Meine Versuche das Preprocessing in normale .py Dateien zu verwandeln. Funktioniert noch nicht.
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import os
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import os
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import pandas as pd
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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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def process_single_file(file_path, user_label, interval='1H', threshold=None):
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"""
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Process a single step count CSV file into a pivoted daily activity DataFrame.
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Parameters
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----------
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file_path : str
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Path to the CSV file.
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user_label : int
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Unique label assigned to the user represented by this file.
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interval : str, optional
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Any valid pandas resampling interval (e.g., '1H', '15T', '30min', '5min').
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threshold : float or None, optional
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Minimum step count value to include in aggregation.
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If None, all values are included (no filtering).
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Returns
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-------
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pd.DataFrame
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A DataFrame where each row represents one day of activity with
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boolean indicators for each time interval, plus temporal and user info.
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"""
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# Load dataset with flexible column handling
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df = pd.read_csv(file_path, delimiter=';')
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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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# Ensure required columns exist
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iphone_df = df[df['device'].str.contains('iPhone', na=False)] # Treat NaN as False
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required_cols = {'device', 'startDate', 'value'}
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if not required_cols.issubset(df.columns):
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raise ValueError(f"Missing required columns in {file_path}: {required_cols - set(df.columns)}")
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# Step 2: Select the desired columns
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# Filter for iPhone devices (ignore NaN safely)
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result = iphone_df[['startDate', 'endDate', 'value']]
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iphone_df = df[df['device'].str.contains('iPhone', na=False)].copy()
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if iphone_df.empty:
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# Step 3: Convert startDate to datetime
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return pd.DataFrame() # Skip empty or invalid files
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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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# 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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iphone_df['startDate'] = pd.to_datetime(
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iphone_df['startDate'], errors='coerce'
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)
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iphone_df.dropna(subset=['startDate'], inplace=True)
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# Round down the startDate to the nearest 15-minute interval
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# Round down to the nearest interval dynamically
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iphone_df['15min_interval'] = iphone_df['startDate'].dt.floor('15T')
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iphone_df['interval_start'] = iphone_df['startDate'].dt.floor(interval)
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# Extract date, time, year, and month for 15-minute intervals
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# Extract date and time components
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iphone_df['date'] = iphone_df['15min_interval'].dt.date
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iphone_df['date'] = iphone_df['interval_start'].dt.date
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iphone_df['time'] = iphone_df['15min_interval'].dt.time
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iphone_df['time'] = iphone_df['interval_start'].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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# Apply threshold filtering if specified
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if threshold is not None:
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iphone_df = iphone_df[iphone_df['value'] > threshold]
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# Group by date and time, summing step values within each interval
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interval_sum = (
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iphone_df.groupby(['date', 'time'])['value']
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.sum()
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.reset_index()
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)
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interval_sum = iphone_df.groupby(['date', 'time', 'Year', 'Month'])['value'].sum().reset_index()
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# Generate a full time range based on the chosen interval
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full_time_range = pd.date_range('00:00', '23:59', freq=interval).time
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# Create a full range of 15-minute intervals (00:00:00 to 23:45:00)
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# Pivot to make one row per date, columns as time intervals
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full_time_range = pd.date_range('00:00', '23:45', freq='15T').time
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pivot_table = interval_sum.pivot(
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index='date', columns='time', values='value'
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).fillna(0)
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# Pivot the data to get one row per day with columns for each 15-minute interval
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# Ensure all intervals exist even if missing in data
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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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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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# Rename columns for clarity
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pivot_table.columns = [f'{str(col)}' for col in pivot_table.columns]
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pivot_table.columns = [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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# Reset index to make 'date' a column again
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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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pivot_table.reset_index(inplace=True)
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# Add day of the week
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# Add temporal features
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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['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['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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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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# One-hot encode day of week
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pivot_table = pd.concat([pivot_table, pd.get_dummies(pivot_table['DayOfWeek'], prefix='DayOfWeek')], axis=1)
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pivot_table = pd.concat(
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[pivot_table, pd.get_dummies(pivot_table['DayOfWeek'], prefix='DayOfWeek')],
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axis=1
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)
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# Step 14: Convert 15-minute interval values to binary (True if > 0, else False)
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# Convert all time-interval columns to boolean (active or not)
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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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for col in pivot_table.columns[1:1 + len(full_time_range)]:
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pivot_table[col] = pivot_table[col].apply(lambda x: True if x > 0 else False)
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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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# Add user identifier
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pivot_table['user'] = 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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# Drop original DayOfWeek (we have the one-hot encoded version)
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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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pivot_table.drop(columns=['DayOfWeek'], inplace=True)
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return pivot_table
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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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def process_stepcount_files(input_folders, output_folder,
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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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files_to_skip=None, interval='1H', threshold=None):
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if f'StepCount{i:02d}.csv' not in files_to_skip]
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"""
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Process multiple step count CSV files from given folders into one aggregated Excel dataset.
