Python 使用带参数的 Pandas groupby() + apply()
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Use Pandas groupby() + apply() with arguments
提问by beta
I would like to use df.groupby()in combination with apply()to apply a function to each row per group.
我想df.groupby()结合使用apply()将函数应用于每组的每一行。
I normally use the following code, which usually works (note, that this is without groupby()):
我通常使用以下代码，它通常有效（注意，这是没有groupby()）：
df.apply(myFunction, args=(arg1,))
With the groupby()I tried the following:
随着groupby()我试过如下：
df.groupby('columnName').apply(myFunction, args=(arg1,))
However, I get the following error:
但是，我收到以下错误：
TypeError: myFunction() got an unexpected keyword argument 'args'
类型错误：myFunction() 得到了一个意外的关键字参数“args”
Hence, my question is: How can I use groupby()and apply()with a function that needs arguments?
因此，我的问题是：如何使用groupby()和apply()需要参数的函数？
采纳答案by MaxU
pandas.core.groupby.GroupBy.applydoes NOT have namedparameter args, but pandas.DataFrame.applydoes have it.
pandas.core.groupby.GroupBy.apply没有命名参数args，但pandas.DataFrame.apply有它。
So try this:
所以试试这个：
df.groupby('columnName').apply(lambda x: myFunction(x, arg1))
or as suggested by @Zero:
或者按照@Zero 的建议：
df.groupby('columnName').apply(myFunction, ('arg1'))
Demo:
演示：
In [82]: df = pd.DataFrame(np.random.randint(5,size=(5,3)), columns=list('abc'))
In [83]: df
Out[83]:
a b c
0 0 3 1
1 0 3 4
2 3 0 4
3 4 2 3
4 3 4 1
In [84]: def f(ser, n):
...: return ser.max() * n
...:
In [85]: df.apply(f, args=(10,))
Out[85]:
a 40
b 40
c 40
dtype: int64
when using GroupBy.applyyou can pass either a named arguments:
使用时，GroupBy.apply您可以传递命名参数：
In [86]: df.groupby('a').apply(f, n=10)
Out[86]:
a b c
a
0 0 30 40
3 30 40 40
4 40 20 30
a tuple of arguments:
一组参数：
In [87]: df.groupby('a').apply(f, (10))
Out[87]:
a b c
a
0 0 30 40
3 30 40 40
4 40 20 30
回答by Brad Solomon
Some confusion here over why using an argsparameter throws an error might stem from the fact that pandas.DataFrame.applydoes have an argsparameter (a tuple), while pandas.core.groupby.GroupBy.applydoes not.
关于为什么使用args参数会引发错误的一些混淆可能源于pandas.DataFrame.apply这样一个事实，即确实有一个args参数（元组），而pandas.core.groupby.GroupBy.apply没有。
So, when you call .applyon a DataFrame itself, you can use this argument; when you call .applyon a groupby object, you cannot.
因此，当您调用.applyDataFrame 本身时，您可以使用此参数；当您调用.applygroupby 对象时，您不能。
In @MaxU's answer, the expression lambda x: myFunction(x, arg1)is passed to func(the first parameter); there is no need to specify additional *args/**kwargsbecause arg1is specified in lambda.
在@MaxU 的回答中，表达式lambda x: myFunction(x, arg1)被传递给func（第一个参数）；不需要指定额外的*args/**kwargs因为arg1是在 lambda 中指定的。
An example:
一个例子：
import numpy as np
import pandas as pd
# Called on DataFrame - `args` is a 1-tuple
# `0` / `1` are just the axis arguments to np.sum
df.apply(np.sum, axis=0) # equiv to df.sum(0)
df.apply(np.sum, axis=1) # equiv to df.sum(1)
# Called on groupby object of the DataFrame - will throw TypeError
print(df.groupby('col1').apply(np.sum, args=(0,)))
# TypeError: sum() got an unexpected keyword argument 'args'
回答by Hitesh Somani
For me
为了我
df2 = df.groupby('columnName').apply(lambda x: my_function(x, arg1, arg2,))
df2 = df.groupby('columnName').apply(lambda x: my_function(x, arg1, arg2,))
worked
工作过
