Is it guaranteed that False == 0 and True == 1, in Python (assuming that they are not reassigned by the user)? For instance, is it in any way guaranteed that the following code will always produce the same results, whatever the version of Python (both ex...

Does Python actually contain a Boolean value? I know that you can do: checker = 1 if checker: #dostuff But I'm quite pedantic and enjoy seeing booleans in Java. For instance: Boolean checker; if (someDecision) { checker = true; } if(checker) {...

I see I can't do: "%b %b" % (True, False) in Python. I guessed %b for b(oolean). Is there something like this?

I would like to use argparse to parse boolean command-line arguments written as "--foo True" or "--foo False". For example: my_program --my_boolean_flag False However, the following test code does not do what I would like: import argparse parser = arg...

I want to do something like: {% if ("view_video" in video_perms) OR purchase_override %} Is that possible?

I tried running this piece of code: path = '/bla/bla/bla' if path is True: print "True" else: print "False" And it prints False. I thought Python treats anything with value as True. Why is this happening?

I have a NumPy array 'boolarr' of boolean type. I want to count the number of elements whose values are True. Is there a NumPy or Python routine dedicated for this task? Or, do I need to iterate over the elements in my script?

In Python, how do I create a numpy array of arbitrary shape filled with all True or all False?

Some column in dataframe df, df.column, is stored as datatype int64. The values are all 1s or 0s. Is there a way to replace these values with boolean values?

I have a DataFrame named df as Order Number Status 1 1668 Undelivered 2 19771 Undelivered 3 100032108 Undelivered 4 2229 Delivered 5 00056 Undelivered I would like to convert the Status column to boolean ...

I use Scilab, and want to convert an array of booleans into an array of integers: >>> x = np.array([4, 3, 2, 1]) >>> y = 2 >= x >>> y array([False, False, True, True], dtype=bool) In Scilab I can use: >>> bool...

I'm working with boolean index in Pandas. The question is why the statement: a[(a['some_column']==some_number) & (a['some_other_column']==some_other_number)] works fine whereas a[(a['some_column']==some_number) and (a['some_other_column']==some_ot...

I'm working with boolean index in Pandas. The question is why the statement: a[(a['some_column']==some_number) & (a['some_other_column']==some_other_number)] works fine whereas a[(a['some_column']==some_number) and (a['some_other_column']==some_ot...

I'm mocking an Post api (written in C#) which return a bool value true or false when called. The content-type is application/json for the request true I am now trying to mock that endpoint in Python using Flask and I'm struggling to make it pass a bool...

For a df table like below, A B C D 0 0 1 1 1 1 2 3 5 7 3 3 1 2 8 why are the double brackets needed for selecting specific columns after boolean indexing? the [['A','C']] part of df[df['A'] < 3][['A','C']]

How to convert true false values in Dataframe as 1 for true and 0 for false COL1 COL2 COL3 COL4 12 TRUE 14 FALSE 13 FALSE 13 TRUE OUTPUT 12 1 14 0 13 0 13 1

Having issue filtering my result dataframe with an or condition. I want my result df to extract all column var values that are above 0.25 and below -0.25. This logic below gives me an ambiguous truth value however it work when I split this filtering in ...

Having issue filtering my result dataframe with an or condition. I want my result df to extract all column var values that are above 0.25 and below -0.25. This logic below gives me an ambiguous truth value however it work when I split this filtering in ...

I assume this is an easy fix and I'm not sure what I'm missing. I have a data frame as such: index c1 c2 c3 2015-03-07 01:27:05 False False True 2015-03-07 01:27:10 False False True ...

import pandas as pd data={'col1':[1,3,3,1,2,3,2,2]} df=pd.DataFrame(data,columns=['col1']) print df col1 0 1 1 3 2 3 3 1 4 2 5 3 6...