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So I'm dropping rows based on column values. My df contains two specific columns that I'm filtering: ['Exclude', 'Lost Flag']. Both columns contain binary values. I dropped all rows where "Exclude" was 1 via:

df = df[df.Exclude !=1]

All is well. However, when I tried to do the same thing with "Lost Flag":

df = df[df.Lost Flag !=1]

I get SyntaxError on the column name. I've triple checked the column name, tried underscore, no space, and tried escaping the space using ``. Then I tried pd.query:

df = df.query('Lost Flag !=1')

And I got the same syntax error. I feel like I'm missing something obvious.

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    Lots of operations don't support names with spaces, just like the dot notation. In any case, it's probably better to use df["col name"] than the dot notation for columns Commented Jan 7, 2020 at 17:49
  • I would agree that it's always better to use df['col name'] for accessing columns, whether there are spaces or not. Unless you have space limitations on your script file for some unknown reason, changing to this notation can help resolve a whole lot of issues I've come across Commented Jan 7, 2020 at 18:11

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try with a ` for space:

df.query('`Lost Flag`!=1')

From docs:

>>> df = pd.DataFrame({'A': range(1, 6), ... 'B': range(10, 0, -2), ... 'C C': range(10, 5, -1)}) >>> df A B C C 0 1 10 10 1 2 8 9 2 3 6 8 3 4 4 7 4 5 2 6 For columns with spaces in their name, you can use backtick quoting. >>> df.query('B == `C C`') A B C C 0 1 10 10
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3 Comments

@awhit may be a pandas upgrade is required to 0.25.0 , i just tried df.query("C C!=10") and it works
+1 wow.... this is why I come to SO... and why I should read completely the "What's new" in every pandas release. OI just learned about "back ticking" nice.
@ScottBoston I am glad one of my answer helped you since many of your's helped me:) bdw, it may interest you to know that 1.0 is coming soon :)

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