DataFrames.jl
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JuliaData
In-memory tabular data in Julia
Documentation update related to #2866. This is a work in progress and my first pull request. Contributions welcome.
First, thank you all for this absolutely beautiful Julia Package :slightly_smiling_face: I am not sure if this is intended, but using `vcat` on `PooledArray` dataframe columns changes their type: ```julia...
@nalimilan: Do we want `DataFrameRows` and `DataFrameColumns` to be considered to be Tables.jl tables and eg. define for them `nrow` and `ncol`?
Currently, when applying a transformation to all columns of a specific type (or subtypes of an abstract type), a pattern such as `transform(df, names(df, Number) .=> f)` is used. Ideally,...
what do you think of the utility of having a method of the flatten function in DataFrames that expands a dataframe type df2 in the form dfexp? ``` df =...
in Pandas: ```py df = pd.DataFrame({ 'paddockId': [0, 0, 1, 1, 2, 2], 'color': ['red', 'blue', 'red', 'blue', 'red', 'blue'], 'count': [3, 4, 3, 4, 3, 4], 'weight': [0.2, 0.3,...
See https://github.com/invenia/Wrangling.jl/issues/10. This should be worked on after #2147 is merged.
This is a request for an `asof` join type, as seen in [pandas](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge_asof.html), [kdb+](https://code.kx.com/q/ref/asof/), [QuestDB](https://questdb.io/docs/reference/sql/join/#asof-join), and probably a few others. This is a rather niche join, but extremely useful for...
It can be defined naively as ```julia function innerjoin!(a, b; kwargs...) leftjoin!(a, b; kwargs..., indicator=:_row_source) subset!(a, :_row_source => ByRow(==("both"))) select!(a, Not(:_row_source)) return a end ``` That is, simply delete (in-place)...
I do not think it is a very felt need, but if it were not too complicated and at risk of unwanted side effects, a generalization of the value parameter...