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# Generate user labels based on file index
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Parameters
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user_labels = list(range(len(file_paths)))
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----------
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input_folders : list of str
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List of folders to scan recursively for CSV files.
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output_folder : str
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Folder path where the combined Excel file will be saved.
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files_to_skip : set or list of str, optional
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Filenames to ignore during processing.
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interval : str, optional
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Any valid pandas resampling interval.
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threshold : float or None, optional
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Minimum value for step count inclusion. If None, all values are used.
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# Process each file with its corresponding user label and concatenate the results
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Returns
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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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-------
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combined_df = pd.concat(processed_dfs, ignore_index=True)
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pd.DataFrame
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Combined DataFrame containing all processed user data.
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"""
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# Save the combined DataFrame to a new Excel file
|
# Ensure skip list is a set for fast lookup
|
||||||
updated_file_path = '/content/combined_aggregated_data_15min_with_threshold.xlsx'
|
files_to_skip = set(files_to_skip or [])
|
||||||
combined_df.to_excel(updated_file_path, index=False)
|
|
||||||
|
|
||||||
# Print the final DataFrame
|
# Collect all CSV file paths
|
||||||
print(combined_df)
|
file_paths = []
|
||||||
|
for folder in input_folders:
|
||||||
|
for root, _, files in os.walk(folder):
|
||||||
|
for fname in files:
|
||||||
|
if fname.endswith('.csv') and fname not in files_to_skip:
|
||||||
|
file_paths.append(os.path.join(root, fname))
|
||||||
|
|
||||||
|
# Assign user labels
|
||||||
|
user_labels = list(range(len(file_paths)))
|
||||||
|
|
||||||
|
# Process each file
|
||||||
|
processed_dfs = []
|
||||||
|
for file_path, user_label in zip(file_paths, user_labels):
|
||||||
|
df = process_single_file(file_path, user_label, interval, threshold)
|
||||||
|
if not df.empty:
|
||||||
|
processed_dfs.append(df)
|
||||||
|
|
||||||
|
# Combine all processed data
|
||||||
|
if not processed_dfs:
|
||||||
|
raise ValueError("No valid data files found for processing.")
|
||||||
|
combined_df = pd.concat(processed_dfs, ignore_index=True)
|
||||||
|
|
||||||
|
# Create output filename dynamically
|
||||||
|
threshold_label = (
|
||||||
|
f"threshold{int(threshold)}" if threshold is not None else "nothreshold"
|
||||||
|
)
|
||||||
|
interval_label = interval.replace(' ', '').replace(':', '')
|
||||||
|
output_filename = f"combined_aggregated_data_{interval_label}_{threshold_label}.xlsx"
|
||||||
|
output_path = os.path.join(output_folder, output_filename)
|
||||||
|
|
||||||
|
# Save to Excel
|
||||||
|
os.makedirs(output_folder, exist_ok=True)
|
||||||
|
combined_df.to_excel(output_path, index=False)
|
||||||
|
|
||||||
|
return combined_df
|
||||||
|
|
||||||
|
|
||||||
def process_file_1_hour_with_threshold(file_path, user_label):
|
# Example usage:
|
||||||
# Load the dataset
|
# combined_df = process_stepcount_files(
|
||||||
df = pd.read_csv(file_path, delimiter=';')
|
# input_folders=['/path/to/data/folder'],
|
||||||
|
# output_folder='/path/to/output/folder',
|
||||||
# Step 1: Filter for iPhone devices
|
# files_to_skip={'StepCount06.csv', 'StepCount10.csv'},
|
||||||
iphone_df = df[df['device'].str.contains('iPhone', na=False)] # Treat NaN as False
