I have a dataframe built out with various calculations performed on different rows. I'm trying to write the dataframe to a csv output, but keep getting the following error:
ArgumentError: `nothing` should not be printed; use `show`, `repr`, or custom output instead.
Here is the code:
filename = "Alldata.csv"
CSV.write(filename, bf)
Here is the stack trace:
[2] print_to_string(::Nothing) at .\strings\io.jl:123
[3] string(::Nothing) at .\strings\io.jl:156
[4] writecell(::Array{UInt8,1}, ::Int32, ::Int32, ::IOStream, ::Nothing, ::CSV.Options{UInt8,UInt8,Nothing,Tuple{}}) at C:\Users\haley.sims\.julia\packages\CSV\ztQqu\src\write.jl:281
[5] macro expansion at C:\Users\haley.sims\.julia\packages\CSV\ztQqu\src\write.jl:182 [inlined]
[6] eachcolumn at C:\Users\haley.sims\.julia\packages\Tables\FXXeK\src\utils.jl:49 [inlined]
[7] writerow(::Array{UInt8,1}, ::Base.RefValue{Int32}, ::Int32, ::IOStream, ::Tables.Schema{(:bid, :building_name, :constr_year, :floor_area, :primary_type, :primary_
[8] (::getfield(CSV, Symbol("##60#61")){getfield(CSV, Symbol("##53#54")){Bool,Tables.Schema{(:bid, :building_name, :constr_year, :floor_area, :primary_type, :primary_occupancy, :n_above, :period, :peak_pop, :eco_pop, :demo_cost, :renew_cost, :replacement_cost......
[9] #open#310(::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::Function, ::getfield(CSV, Symbol("##60#61")){getfie.....
[10] open at .\iostream.jl:367 [inlined]
[11] with(::getfield(CSV, Symbol("##53#54")){Bool,Tables.Schema{(:bid, :building_name, :constr_year, :floor_area, :primary_type, :primary_occupancy, :n_above, :period, :peak_pop, :eco_pop, :demo_cost, :renew_cost, :replacement_cost, :retro_cost, :retro_cost_notes, :perc_1, :perc_2, :perc_3, :perc_4, :perc_5, :perc_6, :perc_7, :perc_8, :perc_9, :perc_10, :perc_11, :n_col, :IM_43, :IM_200, :IM_475, :IM_2475, :PC_43, :PC_200, :PC_475, :PC_2475, :renew_cost3, :temp_cost, :tier3, :fr_HAZ, :cole_HAZ, :IM_975, :collapse_ext, :pcim_975, :theta3, :beta3, :theta4, :beta4, :tier4, :aaf4, :aafr, :pf4_50, :pfr_50, :annual_collapse, :pocc, :pf_col, :ri....
[12] #write#52(::Bool, ::Bool, ::Array{String,1}, ::Function, ::Tables.Schema{(:bid, :building_name, :constr_year, :floor_area, :primary_type, :primary_occupancy, :n_above, :period, :peak_pop, :eco_pop, :demo_cost, :renew_cost, :replacement_cost, :retro_cost, :retro_cost_notes....
[13] write(::Tables.Schema{(:bid, :building_name, :constr_year, :floor_area, :primary_type, :primary_occupancy, :n_above, :period, :peak_pop, :eco_pop, :demo_cost, :renew_cost, :replacement_cost, :retro_cost, :retro_cost_notes, :perc_1, :perc_2, :perc_3, :perc_4, :perc_5, :perc....
