Copying column values up based on other column values

[Codeliner]

On Sunday, January 3, 2021 at 8:17:16 PM UTC+2, Codeliner wrote:
> 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?


can something done along these lines?

df['run2'] = df['b'].apply(lambda x: df['b'].shift(1) if x > 1 else df['b'])