Julia Computing releases DataFrames.jl 1.0 to allow data scientists to work effectively with tabular data. DataFrames is an equivalent to pandas library that allows data scientists to manipulate large datasets for gaining insights. The latest release of DataFrames brings new capabilities for users to effectively handle and analyze data.
Julia lang is gaining popularity in the data science landscape due to its ability to quickly process a colossal amount of datasets. According to various reports, Julia is faster than Python, giving data scientists an edge while analyzing a plethora of information at once.
However, DataFrames is not the only tool for working with tabular data in Julia. Depending on the use cases, one can also leverage TypedTables and JuliDB. While TypedTables is used to obtain optimized performance when the table does not contains thousands of columns, JulidDB is ideal for when you are handling large datasets that cannot fit in the available memory.
Also Read: Julia Programming 1.5 Released – What’s New
One of the crucial features of Julia is that it allows you to switch between the libraries effectively. For instance, you can use Query.jl code to manipulate data in a DataFrame, JuliaDB, and more. Julia DataFrames is available through Julia packages and can be installed using the command Pkg.add(“DataFrames”).
A wide range of libraries for statistics, machine learning, plotting, data wrangling, and more are integrated with Julia DataFrames to streamline the data science workflows.
