When I have data in columns, I can construct a DataFrame like this: using Dataframes nameData = ["Alice", "Bob"] surnameData = ["Smith", "Jones"] df = DataFrame(name = nameData, surname = surnameData) However I have data in rows: row1 = ["Alice", "Smith"] row2 = ["Bob", "Jones"] df = DataFrame(??…

Two options Initialize a data frame with no rows like DataFrame(name = String[], surname = String[], then do for row in rows # your collection of rows push!(df, row) end Write a small helper function that turns each row into a named tuple function make_better_row(vec) (name = vec[1], s…

If you can put rows into a vector of named tuples: julia> row1 = (name="Alice", age=20) julia> row2 = (name="Bob", age=30) julia> DataFrame([row1, row2]) 2×2 DataFrame │ Row │ name │ age │ │ │ String │ Int64 │ ├─────┼────────┼───────┤ │ 1 │ Alice │ 20 │ │ 2 │ Bob │ 30 │

Is this of any use here [image] [ANN] DataFrameDBs.jl Data I don’t work with kdb+, DataFrameDBs is primarily inspired by ClickHouse (and my own experience on developing columnar DB with c++). Performance of DataFrameDBs competes with ClickHouse when ClickHouse runs on a si…

And what is the problem of converting rows to columns? You can push rows to DataFrame as @pdeffebach describes, But this may not be very effective, because it will lead to several resizing of columns vectors. I would do something like that (I assume that rows is an array of arrays): julia> colum…

One thing to remember is that Julia is very good at starting something from nothing and push!ing them, julia doubles the memory used by a vector each time it runs out of space. So it’s performance shouldn’t be an issue. But this also works.

Since this push! is pushing to DataFrame, it is possible that there can be other implementation issues. At least there is a recommendation in DataFrames manual to use columns approach: Getting Started · DataFrames.jl

[image] pdeffebach: So it’s performance shouldn’t be an issue. It depends on the number of rows [image] pdeffebach: julia doubles the memory used by a vector each time it runs out of space It was this process that I meant by “several resizes”. This is the usual behavior for arrays in …

Yet another way: rows = [["Alice", "Smith"], ["Bob", "Jones"]] DataFrame((name = r[1], surname = r[2]) for r in rows)

Thank you for your suggestions. I’m checking them out. Meanwhile I have continued my search and found out that CSV package also produces DataFrames and (presumably) engineered for row-aligned data. It also accepts IOBuffer as an input. However I encountered a problem. This code works and produces …

[image] nnn: Or rather it produces 0x2 DataFrame without rows. Why? I’m guessing because the io pointer is at the end of the buffer, so when the reader looks, it sees the end immediately and closes. Try seek(io, 0) before calling CSV.read() to move the pointer back to the beginning

Construct Julia Dataframe from row data

New to Julia

johnh March 19, 2020, 8:35pm 4

Is this of any use here

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Construct Julia Dataframe from row data

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