populating dataframe column from BOOLEAN column with TRUE value

I have this df:

data = {'book': [True, False, False, False, False],
 'apple': [False, False, True, False, False],
 'cat': [False, False, False, False, True],
 'pigeon': [False, True, False, False, False],
 'shirt': [False, False, False, True, False]}

df = pd.DataFrame(data)

Then I want create a new column, df['category'] that takes in as value, the column’s name whose value is true.

So that df['category'] for each TRUE value column as follows:

book - stationery, apple - fruit,  cat - animal, pigeon - bird, shirt - clothes 

NO 2 columns have TRUE value in a row.

Expected output:

>>> df
    book    apple    cat    pigeon  shirt    category
0   True    False   False   False   False    stationery
1   False   False   False   True    False    bird 
2   False   True    False   False   False    fruit
3   False   False   False   False   True     clothes
4   False   False   True    False   False    animal

Answer

Simple..use idxmax along axis=1 to get the name of column having True value, then map the name to the corresponding category

d = {'book': 'stationery', 'pigeon': 'bird', 
     'apple': 'fruit', 'shirt': 'clothes', 'cat': 'animal'}

df['category'] = df.idxmax(1).map(d)

---

    book  apple    cat  pigeon  shirt    category
0   True  False  False   False  False  stationery
1  False  False  False    True  False        bird
2  False   True  False   False  False       fruit
3  False  False  False   False   True     clothes
4  False  False   True   False  False      animal