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:

```
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:

```
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:

```
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.