Ufdjrw

I would like to filter rows by a function of each row, e.g.

def f(row):
 return sin(row['velocity'])/np.prod(['masses']) > 5

df = pandas.DataFrame(...)
filtered = df[apply_to_all_rows(df, f)]

Or for another more complex, contrived example,

def g(row):
 if row['col1'].method1() == 1:
 val = row['col1'].method2() / row['col1'].method3(row['col3'], row['col4'])
 else:
 val = row['col2'].method5(row['col6'])
 return np.sin(val)

df = pandas.DataFrame(...)
filtered = df[apply_to_all_rows(df, g)]

How can I do so?

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

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

I canot comment on duckworthd's answer, but it is not perfectly working. It crashes when the dataframe is empty:

df = pandas.DataFrame(columns=['a', 'b', 'c'])
df[df.apply(lambda x: x['b'] > x['c'], axis=1)]

Outputs:

ValueError: Must pass DataFrame with boolean values only

To me it looks like a bug in pandas, since is definitively a valid set of boolean values.

The best approach I've found is, instead of using reduce=True to avoid errors for empty df (since this arg is deprecated anyway), just check that df size > 0 before applying the filter:

def my_filter(row):
 if row.columnA == something:
 return True

 return False

if len(df.index) > 0:
 df[df.apply(my_filter, axis=1)]