|
# interval='30T', # Any valid pandas frequency, e.g. '5T', '10T', '2H', etc.
|
||||||
|
# threshold=25
|
||||||
# Step 2: Select the desired columns
|
# )
|
||||||
result = iphone_df[['startDate', 'endDate', 'value']]
|
|
||||||
|
|
||||||
# Step 3: Convert startDate to datetime
|
|
||||||
iphone_df['startDate'] = pd.to_datetime(iphone_df['startDate'], format='%Y-%m-%d %H:%M:%S %z')
|
|
||||||
|
|
||||||
# Step 4: Round down the startDate to the nearest 1-hour interval
|
|
||||||
iphone_df['1hr_interval'] = iphone_df['startDate'].dt.floor('H')
|
|
||||||
|
|
||||||
# Step 5: Extract date and time
|
|
||||||
iphone_df['date'] = iphone_df['1hr_interval'].dt.date
|
|
||||||
iphone_df['time'] = iphone_df['1hr_interval'].dt.time
|
|
||||||
|
|
||||||
# Step 6: Group by date and time, then sum the values for 1-hour intervals
|
|
||||||
iphone_df_filtered = iphone_df[iphone_df['value'] > 25].dropna(subset=['value'])
|
|
||||||
interval_sum = iphone_df.groupby(['date', 'time'])['value'].sum().reset_index()
|
|
||||||
|
|
||||||
# Step 7: Pivot the data to get one row per day with columns for each 1-hour interval
|
|
||||||
pivot_table = interval_sum.pivot(index='date', columns='time', values='value').fillna(0)
|
|
||||||
|
|
||||||
# Step 8: Create a full range of 1-hour intervals (00:00:00 to 23:00:00)
|
|
||||||
full_time_range = pd.date_range('00:00', '23:00', freq='H').time
|
|
||||||
|
|
||||||
# Step 9: Reindex to include all possible 1-hour intervals and fill missing values with 0
|
|
||||||
pivot_table = pivot_table.reindex(columns=full_time_range, fill_value=0)
|
|
||||||
|
|
||||||
# Step 10: Rename columns to reflect 1-hour intervals
|
|
||||||
pivot_table.columns = [f'{str(col)}' for col in pivot_table.columns]
|
|
||||||
|
|
||||||
# Step 11: Reset index to have 'date' as a column instead of an index
|
|
||||||
pivot_table.reset_index(inplace=True)
|
|
||||||
|
|
||||||
# Step 12: Add day of the week, month, and year columns
|
|
||||||
pivot_table['DayOfWeek'] = pd.to_datetime(pivot_table['date']).dt.day_name()
|
|
||||||
pivot_table['Month'] = pd.to_datetime(pivot_table['date']).dt.month
|
|
||||||
pivot_table['Year'] = pd.to_datetime(pivot_table['date']).dt.year
|
|
||||||
|
|
||||||
# Step 13: One-hot encode the 'DayOfWeek' column
|
|
||||||
pivot_table = pd.concat([pivot_table, pd.get_dummies(pivot_table['DayOfWeek'], prefix='DayOfWeek')], axis=1)
|
|
||||||
|
|
||||||
# Step 14: Convert 1-hour interval values to binary (True if > 0, else False)
|
|
||||||
for col in pivot_table.columns[1:25]: # Skip the 'date' column and focus on 1-hour intervals
|
|
||||||
pivot_table[col] = pivot_table[col].apply(lambda x: True if x > 0 else False)
|
|
||||||
|
|
||||||
# Step 15: Add 'user' column with the specified user label
|
|
||||||
pivot_table['user'] = user_label
|
|
||||||
|
|
||||||
# Print which file is currently being processed
|
|
||||||
print(f"Processing file: {file_path}, User label: {user_label}")
|
|
||||||
|
|
||||||
# Step 16: Drop the 'DayOfWeek' column as it has been one-hot encoded
|
|
||||||
pivot_table.drop(columns=['DayOfWeek'], inplace=True)
|
|
||||||
|
|
||||||
return pivot_table
