How to get dtypes of columns in julia dataframe

user_4684861 0
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How to get dtypes of all columns and particular column(s) in julia. To be specific what is the pandas equivalent of df.dtypes in julia?

For example, I have a df like below,

│ Row │ Id    │ name   │ item location │
│     │ Int64 │ String │ String        │
├─────┼───────┼────────┼───────────────┤
│ 1   │ 1     │ A      │ xyz           │
│ 2   │ 2     │ B      │ abc           │
│ 3   │ 3     │ C      │ def           │
│ 4   │ 4     │ D      │ ghi           │
│ 5   │ 5     │ E      │ xyz           │
│ 6   │ 6     │ F      │ abc           │
│ 7   │ 7     │ G      │ def           │
│ 8   │ 8     │ H      │ ghi           │
│ 9   │ 9     │ I      │ xyz           │
│ 10  │ 10    │ J      │ abc           │

Expected output:

{'id':  Int64, 'name': String, 'item location': String}

How to get dtypes, i.e., Int64 │ String │ String in Julia?

julia
2 Answers

You have specified two different expected outputs so I show here how to get both:

julia> df = DataFrame("Id" => 1, "name" => "A", "item_location" => "xyz")
1×3 DataFrame
│ Row │ Id    │ name   │ item_location │
│     │ Int64 │ String │ String        │
├─────┼───────┼────────┼───────────────┤
│ 1   │ 1     │ A      │ xyz           │

julia> eltype.(eachcol(df))
3-element Array{DataType,1}:
 Int64
 String
 String

julia> Dict(names(df) .=> eltype.(eachcol(df)))
Dict{String,DataType} with 3 entries:
  "Id"            => Int64
  "name"          => String
  "item_location" => String

additionally, if you wanted to store the result in a DataFrame instead of a Dict you can write (see mapcols documentation here):

julia> mapcols(eltype, df)
1×3 DataFrame
│ Row │ Id       │ name     │ item_location │
│     │ DataType │ DataType │ DataType      │
├─────┼──────────┼──────────┼───────────────┤
│ 1   │ Int64    │ String   │ String        │

And if you would want to have a NamedTuple storing this information write (the documentation of Tables.columntable is here):

julia> map(eltype, Tables.columntable(df))
(Id = Int64, name = String, item_location = String)

(in this case note that for very wide tables this might have some extra compilation cost as each time you call it you potentially get a new type of NamedTuple)

user_12695670

You can also use describe(df) which is a catchall for learning about the columns in your data frame.

user_88918770
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