Pandas apply function with different argument values to different columns

Question

I have two data frames, one (p) df contains columns to be transformed, second (a) contains transformation parameter in form of pd.series:

p=np.random.rand(5,3) #create data frame
cols=["A","B","C"]
df1=pd.DataFrame(p,columns=cols)
a=np.array([0.3,0.4,0.5]) # create series of transform parameters
a=pd.Series(a,index=cols)

I wander how to iterate over df columns to transform each one with appropriate transform parameter, something like below:

df1.apply(stats.boxcox,lmbda=a)

which of course not works. My temporary solution is just a brute force function:

def boxcox_transform(df,lambdas):
    df1=pd.DataFrame(index=df.index)
    for column in list(df):
        df1[column]=stats.boxcox(df[column],lambdas[column])
    return(df1)
boxcox_transform(df1,a)

I wander is there any more elegant solution like for example R CRAN mapply which can iterate over two lists

Ian Kent · Accepted Answer · 2018-01-16 22:04:10Z

2

You can use a lambda:

result_df = df1.apply(lambda col: stats.boxcox(col, a.loc[col.name]))

answered Jan 16, 2018 at 22:04

Ian Kent

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3 Comments

jarekj71 Over a year ago

Perfect. Thanks!

ju. Over a year ago

May I ask what is a?

Ian Kent Over a year ago

@ju. Look at the question. It's a Series defined by the asker.

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