Adding another condition for df column comparison

[Mark17]

Hi all,

Here's some code:

print('Total number of changes in DTE value is {}'.format((df['DTE'] != df['DTE'].shift(1)).sum()))
print('Total number of changes in DTE value and DTE < 151 is {}'.format((df['DTE'] != df['DTE'].shift(1)) \
                                                                        and (df['DTE']<151).sum()))

Line 1 correctly prints out the sum, but line 2 gives a ValueError about the series being ambiguous. My intent was just to add a second condition. If the syntax works for one, then why not for both?

Mark

[deanhystad]

I think you want this:

(df['DTE'] != df['DTE'].shift(1) & df['DTE'] < 151)

In pandas you use & instead of "and", | instead of "or" and ~ instead of "not"

Your code is hard to read because the lines are long. I would split the calculation from the print

value = (df['DTE'] != df['DTE'].shift(1) & df['DTE'] < 151).sum()
print(f'Total number of changes in DTE value and DTE < 151 is {value}')

[Mark17]

I think you want this:

(df['DTE'] != df['DTE'].shift(1) & df['DTE'] < 151)

In pandas you use & instead of "and", | instead of "or" and ~ instead of "not"

Your code is hard to read because the lines are long. I would split the calculation from the print

value = (df['DTE'] != df['DTE'].shift(1) & df['DTE'] < 151).sum()
print(f'Total number of changes in DTE value and DTE < 151 is {value}')

That gives me: TypeError: unsupported operand type(s) for &: 'float' and 'float'. Wrapping each of the individual & arguments in parentheses works:

print('Total number of changes in DTE value is {}'.format((df['DTE'] != df['DTE'].shift(1)).sum()))
value = ((df['DTE'] != df['DTE'].shift(1)) & (df['DTE'] < 151)).sum()
print(f'Total number of changes in DTE value and DTE < 151 is {value}')

Thanks for your help!