Pandas: DataFrame 行上的复杂过滤器

我希望根据每一行的函数对行进行过滤，例如。

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


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

或者再举一个更复杂的人为的例子,

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)]

我该怎么做？

111113 次浏览
小开

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
小开
最佳答案

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
小开

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. For a solution refer to Roy Hyunjin Han's answer.

小开

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

小开

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)]
小开

You can use the loc property for slice you dataframe.

According documentation, loc can have a callable function as argument.

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


# define lambda function
In [5]: myfilter = lambda x: x['b'] > x['c']


# use my lambda in loc
In [6]: df1 = df.loc[myfilter ]

if you want to combine your filter function myfilter with other filter criteria

df1 = df.loc[myfilter ].loc[(df.b >= 0.5)]

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