Compare columns of two dataframes with custom functions

Question

Given the following two dataframes:

df1 = pd.DataFrame(data={'unicorn': ['blue', 'red', 'piNk'], 'size': [3, 4, 6]})
df2 = pd.DataFrame(data={'unicorn': ['red'], 'size': [2]})

df1:

  unicorn  size
0    blue     3
1     red     4
2    piNk     6

df2 (always has one row):

  unicorn  size
0     red     2

How can I compare the rows of both dataframes column-wise using custom comparison functions like this (simplified):

def unicorn_comparison(str1, str2) -> float:
  return 100.0 if str1 == str2 else 0.0

and

def size_comparison(nr1, nr2) -> float:
  return 100.0 if nr1 < nr2 else 0.0

Expected result:

   unicorn  size
0      0.0   0.0
1    100.0   0.0
2      0.0   0.0

Answer 1

its way. first; add df2's column of you want.

df1['unicorn2'] = df2['unicorn']

after; You can use "application loop". You can run the logic of the you want in the "application loop".

def function(x):
    # your logic
  return x
df1_result = df1.apply(function)

Answer 2

for col in df1:
    df1[col] = (df1[col] == df2[col].loc[0]).replace({True: 100, False: 0})

This will overwrite your df1, or you can make a copy of it first.