I have implemented the following code which works as intended. However, I would like to improve my code in terms of performance and efficiency
Code in Question
import pandas as pd from scipy.stats import norm # data frame of length 40,000 rows, containing 25 columns for indx in df.index: matrix_ordered_first = df.loc[indx].rank(method='first',na_option='bottom') matrix_ordered_avg = df.loc[indx].rank(method='average', na_option='bottom') matrix_ordered_first.loc[df.loc[indx] == 0] = matrix_ordered_avg matrix_computed = norm.ppf(matrix_ordered_first / (len(df.columns) + 1)) df.loc[indx] = matrix_computed.T
A peak of the dataframe
Here is a part view of my dataframe df:
s s1 s2 s3 s4 ... s21 s23 s24 s25 0 NaN 5.406999 5.444658 4.640154 ... 4.633389 5.517850 NaN 6.121492 1 NaN 2.147866 1.758245 1.274754 ... 1.465129 1.200157 NaN 1.789203 2 2.872652 5.492498 2.547415 3.754654 ... 3.686420 1.540947 4.405961 1.715685 3 NaN 46.316837 27.197062 72.910797 ... NaN 46.812153 NaN NaN 4 1.365775 1.329316 1.852473 1.208155 ... 1.489296 1.313321 1.462968 1.249645 [5 rows x 25 columns]
The code above is the part of a long python script in which this part runs slower than the other parts of the program. So what I am trying to do in the above code is to iterate over the data frame in a row-wise fashion. Then, for each row I have to perform a chain of pandas ranking operations followed by a statistical test equivalent to the "One-tail test”.Finally, transpose the matrix which will then be fed as a row for the data frame.
How can I improve this block of code in terms of efficiency, speed, and performance?
On a separate note, I not experienced in pandas so my code may seem amateur and for that I kindly seek your guidance.
Thank you so much in advance,