pandas: filter rows of DataFrame with operator chaining
Filters can be chained using a Pandas query:
df = pd.DataFrame(np.random.randn(30, 3), columns=['a','b','c'])
df_filtered = df.query('a > 0').query('0 < b < 2')
Filters can also be combined in a single query:
df_filtered = df.query('a > 0 and 0 < b < 2')
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
The answer from @lodagro is great. I would extend it by generalizing the mask function as:
def mask(df, f):
return df[f(df)]
Then you can do stuff like:
df.mask(lambda x: x[0] < 0).mask(lambda x: x[1] > 0)