[14] #write#51(::Char, ::Char, ::Nothing, ::Nothing, ::Char, ::Char, ::Char, ::Nothing, ::Bool, ::String, ::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{(),Tuple{}}}, ::typeof(CSV.write), ::String, ::DataFrame) at C:\Users\haley.sims\.julia\packages\CSV\ztQqu\src\write.jl:60
[15] write(::String, ::DataFrame) at C:\Users\haley.sims\.julia\packages\CSV\ztQqu\src\write.jl:53
[16] top-level scope at In[177]:3
The dataframe exists and is populated since I can print it:
│ Row │ bid │ building_name │ constr_year │ floor_area │ primary_type │ primary_occupancy │ n_above │ period │ peak_pop │ eco_pop │ demo_cost │ renew_cost │ replacement_cost │ retro_cost │ retro_cost_notes │ perc_1 │ perc_2 │ perc_3 │ perc_4 │ perc_5 │ perc_6 │ perc_7 │ perc_8 │ perc_9 │ perc_10 │ perc_11 │ n_col │ IM_43 │ IM_200 │ IM_475 │ IM_2475 │ PC_43 │ PC_200 │ PC_475 │ PC_2475 │ renew_cost3 │ temp_cost │ tier3 │ fr_HAZ │ cole_HAZ │ IM_975 │ collapse_ext │ pcim_975 │ theta3 │ beta3 │ theta4 │ beta4 │ tier4 │ aaf4 │ aafr │ pf4_50 │ pfr_50 │ annual_collapse │ pocc │ pf_col │ risk_individual │ risk_individual_perc │
│ │ String │ String │ Int64⍰ │ Int64 │ String │ String⍰ │ Int64⍰ │ Float64⍰ │ Float64 │ Float64 │ Missing │ Float64⍰ │ Float64 │ Float64⍰ │ Union{Missing, String} │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Int64⍰ │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │ Float64 │ Float64 │ Float64 │ Float64⍰ │ Float64⍰ │ String │ Float64⍰ │ Float64⍰ │ Int64 │ Float64⍰ │ Float64⍰ │ Float64 │ Float64 │ Float64 │ Float64 │ Union… │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │
├─────┼────────┼─────────────────────────────────────┼─────────────┼────────────┼──────────────┼───────────────────┼─────────┼──────────┼──────────┼─────────┼───────────┼────────────┼──────────────────┼────────────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼────────┼───────┼────────┼────────┼─────────┼───────┼─────────┼─────────┼─────────┼─────────────┼───────────┼────────┼──────────┼──────────┼────────┼──────────────┼──────────┼─────────┼──────────┼─────────┼──────────┼────────┼───────────┼────────────┼──────────┼──────────┼─────────────────┼──────────┼──────────┼─────────────────┼──────────────────────┤
│ 1 │ 22 │ LOWER MALL RESEARCH STATION │ 1960 │ 75229 │ C2 │ Laboratory │ 4 │ 0.37 │ 206.874 │ 68.9579 │ missing │ 47.8929 │ 56.3446 │ missing │ Lab wing 95% vulnerable - Office wing far less vulnerable at 5% collapse. New shear and frame ductility required. Non structural risk high. │ 0.85 │ 0.85 │ 0.9 │ 0.9 │ 0.8 │ 0.85 │ 0.9 │ 0.9 │ 0.9 │ 0.85 │ 0.9 │ 11 │ 43 │ 200 │ 475 │ 2475 │ 0 │ 0.0 │ 0.098 │ 0.42 │ 22.969 │ 47.8929 │ III │ missing │ missing │ 975 │ 0.872727 │ 0.9 │ 2294.63 │ 0.951074 │ 572.462 │ 0.398816 │ V │ 0.0970286 │ 0.00312973 │ 0.992183 │ 0.144857 │ 0.00179141 │ 0.333333 │ 0.785455 │ 0.000469024 │ 0.0469024 │
