How to drop a row in Pandas if a third letter in a column is W?

Question

I have a dataframe of this kind:

And I want to drop all the rows where the third letter in the first column is a ‘W’ to give an output:

ID	Jan	Feb	Mar
20S22	0	0	1
200122	0	0	1

It is a very large dataframe and I tried doing something like this:

df[df.ID[2] != 'W']

But this only selects the item in the second row. I could potentially iterate over the dataframe but wanted to see if there was a better option.

Asked By: Ray234

||

Answer 1

You are almost there. Use:

df= df[df['ID'].str[2].ne('W')]

you might want to reset the index after this selection

Answered By: user19077881

Answer 2

You can use regex to find the 3rd character

out = df[df['ID'].str.contains('^.{2}(?!W)')]
# or
out = df[df['ID'].str.match('.{2}(?!W)')]
# or
out = df[df['ID'].str.match('.{2}[^W]')]

NOTE: difference between str.contains and str.match is that str.match match the string from beginning of the target.

$ print(out)

       ID  Jan  Feb  Mar
1   20S22    0    0    1
3  200122    0    0    1

Answered By: Ynjxsjmh

Question: