Which option reduces memory when loading a large CSV with pandas?

Problem Statement

Explanation

Specifying dtypes and selecting only needed columns cuts RAM usage. Parsing dates during read can also help if types are clear. For huge files, consider chunksize to stream data and process in batches.

Code Solution

SolutionRead Only

pd.read_csv('data.csv', usecols=['id','price'], dtype={'id':'int32','price':'float32'})

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Python Libraries & Real-World Usage

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Which option reduces memory when loading a large CSV with pandas?

Problem Statement

Explanation

Code Solution

Practice Sets

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Master Interviews Anywhere, Anytime

Which option reduces memory when loading a large CSV with pandas?

Problem Statement

Explanation

Code Solution

Practice Sets

Related Questions

Is Python compiled, interpreted, or both?

Which of these joins two lists to make one new list?

Which loop runs until a condition becomes false?

Which method gives the floor value of a float?

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Master Interviews
Anywhere, Anytime