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 size
filepath = 'large_file.csv'
chunksize = 1000
 
# Create the iterator
csv_iterator = pd.read_csv(filepath, chunksize=chunksize)
 
# Iterate over the chunks and process the data
for 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.