Drop rows in dataframe that are between two dates Pandas

Hello so I have the table below:

import pandas as pd

raw_data = {
    'vendor_id': [1, 2, 3, 4, 5, 6], 
    'name': ['vendor_schmendor', 'parts_r_us', 'vendor_king', 'vendor_diagram', 'venny', 'vendtriloquist'], 
    'contract_sign_date': ['2018-09-01', '2018-09-03', '2018-10-11', '2018-08-21', '2018-08-13', '2018-10-29'],
    'total_spend' :[34324, 23455, 77654, 23334, 94843, 23444]}

df = pd.DataFrame(raw_data, columns = ['vendor_id', 'name', 'contract_sign_date', 'total_spend'])

I was given a task where I have to drop all the rows where the contract_sign_date is between "2018-09-01" and "2018-10-13", this is my solution (although it doesn’t work):

alter = df.drop((df['contract_sign_date'] == "2018-09-01") & (df['contract_sign_date'] == "2018-10-13"))

the output throws: KeyError: '[False, False, False, False, False, False] not found in axis'

So can anyone provide a code so in order that I can construct what I was desired for?

>Solution :

Your condition is to check simultaneous equality with two different values (a == b) and (a==c), which is impossible.

Use between and the boolean NOT operator ~:

alter = df[~df['contract_sign_date'].between("2018-09-01", "2018-10-13")]

output:

   vendor_id            name contract_sign_date  total_spend
3          4  vendor_diagram         2018-08-21        23334
4          5           venny         2018-08-13        94843
5          6  vendtriloquist         2018-10-29        23444

NB. we’re using strings here as the YYYY-MM-DD format enables direct comparison, with a different format you would need to use a datetime type