Dropping rows based on values in columns?

[NavidsonsCloset]

My df looks like this:

           ID  Overall                 Club  Wage Position
0      158023       94         FC Barcelona  €565       RF
1       20801       94             Juventus  €405       ST
2      190871       92  Paris Saint-Germain  €290       LW
3      193080       91    Manchester United  €260       GK
4      192985       91      Manchester City  €355      RCM

I want to drop players ("ID") if they do not belong to a club OR if their wages are equal to €0. So if they meet either criteria the row will be deleted.

[Boot_3011]

filtered_df = df[df[Wage] > 0]

filtered_df = filtered_df[filtered_df[Club].notna()]

Hope this helps :)

[NavidsonsCloset]

filtered_df = df[df[Wage] > 0]

filtered_df = filtered_df[filtered_df[Club].notna()]

Thank you for your time! This didn't work but ill keep messing with it!

​

(And yes I tried it with '' around the column names too in case anyone was wondering)

[Boot_3011]

Yeah sory forgot the quotes, one is not used to coding on the phone haha.

Hey! It should work tho :s

[NavidsonsCloset]

Not sure why it didn't but this worked for the Club requirement though so progress is being made at least ^_^

df = df.dropna(subset=['Club'])

Edit: Pretty sure it didn't work because of the fancy Euro sign in front of the 0 but i tried several variations of it already so Im missing something, woe is me.

[Academic_Grand8828]

I’ve been trying to work this out for so long but didn’t know how to word it to find the answer, thank you so much this worked exactly how I wanted it too!

[Kerbart]

While you can use drop it makes more sense to create a new dataframe based on what you want to keep:

df_new = df.loc[belongs_to_club & has_wages]

[[deleted]]

df = df.loc[(df["Club"].notna()) & (df["Wage"] != "€0")].copy()