Most operations in pandas can be accomplished with operator chaining (groupby, aggregate, apply, etc), but the only way I’ve found to filter rows is via normal bracket indexing
df_filtered = df[df['column'] == value]
This is unappealing as it requires I assign df to a variable before being able to filter on its values. Is there something more like the following?
df_filtered = df.mask(lambda x: x['column'] == value)
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Answer
I’m not entirely sure what you want, and your last line of code does not help either, but anyway:
“Chained” filtering is done by “chaining” the criteria in the boolean index.
In [96]: df Out[96]: A B C D a 1 4 9 1 b 4 5 0 2 c 5 5 1 0 d 1 3 9 6 In [99]: df[(df.A == 1) & (df.D == 6)] Out[99]: A B C D d 1 3 9 6
If you want to chain methods, you can add your own mask method and use that one.
In [90]: def mask(df, key, value):
....: return df[df[key] == value]
....:
In [92]: pandas.DataFrame.mask = mask
In [93]: df = pandas.DataFrame(np.random.randint(0, 10, (4,4)), index=list('abcd'), columns=list('ABCD'))
In [95]: df.ix['d','A'] = df.ix['a', 'A']
In [96]: df
Out[96]:
A B C D
a 1 4 9 1
b 4 5 0 2
c 5 5 1 0
d 1 3 9 6
In [97]: df.mask('A', 1)
Out[97]:
A B C D
a 1 4 9 1
d 1 3 9 6
In [98]: df.mask('A', 1).mask('D', 6)
Out[98]:
A B C D
d 1 3 9 6