│ 2 │ 48 │ ANTHROPOLOGY AND SOCIOLOGY BUILDING │ 1975 │ 72384 │ C2 │ Office │ 3 │ 0.2 │ 267.931 │ 60.2845 │ missing │ 24.1623 │ 25.4339 │ 21.6189 │ Wings B and C highly vulnerable to collapse. Lateral strength weakness very high, connections of roofs minimal. Mitigation would need to address adjacent ANSO buildings. Modelling includes assumtions about soil stability and foundation response. │ 0.95 │ 0.5 │ 0.5 │ 0.5 │ 0.5 │ 0.5 │ 0.7 │ 0.7 │ 0.5 │ 0.5 │ 0.5 │ 11 │ 43 │ 200 │ 475 │ 2475 │ 0 │ 0.0 │ 0.016 │ 0.582 │ 16.414 │ 24.1623 │ IV │ missing │ missing │ 975 │ 0.577273 │ 0.9 │ 2111.45 │ 0.657641 │ 572.462 │ 0.398816 │ V │ 0.0561079 │ 0.00273608 │ 0.939517 │ 0.127859 │ 0.00179141 │ 0.225 │ 0.519545 │ 0.000209412 │ 0.0209412 │
│ 3 │ 148 │ CHEMISTRY B BLOCK, SOUTH WING │ 1959 │ 73590 │ C1 │ Laboratory │ 3 │ 0.51 │ 180.63 │ 60.21 │ missing │ 41.1035 │ 48.357 │ missing │ missing │ 0.05 │ 0.05 │ 0.2 │ 0.1 │ 0.1 │ 0.0 │ 0.3 │ 0.3 │ 0.3 │ 0.8 │ 0.3 │ 10 │ 43 │ 200 │ 475 │ 2475 │ 0 │ 0.0 │ 0.0 │ 0.517 │ 26.943 │ 48.357 │ IV │ missing │ missing │ 975 │ 0.25 │ 0.76 │ 2452.13 │ 0.217791 │ 753.719 │ 0.425775 │ V │ 0.018312 │ 0.0027327 │ 0.599724 │ 0.127711 │ 0.00135172 │ 0.333333 │ 0.225 │ 0.000101379 │ 0.0101379 ```
You are probably on Julia 1.0. Try switching to Julia 1.3.1 (current release) and the problem should disappear.
If you have to stick to Julia 1.0 the problem is that nothing value is not allowed to be printed in this version (it is allowed now in Julia 1.3.1 so that is why switching solves your problem). In order to solve it the simplest thing is to replace nothing with missing (as probably if you have nothing in your data then missing is not used in it so it should be safe to use it as a replacement).
The code that will work in this case is:
filename = "Alldata.csv"
CSV.write(filename, something.(bf, missing))
After this operation in the saved file each occurrence of nothing in your original data frame will be stored as an empty field in a specific row (and when you read back the file using CSV.read it will take a missing value in the resulting data frame).
I am trying to make a survey site, but I have not been able to normalize the database. I do not know how to correlate questions and answers in the database. There is a very strong relationship between questionnaire and questionnaire. Because I have the QuestionID in the question table as the primary Key, the ID numbers of the question numbers are unique. This is how the answer table works. How am I supposed to do that?
┌────────────────┬─────────────┬────────────┬────────────┬──────────────┐
│ Member │ Survey │ Question │ Choice │ Category │
├────────────────┼─────────────┼────────────┼────────────┼──────────────┤
│ ID │ SurveyID │ QuestionID │ ChoiceID │ ID │
│ FirstName │ SurveyorID │ Question │ Choice │ CategoryName │
│ LastName │ SurveyTitle │ SurveyID │ QuestionID │ │
│ Mail │ CategoryID │ │ │ │
│ Password │ │ │ │ │
│ NumberOfSurvey │ │ │ │ │
└────────────────┴─────────────┴────────────┴────────────┴──────────────┘
I have come up with a structure which might work for your case with few or no modifications. The database structure is in the Image attached. All the columns marked with * are the ones that are to be set as a primary key. If you are not sure of how to set multiple columns as primary keys, please refer Composite Keys.
