An Introduction to DataFrames

Bogumił Kamiński, April 30, 2019

The tutorial is for DataFrames 0.18.1.

A brief introduction to basic usage of DataFrames. Tested under Julia 1.1, CSV 0.4.3, CSVFiles 0.15.0, CategoricalArrays 0.5.2, DataFrames 0.18.1, DataFramesMeta 0.4.1, Feather 0.5.1, FileIO 1.0.6, FreqTables 0.3.1, PooledArrays 0.5.1, StatsPlots 0.10.2, Tables 0.1.19. Also package BenchmarkTools 0.4.2 is used as a utility.

I will try to keep the material up to date as the packages evolve.

This tutorial covers DataFrames, CSV, CSVFiles, Feather, and CategoricalArrays, as they constitute the core of DataFrames.

In the last extras part mentions selected functionalities of selected useful packages that I find useful for data manipulation, currently those are: FreqTables, DataFramesMeta, StatsPlots.

Setting up Jupyter Notebook for work with DataFrames.jl

By default Jupyter Notebook will limit the number of rows and columns when displaying a data frame to roughly fit the screen size (like in the REPL).

You can override this behavior by setting ENV["COLUMNS"] or ENV["LINES"] variables to hold the maximum width and height of output in characters respectively before using the notebook function. Alternatively you can add the following entry "COLUMNS": "1000", "LINES": "100" to "env" variable in your Jupyter kernel file. See here for information about location and specification of Jupyter kernels.

TOC

File	Topic
01_constructors.ipynb	Creating DataFrame and conversion
02_basicinfo.ipynb	Getting summary information
03_missingvalues.ipynb	Handling missing values
04_loadsave.ipynb	Loading and saving DataFrames
05_columns.ipynb	Working with columns of DataFrame
06_rows.ipynb	Working with row of DataFrame
07_factors.ipynb	Working with categorical data
08_joins.ipynb	Joining DataFrames
09_reshaping.ipynb	Reshaping DataFrames
10_transforms.ipynb	Transforming DataFrames
11_performance.ipynb	Performance tips
12_pitfalls.ipynb	Possible pitfalls
13_extras.ipynb	Additional interesting packages

Changelog:

Date	Changes
2017-12-05	Initial release
2017-12-06	Added description of insert!, merge!, empty!, categorical!, delete!, DataFrames.index
2017-12-09	Added performance tips
2017-12-10	Added pitfalls
2017-12-18	Added additional worthwhile packages: FreqTables and DataFramesMeta
2017-12-29	Added description of filter and filter!
2017-12-31	Added description of conversion to Matrix
2018-04-06	Added example of extracting a row from a DataFrame
2018-04-21	Major update of whole tutorial
2018-05-01	Added byrow! example
2018-05-13	Added StatPlots package to extras
2018-05-23	Improved comments in sections 1 do 5 by Jane Herriman
2018-07-25	Update to 0.11.7 release
2018-08-25	Update to Julia 1.0 release: sections 1 to 10
2018-08-29	Update to Julia 1.0 release: sections 11, 12 and 13
2018-09-05	Update to Julia 1.0 release: FreqTables section
2018-09-10	Added CSVFiles section to chapter on load/save
2018-09-26	Updated to DataFrames 0.14.0
2018-10-04	Updated to DataFrames 0.14.1, added haskey and repeat
2018-12-08	Updated to DataFrames 0.15.2
2018-01-03	Updated to DataFrames 0.16.0, added serialization instructions
2018-01-18	Updated to DataFrames 0.17.0, added passmissing
2018-01-27	Added Feather.jl file read/write
2018-01-30	Renamed StatPlots.jl to StatsPlots.jl and added Tables.jl
2018-02-08	Added groupvars and groupindices functions
2018-04-27	Updated to DataFrames 0.18.0, dropped JLD2.jl
2018-04-30	Updated handling of missing values description

Core functions summary

1. Constructors: DataFrame, DataFrame!, Tables.rowtable, Tables.columntable, Matrix
2. Getting summary: size, nrow, ncol, describe, names, eltypes, first, last, getindex, setindex!, @view
3. Handling missing: missing (singleton instance of Missing), ismissing, Missings.T, skipmissing, replace, replace!, coalesce, allowmissing, disallowmissing, allowmissing!, completecases, dropmissing, dropmissing!, disallowmissing, disallowmissing!, passmissing
4. Loading and saving: CSV (package), CSVFiles (package), Serialization (module), CSV.read, CSV.write, save, load, serialize, deserialize, Feather.write, Feather.read, Feather.materialize (from Feather)
5. Working with columns: rename, rename!, names!, hcat, insertcol!, DataFrames.hcat!, deletecols, deletecols!, empty!, categorical!, DataFrames.index, permutedims!, haskey, select, select!
6. Working with rows: sort!, sort, issorted, append!, vcat, push!, view, filter, filter!, deleterows!, unique, nonunique, unique!, repeat, parent, parentindices
7. Working with categorical: categorical, cut, isordered, ordered!, levels, unique, levels!, droplevels!, get, recode, recode!
8. Joining: join
9. Reshaping: stack, melt, stackdf, meltdf, unstack
10. Transforming: groupby, vcat, by, aggregate, eachcol, eachrow, mapcols, parent, groupvars, groupindices
11. Extras:
    • FreqTables: freqtable, prop
    • DataFramesMeta: @with, @where, @select, @transform, @orderby, @linq, by, based_on, byrow!
    • StatsPlots: @df, plot, density, histogram,boxplot, violin