How to preprocess data in a data table? (e.g. change a value in each row for some feature)

Question

I Have a general question - how to preprocess data in Orange. E.g. I want to do something with each row value of some feature. How can I do this visually? And how can I do this via Python Script?

So far, I have only some ideas how to do this via python script:

Here is my code:

minimum = min(list(map(lambda row: row[0], in_data))).value

from copy import deepcopy
out_data = deepcopy(in_data)

for row in out_data:
    row[0].value = row[0].value - minimum

But it fails, because seems like the Value object is immutable... Is there any way to create my own Value object with new data, and then do something like:

out_data = list(map(lambda row: Value(row[0].variable, [row[0].value - minimum]) + row[1:], in_data))

I am also interested in a "Visual widget-based" alternative, if it exists...

Answer 1

To scale the data, you can use Preprocess widget. To construct new features, e.g.

new_feature = old_feature - some_values

you can use Feature Constructor widget.

Answer 2

Something like this worked for my case:

minimum = min(list(map(lambda row: row[0].value, in_data)))

from copy import deepcopy
out_data = deepcopy(in_data)

import Orange
for i in range(len(out_data)):
    out_data[i,0] = Orange.data.Value(out_data[i,0].variable, out_data[i,0].value - minimum)

Interesting is that in order to plot the out_data, I needed additional "Select Columns" widget before "Scatter Plot".

In addition I couldn't combine two out_data (one from one source and one from another) on one Scatter Plot... But this seems to be an other question...