Getting Started

Installation

The DataFrames package is available through the Julia package system and can be installed using the following commands:

using Pkg
Pkg.add("DataFrames")

Throughout the rest of this tutorial, we will assume that you have installed the DataFrames package and have already typed using DataFrames to bring all of the relevant variables into your current namespace.

Note

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 the 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 may want to set the maximum number of data frame rows to print to 100 and the maximum output width in characters to 1000 for every Julia session using some Jupyter kernel file (numbers 100 and 1000 are only examples and can be adjusted). In such case add a "COLUMNS": "1000", "LINES": "100" entry to the "env" variable in this Jupyter kernel file. See here for information about location and specification of Jupyter kernels.

The `DataFrame` Type

Objects of the DataFrame type represent a data table as a series of vectors, each corresponding to a column or variable. The simplest way of constructing a DataFrame is to pass column vectors using keyword arguments or pairs:

julia> using DataFrames

julia> df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"])
4×2 DataFrame
│ Row │ A     │ B      │
│     │ Int64 │ String │
├─────┼───────┼────────┤
│ 1   │ 1     │ M      │
│ 2   │ 2     │ F      │
│ 3   │ 3     │ F      │
│ 4   │ 4     │ M      │

Columns can be accessed via df.col or df[:col]. The latter syntax is more flexible as it allows passing a variable holding the name of the column, and not only a literal name. Note that column names are symbols (:col or Symbol("col")) rather than strings ("col"). Columns can also be accessed using an integer index specifying their position.

julia> df.A
4-element Array{Int64,1}:
 1
 2
 3
 4

julia> df.A === df[:A]
true

julia> df.A === df[1]
true

julia> firstcolumn = :A
:A

julia> df[firstcolumn] === df.A
true

Column names can be obtained using the names function:

julia> names(df)
2-element Array{Symbol,1}:
 :A
 :B

Constructing Column by Column

It is also possible to start with an empty DataFrame and add columns to it one by one:

julia> df = DataFrame()
0×0 DataFrame


julia> df.A = 1:8
1:8

julia> df.B = ["M", "F", "F", "M", "F", "M", "M", "F"]
8-element Array{String,1}:
 "M"
 "F"
 "F"
 "M"
 "F"
 "M"
 "M"
 "F"

julia> df
8×2 DataFrame
│ Row │ A     │ B      │
│     │ Int64 │ String │
├─────┼───────┼────────┤
│ 1   │ 1     │ M      │
│ 2   │ 2     │ F      │
│ 3   │ 3     │ F      │
│ 4   │ 4     │ M      │
│ 5   │ 5     │ F      │
│ 6   │ 6     │ M      │
│ 7   │ 7     │ M      │
│ 8   │ 8     │ F      │

The DataFrame we build in this way has 8 rows and 2 columns. This can be checked using the size function:

julia> size(df, 1)
8

julia> size(df, 2)
2

julia> size(df)
(8, 2)

Constructing Row by Row

It is also possible to fill a DataFrame row by row. Let us construct an empty data frame with two columns (note that the first column can only contain integers and the second one can only contain strings):

julia> df = DataFrame(A = Int[], B = String[])
0×2 DataFrame

Rows can then be added as tuples or vectors, where the order of elements matches that of columns:

julia> push!(df, (1, "M"))
1×2 DataFrame
│ Row │ A     │ B      │
│     │ Int64 │ String │
├─────┼───────┼────────┤
│ 1   │ 1     │ M      │

julia> push!(df, [2, "N"])
2×2 DataFrame
│ Row │ A     │ B      │
│     │ Int64 │ String │
├─────┼───────┼────────┤
│ 1   │ 1     │ M      │
│ 2   │ 2     │ N      │

Rows can also be added as Dicts, where the dictionary keys match the column names:

julia> push!(df, Dict(:B => "F", :A => 3))
3×2 DataFrame
│ Row │ A     │ B      │
│     │ Int64 │ String │
├─────┼───────┼────────┤
│ 1   │ 1     │ M      │
│ 2   │ 2     │ N      │
│ 3   │ 3     │ F      │

Note that constructing a DataFrame row by row is significantly less performant than constructing it all at once, or column by column. For many use-cases this will not matter, but for very large DataFrames this may be a consideration.