|
|
||||||
|
|
||||||
# List of files to skip
|
|
||||||
files_to_skip = {'StepCount06.csv','StepCount10.csv','StepCount12.csv', 'StepCount13.csv', 'StepCount15.csv', 'StepCount17.csv',
|
|
||||||
'StepCount18.csv', 'StepCount20.csv', 'StepCount24.csv', 'StepCount27.csv','StepCount31.csv','StepCount32.csv',
|
|
||||||
'StepCount42.csv', 'StepCount46.csv'}
|
|
||||||
|
|
||||||
# Generate file paths, skipping specified files
|
|
||||||
file_paths = [f'/content/drive/My Drive/Data/iOS/StepCount{i:02d}.csv' for i in range(1, 47)
|
|
||||||
if f'StepCount{i:02d}.csv' not in files_to_skip]
|
|
||||||
|
|
||||||
# Generate user labels based on file index
|
|
||||||
user_labels = list(range(len(file_paths)))
|
|
||||||
|
|
||||||
# Process each file with its corresponding user label and concatenate the results
|
|
||||||
processed_dfs = [process_file(file_path, user_label) for file_path, user_label in zip(file_paths, user_labels)]
|
|
||||||
combined_df = pd.concat(processed_dfs, ignore_index=True)
|
|
||||||
|
|
||||||
# Save the combined DataFrame to a new Excel file
|
|
||||||
updated_file_path = '/content/combined_aggregated_data_1hr_withthreshold.xlsx'
|
|
||||||
combined_df.to_excel(updated_file_path, index=False)
|
|
||||||
|
|
||||||
# Print the final DataFrame
|
|
||||||
print(combined_df)
|
|
||||||
|
|
||||||
|
process_stepcount_files(["Step_Data_Project_India/Rest_of_the_World", "Step_Data_Project_India/Europe"], "Step_Data_Project_India/OuptutIndiaTest", interval="1H")
|
||||||
@@ -0,0 +1,36 @@
|
|||||||
|
import data_preprocessing
|
||||||
|
|
||||||
|
# Example usage:
|
||||||
|
# combined_df = process_stepcount_files(
|
||||||
|
# input_folders=[
|
||||||
|
# '/content/drive/My Drive/Data/iOS',
|
||||||
|
# '/content/drive/My Drive/Data/Watch'
|
||||||
|
# ],
|
||||||
|
# output_folder='/content/drive/My Drive/Data/Results',
|
||||||
|
# files_to_skip={
|
||||||
|
# 'StepCount06.csv', 'StepCount10.csv', 'StepCount12.csv',
|
||||||
|
# 'StepCount13.csv', 'StepCount15.csv', 'StepCount17.csv',
|
||||||
|
# 'StepCount18.csv', 'StepCount20.csv', 'StepCount24.csv',
|
||||||
|
# 'StepCount27.csv', 'StepCount31.csv', 'StepCount32.csv',
|
||||||
|
# 'StepCount42.csv', 'StepCount46.csv'
|
||||||
|
# },
|
||||||
|
# interval='15T', # or '1H'
|
||||||
|
# threshold=25 # or None
|
||||||
|
# )
|
||||||
|
|
||||||
|
input_folders=[
|
||||||
|
'Step_Data_Project_India/Europe/Europe',
|
||||||
|
'Step_Data_Project_India/Rest_of_the_World'
|
||||||
|
]
|
||||||
|
output_folder='Step_Data_Project_India/Preprocessing_Results'
|
||||||
|
files_to_skip={
|
||||||
|
'StepCount06.csv', 'StepCount10.csv', 'StepCount12.csv',
|
||||||
|
'StepCount13.csv', 'StepCount15.csv', 'StepCount17.csv',
|
||||||
|
'StepCount18.csv', 'StepCount20.csv', 'StepCount24.csv',
|
||||||
|
'StepCount27.csv', 'StepCount31.csv', 'StepCount32.csv',
|
||||||
|
'StepCount42.csv', 'StepCount46.csv'
|
||||||
|
}
|
||||||
|
interval='15T'
|
||||||
|
threshold=25
|
||||||
|
|
||||||
|
combined_df = data_preprocessing.process_stepcount_files(input_folders, output_folder, files_to_skip, interval, threshold)
|
||||||
Reference in New Issue
Block a user