┌────────────────┬─────────────┬────────────┬────────────┬──────────────┐
│ Member │ Survey │ Question │ Choice │ Category │
├────────────────┼─────────────┼────────────┼────────────┼──────────────┤
│ MemberID* │ SurveyID* │ QuestionID*│ ChoiceID* │ CategoryID* │
│ FirstName │ MemberID* │ Question │ Choice │ CategoryName │
│ LastName │ SurveyTitle │ SurveyID* │ QuestionID*│ │
│ Mail │ CategoryID* │ │ │ │
│ Password │ │ │ │ │
│ NumberOfSurvey │ │ │ │ │
└────────────────┴─────────────┴────────────┴────────────┴──────────────┘
Good Luck!
I am trying to read a tab delimited file with all data present into julia. It saves all the columns as NullableArrays.NullableArray{Int64,1} although I specified the type:
data = CSV.read("../datasets/baby.dat"; delim='\t', types=[Int, Float64, Float64, Float64, Float64, Float64])
The dataset is from http://stat.ethz.ch/Teaching/Datasets/baby.dat
I want to do a regression with the dataset, but the glm.jl Package gives an error with Nullable Arrays ...
Any ideas?
The complete error message is:
fit(GeneralizedLinearModel, #formula(Survival2 ~
Weight+Age+X1.Apgar+X5.Apgar+pH), data, Binomial(), ProbitLink())
ERROR: Non-call expression encountered
Stacktrace:
[1] dospecials(::Expr) at
/.julia/v0.6/DataFrames/src/statsmodels/formula.jl:97
[2] collect_to!(::Array{Symbol,1},
::Base.Generator{Array{Any,1},DataFrames.#dospecials}, ::Int64,::Int64) at
./array.jl:508
[3] collect_to_with_first!(::Array{Symbol,1}, ::Symbol,
::Base.Generator{Array{Any,1},DataFrames.#dospecials}, ::Int64) at
./array.jl:495
[4] _collect(::Array{Any,1},
::Base.Generator{Array{Any,1},DataFrames.#dospecials}, ::Base.EltypeUnknown,
::Base.HasShape) at ./array.jl:489
[5] map(::Function, ::Array{Any,1}) at ./abstractarray.jl:1868
[6] dospecials(::Expr) at
.julia/v0.6/DataFrames/src/statsmodels/formula.jl:101
[7] DataFrames.Terms(::DataFrames.Formula) at
.julia/v0.6/DataFrames/src/statsmodels/formula.jl:209
[8] #ModelFrame#127(::Array{Any,1}, ::Type{T} where T, ::DataFrames.Formula, ::DataFrames.DataFrame) at .julia/v0.6/DataFrames/src/statsmodels/formula.jl:333
[9] (::Core.#kw#Type)(::Array{Any,1}, ::Type{DataFrames.ModelFrame}, ::DataFrames.Formula, ::DataFrames.DataFrame) at ./<missing>:0
[10] #fit#153(::Dict{Any,Any}, ::Array{Any,1}, ::Function, ::Type{GLM.GeneralizedLinearModel}, ::DataFrames.Formula, ::DataFrames.DataFrame, ::Distributions.Binomial{Float64}, ::Vararg{Any,N} where N) at .julia/v0.6/DataFrames/src/statsmodels/statsmodel.jl:52
[11] fit(::Type{GLM.GeneralizedLinearModel}, ::DataFrames.Formula, ::DataFrames.DataFrame, ::Distributions.Binomial{Float64}, ::GLM.ProbitLink) at .julia/v0.6/DataFrames/src/statsmodels/statsmodel.jl:52
[12] eval(::Module, ::Any) at ./boot.jl:235
[13] eval(::Any) at ./boot.jl:234
[14] macro expansion at .julia/v0.6/Atom/src/repl.jl:186 [inlined]
[15] anonymous at ./<missing>:?
I assume that you want to get a DataFrame. Unfortunately your file is not tab-delimited. This is how you can load it into a DataFrame:
using DataFrames
data = split.(readlines("baby.dat"))
types = [Int, Float64, Float64, Float64, Float64, Float64]
df = DataFrame([parse.(t, getindex.(data[2:end], i)) for (i, t) in enumerate(types)],
Symbol.(replace.(data[1], ".", "")))
Observe that I remove . from names of columns as later GLM package has problem with them.
