Python Dataframe: Remove Rows from Dataframe Using a Loop

Question

I wanted to remove certain rows from my pandas dataframe. I did it the manual way of spelling out each ITEM number that I didn't want included.

How do I do the same task as shown in the code below but using a loop?

df_adhoc_1_final = df_adhoc_1_final[df_adhoc_1_final['ITEM'].str.contains('4888') == False]
df_adhoc_1_final = df_adhoc_1_final[df_adhoc_1_final['ITEM'].str.contains('4889') == False]
df_adhoc_1_final = df_adhoc_1_final[df_adhoc_1_final['ITEM'].str.contains('4890') == False]
df_adhoc_1_final = df_adhoc_1_final[df_adhoc_1_final['ITEM'].str.contains('4891') == False]
df_adhoc_1_final = df_adhoc_1_final[df_adhoc_1_final['ITEM'].str.contains('4892') == False]
df_adhoc_1_final = df_adhoc_1_final[df_adhoc_1_final['ITEM'].str.contains('4893') == False]

Answer 1

A loop is unnecessary here. There is almost always a vectorised, non-loopy approach with any pandas operation. Here's one way to do it.

First, initialise a list of codes -

codes = ['4888', '4889', ... '4893']

Or,

codes = np.arange(4888, 4894).astype(str)

Now, filter using str.contains . You'll need to join each code as a single regex using the | OR pipe -

df = df[~df['ITEM'].str.contains('|'.join(codes))]

If the codes are the only thing in the ITEM column, you can use isin -

df = df[~df['ITEM'].isin(codes)]

Answer 2

how about:

for val in ['4888','4889','4890','4891','4892','4893']:
    df_adhoc_1_final = df_adhoc_1_final[df_adhoc_1_final['ITEM'].str.contains(val) == False]