What is the best way to perform vectorized calculations on a pandas dataframe, replacing values with the data from the previous row if a certain condition is not satisfied

My current approach involves using a for loop with a conditional statement that triggers a calculation and updates the column in a dataframe when true. However, if the condition is not met, the data from the previous row is carried over to the new row.

Here is a snippet of the pseudocode I am working with:


for index in range(len(dataframe[column1])):
  if condition == True:
    dataframe.at[index, column3] = dataframe.at[index, column1] - dataframe.at[index, column2]
  else:
    dataframe.at[index, column3] = dataframe.at[index-1, column3]

I realize that vectorization may not be suitable when the calculation for the current row depends on the previous one. However, since the calculation for column 3 does not rely on the previous row but simply transfers the previous value, could we potentially optimize this process through vectorization to enhance runtime performance?

pythonpandasdataframefor-loopvectorization

Answer №1

To achieve this in a more efficient manner, you can utilize vectorization.

Initial Dataset

   x0  x1  x2
0   8   3   5
1   6   9   4
2   2   7   1
3   3   5   9
4   5   2   6
5   8   1   3
6   4   8   2
7   7   6   4
8   1   4   7
9   9   3   1

Implementation

import numpy as np

df_temp = df.assign(x3=np.where(df['x0']>3, df['x1']-df['x2'], np.nan)).ffill().fillna(0).astype(int)
print(df_temp)

Outcome

   x0  x1  x2  x3
0   8   3   5  -2
1   6   9   4   5
2   2   7   1   6
3   3   5   9   6
4   5   2   6  -4
5   8   1   3  -2
6   4   8   2   6
7   7   6   4   2
8   1   4   7  -3
9   9   3   1   2

This method leverages Numpy's where function for conditional operations. It subtracts values from one column based on a condition and fills temporary null values using ffill. Additionally, fillna(0) inserts zeros where the condition is not satisfied in the first row due to lack of preceding values.

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