Now you can check that all is as desired:
julia> showcols(df)
247×6 DataFrames.DataFrame
│ Col # │ Name │ Eltype │ Missing │ Values │
├───────┼──────────┼─────────┼─────────┼──────────────────┤
│ 1 │ Survival │ Int64 │ 0 │ 1 … 0 │
│ 2 │ Weight │ Float64 │ 0 │ 1350.0 … 790.0 │
│ 3 │ Age │ Float64 │ 0 │ 32.0 … 27.0 │
│ 4 │ X1Apgar │ Float64 │ 0 │ 4.0 … 4.0 │
│ 5 │ X5Apgar │ Float64 │ 0 │ 7.0 … 8.0 │
│ 6 │ pH │ Float64 │ 0 │ 7.25 … 7.35 │
julia> head(df)
6×6 DataFrames.DataFrame
│ Row │ Survival │ Weight │ Age │ X1Apgar │ X5Apgar │ pH │
├─────┼──────────┼────────┼──────┼─────────┼─────────┼──────┤
│ 1 │ 1 │ 1350.0 │ 32.0 │ 4.0 │ 7.0 │ 7.25 │
│ 2 │ 0 │ 725.0 │ 27.0 │ 5.0 │ 6.0 │ 7.36 │
│ 3 │ 0 │ 1090.0 │ 27.0 │ 5.0 │ 7.0 │ 7.42 │
│ 4 │ 0 │ 1300.0 │ 24.0 │ 9.0 │ 9.0 │ 7.37 │
│ 5 │ 0 │ 1200.0 │ 31.0 │ 5.0 │ 5.0 │ 7.35 │
│ 6 │ 0 │ 590.0 │ 22.0 │ 9.0 │ 9.0 │ 7.37 │
julia> tail(df)
6×6 DataFrames.DataFrame
│ Row │ Survival │ Weight │ Age │ X1Apgar │ X5Apgar │ pH │
├─────┼──────────┼────────┼──────┼─────────┼─────────┼──────┤
│ 1 │ 1 │ 1120.0 │ 28.0 │ 7.0 │ 7.0 │ 7.33 │
│ 2 │ 1 │ 1020.0 │ 28.0 │ 5.0 │ 7.0 │ 7.34 │
│ 3 │ 1 │ 1320.0 │ 28.0 │ 6.0 │ 6.0 │ 7.24 │
│ 4 │ 0 │ 900.0 │ 27.0 │ 5.0 │ 6.0 │ 7.37 │
│ 5 │ 1 │ 1150.0 │ 27.0 │ 4.0 │ 7.0 │ 7.37 │
│ 6 │ 0 │ 790.0 │ 27.0 │ 4.0 │ 8.0 │ 7.35 │
Now the GLM part (notice the correct way to call GLM):
julia> using GLM
julia> glm(#formula(Survival ~ Weight+Age+X1Apgar+X5Apgar+pH), df, Binomial(), ProbitLink())
StatsModels.DataFrameRegressionModel{GLM.GeneralizedLinearModel{GLM.GlmResp{Array{Float64,1},Distributions.Binomial{Float64},GLM.ProbitLink},GLM.DensePredChol{Float64,Base.LinAlg.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}
Formula: Survival ~ 1 + Weight + Age + X1Apgar + X5Apgar + pH
Coefficients:
Estimate Std.Error z value Pr(>|z|)
(Intercept) -0.563327 8.36692 -0.0673279 0.9463
Weight 0.00213458 0.000479601 4.45074 <1e-5
Age 0.0996481 0.0444713 2.24073 0.0250
X1Apgar 0.0698717 0.0646315 1.08108 0.2797
X5Apgar 0.0371294 0.0703724 0.527614 0.5978
pH -0.624956 1.11015 -0.562946 0.5735
You can check that the results are the same as in R for this model.