# Iterate over all columns in a DataFrame

**URL:** <https://discourse.julialang.org/t/iterate-over-all-columns-in-a-dataframe/61697>\
**Category:** New to Julia\
**Tags:** dataframes\
**Created:** [May 23, 2021, 10:57pm UTC](https://discourse.julialang.org/t/iterate-over-all-columns-in-a-dataframe/61697 "2021-05-23T22:57:50Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![VivMendes](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/vivmendes/32/16477_2.png) [@VivMendes](https://discourse.julialang.org/u/VivMendes)\
**Post date:** [May 23, 2021, 10:57pm UTC](https://discourse.julialang.org/t/iterate-over-all-columns-in-a-dataframe/61697/1 "2021-05-23T22:57:51Z")

</div>

Hi. Given my poor knowledge of DataFrames, I am stuck with a stupid little problem. I have a sizeable time-series data frame involving variables expressed in millions/billions of dollars. I want to transform each column such that instead of dollars, I will have an index: the first observation across all columns will be equal to 100. Doing it to one particular column is extremely easy; the problem is to generalize it to the entire set of columns. I did my homework the best I could, and I had a look at the most recent Julia-DataFrames-Tutorial by Bogumił Kamiński but had no success. Help will be very much appreciated.  
An MWE is like this:

```julia
df = DataFrame(W = [20, 22, 19, 21, 23, 25, 28, 30], 
               X = [210, 220, 230, 240, 250, 245, 260, 270],
               Z = [400, 320, 419, 426, 430, 440, 435, 444])

```

A for loop through the X column will be:

```julia
t = 8;
X1 = df[1,2]*100/df[1,2] # Fixing the initial value to 100
Xn = length(X1)
Xt = [X1 zeros(Xn, t-1)]; 

for i = 1:t-1
    Xt[i+1] = df[:,2][i+1] *100/ df[1,2];
end
Xt

```

And my Index of X will come out as:

```julia
1×8 Matrix{Float64}:
 100.0 104.762 109.524 114.286 119.048 116.667 123.81 128.571

```

But what is the trick I should use to apply the loop to all columns (which are not just 3 like this MWE)? This problem looks like a simple one (that is so in MATLAB). I tried many variations but always got stuck with some error.  
Thanks.

---

<div class="post-metadata">

**Author:** ![DataFrames](https://avatars.discourse-cdn.com/v4/letter/d/e19b73/32.png) [@DataFrames](https://discourse.julialang.org/u/DataFrames)\
**Post date:** [May 23, 2021, 11:43pm UTC](https://discourse.julialang.org/t/iterate-over-all-columns-in-a-dataframe/61697/2 "2021-05-23T23:43:41Z")

</div>

assume you only need Float64 calculation

```julia
function change_data(x)
     x = convert(Vector{Float64}, x)
     f_v = x[1]
     x .* 100 ./ f_v
 end

mapcols!(change_data, df)

```

---

<div class="post-metadata">

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\
**Post date:** [May 24, 2021, 5:30am UTC](https://discourse.julialang.org/t/iterate-over-all-columns-in-a-dataframe/61697/3 "2021-05-24T05:30:19Z")

</div>

Easy to read but not in place:

```julia
julia> DataFrame([c ./ first(c) for c ∈ eachcol(df)], names(df))

```

an in place loop could be written as

```julia
julia> for c ∈ eachcol(df)
           c ./= first(c)
       end

```

but in this case will fail as you have `Int` columns, but the division will produce `Float64`s - not a problem if your original data is `Float64` anyway, otherwise you need to convert like in the answer above.

---

<div class="post-metadata">

**Author:** ![VivMendes](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/vivmendes/32/16477_2.png) [@VivMendes](https://discourse.julialang.org/u/VivMendes)\
**Post date:** [May 24, 2021, 9:04am UTC](https://discourse.julialang.org/t/iterate-over-all-columns-in-a-dataframe/61697/4 "2021-05-24T09:04:39Z")

</div>

@DataFrames and @nilshg thanks a lot.

Both approaches work to perfection: simple, clear, and fast. I could choose any one of them as a solution. The first was chosen just because it came out first.