Data comes in so many forms (mainly; images, text, and audio) in this data-driven age, we need to read data for our every next project. In this article, I will take you through 4 ways to read datasets with Python programming language.
How to Read Large Datasets with Python?
When you need to read large datasets which size is larger than RAM, your system will run out of RAM while reading such amount of data which can also lead to a shutdown of your system or system crash.
Data Scientists often use Python Pandas to work with tables. While Pandas is great for small to medium size datasets, larger ones are problematic.
Below are the 4 best ways to read large datasets using the Python programming language.
Pandas is an Open Source library which is used to provide high performance and easy to use data structures and various data analysis tools for the Python programming language. Let’s see how to use Pandas to read large datasets with Python:
import pandas as pd train1 = pd.read_csv("train.csv")
Dask is a flexible library in Python for parallel computing. It is made up of dynamic task planning and various Big Data tools. Let’s see how to use Dask to read large datasets:
import dask.dataframe as dd train2 = dd.read_csv("train.csv").compute()
Datatable is a python library for working with tabular data. It supports out of heavy and large datasets, data processing, and flexible APIs. Let’s see how to use Datatable to read large datasets:
import datatable as dt train3 = dt.fread("train.csv")
The RAPIDS is a data science framework which includes a collection of Python libraries for running end-to-end data science pipelines entirely on the GPU. Let’s see how to use it to read large datasets:
import cudf train4 = cudf.read_csv("train.csv")
This is how we can use these 4 libraries for reading large and heavy datasets. I hope you liked this article on how to read large datasets with Python programming language. Feel free to ask your valuable questions in the comments section below.