Converting a Text File with JSON Values to CSV

Introduction

In this article, we will explore how to convert a text file containing JSON values to CSV format. This task can be achieved using Python programming language and the required libraries are json and pandas. We’ll also discuss some alternatives for large files.

JSON Data Format

Before diving into code examples, let’s briefly review the JSON data format:

• It is a lightweight data interchange format.
• It is easy to read and write and works on many operating systems.
• A JSON file can contain only string values, numbers, booleans, objects (key-value pairs), arrays, and null.

Sample JSON Data

Below is the sample JSON data in our text file:

{"id": "testid1","title": "testtitle1","link": "testlink1","description": "testdes2","entities": ["en1", "en2"]}
{"id": "testid2","title": "testtitle2","link": "testlink2","description": "testdes2","entities": [""]}
{"id": "testid1","title": "testtitle1","link": "testlink1","description": "testdesc","entities": ["en1", "en2", "en3"]}

CSV Data Format

Below is the desired CSV data format for our sample JSON data:

id  title   link    description entities__001   entities__002   entities__003
testid1 testtitle1  testlink1   testdes2    en1 en2 
testid2 testtitle2  testlink2   testdes2            
testid1 testtitle1  testlink1   testdesc    en1 en2 en3

Converting JSON to CSV Using Python

To achieve the desired output, we’ll use the following steps:

Step 1: Read and Process the JSON Data

We will read our text file line by line using a for loop, process each string as JSON data, and store the resulting dictionaries in a list.

import json

# initialize an empty list to hold processed data
processed_data = []

with open(r".\data_file.txt") as f:
    # iterate over the lines in our file (to convert from string to json)
    for line in f:
        try:
            # attempt to parse each string as a JSON object
            data = json.loads(line)
            processed_data.append(data)

except json.JSONDecodeError:
    print("There was an error parsing the JSON. The file may contain invalid or malformed JSON.")

Step 2: Flatten the Data

To convert the entities field from arrays to new columns in our CSV, we will iterate over each dictionary and create a new key-value pair for each entity.

import pandas as pd

# Iterate over the processed data (to add new fields)
for document in processed_data:
    # iterate through the entities list
    for i in range(len(document["entities"])):
        # create a new field with the same index as the original entity
        document[f"entities_{i+1}"] = document["entities"][i]

# Create DataFrame from our processed data
df = pd.DataFrame(processed_data)

Step 3: Remove Original Column and Save to CSV

We’ll remove the entities column if it exists, and save the resulting DataFrame as a CSV file.

# Drop the 'entities' column (if needed)
del df["entities"]

# Save our processed data as a csv file
df.to_csv(r"./out_folder/out_data.csv", index=False)

Handling Large Files

When dealing with large files, you’ll need to be mindful of memory usage. The above method works but can be inefficient for very large files due to its reliance on RAM.

For such cases, a different approach is to process the file in chunks or streams:

import json
import pandas as pd

with open(r".\data_file.txt", 'r') as f:
    # Iterate over the lines in our file (to convert from string to json)
    for line in f:
        try:
            # Attempt to parse each string as a JSON object
            data = json.loads(line)

            # Create DataFrame for processing chunk of data
            df_chunk = pd.DataFrame([data])

            # Add new field with index for entity
            df_chunk[f"entities_{len(df_chunk) - 1}"] = [df_chunk.iloc[0]['entities'][0]]

        except json.JSONDecodeError:
            print("There was an error parsing the JSON. The file may contain invalid or malformed JSON.")

Best Practices and Alternatives

Here are some best practices when working with text files, JSON data, and CSV formats:

• Always validate user input: Ensure that the data you’re converting meets your requirements by validating it before processing.
• Handle exceptions: Anticipate potential errors in your code and handle them properly to prevent application crashes or unexpected behavior.
• Consider using alternative libraries or tools if you encounter issues with memory usage or performance.

By following these guidelines and techniques, you’ll be able to efficiently convert text files with JSON values into CSV format using Python.

Last modified on 2024-03-31