Pandas provides a way to read large CSV files in chunks using the read_csv function with the chunksize parameter. The chunksize parameter specifies the number of rows to read at a time, and returns a TextFileReader object that can be iterated over to get the data in chunks.
Here is an example of how to use the read_csv iterator to read a large CSV file in chunks:
import pandas as pd# Set the file path and chunk sizefilepath = 'large_file.csv'chunksize = 1000# Create the iteratorcsv_iterator = pd.read_csv(filepath, chunksize=chunksize)# Iterate over the chunks and process the datafor chunk in csv_iterator: # Do something with the chunk of data process_data(chunk) |
In this example, we set the file path to the large CSV file and specify a chunk size of 1000 rows. We then create an iterator using the pd.read_csv function with the chunksize parameter. The iterator returns a TextFileReader object that we can iterate over in a for loop.
Inside the loop, we can process the chunk of data by passing it to a function called process_data, which can perform any necessary transformations or analyses on the data.
By using the read_csv iterator in this way, we can efficiently process large CSV files that would otherwise be too large to fit into memory.