Fix Python – Logical operators for Boolean indexing in Pandas

I’m working with a 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_other_number)]

exits with error?
Example:
a = pd.DataFrame({‘x’:[1,1],’y’:[10,20]})

In: a[(a[‘x’]==….

Fix Python – Is False == 0 and True == 1 an implementation detail or is it guaranteed by the language?

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 existing and, likely, future ones)?
0 == False # True
1 == True # True
[‘zero’, ‘one’][False]….

Fix Python – Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

I want to filter my dataframe with an or condition to keep rows with a particular column’s values that are outside the range [-0.25, 0.25]. I tried:
df = df[(df[‘col’] < -0.25) or (df['col'] > 0.25)]

But I get the error:

Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

….

Fix Python – Parsing boolean values with argparse

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 = argparse.ArgumentParser(description=”My parser”)
parser.add_argument(“–my_bool”, type=bool)
cmd_lin….