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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) 
923
Slicing Rows and Columns by Index PositionWhen slicing by index position in Pandas, the start index is included in the output, but the stop index is one step beyond the row you want to select. So the slice return row 0 and row 1, but does not return row 2. The second slice [:] indicates that all columns are required.
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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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