How to efficiently merge multiple JSON files into a single DataFrame in Python 3.9 using pandas?
I'm trying to figure out I'm sure I'm missing something obvious here, but I'm trying to merge several JSON files into a single pandas DataFrame, but I'm running into performance optimization when dealing with a large number of files (around 100)... Each JSON file has a similar structure, but some fields may be missing in certain files. I want to ensure that all the data is included without throwing errors due to missing fields. Here’s a simplified version of what I’m doing: ```python import pandas as pd import os json_dir = 'path/to/json/files' json_files = [f for f in os.listdir(json_dir) if f.endswith('.json')] # Initialize an empty list to hold DataFrames df_list = [] for file in json_files: file_path = os.path.join(json_dir, file) try: df = pd.read_json(file_path) df_list.append(df) except ValueError as e: print(f'behavior reading {file}: {e}') # Handling case where file may not be a valid JSON # Concatenate all DataFrames into one merged_df = pd.concat(df_list, ignore_index=True) ``` This works, but it seems rather slow, especially when scaling up the number of files. I also noticed that if one JSON file has a missing field that others have, it fills those entries with NaN, which is fine, but it feels inefficient. I’ve read about using `pd.json_normalize()` but I wasn't sure how to implement it in this context. Is there a more efficient way to read and merge these JSON files in pandas, perhaps by streamlining how I handle missing fields or improving the read process? Any best practices for merging large datasets would be greatly appreciated. My development environment is Ubuntu. Any help would be greatly appreciated! My development environment is macOS. I'm working on a mobile app that needs to handle this. What would be the recommended way to handle this?