[FIXED] Using value_counts with False/True. How to get false/true = 0 when there is any false or true

Issue

I am writing an application that generates a report. One part of the output shows how many matches are between two columns (match = True, no match = False). In order to do that I use df.column.value_counts() which output is

False    2
True     1
Name: column, dtype: int64

So I was thinking to show the result in the report something like

# This is a simplify example
print(" Position with same genotype: ", df.column.value_counts()[1])
print(" Position with different genotype: ", df.column.value_counts()[0])

I have noticed that the order of this output change depending on what False or True is found first in the table ( I think I can solve this with sort ). However, another bigger concern I am thinking of is if there is no False or non True are found in the table the output is:

# For example not Falses
True     3
Name: column, dtype: int64

I was expected this:

# For example not Falses
True     3
False    0
Name: column, dtype: int64

How can I solve this to avoid future errors? It is quite unlike that happen, it is going to be at least one false or true but I want to be sure that not error happens as this application is going to be run many times with different samples so theoretically this situation can happen.

Solution

Series.get

We can use the get method of the pandas series and specify the default value as 0

s = df['column'].value_counts()

print("Position with same genotype: ", s.get(True, 0))
print("Position with different genotype: ", s.get(False, 0))

Result

Position with same genotype:  2
Position with different genotype:  1

Answered By - Shubham Sharma