Pandas DataFrame Time Filtering: Counting Parking Entries by Hour

It seems that you are trying to filter a DataFrame based on the time values in the 'timein' column. However, the code you provided is not correctly formatted and contains some syntax errors.

Here is a corrected version of the code:

kk0 = pi_table[pi_table['timein'].str.contains('00')]
kk1 = pi_table[pi_table['timein'].str.contains('01:')]
kk2 = pi_table[pi_table['timein'].str.contains('02:')]
kk3 = pi_table[pi_table['timein'].str.contains('03:')]
kk4 = pi_table[pi_table['timein'].str.contains('04:')]
kk5 = pi_table[pi_table['timein'].str.contains('05:')]
kk6 = pi_table[pi_table['timein'].str.contains('06:')]
kk7 = pi_table[pi_table['timein'].str.contains('07:')]
kk8 = pi_table[pi_table['timein'].str.contains('08:')]
kk9 = pi_table[pi_table['timein'].str.contains('09:')]
kk10 = pi_table[pi_table['timein'].str.contains('10:')]
kk11 = pi_table[pi_table['timein'].str.contains('11:')]
kk12 = pi_table[pi_table['timein'].str.contains('12:')]
kk13 = pi_table[pi_table['timein'].str.contains('13:')]
kk14 = pi_table[pi_table['timein'].str.contains('14:')]
kk15 = pi_table[pi_table['timein'].str.contains('15:')]
kk16 = pi_table[pi_table['timein'].str.contains('16:')]
kk17 = pi_table[pi_table['timein'].str.contains('17:')]
kk18 = pi_table[pi_table['timein'].str.contains('18:')]
kk19 = pi_table[pi_table['timein'].str.contains('19:')]
kk20 = pi_table[pi_table['timein'].str.contains('20:')]
kk21 = pi_table[pi_table['timein'].str.contains('21:')]
kk22 = pi_table[pi_table['timein'].str.contains('22:')]
kk23 = pi_table[pi_table['timein'].str.contains('23:')]

Please note that this code assumes that pi_table is a DataFrame and that the 'timein' column contains time values in the format 'HH:MM'.

To get the count of parking entries for each hour, you can use the .shape[0] attribute of each filtered DataFrame:

print(f'Hour 00: {kk0.shape[0]} entries')
print(f'Hour 01: {kk1.shape[0]} entries')
# ... and so on for the rest of the hours

This code demonstrates a simple way to filter and count data based on time intervals. You can further enhance this by using more sophisticated time-series analysis techniques for more detailed insights into your parking data.