File size: 1,470 Bytes
c5ef85d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | import pandas as pd
# List of the files to process
file_names = ['4_layer_output.csv', '5_layer_output.csv']
output_file = '6_layer_output.csv'
# List to hold the extracted dataframes
extracted_data = []
for file in file_names:
try:
# usecols=lambda x: 'rolling' in x
# This checks the header (first row) and extracts only columns with 'rolling' in the name
df = pd.read_csv(file, usecols=lambda x: 'rolling' in x)
# Add the filename as a prefix to the columns to keep them distinct
# e.g., 'column_name' becomes '4_layer_output_column_name'
prefix = file.replace('.csv', '') + '_'
df = df.add_prefix(prefix)
extracted_data.append(df)
print(f"Processed {file}: Extracted {df.shape[1]} columns.")
except FileNotFoundError:
print(f"Error: The file '{file}' was not found.")
except Exception as e:
print(f"An error occurred while reading '{file}': {e}")
# Check if we have data to combine
if extracted_data:
# Concatenate the dataframes side-by-side (axis=1)
# This assumes the rows in both files correspond to the same samples/timestamps
combined_df = pd.concat(extracted_data, axis=1)
# Save the result to a new CSV file
combined_df.to_csv(output_file, index=False)
print(f"\nSuccess! Combined data saved to '{output_file}'")
else:
print("\nNo columns were extracted.") |