On Sunday, January 3, 2021 at 7:08:49 PM UTC+2, Jason Friedman wrote: > > > > import numpy as np > > import pandas as pd > > from numpy.random import randn > > df=pd.DataFrame(randn(5,4),['A','B','C','D','E'],['W','X','Y','Z']) > > > > W X Y Z > > A -0.183141 -0.398652 0.909746 0.332105 > > B -0.587611 -2.046930 1.446886 0.167606 > > C 1.142661 -0.861617 -0.180631 1.650463 > > D 1.174805 -0.957653 1.854577 0.335818 > > E -0.680611 -1.051793 1.448004 -0.490869 > > > > is there a way to create a column S - which will copy column column Y > > values UP- if values in column Y are above 1 - otherwise return new value > > above zero?.I made this manually: > > > > S: > > > > A 1.446886 > > B 1.446886 > > C 1.854577 > > D 1.854577 > > E 1.448004 > > > Here's one solution. No consideration to performance. > import numpy as np > import pandas as pd > from numpy.random import randn > df=pd.DataFrame(randn(5,4),['A','B','C','D','E'],['W','X','Y','Z']) > print(df) > > y_series = df["Y"] > for i in range(len(y_series)): > if i == len(y_series) - 1: > # Last one, nothing to copy > break > if y_series[i+1] > 1: > y_series[i] = y_series[i+1] > > df["Y"] = y_series > print(df)
Thank you Jason for this lovely for loop - is there a way to make this with pandas series or numpy arrays? for maximum speed? -- https://mail.python.org/mailman/listinfo/python-list
