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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) 
481
Using the size() or count() method with pandas. DataFrame. groupby() will generate the count of a number of occurrences of data present in a particular column of the dataframe.
Use count_nonzero() to count True elements in NumPy array In Python, False is equivalent to 0 , whereas True is equivalent to 1 i.e. a non-zero value. Numpy module provides a function count_nonzero(arr, axis=None), which returns the count of non zero values in a given numpy array.
You can count the number of duplicate rows by counting True in pandas. Series obtained with duplicated() . The number of True can be counted with sum() method. If you want to count the number of False (= the number of non-duplicate rows), you can invert it with negation ~ and then count True with sum() .
So you need value_counts ?
df.col_name.value_counts() Out[345]: False 6 True 1 Name: has_cancer, dtype: int64 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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