Importing Identical Text Files from Different Subfolders and Merging Them as a Single DataFrame in Python

In this article, we will explore the process of importing identical text files from different subfolders, merging them into a single DataFrame, and handling period information.

Background

When working with data from multiple sources, it’s common to have similar file structures but differing content. In such cases, using techniques like file path manipulation and data merging can help streamline the data collection process.

In this article, we’ll focus on importing text files with identical column names from different subfolders and merging them into a single DataFrame in Python. We will use popular libraries like pandas for data manipulation and pathlib to handle file paths.

Understanding File Paths

Before diving into the code, let’s understand how file paths work in Python:

• The pathlib library is used to manipulate file paths.
• The glob() function returns a list of path objects that match a specified pattern.
• The parent.name.split('_')[1] expression extracts the period name from a given file path.

Importing Identical Text Files

To import identical text files from different subfolders, we need to use the following approach:

1. Use glob() to find all .txt files under the specified root directory.
2. Extract the period name from each file path using parent.name.split('_')[1].
3. Read the contents of each file into a DataFrame using pd.read_csv().
4. Concatenate the DataFrames from different periods using pd.concat().

Code

# Import necessary libraries
import pandas as pd
import pathlib

# Define the root directory
root_dir = './Main_folder/'

# Initialize an empty dictionary to store DataFrames for each period
data = {}

# Iterate over all `.txt` files in the specified root directory
for filename in pathlib.Path(root_dir).glob('**/Cells.txt'):
    # Extract the period name from the file path
    period = filename.parent.name.split('_')[1]
    
    # Read the contents of the current file into a DataFrame
    df = pd.read_csv(filename)
    
    # Add the DataFrame to the dictionary with the period as the key
    data[period] = df

# Concatenate the DataFrames from different periods
Cells = pd.concat(data).drop_duplicates().reset_index(drop=True)

# Rename the index column to 'Period'
Cells = Cells.rename(columns={'index': 'Period'})

# Print the resulting DataFrame
print(Cells)

Explanation of the Code

Here’s a step-by-step explanation of the code:

1. We import the necessary libraries: pandas for data manipulation and pathlib to handle file paths.
2. We define the root directory using ./Main_folder/.
3. We initialize an empty dictionary called data to store DataFrames for each period.
4. Inside the loop, we use glob() to find all .txt files under the specified root directory. We then extract the period name from each file path by splitting the parent directory’s name and taking the second element (split('_')[1]).
5. Next, we read the contents of each file into a DataFrame using pd.read_csv().
6. We add each DataFrame to the dictionary with the corresponding period as the key.
7. Finally, we concatenate the DataFrames from different periods using pd.concat() and drop duplicate rows to create the final merged DataFrame.

Handling Period Information

To handle period information when appending files, we can simply include a new column in the resulting DataFrame that stores the period name:

Cells['Period'] = Cells.index.str[:4]

This code extracts the first four characters from each index (i.e., the period name) and assigns it to a new ‘Period’ column.

Tips and Variations

Here are some additional tips and variations to consider when working with this approach:

• Use os.walk() instead of glob(): If you need to process all subdirectories recursively, use os.walk() instead of glob().
• Handle file encoding issues: Be aware that file encoding can be a problem if the files contain non-ASCII characters. Consider using the encoding parameter when reading CSV files.
• Improve performance for large datasets: If you’re dealing with very large datasets, consider using more efficient data structures or libraries like Dask.

Conclusion

In this article, we’ve explored how to import identical text files from different subfolders and merge them into a single DataFrame in Python. We covered the basics of file path manipulation and provided an example code snippet that handles period information when appending files. With these tips and techniques, you can streamline your data collection process and focus on analyzing the results more efficiently.

Last modified on 2025-01-22