# Converting columns in Dataframe from Int to Float type

**URL:** <https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207>\
**Category:** New to Julia\
**Tags:** question, dataframes, convert\
**Created:** [June 11, 2017, 11:46am UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207 "2017-06-11T11:46:17Z")\
**Posts on this page:** 9\
**Page:** 1

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**Author:** ![Saran\_S](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/saran_s/32/1155_2.png) [@Saran\_S](https://discourse.julialang.org/u/Saran_S)\
**Post date:** [June 11, 2017, 11:46am UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/1 "2017-06-11T11:46:17Z")

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Hello,

Below is the dataframe which i am working on and i would like to know if there is any alternative way to convert `[:Age, :Salary]` columns which is **Int64** to **Float64**

```julia
10×4 DataFrames.DataFrame
│ Row │ Country │ Age │ Salary │ Purchased │
├─────┼───────────┼─────┼────────┼───────────┤
│ 1 │ "France" │ 44 │ 72000 │ "No" │
│ 2 │ "Spain" │ 27 │ 48000 │ "Yes" │
│ 3 │ "Germany" │ 30 │ 54000 │ "No" │
│ 4 │ "Spain" │ 38 │ 61000 │ "No" │
│ 5 │ "Germany" │ 40 │ NA │ "Yes" │
│ 6 │ "France" │ 35 │ 58000 │ "Yes" │
│ 7 │ "Spain" │ NA │ 52000 │ "No" │
│ 8 │ "France" │ 48 │ 79000 │ "Yes" │
│ 9 │ "Germany" │ 50 │ 83000 │ "No" │
│ 10 │ "France" │ 37 │ 67000 │ "Yes" │

```

`>data1 = readtable("Data1.csv", eltypes = [String,Float64,Float64,String]);`

```julia
10×4 DataFrames.DataFrame
│ Row │ Country │ Age │ Salary │ Purchased │
├─────┼───────────┼──────┼─────────┼───────────┤
│ 1 │ "France" │ 44.0 │ 72000.0 │ "No" │
│ 2 │ "Spain" │ 27.0 │ 48000.0 │ "Yes" │
│ 3 │ "Germany" │ 30.0 │ 54000.0 │ "No" │
│ 4 │ "Spain" │ 38.0 │ 61000.0 │ "No" │
│ 5 │ "Germany" │ 40.0 │ NA │ "Yes" │
│ 6 │ "France" │ 35.0 │ 58000.0 │ "Yes" │
│ 7 │ "Spain" │ NA │ 52000.0 │ "No" │
│ 8 │ "France" │ 48.0 │ 79000.0 │ "Yes" │
│ 9 │ "Germany" │ 50.0 │ 83000.0 │ "No" │
│ 10 │ "France" │ 37.0 │ 67000.0 │ "Yes" │

```

Currently i am using the above method to convert from Int to Float while reading. And as you can see if i work on the other datasets with lots of columns, it will get tedious for sure. Kindly let me know if there is any efficient way to achieve this.

Thank You.

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<div class="post-metadata">

**Author:** ![joshbode](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/joshbode/32/260_2.png) [@joshbode](https://discourse.julialang.org/u/joshbode)\
**Post date:** [June 11, 2017, 3:05pm UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/2 "2017-06-11T15:05:50Z")

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If the data file being read in by `readtable` has decimal points in the float column, then the column in the resulting `DataFrame` will automatically be constructed as a `DataVector{Float64}`.

e.g.  
`test.csv`:

```julia
X,Y
1,1.0
2,2.0

```

```julia
julia> x = readtable("/tmp/test.csv")
2×2 DataFrames.DataFrame
│ Row │ X │ Y │
│ 1 │ 1 │ 1.0 │
│ 2 │ 2 │ 2.0 │

julia> eltypes(x)
2-element Array{Type,1}:
 Int64
 Float64

```

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<div class="post-metadata">

**Author:** ![Saran\_S](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/saran_s/32/1155_2.png) [@Saran\_S](https://discourse.julialang.org/u/Saran_S)\
**Post date:** [June 11, 2017, 3:40pm UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/3 "2017-06-11T15:40:36Z")

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Dataframe which i have been working on has two Int64 Columns ` [:Age, :Salary]` which i want to be read as float hence i use  
`>data1 = readtable("Data1.csv", eltypes = [String,Float64,Float64,String]);`

So if i have large set of columns with Int64 datatype i would like that to be read in as Float64 in efficient way instead of specifying all the datatype in the order in `eltypes` while reading data using `readtable`

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**Author:** ![dpsanders](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dpsanders/32/3573_2.png) [@dpsanders](https://discourse.julialang.org/u/dpsanders)\
**Post date:** [June 11, 2017, 4:15pm UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/4 "2017-06-11T16:15:33Z")

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Why would you want them to be read as floats?