Constructing from another table type

DataFrames supports the Tables.jl interface for interacting with tabular data. This means that a DataFrame can be used as a "source" to any package that expects a Tables.jl interface input, (file format packages, data manipulation packages, etc.). A DataFrame can also be a sink for any Tables.jl interface input. Some example uses are:

df = DataFrame(a=[1, 2, 3], b=[:a, :b, :c])

# write DataFrame out to CSV file
CSV.write("dataframe.csv", df)

# store DataFrame in an SQLite database table
SQLite.load!(df, db, "dataframe_table")

# transform a DataFrame through Query.jl package
df = df |> @map({a=_.a + 1, _.b}) |> DataFrame

Working with Data Frames

Examining the Data

The default printing of DataFrame objects only includes a sample of rows and columns that fits on screen:

julia> df = DataFrame(A = 1:2:1000, B = repeat(1:10, inner=50), C = 1:500)
500×3 DataFrame
│ Row │ A     │ B     │ C     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 1     │ 1     │
│ 2   │ 3     │ 1     │ 2     │
│ 3   │ 5     │ 1     │ 3     │
│ 4   │ 7     │ 1     │ 4     │
⋮
│ 496 │ 991   │ 10    │ 496   │
│ 497 │ 993   │ 10    │ 497   │
│ 498 │ 995   │ 10    │ 498   │
│ 499 │ 997   │ 10    │ 499   │
│ 500 │ 999   │ 10    │ 500   │

Printing options can be adjusted by calling the show function manually: show(df, allrows=true) prints all rows even if they do not fit on screen and show(df, allcols=true) does the same for columns.

The first and last functions can be used to look at the first and last rows of a data frame (respectively):

julia> first(df, 6)
6×3 DataFrame
│ Row │ A     │ B     │ C     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 1     │ 1     │
│ 2   │ 3     │ 1     │ 2     │
│ 3   │ 5     │ 1     │ 3     │
│ 4   │ 7     │ 1     │ 4     │
│ 5   │ 9     │ 1     │ 5     │
│ 6   │ 11    │ 1     │ 6     │

julia> last(df, 6)
6×3 DataFrame
│ Row │ A     │ B     │ C     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 989   │ 10    │ 495   │
│ 2   │ 991   │ 10    │ 496   │
│ 3   │ 993   │ 10    │ 497   │
│ 4   │ 995   │ 10    │ 498   │
│ 5   │ 997   │ 10    │ 499   │
│ 6   │ 999   │ 10    │ 500   │

Also notice that when DataFrame is printed to the console or rendered in HTML (e.g. in Jupyter Notebook) you get an information about type of elements held in its columns. For example in this case:

julia> DataFrame(a = 1:2, b = [1.0, missing],
                 c = categorical('a':'b'), d = [1//2, missing])
2×4 DataFrame
│ Row │ a     │ b        │ c            │ d         │
│     │ Int64 │ Float64⍰ │ Categorical… │ Rationa…⍰ │
├─────┼───────┼──────────┼──────────────┼───────────┤
│ 1   │ 1     │ 1.0      │ 'a'          │ 1//2      │
│ 2   │ 2     │ missing  │ 'b'          │ missing   │

we can observe that:

the first column :a can hold elements of type Int64;
the second column :b can hold Float64 or Missing, which is indicated by ⍰ printed after the name of type;
the third column :c can hold categorical data; here we notice …, which indicates that the actual name of the type was long and got truncated;
the type information in fourth column :d presents a situation where the name is both truncated and the type allows Missing.

Taking a Subset

Specific subsets of a data frame can be extracted using the indexing syntax, similar to matrices. The colon : indicates that all items (rows or columns depending on its position) should be retained:

julia> df[1:3, :]
3×3 DataFrame
│ Row │ A     │ B     │ C     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 1     │ 1     │
│ 2   │ 3     │ 1     │ 2     │
│ 3   │ 5     │ 1     │ 3     │

julia> df[[1, 5, 10], :]
3×3 DataFrame
│ Row │ A     │ B     │ C     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 1     │ 1     │
│ 2   │ 9     │ 1     │ 5     │
│ 3   │ 19    │ 1     │ 10    │

