pandas: complex filter on rows of DataFrame

Specify reduce=True to handle empty DataFrames as well.

import pandas as pd

t = pd.DataFrame(columns=['a', 'b'])
t[t.apply(lambda x: x['a'] > 1, axis=1, reduce=True)]

https://crosscompute.com/n/jAbsB6OIm6oCCJX9PBIbY5FECFKCClyV/-/apply-custom-filter-on-rows-of-dataframe

You can do this using DataFrame.apply, which applies a function along a given axis,

In [3]: df = pandas.DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c'])

In [4]: df
Out[4]: 
          a         b         c
0 -0.001968 -1.877945 -1.515674
1 -0.540628  0.793913 -0.983315
2 -1.313574  1.946410  0.826350
3  0.015763 -0.267860 -2.228350
4  0.563111  1.195459  0.343168

In [6]: df[df.apply(lambda x: x['b'] > x['c'], axis=1)]
Out[6]: 
          a         b         c
1 -0.540628  0.793913 -0.983315
2 -1.313574  1.946410  0.826350
3  0.015763 -0.267860 -2.228350
4  0.563111  1.195459  0.343168

Suppose I had a DataFrame as follows:

In [39]: df
Out[39]: 
      mass1     mass2  velocity
0  1.461711 -0.404452  0.722502
1 -2.169377  1.131037  0.232047
2  0.009450 -0.868753  0.598470
3  0.602463  0.299249  0.474564
4 -0.675339 -0.816702  0.799289

I can use sin and DataFrame.prod to create a boolean mask:

In [40]: mask = (np.sin(df.velocity) / df.ix[:, 0:2].prod(axis=1)) > 0

In [41]: mask
Out[41]: 
0    False
1    False
2    False
3     True
4     True

Then use the mask to select from the DataFrame:

In [42]: df[mask]
Out[42]: 
      mass1     mass2  velocity
3  0.602463  0.299249  0.474564
4 -0.675339 -0.816702  0.799289