---

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**Author:** ![alasaadstat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/alasaadstat/32/975_2.png) [@alasaadstat](https://discourse.julialang.org/u/alasaadstat)\
**Post date:** [June 11, 2017, 4:38pm UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/6 "2017-06-11T16:38:31Z")

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Here is my solution:

```julia
using DataFrames

c1 = ["France", "Spain", "Germany", "Spain", "Germany", "France", "Spain"];
c2 = @data [44, 27, 30, 38, 40, 35, NA];
c3 = @data [72000, 48000, 54000, 61000, NA, 58000, 52000];
c4 = ["No", "Yes", "No", "No", "Yes", "Yes", "No"];

data1 = DataFrame(Country = c1, Age = c2, Salary = c3, Purchased = c4)

# Convert to Float64
data1[:Salary] = data1[:Salary] * 1.
data1[:Age] = data1[:Age] * 1.

# alternatively
data1[:Age] = map(x -> isna(x) ? NA : convert(Float64, x), data1[:Age])
data1[:Salary] = map(x -> isna(x) ? NA : convert(Float64, x), data1[:Salary])

# View Result
data1
# 7×4 DataFrames.DataFrame
# │ Row │ Country │ Age │ Salary │ Purchased │
# ├─────┼───────────┼──────┼─────────┼───────────┤
# │ 1 │ "France" │ 44.0 │ 72000.0 │ "No" │
# │ 2 │ "Spain" │ 27.0 │ 48000.0 │ "Yes" │
# │ 3 │ "Germany" │ 30.0 │ 54000.0 │ "No" │
# │ 4 │ "Spain" │ 38.0 │ 61000.0 │ "No" │
# │ 5 │ "Germany" │ 40.0 │ NA │ "Yes" │
# │ 6 │ "France" │ 35.0 │ 58000.0 │ "Yes" │
# │ 7 │ "Spain" │ NA │ 52000.0 │ "No" │

# Convert back to Int64
data1[:Age] = map(x -> isna(x) ? NA : convert(Int64, x), data1[:Age])
data1[:Salary] = map(x -> isna(x) ? NA : convert(Int64, x), data1[:Salary])

#View Result
data1
# 7×4 DataFrames.DataFrame
# │ Row │ Country │ Age │ Salary │ Purchased │
# ├─────┼───────────┼─────┼────────┼───────────┤
# │ 1 │ "France" │ 44 │ 72000 │ "No" │
# │ 2 │ "Spain" │ 27 │ 48000 │ "Yes" │
# │ 3 │ "Germany" │ 30 │ 54000 │ "No" │
# │ 4 │ "Spain" │ 38 │ 61000 │ "No" │
# │ 5 │ "Germany" │ 40 │ NA │ "Yes" │
# │ 6 │ "France" │ 35 │ 58000 │ "Yes" │
# │ 7 │ "Spain" │ NA │ 52000 │ "No" │

```

---

<div class="post-metadata">

**Author:** ![Saran\_S](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/saran_s/32/1155_2.png) [@Saran\_S](https://discourse.julialang.org/u/Saran_S)\
**Post date:** [June 11, 2017, 4:50pm UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/7 "2017-06-11T16:50:42Z")

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@alasaadstat Thank You for the alternative solution.

