Make effective use of data types to prevent crashing of memory

Pandas is a popular Python package for data science, as it offers powerful, expressive, and flexible data structures for data explorations and visualization. But when it comes to handling large-sized datasets, it fails, as it cannot process larger than memory data.

Pandas offer a vast list of API for data explorations and visualization, which makes it more popular among the data scientist community. Dask, modin, Vaex are some of the open-source packages that can scale up the performance of Pandas library and handle large-sized datasets.

When the size of the dataset is comparatively larger than memory using such libraries is preferred, but when dataset size comparatively equal or smaller to memory size, we can optimize the memory usage while reading the dataset. In this article, we will discuss how to optimize memory usage while loading the dataset using pandas.read_csv(),**pandas.read_excel() orpandas.read_excel()**functions.

#machine-learning #education #pandas #optimize pandas memory usage for large datasets #pandas memory #datasets