Thanks Michael,
I been thinking about this all day. Yes, basically I am going to have to
create a macro *CSVreadtable* that mimics the *readtable* command, but in
the expantion uses *CSV.read*. The macro will manually constructs a
similar readtable sized dataframe array, but use the column types I specify
or inherit from the original readtable command. The macro can use the
current CSV.read parameters.
So this would work.
*df1_CSVreadtable = CSVreadtable("$df1_path"; types=Dict(1=>String))*
so a:
*eltypes(df1_CSVreadtable)*
3-element Array{Type,1}:
Int32
String
String
Anyway, I was looking for a quick fix, but it least I will learn some
Julia.
On Thursday, November 3, 2016 at 4:05:23 PM UTC-4, Michael Borregaard wrote:
>
> DataFrames is currently undergoing a very major change. Looks like CSV
> creates the new type of DataFrames. I hope someone can help you with using
> that. As a workaround, on the normal DataFrames version, I have generally
> just replaced with a string representation:
> ```
> df[:account_numbers] = ["$account_number" for account_number in
> df[:account_numbers]]
>
> On Thu, Nov 3, 2016 at 3:05 PM, LeAnthony Mathews <leant...@gmail.com
> <javascript:>> wrote:
>
>> Sure, so I need col #1 in my CSV to be a string in my data frame.
>>
>> So as a test I tried to load the file 3 different ways:
>>
>> *df1_CSV = CSV.read("$df1_path"; types=Dict(1=>String)) #forcing the
>> column to stay a string*
>> *df1_readtable = readtable("$df1_path") #Do not know how to force the
>> column to stay a string*
>> *df1_convertDF = convert(DataFrame, df1_CSV)*
>>
>> Here is the output: If they are all dataframes then showcols should work
>> an all three df1:
>>
>> *julia> names(df1_CSV)*
>> 3-element Array{Symbol,1}:
>> :account_number
>> Symbol("Discharge Date")
>> :site
>>
>> *julia> names(df1_readtable)*
>> 3-element Array{Symbol,1}:
>> :account_number
>> :Discharge_Date
>> :site
>>
>> *julia> names(df1_convertDF)*
>> 3-element Array{Symbol,1}:
>> :account_number
>> Symbol("Discharge Date")
>> :site
>>
>>
>> *julia> eltypes(df1_CSV)*
>> 3-element Array{Type,1}:
>> Nullable{String}
>> Nullable{WeakRefString{UInt8}}
>> Nullable{WeakRefString{UInt8}}
>>
>> *julia> eltypes(df1_readtable)*
>> 3-element Array{Type,1}:
>> Int32 *#Do not know how to force the column to stay a string*
>> String
>> String
>>
>> *julia> eltypes(df1_convertDF)*
>> 3-element Array{Type,1}:
>> Nullable{String}
>> Nullable{WeakRefString{UInt8}}
>> Nullable{WeakRefString{UInt8}}
>>
>> *julia> showcols(df1_convertDF)*
>> 1565x3 DataFrames.DataFrame
>> ERROR: MethodError: no method matching
>> countna(::NullableArrays.NullableArray{St
>> ring,1})
>> Closest candidates are:
>> countna(::Array{T,N}) at
>> C:\Users\lmathews\.julia\v0.5\DataFrames\src\other\ut
>> ils.jl:115
>> countna(::DataArrays.DataArray{T,N}) at
>> C:\Users\lmathews\.julia\v0.5\DataFram
>> es\src\other\utils.jl:128
>> countna(::DataArrays.PooledDataArray{T,R<:Integer,N}) at
>> C:\Users\lmathews\.ju
>> lia\v0.5\DataFrames\src\other\utils.jl:143
>> in colmissing(::DataFrames.DataFrame) at
>> C:\Users\lmathews\.julia\v0.5\DataFram
>> es\src\abstractdataframe\abstractdataframe.jl:657
>> in showcols(::Base.TTY, ::DataFrames.DataFrame) at
>> C:\Users\lmathews\.julia\v0.
>> 5\DataFrames\src\abstractdataframe\show.jl:574
>> in showcols(::DataFrames.DataFrame) at
>> C:\Users\lmathews\.julia\v0.5\DataFrames
>> \src\abstractdataframe\show.jl:581
>>
>> *julia> showcols(df1_readtable)*
>> 1565x3 DataFrames.DataFrame
>> │ Col # │ Name │ Eltype │ Missing │
>> ├───────┼────────────────┼────────┼─────────┤
>> │ 1 │ account_number │ Int32 │ 0 │
>> │ 2 │ Discharge_Date │ String │ 0 │
>> │ 3 │ site │ String │ 0 │
>>
>> *julia> showcols(df1_CSV)*
>> 1565x3 DataFrames.DataFrame
>> ERROR: MethodError: no method matching
>> countna(::NullableArrays.NullableArray{St
>> ring,1})
>> Closest candidates are:
>> countna(::Array{T,N}) at
>> C:\Users\lmathews\.julia\v0.5\DataFrames\src\other\ut
>> ils.jl:115
>> countna(::DataArrays.DataArray{T,N}) at
>> C:\Users\lmathews\.julia\v0.5\DataFram
>> es\src\other\utils.jl:128
>> countna(::DataArrays.PooledDataArray{T,R<:Integer,N}) at
>> C:\Users\lmathews\.ju
>> lia\v0.5\DataFrames\src\other\utils.jl:143
>> in colmissing(::DataFrames.DataFrame) at
>> C:\Users\lmathews\.julia\v0.5\DataFram
>> es\src\abstractdataframe\abstractdataframe.jl:657
>> in showcols(::Base.TTY, ::DataFrames.DataFrame) at
>> C:\Users\lmathews\.julia\v0.
>> 5\DataFrames\src\abstractdataframe\show.jl:574
>> in showcols(::DataFrames.DataFrame) at
>> C:\Users\lmathews\.julia\v0.5\DataFrames
>> \src\abstractdataframe\show.jl:581
>>
>>
>>
>> On Thursday, November 3, 2016 at 8:54:19 AM UTC-4, Michael Borregaard
>> wrote:
>>>
>>> The result of CSV should be a DataFrame by default. What return type do
>>> you get?
>>>
>>
>
