Processing Large CSV Files in Pandas

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Large CSV files

First step of any data science project is to analyze the dataset. Sometimes these datasets are fairly large with millions or billions of rows. It becomes a challenge to import and analyze these on laptops or AWS instances. Here is an approach to solving this problem.

For this illustration, we are going to use citibike dataset. This dataset is from the NYC Citibike system and contains anonymized trip data for July 2019.

Pandas read_csv chunksize

Pandas ‘read_csv’ method gives a nice way to handle large files. Parameter ‘chunksize’ supports optionally iterating or breaking of the file into chunks.

By specifying a chunksize to read_csv, the return value will be an iterable object of type TextFileReader.

Example

Here is the sample code for reading the CSV file in chunks of 1000 and print the shape of each chunk.

import pandas as pd
csv_url = 'https://s3.amazonaws.com/tripdata/201907-citibike-tripdata.csv.zip'
c_size = 1000

# load the big file in smaller chunks
for data_chunk in pd.read_csv(csv_url, chunksize=c_size):
    print(data_chunk.shape)

Let’s now do some processing on these chunks. We want to calculate the counts of trips started for each station:

results = pd.DataFrame({})
for data_chunk in pd.read_csv(csv_url, chunksize=c_size):
    results = pd.concat(results, data_chunk['start station name'].value_counts)

results = results.groupby(results.index).sum() 

Specifying iterator=True will also return the TextFileReader object:

reader = pd.read_csv('tmp.sv', sep='|', iterator=True)

reader.get_chunk(5)