To concatenate CSV files in Python, you can use the
pandas library. Pandas provides a convenient function called
concat() that allows you to concatenate multiple DataFrames, including those loaded from CSV files. Here’s an example of how you can do it:
import pandas as pd import glob # Get a list of CSV files in a directory csv_files = glob.glob('path/to/csv/files/*.csv') # Create an empty list to store the DataFrames dfs =  # Iterate over the CSV files and load them into DataFrames for file in csv_files: df = pd.read_csv(file) dfs.append(df) # Concatenate the DataFrames concatenated_df = pd.concat(dfs) # Save the concatenated DataFrame to a new CSV file concatenated_df.to_csv('path/to/output.csv', index=False)
In the code above, we first use the
glob module to get a list of CSV files in a directory. Then, we create an empty list called
dfs to store the DataFrames. We iterate over each CSV file, load it into a DataFrame using
pd.read_csv(), and append it to the
After loading all the CSV files, we use
pd.concat() to concatenate the DataFrames into a single DataFrame called
concatenated_df. Finally, we use the
to_csv() method of the concatenated DataFrame to save it as a new CSV file.
Make sure to replace
'path/to/csv/files/' with the actual path to the directory where your CSV files are located, and
'path/to/output.csv' with the desired path and filename for the output file.