How to Restrict Memory Usage in Python Code with Pandas
Introduction: Python's Pandas library is a powerful tool for data manipulation and analysis. However, working with large datasets can sometimes lead to excessive memory consumption, causing performance issues or even crashing the application. In this blog, we will explore various techniques to restrict memory usage in Python code with Pandas. By the end of this tutorial, you will have practical strategies to handle large datasets efficiently and avoid memory-related problems. 1. Use chunksize for Large CSV Files: When reading large CSV files with pd.read_csv() , you can use the chunksize parameter to read the file in smaller chunks. This way, you won't load the entire file into memory at once, saving memory and allowing you to process the data in manageable portions. import pandas as pd chunksize = 10000 # Set an appropriate chunk size for chunk in pd.read_csv( 'large_data.csv' , chunksize=chunksize): # Process each chunk here 2. Select Specific Colu...