Python | Count Number of False Statements in 3 Rows

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

Counting the number of True Booleans in a Python List

True is equal to 1.

>>> sum([True, True, False, False, False, True])
3

How to count rows where condition is false Pandas?

If you want to count the rows where isValid outputs False:

(~dataset['NUM'].apply(isValid)).sum()

output: 0

edit

m = dataset['NUM'].apply(isValid)
report["NUM"] = (~m).sum()
dataset2 = dataset[m]

Pandas count true boolean values per row

Apply sum over columns axis:

df['count'] = df[['bool2', 'bool3']].sum(axis=1)

Output:

>>> df
   num1  num2  bool1  bool2  bool3  count
0    20    30   True  False   True      1
1    10     5  False   True   True      2

Count number of trues in a numpy array in each row?

We can simply do this by providing an axis argument to the sum function:

arr.sum(axis=1)

How to count number of False values that precede at least one True with Numpy array?

As a oneliner:

import numpy as np

x = np.array([[0,0,1,0],[1,1,0,1],[1,1,1,1]], dtype='bool')
np.max((~x).cumsum(axis = 1) * x, axis = 1 )

# array([2, 1, 0])

To show the calculation:

not_x = ~x    # Reverse the Trues and Falses
temp = not_x.cumsum( axis = 1 )  # cumsum the Falses by row.
temp

# array([[1, 2, 2, 3],
#        [0, 0, 1, 1],
#        [0, 0, 0, 0]])

temp *= x    # Zero the result only where x is False
temp 
# array([[0, 0, 2, 0],
#        [0, 0, 0, 1],
#        [0, 0, 0, 0]])     

temp.max( axis = 1 )  # Find the max in each row.
# array([2, 1, 0])

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