Given a dataframe :
Animal wings claws horns
0 Ant No No No
1 Bat Yes Yes No
2 Cat No Yes No
I am trying to print corresponding value of 'Animal' column where the value of given column is 'Yes'.
Eg1: Given column is 'claws', output should be Bat and Cat.
Eg2: Given column is 'wings', output should be Bat
You can filter each column separately - convert Animal to index by DataFrame.set_index and compare by DataFrame.eq, then filter by columns names and index convert to list:
df1 = df.set_index('Animal').eq('Yes')
print (df1.index[df1['wings']].tolist())
['Bat']
print (df1.index[df1['claws']].tolist())
['Bat', 'Cat']
print (df1.index[df1['horns']].tolist())
[]
Or is possible create dictionary use dict comprehension:
df1 = df.set_index('Animal').eq('Yes')
d = {col: df1.index[df1[col]].tolist() for col in df1}
print (d)
{'wings': ['Bat'], 'claws': ['Bat', 'Cat'], 'horns': []}
Or if want separator add join:
d1 = {col: ','.join(df1.index[df1[col]]) for col in df1}
print (d1)
{'wings': 'Bat', 'claws': 'Bat,Cat', 'horns': ''}
Or if want filter all columns to Series with index by Animal and values by all Yes values with separator , use DataFrame.dot with transpose DataFrame:
df1 = df.set_index('Animal').eq('Yes')
s = df1.T.dot(df1.index + ',').str.strip(',')
print (s)
wings Bat
claws Bat,Cat
horns
dtype: object
print (s['wings'])
Bat
print (s['claws'])
Bat,Cat
print (s['horns'])
