Pandas Drop Behavior

Question

I just tried answering another question here on SO and ran into an issue of filtering a dataframe using pd.drop. Here is the example I gave:

import pandas as pd
import langdetect
df = pd.DataFrame({'Sentence':['es muy bueno','run, Forest! Run!','Ήξερα ότι θα εξετάζατε τον Μεταφραστή Google', 'This is Certainly en']})
df['Language'] = df['Sentence'].apply(lambda x: langdetect.detect(x))
# output
                                       Sentence Language
0                                  es muy bueno       es
1                             run, Forest! Run!       ro
2  Ήξερα ότι θα εξετάζατε τον Μεταφραστή Google       el
3                          This is Certainly en       en

Now I wanted to drop all rows where language is not en. When using df.drop(df['Language'] != 'en') it unexpectedly returns:

                                       Sentence Language
2  Ήξερα ότι θα εξετάζατε τον Μεταφραστή Google       el
3                          This is Certainly en       en

However, when I take the Boolean indexing it returns:

df['Language'] != 'en'
# output 

0     True
1     True
2     True
3    False
Name: Language, dtype: bool

Now, I can get around this by using df.loc[df['Language'] == 'en']. But I am wondering why drop is behaving this way or if I've done something wrong?

Answer 1

drop require index or column label

df.drop(df.index[(df['Language'] != 'en')])
Out[303]: 
            Sentence Language
3  ThisisCertainlyen       en

Answer 2

Pandas drop takes index or column label

labels : single label or list-like

Index or column labels to drop.

When you pass the following to df.drop on default axis (which is 0), its dropping rows 0 and 1 - corresponding to False(0) and True(1)

df['Language'] != 'en'

0     True
1     True
2     True
3    False

Though it can be done using df.drop as in @Wen's answer, the most idiomatic way would be to go for boolean indexing or df.query