Working with large files efficiently in NumPy - Cheat Sheet & Quick Revision

Recall & Review

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What is memory mapping in NumPy?

Memory mapping allows NumPy to access data stored in a file on disk as if it were in memory, without loading the entire file at once. This helps work with large files efficiently.

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How does np.memmap help when working with large files?

np.memmap creates an array-like object that accesses data directly from disk. It loads only the needed parts into memory, saving RAM and speeding up processing.

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Why should you avoid loading entire large files into memory?

Loading large files fully can cause your computer to slow down or crash because it uses too much RAM. Using techniques like memory mapping avoids this problem.

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What is chunking when processing large files?

Chunking means reading or processing a file in small parts (chunks) instead of all at once. This reduces memory use and helps handle big data smoothly.

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How can you save a large NumPy array efficiently?

You can save large arrays using np.save or np.savez_compressed to store data in binary format, which is faster and smaller than text files.

What does np.memmap do?

A Loads the entire file into memory

B Creates a memory-mapped array accessing data on disk

C Deletes large files to save space

D Converts arrays to text files

np.memmap creates an array-like object that accesses data directly from disk without loading it all into memory.

Why is chunking useful when working with large files?

A It reduces memory usage by processing small parts

B It speeds up internet downloads

C It compresses files automatically

D It converts files to images

Chunking processes data in small parts, reducing memory use and helping handle large files efficiently.

Which of these is a benefit of memory mapping?

A Automatically fixing corrupted files

B Faster CPU speed

C Accessing large files without loading all data into RAM

D Increasing file size

Memory mapping lets you access large files on disk as if they were in memory, without loading everything at once.

What file format does np.save use?

A Binary file

B JSON file

C CSV file

D Text file

np.save stores NumPy arrays in a binary format, which is efficient for saving and loading large data.

What happens if you try to load a very large file fully into memory?

A The file becomes smaller

B The file is automatically compressed

C Nothing special happens

D Your computer may slow down or crash

Loading very large files fully can use too much RAM, causing slowdowns or crashes.

Explain how memory mapping helps when working with large files in NumPy.

Think about how you can look at a big book without reading every page at once.

Describe the concept of chunking and why it is useful for large file processing.

Imagine eating a large pizza slice by slice instead of all at once.