I wondering if we could read it as float from `readtable` function. Instead of

`>data1 = readtable("Data1.csv", eltypes = [String,Float64,Float64,String]);`

OR even the below solution

`> data1 = readtable("Data1.csv")`

```julia
10×4 DataFrames.DataFrame
│ Row │ Country │ Age │ Salary │ Purchased │
├─────┼───────────┼─────┼────────┼───────────┤
│ 1 │ "France" │ 44 │ 72000 │ "No" │
│ 2 │ "Spain" │ 27 │ 48000 │ "Yes" │
│ 3 │ "Germany" │ 30 │ 54000 │ "No" │
│ 4 │ "Spain" │ 38 │ 61000 │ "No" │
│ 5 │ "Germany" │ 40 │ NA │ "Yes" │
│ 6 │ "France" │ 35 │ 58000 │ "Yes" │
│ 7 │ "Spain" │ NA │ 52000 │ "No" │
│ 8 │ "France" │ 48 │ 79000 │ "Yes" │
│ 9 │ "Germany" │ 50 │ 83000 │ "No" │
│ 10 │ "France" │ 37 │ 67000 │ "Yes" │

```

```julia
>for c = eachcol(data1)
  if eltype(c[2]) <: Integer
    data1[c[1]] = data1[c[1]] .* 1.0
  end
end

```

```julia
10×4 DataFrames.DataFrame
│ Row │ Country │ Age │ Salary │ Purchased │
├─────┼───────────┼──────┼─────────┼───────────┤
│ 1 │ "France" │ 44.0 │ 72000.0 │ "No" │
│ 2 │ "Spain" │ 27.0 │ 48000.0 │ "Yes" │
│ 3 │ "Germany" │ 30.0 │ 54000.0 │ "No" │
│ 4 │ "Spain" │ 38.0 │ 61000.0 │ "No" │
│ 5 │ "Germany" │ 40.0 │ NA │ "Yes" │
│ 6 │ "France" │ 35.0 │ 58000.0 │ "Yes" │
│ 7 │ "Spain" │ NA │ 52000.0 │ "No" │
│ 8 │ "France" │ 48.0 │ 79000.0 │ "Yes" │
│ 9 │ "Germany" │ 50.0 │ 83000.0 │ "No" │
│ 10 │ "France" │ 37.0 │ 67000.0 │ "Yes" │

```

---

<div class="post-metadata">

**Author:** ![Saran\_S](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/saran_s/32/1155_2.png) [@Saran\_S](https://discourse.julialang.org/u/Saran_S)\
**Post date:** [June 11, 2017, 5:01pm UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/8 "2017-06-11T17:01:47Z")

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@dpsanders  
The reason on why i want to convert is to remove the `NA` type and replace them with mean of the column.

```julia
function nameanfunc!{DataFrame}(dtfrm::DataFrame)
  for i = 1:size(dtfrm,2)
      if eltype(dtfrm[:,i]) <: AbstractFloat
        fillnamean(dtfrm, i)
      end
  end
end

function fillnamean{DataFrame}(dtfr::DataFrame, ind::Int64)
  @inbounds dtfr[dtfr[:,ind].na, ind] = mean(dropna(dtfr[:,ind]))
end

```

If i use the above code it will not replace the `NA` type with mean as mean is of Float64 and and column is of DataArray{Int64,1} type.  
One alternative method would be instead of

`dtfr[dtfr[:,ind].na, ind] = mean(dropna(dtfr[:,ind]))`

in **fillnamean** function i wil have to use this

`eltype((dtfr[:,ind])) <:AbstractFloat ? mean(dropna(dtfr[:,ind])): trunc(Int64, mean(dropna(dtfr[:,ind])))`

---

<div class="post-metadata">

**Author:** ![joshbode](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/joshbode/32/260_2.png) [@joshbode](https://discourse.julialang.org/u/joshbode)\
**Post date:** [June 11, 2017, 8:46pm UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/9 "2017-06-11T20:46:09Z")

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How about this? Read everything as-is and just convert the types that need to be:

```julia
using DataArrays
using DataFrames

data1 = readtable("Data1.csv")
data1[:Salary] = convert(DataVector{Float64}, data1[:Salary])

```

---

<div class="post-metadata">

**Author:** ![Saran\_S](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/saran_s/32/1155_2.png) [@Saran\_S](https://discourse.julialang.org/u/Saran_S)\
**Post date:** [June 12, 2017, 1:57am UTC](https://discourse.julialang.org/t/converting-columns-in-dataframe-from-int-to-float-type/4207/10 "2017-06-12T01:57:13Z")

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@joshbode. Thank you. Its Alternative method to multiplying by 1.0.