julia> df[:, [:A, :B]]
500×2 DataFrame
│ Row │ A     │ B     │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 1     │
│ 2   │ 3     │ 1     │
│ 3   │ 5     │ 1     │
│ 4   │ 7     │ 1     │
⋮
│ 496 │ 991   │ 10    │
│ 497 │ 993   │ 10    │
│ 498 │ 995   │ 10    │
│ 499 │ 997   │ 10    │
│ 500 │ 999   │ 10    │

julia> df[1:3, [:B, :A]]
3×2 DataFrame
│ Row │ B     │ A     │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 1     │
│ 2   │ 1     │ 3     │
│ 3   │ 1     │ 5     │

julia> df[[3, 1], [:C]]
2×1 DataFrame
│ Row │ C     │
│     │ Int64 │
├─────┼───────┤
│ 1   │ 3     │
│ 2   │ 1     │

Do note that df[[:A]] and df[:, [:A]] return a DataFrame object, while df[:A] and df[:, :A] return a vector:

julia> df[[:A]]
500×1 DataFrame
│ Row │ A     │
│     │ Int64 │
├─────┼───────┤
│ 1   │ 1     │
│ 2   │ 3     │
│ 3   │ 5     │
│ 4   │ 7     │
⋮
│ 496 │ 991   │
│ 497 │ 993   │
│ 498 │ 995   │
│ 499 │ 997   │
│ 500 │ 999   │

julia> df[[:A]] == df[:, [:A]]
true

julia> df[:A]
500-element Array{Int64,1}:
   1
   3
   5
   7
   9
  11
   ⋮
 991
 993
 995
 997
 999

julia> df[:A] == df[:, :A]
true

In the first cases, [:A] is a vector, indicating that the resulting object should be a DataFrame, since a vector can contain one or more column names. On the other hand, :A is a single symbol, indicating that a single column vector should be extracted.

The indexing syntax can also be used to select rows based on conditions on variables:

julia> df[df.A .> 500, :]
250×3 DataFrame
│ Row │ A     │ B     │ C     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 501   │ 6     │ 251   │
│ 2   │ 503   │ 6     │ 252   │
│ 3   │ 505   │ 6     │ 253   │
│ 4   │ 507   │ 6     │ 254   │
⋮
│ 246 │ 991   │ 10    │ 496   │
│ 247 │ 993   │ 10    │ 497   │
│ 248 │ 995   │ 10    │ 498   │
│ 249 │ 997   │ 10    │ 499   │
│ 250 │ 999   │ 10    │ 500   │

julia> df[(df.A .> 500) .& (300 .< df.C .< 400), :]
99×3 DataFrame
│ Row │ A     │ B     │ C     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 601   │ 7     │ 301   │
│ 2   │ 603   │ 7     │ 302   │
│ 3   │ 605   │ 7     │ 303   │
│ 4   │ 607   │ 7     │ 304   │
⋮
│ 95  │ 789   │ 8     │ 395   │
│ 96  │ 791   │ 8     │ 396   │
│ 97  │ 793   │ 8     │ 397   │
│ 98  │ 795   │ 8     │ 398   │
│ 99  │ 797   │ 8     │ 399   │

While the DataFrames package provides basic data manipulation capabilities, users are encouraged to use querying frameworks for more convenient and powerful operations:

the Query.jl package provides a LINQ-like interface to a large number of data sources
the DataFramesMeta.jl package provides interfaces similar to LINQ and dplyr

See the Data manipulation frameworks section for more information.

Summarizing Data

The describe function returns a data frame summarizing the elementary statistics and information about each column:

julia> df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"])

julia> describe(df)
2×8 DataFrame
│ Row │ variable │ mean   │ min │ median │ max │ nunique │ nmissing │ eltype   │
│     │ Symbol   │ Union… │ Any │ Union… │ Any │ Union…  │ Nothing  │ DataType │
├─────┼──────────┼────────┼─────┼────────┼─────┼─────────┼──────────┼──────────┤
│ 1   │ A        │ 2.5    │ 1   │ 2.5    │ 4   │         │          │ Int64    │
│ 2   │ B        │        │ F   │        │ M   │ 2       │          │ String   │

