Working with large datasets strategies in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is chunking when working with large datasets in pandas?

Chunking means reading or processing the data in smaller parts (chunks) instead of loading the entire dataset at once. This helps manage memory better.

beginner

Why should you use data types like 'category' for columns in large datasets?

Using 'category' data type reduces memory usage by storing repeated values efficiently, which speeds up processing for columns with many repeated values.

beginner

How does filtering data early help when working with large datasets?

Filtering data early means selecting only the needed rows or columns before heavy processing. This reduces the amount of data to handle, saving time and memory.

intermediate

What is the benefit of using 'dask' or similar libraries with pandas for large datasets?

Dask allows you to work with datasets larger than memory by breaking them into smaller parts and processing them in parallel, making big data handling easier.

intermediate

How can saving intermediate results help when working with large datasets?

Saving intermediate results to disk lets you avoid repeating expensive computations and recover progress if the process stops, improving efficiency.

What is the main reason to use chunking when reading large CSV files with pandas?

A To reduce memory usage by loading data in smaller parts

B To speed up the reading by loading all data at once

C To convert data types automatically

D To sort data while reading

Chunking reads the file in smaller pieces, which helps reduce memory usage.

Which pandas data type is best for columns with many repeated string values to save memory?

A int64

B category

C float64

D object

The 'category' type stores repeated values efficiently, saving memory.

What is a good practice before performing heavy computations on large datasets?

A Load data twice for safety

B Convert all data to strings

C Sort data randomly

D Filter data to keep only needed rows and columns

Filtering early reduces data size, making computations faster and less memory-intensive.

Which library helps pandas handle datasets larger than memory by parallel processing?

A NumPy

B Seaborn

C Dask

D Matplotlib

Dask breaks data into parts and processes them in parallel, enabling big data handling.

Why save intermediate results when working with large datasets?

A To avoid repeating expensive computations

B To increase memory usage

C To slow down processing

D To delete original data

Saving intermediate results helps resume work and saves time by not repeating steps.

Explain three strategies to efficiently work with large datasets in pandas.

Think about how to reduce memory use and processing time.

Describe how libraries like Dask help when pandas alone cannot handle large datasets.

Consider how to process big data in pieces.