Copying column values up based on other column values
Codeliner
semantina at gmail.com
Sun Jan 3 13:16:55 EST 2021
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?
More information about the Python-list
mailing list