given

patient_id  test_result has_cancer
0   79452   Negative    False
1   81667   Positive    True
2   76297   Negative    False
3   36593   Negative    False
4   53717   Negative    False
5   67134   Negative    False
6   40436   Negative    False

how to count False or True in a column , in python?

I had been trying:

# number of patients with cancer

number_of_patients_with_cancer= (df["has_cancer"]==True).count()
print(number_of_patients_with_cancer)

So you need value_counts ?

df.col_name.value_counts()
Out[345]: 
False    6
True     1
Name: has_cancer, dtype: int64

If has_cancer has NaNs:

false_count = (~df.has_cancer).sum()

If has_cancer does not have NaNs, you can optimise by not having to negate the masks beforehand.

false_count = len(df) - df.has_cancer.sum()

And similarly, if you want just the count of True values, that is

true_count = df.has_cancer.sum()

If you want both, it is

fc, tc = df.has_cancer.value_counts().sort_index().tolist()

0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
8    False
9    False

If the panda series above is called example

example.sum()

Then this code outputs 1 since there is only one True value in the series. To get the count of False

len(example) - example.sum()

number_of_patients_with_cancer = df.has_cancer[df.has_cancer==True].count()

Just sum the column for a count of the Trues. False is just a special case of 0 and True a special case of 1. The False count would be your row count minus that. Unless you’ve got na‘s in there.

Consider your above data frame as a df

True_Count = df[df.has_cancer == True]

len(True_Count)