DataFrames.jl isn't the only tool for working with tabular data in Julia – as noted below, there are some other great libraries for certain use-cases – but it provides great data wrangling functionality through a familiar interface.

In-memory tabular data in Julia. Contribute to JuliaData/DataFrames.jl development by creating an account on GitHub.

This way of using functions to wrap around basic functionality in programming languages and packages is quite common. Basically, you can think of Julia and DataFrames.jl as providers of building blocks. They provide very generic building blocks which allow you to build things for your specific ...

─────┼─────────────────────────────────── 1 │ a 1 None x 2 │ None 2 j y 3 │ b 3 k None 4 │ None 4 h z julia> df .= ifelse.(df .== "None", missing, df) 4×4 DataFrame Row │ a b c d │ String? Int64? String? String? ─────┼─────────────────────────────────── 1 │ a 1 missing x 2 │ missing 2 j y 3 │ b 3 k missing 4 │ missing 4 h z ... This document was generated with Documenter.jl version 1.14.1 on Friday 17 October 2025.

DataFrames.jl has the functions select, transform, and combine, as well as the in-place select! and transform! for manipulating data frames. DataFramesMeta.jl provides the macros @select, @transform, @combine, @select!, and @transform! to mirror these functions with more convenient syntax.

Hence, for example, if the user ... available options, check PrettyTables.jl documentation. Objects of the DataFrame type represent a data table as a series of vectors, each corresponding to a column or variable....

September 23, 2023 - DataFrames.jl is a package written for and in the Julia language offering flexible and efficient handling of tabular data sets in memory. Thanks to Julia's unique strengths, it provides an appealing set of features: Rich support for standard data processing tasks and excellent flexibility and ...

January 27, 2021 - DataFrames: http://juliadata.github.io/DataFrames.jl/stable/, https://en.wikibooks.org/wiki/Introducing_Julia/DataFrames

In-memory tabular data in Julia. Contribute to JuliaData/DataFrames.jl development by creating an account on GitHub.

You now know how to create a DataFrame from data that you already have in your Julia session. In the next section we show how to load data to a DataFrame from disk. Here we focus on one of the most common scenarios, where one has data stored on disk in the CSV format. First make sure you have CSV.jl installed.

Pandas uses NaN for representing both missing data and the floating point "not a number" value. Julia defines a special value missing for representing missing data. DataFrames.jl respects general rules in Julia in propagating missing values by default. If necessary, the skipmissing function ...

In the example below, you can also see some of DataFrameMacros.jl's multi-column features, where mean is applied to both age columns at once by selecting them with the r"age" regex. The new column names are then derived using the "{}" shortcut which splices the transformed column names into a string. julia> using DataFrames, DataFrameMacros, Chain, Statistics julia> df = DataFrame(name=["John", "Sally", "Roger"], age=[54.0, 34.0, 79.0], children=[0, 2, 4]) 3×3 DataFrame Row │ name age children │ String Float64 Int64 ─────┼───────────────────

Below detailed common rules for all transformation functions supported by DataFrames.jl are explained and compared.

In simple cases, when compilation latency of CSV.jl might be an issue, using the DelimitedFiles module from the Julia standard library can be considered. Here is an example showing how to read in the data and perform its post-processing: julia> using DelimitedFiles, DataFrames julia> path = joinpath(pkgdir(DataFrames), "docs", "src", "assets", "iris.csv"); julia> data, header = readdlm(path, ',', header=true); julia> iris_raw = DataFrame(data, vec(header)) 150×5 DataFrame Row │ SepalLength SepalWidth PetalLength PetalWidth Species │ Any Any Any Any Any ─────┼──────

Bogumił Kamiński is an Associate Professor and Head of the Decision Analysis and Support Unit at the Warsaw School of Economics. He is also the author of the DataFrames.jl package and a prolific Julia ecosystem contributor.