Count occurences of True/False in column of dataframe

Use pd.Series.value_counts():

>> df = pd.DataFrame({'boolean_column': [True, False, True, False, True]})
>> df['boolean_column'].value_counts()
True     3
False    2
Name: boolean_column, dtype: int64

If you want to count False and True separately you can use pd.Series.sum() + ~:

>> df['boolean_column'].values.sum()  # True
3
>> (~df['boolean_column']).values.sum() # False
2

With Pandas, the natural way is using value_counts:

df = pd.DataFrame({'A': [True, False, True, False, True]})

print(df['A'].value_counts())

# True     3
# False    2
# Name: A, dtype: int64

To calculate True or False values separately, don't compare against True / False explicitly, just sum and take the reverse Boolean via ~ to count False values:

print(df['A'].sum())     # 3
print((~df['A']).sum())  # 2

This works because bool is a subclass of int, and the behaviour also holds true for Pandas series / NumPy arrays.

Alternatively, you can calculate counts using NumPy:

print(np.unique(df['A'], return_counts=True))

# (array([False,  True], dtype=bool), array([2, 3], dtype=int64))

This alternative works for multiple columns and/or rows as well. 

df[df==True].count(axis=0)

Will get you the total amount of True values per column. For row-wise count, set axis=1. 

df[df==True].count().sum()

Adding a sum() in the end will get you the total amount in the entire DataFrame.

I couldn't find here what I exactly need. I needed the number of True and False occurrences for further calculations, so I used:

true_count = (df['column']).value_counts()[True]
False_count = (df['column']).value_counts()[False]

Where df is your DataFrame and column is the column with booleans.