Menu
df3[10, :A] = missing
df3[15, :B] = missing
df3[15, :C] = missing
Even NA is not working.
I am getting an error
MethodError: Cannot
convertan object of type Missings.Missing to an object of type Int64 This may have arisen from a call to the constructor Int64(...), since type constructors fall back to convert methods. Stacktrace: [1] setindex!(::Array{Int64,1}, ::Missings.Missing, ::Int64) at ./array.jl:583 [2] insert_single_entry!(::DataFrames.DataFrame, ::Missings.Missing, ::Int64, ::Symbol) at /home/jrun/.julia/v0.6/DataFrames/src/dataframe/dataframe.jl:361 [3] setindex!(::DataFrames.DataFrame, ::Missings.Missing, ::Int64, ::Symbol) at /home/jrun/.julia/v0.6/DataFrames/src/dataframe/dataframe.jl:448 [4] include_string(::String, ::String) at ./loading.jl:522
862
That in mind, how can we use conditionals in order to detect our missing values? In Julia, we actually use methods in order to do this. The two methods are isnan() and ismissing(). In most cases, you might not have to drop down to the level of using conditionals to process your data.
Julia provides support for representing missing values in the statistical sense, that is for situations where no value is available for a variable in an observation, but a valid value theoretically exists. Missing values are represented via the missing object, which is the singleton instance of the type Missing .
Create an empty Julia DataFrame by enclosing column names and datatype of column inside DataFrame() function. Now you can add rows one by one using push!() function. This is like row binding.
You can simply create a data frame using the DataFrame() function. You can mention the columns and their values in between the brackets of the DataFrame() function as the argument and run it as shown below. Before this you have to tell Julia that you are going to use data frames by using the command 'using DataFrames'.
Use allowmissing! function.
julia> using DataFrames
julia> df = DataFrame(a=[1,2,3])
3×1 DataFrame
│ Row │ a │
│ │ Int64 │
├─────┼───────┤
│ 1 │ 1 │
│ 2 │ 2 │
│ 3 │ 3 │
julia> df.a[1] = missing
ERROR: MethodError: Cannot `convert` an object of type Missing to an object of type Int64
julia> allowmissing!(df)
3×1 DataFrame
│ Row │ a │
│ │ Int64⍰ │
├─────┼────────┤
│ 1 │ 1 │
│ 2 │ 2 │
│ 3 │ 3 │
julia> df.a[1] = missing
missing
julia> df
3×1 DataFrame
│ Row │ a │
│ │ Int64⍰ │
├─────┼─────────┤
│ 1 │ missing │
│ 2 │ 2 │
│ 3 │ 3 │
You can see which columns in a DataFrame allow missing because they are highlighted with ⍰ after type name under column name.
You can also use allowmissing function to create a new DataFrame.
Both functions optionally accept columns that are to be converted.
Finally there is a disallowmissing/disallowmissing! pair that does the reverse (i.e. strips optional Missing union from eltype if a vector actually contains no missing values).
60
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