Of course, one can also compute descrptive statistics directly on individual columns:

julia> using Statistics

julia> mean(df.A)
2.5

Column-Wise Operations

We can also apply a function to each column of a DataFrame with the aggregate function. For example:

julia> df = DataFrame(A = 1:4, B = 4.0:-1.0:1.0)
4×2 DataFrame
│ Row │ A     │ B       │
│     │ Int64 │ Float64 │
├─────┼───────┼─────────┤
│ 1   │ 1     │ 4.0     │
│ 2   │ 2     │ 3.0     │
│ 3   │ 3     │ 2.0     │
│ 4   │ 4     │ 1.0     │

julia> aggregate(df, sum)
1×2 DataFrame
│ Row │ A_sum │ B_sum   │
│     │ Int64 │ Float64 │
├─────┼───────┼─────────┤
│ 1   │ 10    │ 10.0    │

julia> aggregate(df, [sum, prod])
1×4 DataFrame
│ Row │ A_sum │ B_sum   │ A_prod │ B_prod  │
│     │ Int64 │ Float64 │ Int64  │ Float64 │
├─────┼───────┼─────────┼────────┼─────────┤
│ 1   │ 10    │ 10.0    │ 24     │ 24.0    │

Handling of Columns Stored in a `DataFrame`

Functions that transform a DataFrame to produce a new DataFrame always perform a copy of the columns by default, for example:

julia> df = DataFrame(A = 1:4, B = 4.0:-1.0:1.0)
4×2 DataFrame
│ Row │ A     │ B       │
│     │ Int64 │ Float64 │
├─────┼───────┼─────────┤
│ 1   │ 1     │ 4.0     │
│ 2   │ 2     │ 3.0     │
│ 3   │ 3     │ 2.0     │
│ 4   │ 4     │ 1.0     │

julia> df2 = copy(df);

julia> df2.A === df.A
false

On the other hand, in-place functions, whose names end with !, may mutate the column vectors of the DataFrame they take as an argument, for example:

julia> x = [3, 1, 2];

julia> df = DataFrame(x=x)
3×1 DataFrame
│ Row │ x     │
│     │ Int64 │
├─────┼───────┤
│ 1   │ 3     │
│ 2   │ 1     │
│ 3   │ 2     │

julia> sort!(df)
3×1 DataFrame
│ Row │ x     │
│     │ Int64 │
├─────┼───────┤
│ 1   │ 1     │
│ 2   │ 2     │
│ 3   │ 3     │

julia> x
3-element Array{Int64,1}:
 1
 2
 3

julia> df.x[1] = 100
100

julia> df
3×1 DataFrame
│ Row │ x     │
│     │ Int64 │
├─────┼───────┤
│ 1   │ 100   │
│ 2   │ 2     │
│ 3   │ 3     │

julia> x
3-element Array{Int64,1}:
 100
   2
   3

In-place functions are safe to call, except when a view of the DataFrame (created via a view, @view or groupby) or when a DataFrame created with copycols=false (or with the DataFrame! function) are in use.

It is possible to have a direct access to a column col of a DataFramedf using the syntaxes df.col, df[:col], via the eachcol function, by accessing a parent of a view of a column of a DataFrame, or simply by storing the reference to the column vector before the DataFrame was created with copycols=false (or with the DataFrame! function).

julia> x = [3, 1, 2];

julia> df = DataFrame(x=x)
3×1 DataFrame
│ Row │ x     │
│     │ Int64 │
├─────┼───────┤
│ 1   │ 3     │
│ 2   │ 1     │
│ 3   │ 2     │

julia> df.x == x
true

julia> df[1] !== x
true

julia> eachcol(df, false)[1] === df.x
true

Note that a column obtained from a DataFrame using one of these methods should not be mutated without caution.

The exact rules of handling columns of a DataFrame are explained in The design of handling of columns of a DataFrame section of the manual.

Importing and Exporting Data (I/O)

For reading and writing tabular data from CSV and other delimited text files, use the CSV.jl package.

If you have not used the CSV.jl package before then you may need to install it first:

using Pkg
Pkg.add("CSV")

The CSV.jl functions are not loaded automatically and must be imported into the session.

using CSV

A dataset can now be read from a CSV file at path input using

CSV.read(input)

A DataFrame can be written to a CSV file at path output using

df = DataFrame(x = 1, y = 2)
CSV.write(output, df)

The behavior of CSV functions can be adapted via keyword arguments. For more information, see ?CSV.read and ?CSV.write, or checkout the online CSV.jl documentation.