Combine and clean data files with Python Pandas

Table of Contents

1. Introduction
2. Business Requirement
3. Sample Data Files
4. Python Code Overview
5. Setting Up the Environment
6. Importing Libraries
7. Reading and Combining Files
8. Writing the Combined File
9. Testing the Code
10. Conclusion

Introduction

In this article, we will discuss how to write a file combiner utility using the Python panda library and G log. The utility will help us massage, transform, and clean multiple data files and combine them into a single file for analysis purposes. The number of source files can vary, and the utility needs to handle this dynamically. We will provide a step-by-step guide on how to implement this utility in Python, as well as a demonstration of the code execution.

Business Requirement

The business requirement is to combine multiple data files into a single file for analysis purposes. The number of source files can vary, and the utility needs to be able to handle an unknown number of files. The files come from various sources and may contain data for different entities. The goal is to Create a dynamic method that can Read and combine these files into one file.

Sample Data Files

Before we dive into the code, let's take a look at the sample data files. These files contain sales order processing data from different legal entities or countries. Each file is delimited by a pipe character ('|') and contains data for a specific entity. We need to combine these files to perform analysis across multiple entities.

Python Code Overview

The Python code for the file combiner utility is fairly straightforward. We will be using the panda library to read and manipulate data frames, as well as the G log library to search for files. The code will consist of the following steps:

1. Setting up the environment
2. Importing the required libraries
3. Reading and combining the files
4. Writing the combined file
5. Testing the code

We will now proceed with the implementation of each step in Detail.

Setting Up the Environment

To get started, You need to set up your Python environment. Make sure you have Python installed on your system and a code editor of your choice. We recommend using Python Spider IDE for its simplicity and ease of use.

Importing Libraries

The first step is to import the required libraries. We will need the panda library for reading and manipulating data frames, and the G log library for file searching. Use the following code to import the libraries:

import pandas as pd
import glob

Reading and Combining Files

After importing the necessary libraries, we need to specify the path where our data files are located. This path will be a STRING variable containing the folder path. We will then use the glob function to search for all CSV files within the specified path. The output of this search will be stored in a variable called all_files, which will be a list of all the file names.

path = "C:\Path\To\Files"
all_files = glob.glob(path + "/*.csv")

Next, we will create a dynamic array to hold the data frames of each file. Since we don't know in advance how many files there are, we will use a for loop to iterate over all the files. Inside the loop, we will use the pandas read_csv function to read each file and append it to the array.

data_frames = []
for file in all_files:
    df = pd.read_csv(file, sep="|")
    data_frames.append(df)

Finally, we will use the pandas concat function to concatenate all the data frames in the array, vertically. This will combine all the files into a single data frame.

combined_data = pd.concat(data_frames, axis=0, ignore_index=True)

Writing the Combined File

Once we have the combined data frame, we can write it back to a CSV file for further analysis. We will specify the path and file name for the output file, and use the pandas to_csv function to write the data frame to the file.

output_file = "output/all_data.csv"
combined_data.to_csv(output_file, index=False)

Testing the Code

To test the code, you can copy some CSV files into the specified folder and execute the Python script. The script will search for all the CSV files in the folder, combine them into a single data frame, and write the combined data to the output file. You can then open the output file to verify that the data has been successfully combined.

Conclusion

In this article, we have discussed how to write a file combiner utility using the Python panda library and G log. We covered the business requirement of combining multiple data files into a single file for analysis purposes. We provided a step-by-step guide on how to implement the utility in Python, along with a demonstration of the code execution. By following the steps outlined in this article, you can create a dynamic file combiner utility that can handle an unknown number of source files.

Pros:

• Handles a dynamic number of source files
• Uses the panda library for efficient data manipulation
• Can be easily extended for additional functionality

Cons:

• Requires basic knowledge of Python programming