How could I create the column called "reason", which shows which strings matched?
match_d = {"col_a":["green", "purple"], "col_b":["weak", "stro", "strong"],...}
df
fruit col_a col_b
0 apple yellow NaN
1 pear blue NaN
2 banana green strong
3 cherry green heavy
4 grapes brown light
...
Expected Output
fruit col_a col_b reason
0 apple yellow NaN NaN
1 pear blue NaN NaN
2 banana green strong col_a:["green"], col_b:["stro", "strong"]
3 cherry green heavy col_a:["green"]
4 grapes brown light
Use nested list comprehension for join matched values by match_d and then join values with columns names if not empty strings:
match_d = {"col_a":["green", "purple"], "col_b":["weak", "stro", "strong"]}
cols = list(match_d.keys())
L = [[','.join(z for z in match_d[x] if pd.notna(y) and z in y)
for y in df[x]] for x in df[cols]]
df['reason'] = [np.nan if ''.join(x) == '' else ';'.join(f'{a}:[{b}]'
for a, b in zip(cols, x) if b != '')
for x in zip(*L)]
print (df)
fruit col_a col_b reason
0 apple yellow NaN NaN
1 pear blue NaN NaN
2 banana green strong col_a:[green];col_b:[stro,strong]
3 cherry green heavy col_a:[green]
4 grapes brown light NaN
Alternative solution with .apply:
match_d = {"col_a":["green", "purple"], "col_b":["weak", "stro", "strong"]}
cols = list(match_d.keys())
df1 = df[cols].apply(lambda x: [','.join(z for z in match_d[x.name]
if pd.notna(y) and z in y) for y in x])
df['reason'] = [np.nan if ''.join(x) == '' else ';'.join(f'{a}:[{b}]'
for a, b in zip(cols, x) if b != '')
for x in df1.to_numpy()]
EDIT: For add substring for not missing values in column reason use:
m = df['reason'].notna()
df.loc[m, 'reason'] = 'fruit[not_empty];' + df.loc[m, 'reason']
print (df)
fruit col_a col_b reason
0 apple yellow NaN NaN
1 pear blue NaN NaN
2 banana green strong fruit[not_empty];col_a:[green];col_b:[stro,str...
3 cherry green heavy fruit[not_empty];col_a:[green]
4 grapes brown light NaN
EDIT1: You ca wrap solution to function:
def func(df, match_d):
cols = list(match_d.keys())
df1 = df[cols].apply(lambda x: [','.join(z for z in match_d[x.name]
if pd.notna(y) and z in y) for y in x])
s = [np.nan if ''.join(x) == '' else ';'.join(f'{a}:[{b}]'
for a, b in zip(cols, x) if b != '')
for x in df1.to_numpy()]
return s
d = {"col_a":["green", "purple"], "col_b":["weak", "stro", "strong"]}
d1 = {"col_a":["blue", "yeallow"], "col_b":["light"]}
df['reason'] = df.pipe(func, d)
df['reason1'] = df.pipe(func, d1)
print (df)
fruit col_a col_b reason reason1
0 apple yellow NaN NaN NaN
1 pear blue NaN NaN col_a:[blue]
2 banana green strong col_a:[green];col_b:[stro,strong] NaN
3 cherry green heavy col_a:[green] NaN
4 grapes brown light NaN col_b:[light]