如何在 Julia 中将缺失值插入我的数据框中 [英] How do I insert missing values to my dataframe in Julia

问题描述

df3[10, :A] = missing
df3[15, :B] = missing
df3[15, :C] = missing

即使 NA 也不起作用.

Even NA is not working.

我遇到了一个错误

MethodError: 无法将 Missings.Missing 类型的对象 convert 转换为 Int64 类型的对象这可能源于对构造函数 Int64(...) 的调用，因为类型构造函数回退到转换方法.堆栈跟踪:[1] setindex!(::Array{Int64,1}, ::Missings.Missing, ::Int64) 在 ./array.jl:583[2] insert_single_entry!(::DataFrames.DataFrame, ::Missings.Missing, ::Int64, ::Symbol) 在/home/jrun/.julia/v0.6/DataFrames/src/dataframe/dataframe.jl:361[3] setindex!(::DataFrames.DataFrame, ::Missings.Missing, ::Int64, ::Symbol) 在/home/jrun/.julia/v0.6/DataFrames/src/dataframe/dataframe.jl:448[4] include_string(::String, ::String) at ./loading.jl:522

MethodError: Cannot convert an 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

推荐答案

使用allowmissing!函数.

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       │

您可以查看 DataFrame 中的哪些列允许 missing，因为它们在列名下的类型名称后用 ⍰ 突出显示.

You can see which columns in a DataFrame allow missing because they are highlighted with ⍰ after type name under column name.

你也可以使用allowmissing函数来创建一个新的DataFrame.

You can also use allowmissing function to create a new DataFrame.

这两个函数都可选择接受要转换的列.

Both functions optionally accept columns that are to be converted.

最后有一个 disallowmissing/disallowmissing! 对可以做相反的事情(即从 eltypeMissing 联合code> 如果向量实际上不包含缺失值).

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).

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