Problem with Handling Large Datasets

Problem with Handling Large Datasets

Pandas is a great tool when working with tiny datasets, usually ranging from two to three gigabytes. For datasets bigger than this threshold, using Pandas is not recommended. This is because, should the dataset size surpass the available RAM, Pandas loads the full dataset into memory before processing. Memory problems can occur even with smaller datasets since preprocessing and modification creates duplicates of the DataFrame.Despite these drawbacks, by using particular methods, Pandas may be used to manage bigger datasets in Python. Let’s explore these techniques, which let you use Pandas to analyze millions of records and efficiently manage huge datasets in Python....

How to handle Large Datasets in Python?

Use Efficient Datatypes: Utilize more memory-efficient data types (e.g., int32 instead of int64, float32 instead of float64) to reduce memory usage. Load Less Data: Use the use-cols parameter in pd.read_csv() to load only the necessary columns, reducing memory consumption. Sampling: For exploratory data analysis or testing, consider working with a sample of the dataset instead of the entire dataset. Chunking: Use the chunksize parameter in pd.read_csv() to read the dataset in smaller chunks, processing each chunk iteratively. Optimizing Pandas dtypes: Use the astype method to convert columns to more memory-efficient types after loading the data, if appropriate. Parallelizing Pandas with Dask: Use Dask, a parallel computing library, to scale Pandas workflows to larger-than-memory datasets by leveraging parallel processing....