Chunking and filtering can be used together to process large data sets efficiently while only retaining the relevant data in memory. This is particularly useful when working with data sets that are too large to fit into memory all at once.
Here's an example of how to use chunking and filtering together:
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'] # process the filtered chunk here (e.g. compute statistics) # ... # 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 process the filtered chunk (e.g. compute statistics) as required.
After processing all the chunks, we concatenate them into a single DataFrame using pd.concat() and do further processing on the filtered DataFrame.
By using chunking and filtering together, we can process large data sets efficiently while only retaining the relevant data in memory.