pandas: filter rows of DataFrame with operator chaining

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

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')

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)

Since version 0.18.1 the .loc method accepts a callable for selection. Together with lambda functions you can create very flexible chainable filters:

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df.loc[lambda df: df.A == 80]  # equivalent to df[df.A == 80] but chainable

df.sort_values('A').loc[lambda df: df.A > 80].loc[lambda df: df.B > df.A]

If all you're doing is filtering, you can also omit the .loc.