' column with the specified user label |
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pivot_table['user'] = user_label |
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# Print which file is currently being processed |
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print(f"Processing file: {file_path}, User label: {user_label}") |
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# Step 16: Drop the 'DayOfWeek' column as it has been one-hot encoded |
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pivot_table.drop(columns=['DayOfWeek'], inplace=True) |
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return pivot_table |
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# List of files to skip |
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files_to_skip = {'StepCount06.csv','StepCount10.csv','StepCount12.csv', 'StepCount13.csv', 'StepCount15.csv', 'StepCount17.csv', |
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'StepCount18.csv', 'StepCount20.csv', 'StepCount24.csv', 'StepCount27.csv','StepCount31.csv','StepCount32.csv', |
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'StepCount42.csv', 'StepCount46.csv'} |
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|
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# Generate file paths, skipping specified files |
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file_paths = [f'/content/drive/My Drive/Data/iOS/StepCount{i:02d}.csv' for i in range(1, 47) |
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if f'StepCount{i:02d}.csv' not in files_to_skip] |
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|
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# Generate user labels based on file index |
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user_labels = list(range(len(file_paths))) |
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# Process each file with its corresponding user label and concatenate the results |
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processed_dfs = [process_file(file_path, user_label) for file_path, user_label in zip(file_paths, user_labels)] |
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combined_df = pd.concat(processed_dfs, ignore_index=True) |
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|
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# Save the combined DataFrame to a new Excel file |
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updated_file_path = '/content/combined_aggregated_data_1hr_withthreshold.xlsx' |
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combined_df.to_excel(updated_file_path, index=False) |
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# Print the final DataFrame |
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print(combined_df) |
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