Issue
I have a problem with regards as to how to appropriately code this condition. I'm currently creating a new pandas column in my dataframe, new_column
, which performs a subtraction on the values in column test
, based on what index of the data we are at. I'm currently using this code to get it to subtract a different value every 4 times:
subtraction_value = 3
subtraction_value = 6
data = pd.DataFrame({"test":[12, 4, 5, 4, 1, 3, 2, 5, 10, 9]}
data['new_column'] = np.where(data.index%4,
data['test']-subtraction_value,
data['test']-subtraction_value_2)
print (data['new_column']
[6,1,2,1,-5,0,-1,3,4,6]
However, I now wish to get it performing the higher subtraction on the first two positions in the column, and then 3 subtractions with the original value, another two with the higher subtraction value, 3 small subtractions, and so forth. I thought I could do it this way, with an |
condition in my np.where
statement:
data['new_column'] = np.where((data.index%4) | (data.index%5),
data['test']-subtraction_value,
data['test']-subtraction_value_2)
However, this didn't work, and I feel my maths may be slightly off. My desired output would look like this:
print(data['new_column'])
[6,-2,2,1,-2,-3,-4,3,7,6])
As you can see, this slightly shifts the pattern. Can I still use numpy.where()
here, or do I have to take a new approach? Any help would be greatly appreciated!
Solution
As mentioned in the comment section, the output should equal
[6,-2,2,1,-2,-3,-4,2,7,6]
instead of [6,-2,2,1,-2,-3,-4,3,7,6]
according to your logic. Given that, you can do the following:
import pandas as pd
import numpy as np
from itertools import chain
subtraction_value = 3
subtraction_value_2 = 6
data = pd.DataFrame({"test":[12, 4, 5, 4, 1, 3, 2, 5, 10, 9]})
index_pos_large_subtraction = list(chain.from_iterable((data.index[i], data.index[i+1]) for i in range(0, len(data)-1, 5)))
data['new_column'] = np.where(~data.index.isin(index_pos_large_subtraction), data['test']-subtraction_value, data['test']-subtraction_value_2)
# The next line is equivalent to the previous one
# data['new_column'] = np.where(data.index.isin(index_pos_large_subtraction), data['test']-subtraction_value_2, data['test']-subtraction_value)
---------------------------------------------
test new_column
0 12 6
1 4 -2
2 5 2
3 4 1
4 1 -2
5 3 -3
6 2 -4
7 5 2
8 10 7
9 9 6
---------------------------------------------
As you can see, np.where
works fine. Your masking condition is the problem and needs to be adjusted, you are not selecting rows according to your logic.
Answered By - ko3
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