Chunked reading for large files in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is chunked reading when working with large files?

Chunked reading means loading a file in small parts (chunks) instead of all at once. This helps handle big files without using too much memory.

beginner

Why is chunked reading useful in data analysis?

It allows processing large datasets that don't fit in memory by reading and analyzing them piece by piece.

beginner

Which Python library is commonly used for chunked reading of CSV files?

Pandas library with the 'read_csv' function supports chunked reading using the 'chunksize' parameter.

beginner

How do you specify the size of each chunk when reading a file with pandas?

You set the 'chunksize' parameter to the number of rows you want in each chunk, for example: pd.read_csv('file.csv', chunksize=1000).

intermediate

What type of object does pandas return when reading a file in chunks?

It returns an iterator that yields DataFrames, each containing one chunk of the file.

What does the 'chunksize' parameter in pandas.read_csv control?

A File size in MB

B Number of columns to read

C Number of rows per chunk

D Number of files to read

The 'chunksize' parameter sets how many rows pandas reads at a time, creating smaller chunks.

Why might you use chunked reading instead of reading a whole file at once?

A To read only the first row

B To speed up reading small files

C To change file format

D To reduce memory usage

Chunked reading helps reduce memory use by loading parts of the file instead of the entire file.

What type of object do you get when you read a CSV file with chunksize in pandas?

A An iterator of DataFrames

B A list of DataFrames

C A single DataFrame

D A dictionary

Pandas returns an iterator that yields DataFrames for each chunk.

If you want to process a large CSV file in parts, which pandas method is best?

A pd.read_csv with chunksize

B pd.read_excel

C pd.read_json

D pd.read_csv without chunksize

Using 'chunksize' in pd.read_csv allows reading the file in manageable parts.

What happens if you set chunksize too small?

A File is read faster

B More overhead from many small reads

C Memory usage increases

D File won't load

Very small chunks cause more overhead because the program reads many small pieces, which can slow processing.

Explain how chunked reading helps when working with very large data files.

Think about how loading a huge file all at once can cause problems.

Describe how you would use pandas to read a large CSV file in chunks and process each chunk.

Consider how to handle each piece of data one by one.