pandas dataframe apply a function depending on index/column name-CodePudding

multipliers = {'A' : 5, 'B' : 10, 'C' : 15, 'D' : 20}
df = pd.util.testing.makeDataFrame() # a random df with columns A,B,C,D

f = lambda x, col: multipliers[col] * x

Is there Pandas non-loop way to apply f to each column, like df.apply(f, axis = 0, ?)? What I can achieve with loop is

df2 = df.copy()
for c in df.columns:
    df2[c] = f(df[c], c)

(real f is more complex than the above example, please treat f as a black box function of two variables, arg1 is data, arg2 is column name)

CodePudding user response：

Use lambda function and for pass column name use x.name:

np.random.seed(2022)
multipliers = {'A' : 5, 'B' : 10, 'C' : 15, 'D' : 20}
df = pd.util.testing.makeDataFrame() # a random df with columns A,B,C,D

f = lambda x, col: multipliers[col] * x
df2 = df.copy()
for c in df.columns:
    df2[c] = f(df[c], c)
print (df2.head())
                    A          B          C          D
9CTWXXW3ys   2.308860   6.375789   5.362095 -23.354181
yq1PHBltEO   2.876024   1.950080  15.772909 -13.776645
lWtMioDq6A -11.206739  17.691500 -12.175996  25.957264
lEHcq1pxLr  -6.510434  -6.004475  14.084401  13.999673
xvL04Y66tm  -3.827731  -3.104207  -4.111277   1.440596

df2 = df.apply(lambda x: f(x, x.name))
print (df2.head())
                    A          B          C          D
9CTWXXW3ys   2.308860   6.375789   5.362095 -23.354181
yq1PHBltEO   2.876024   1.950080  15.772909 -13.776645
lWtMioDq6A -11.206739  17.691500 -12.175996  25.957264
lEHcq1pxLr  -6.510434  -6.004475  14.084401  13.999673
xvL04Y66tm  -3.827731  -3.104207  -4.111277   1.440596

CodePudding user response：

You can convert your dictionary to series and transform your function to vectorized operation. For example:

df * pd.Series(multipliers)

You can also use the method transform that accepts a dict of functions:

def func(var):
    # return your function
    return lambda x: x * var

df.transform({k: func(v) for k, v in multipliers.items()})