When working with large files using pd.read_csv() with the chunksize parameter, it is often useful to filter the data in each chunk before processing it. This can reduce the amount of data that needs to be loaded into memory and speed up processing.
Here's an example of how to filter a chunk:
import pandas as pd# specify the file pathfile_path = 'large_file.csv'# specify the chunksize (number of rows to read at a time)chunksize = 1000# initialize an empty list to store the filtered chunksfiltered_chunks = []# loop over the file and read each chunkfor chunk in pd.read_csv(file_path, chunksize=chunksize): # filter the chunk based on a condition filtered_chunk = chunk[chunk['column_name'] == 'filter_value'] # append the filtered chunk to the list of filtered chunks filtered_chunks.append(filtered_chunk)# concatenate the filtered chunks into a single DataFramedf = pd.concat(filtered_chunks, ignore_index=True)# do further processing on the filtered DataFrame# ... |
In this example, we loop over the file using pd.read_csv() and filter each chunk based on a condition. We use the syntax chunk[chunk['column_name'] == 'filter_value'] to filter the chunk based on the value in a specific column.
We then append the filtered chunk to the list of filtered chunks as before.
After processing all the chunks, we concatenate them into a single DataFrame using pd.concat() and do further processing on the filtered DataFrame.
By filtering each chunk before processing it, we can reduce the amount of data that needs to be loaded into memory and speed up processing.