DataFrames.jl
Welcome to the DataFrames documentation!
This resource aims to teach you everything you need to know to get up and running with tabular data manipulation using the DataFrames.jl package. For more illustrations of its usage in conjunction with other packages, the DataFrames Tutorial using Jupyter Notebooks is a good complementary resource.
If there is something you expect DataFrames to be capable of, but cannot figure out how to do, please reach out with questions in Domains/Data on Discourse.
Please report bugs by opening an issue.
You can follow the source links throughout the documentation to jump right to the source files on GitHub to make pull requests for improving the documentation and function capabilities.
Please review DataFrames contributing guidelines before submitting your first PR!
Information on specific versions can be found on the Release page.
Package Manual
- Getting Started
- Installation
- The
DataFrame
Type - Working with Data Frames
- Replacing Data
- Importing and Exporting Data (I/O)
- Database-Style Joins
- The Split-Apply-Combine Strategy
- Reshaping and Pivoting Data
- Sorting
- Categorical Data
- Missing Data
- Data manipulation frameworks
API
Only exported (i.e. available for use without DataFrames.
qualifier after loading the DataFrames.jl package with using DataFrames
) types and functions are considered a part of the public API of the DataFrames.jl package. In general all such objects are documented in this manual (in case some documentation is missing please kindly report an issue here).
All types and functions that are part of public API are guaranteed to go through a deprecation period before being changed or removed.
Please be warned that while Julia allows you to access internal functions or types of DataFrames.jl these can change without warning between versions of DataFrames.jl. In particular it is not safe to directly access fields of types that are a part of public API of the DataFrames.jl package using e.g. the getfield
function. Whenever some operation on fields of defined types is considered allowed an appropriate exported function should be used instead.
Index
DataFrames.AbstractDataFrame
DataFrames.AsTable
DataFrames.ByRow
DataFrames.DataFrame
DataFrames.DataFrameColumns
DataFrames.DataFrameRow
DataFrames.DataFrameRows
DataFrames.GroupKey
DataFrames.GroupKeys
DataFrames.GroupedDataFrame
DataFrames.RepeatedVector
DataFrames.StackedVector
DataFrames.SubDataFrame
Base.append!
Base.copy
Base.delete!
Base.filter
Base.filter!
Base.first
Base.get
Base.hcat
Base.issorted
Base.keys
Base.last
Base.length
Base.names
Base.ndims
Base.pairs
Base.parent
Base.propertynames
Base.push!
Base.repeat
Base.show
Base.similar
Base.size
Base.sort
Base.sort!
Base.sortperm
Base.unique
Base.unique!
Base.vcat
CategoricalArrays.categorical
Compat.eachcol
Compat.eachrow
DataAPI.describe
DataFrames.DataFrame!
DataFrames.allowmissing!
DataFrames.antijoin
DataFrames.categorical!
DataFrames.combine
DataFrames.completecases
DataFrames.crossjoin
DataFrames.disallowmissing!
DataFrames.dropmissing
DataFrames.dropmissing!
DataFrames.flatten
DataFrames.groupby
DataFrames.groupcols
DataFrames.groupindices
DataFrames.innerjoin
DataFrames.insertcols!
DataFrames.leftjoin
DataFrames.mapcols
DataFrames.mapcols!
DataFrames.ncol
DataFrames.nonunique
DataFrames.nrow
DataFrames.order
DataFrames.outerjoin
DataFrames.rename
DataFrames.rename!
DataFrames.repeat!
DataFrames.rightjoin
DataFrames.select
DataFrames.select!
DataFrames.semijoin
DataFrames.stack
DataFrames.transform
DataFrames.transform!
DataFrames.unstack
DataFrames.valuecols
Missings.allowmissing
Missings.disallowmissing