Why is .apply / .map only running against the first column?

Question

All!

I have two [2] pd.DataFrames: df_colors [20,3] and df_master [200,13].

I am attempting to update the values for each item in each row.

df_colors.head(5)

COLOR	CODE
WHITE	0
BLACK	1
GREEN	0
YELLOW	0
...	...
BLUE	0
RED	1

df_master.head(5)

color_0	color_1	color_2	color_3	color_4	...	color_12	color_13
RED	GREEN	YELLOW	PINK	NONE	...	NONE	NONE
BLUE	BLACK	WHITE	GREEN	PINK	...	RED	ORANGE
RED	NONE	NONE	NONE	NONE	...	NONE	NONE
...
BLACK	WHITE	ORANGE	NONE	NONE	...	NONE	NONE
PURPLE	ORANGE	GREEN	WHITE	BLUE	...	RED	NONE

def fun2(x):
  _y1 = df_colors[df_colors.COLOR==x]
  
  if _y1.size > 0:
    _y1 = _y1.values[0][1]
  else:
    continue
  return _y1

col_names = list(df_master.columns)
dat1 = df_master[col_names] = df_master[col_names].map(lambda x: fun(2) if isinstance(x, str), else x)

dat1.head(3)

color_0	color_1	color_2	color_3	color_4	...	color_12	color_13
1	Empty DataFrame Columns: [COLOR,CODE]	Empty DataFrame Columns: [COLOR,CODE]	Empty DataFrame Columns: [COLOR,CODE]	NONE	...	NONE	NONE
0	Empty DataFrame Columns: [COLOR,CODE]	Empty DataFrame Columns: [COLOR,CODE]	Empty DataFrame Columns: [COLOR,CODE]	Empty DataFrame Columns: [COLOR,CODE]	...	Empty DataFrame Columns: [COLOR,CODE]	Empty DataFrame Columns: [COLOR,CODE]
1	NONE	NONE	NONE	NONE	...	NONE	NONE

What I was expecting:

color_0	color_1	color_2	color_3	color_4	...	color_12	color_13
1	0	1	1	NONE	...	NONE	NONE
0	1	0	0	1	...	1	1
1	NONE	NONE	NONE	NONE	...	NONE	NONE
...
1	0	1	NONE	NONE	...	NONE	NONE
1	1	0	WHITE	0	...	1	NONE

Answer 1

Your current logic is unclear, but if you want to replace the colors by their code why not just use map (applymap for older pandas version):

out = df_master.map(df_colors.set_index('COLOR')['CODE'].get)

Output:

   color_0  color_1  color_2  color_3  color_4  color_12 color_13
0      1.0      0.0      0.0      NaN      NaN       NaN     None
1      0.0      1.0      0.0      0.0      NaN       1.0     None
2      1.0      NaN      NaN      NaN      NaN       NaN     None
3      1.0      0.0      NaN      NaN      NaN       NaN     None
4      NaN      NaN      0.0      0.0      0.0       1.0     None