Dropping Multiple Columns from a dataframe

I know how to drop columns from a data frame using Python. But for my problem the data set is vast, the columns I want to drop are grouped together or are basically singularly spread out across the column heading axis. Is there a shorter way to slice or drop all the columns with fewer lines of code rather than to write it out like how I have done. The way I have done it here works but I would like a more summarized way.

The flight_data_copy_final is the variable in which it should be stored.

Here’s my code:

from IPython.display import display

flight_data_copy_version1 = flight_data_copy.drop(flight_data_copy.ix[:,"Year": "FlightDate"].columns, axis=1)
flight_data_copy_version2 = flight_data_copy_version1.drop("TailNum", axis=1)
flight_data_copy_version3 = flight_data_copy_version2.drop("OriginStateFips", axis=1)
flight_data_copy_version4 = flight_data_copy_version3.drop("DestStateFips", axis=1)
flight_data_copy_version5 = flight_data_copy_version4.drop("Diverted", axis=1)
flight_data_copy_version6 = flight_data_copy_version5.drop("Flights", axis=1)
flight_data_copy_final = flight_data_copy.drop(flight_data_copy_version6.ix[:,"FirstDepTime":].columns, axis=1)

print (display (flight_data_copy_final))

Answer

To delete multiple columns at the same time in pandas, you could specify the column names as shown below. The option inplace=True is needed if one wants the change affected column in the same dataframe. Otherwise remove it.

flight_data_copy.drop(['TailNum', 'OriginStateFips', 
                'DestStateFips', 'Diverted'], axis=1, inplace=True)

Source: Python Pandas – Deleting multiple series from a data frame in one command