Pandas groupby 分位数值
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Pandas groupby quantile values
提问by lignin
I tried to calculate specific quantile values from a data frame, as shown in the code below. There was no problem when calculate it in separate lines.
我尝试从数据框中计算特定的分位数值，如下面的代码所示。在单独的行中计算时没有问题。
When attempting to run last 2 lines, I get the following error:
尝试运行最后 2 行时，出现以下错误：
AttributeError: 'SeriesGroupBy' object has no attribute 'quantile(0.25)'
How can I fix this?
我怎样才能解决这个问题？
import pandas as pd
df = pd.DataFrame(
{
'x': [0, 1, 0, 1, 0, 1, 0, 1],
'y': [7, 6, 5, 4, 3, 2, 1, 0],
'number': [25000, 35000, 45000, 50000, 60000, 70000, 65000, 36000]
}
)
f = {'number': ['median', 'std', 'quantile']}
df1 = df.groupby('x').agg(f)
df.groupby('x').quantile(0.25)
df.groupby('x').quantile(0.75)
# code below with problem:
f = {'number': ['median', 'std', 'quantile(0.25)', 'quantile(0.75)']}
df1 = df.groupby('x').agg(f)
回答by YOBEN_S
I prefer def functions
我更喜欢 def 函数
def q1(x):
return x.quantile(0.25)
def q2(x):
return x.quantile(0.75)
f = {'number': ['median', 'std', q1,q2]}
df1 = df.groupby('x').agg(f)
df1
Out[1643]:
number
median std q1 q2
x
0 52500 17969.882211 40000 61250
1 43000 16337.584481 35750 55000
回答by Jurgen Strydom
@WeNYoBen's answer is great. There is one limitation though, and that lies with the fact that one needs to create a new function for every quantile. This can be a very unpythonic exercise if the number of quantiles become large. A better approach is to use a function to create a function, and to rename that function appropriately.
@WeNYoBen的回答很棒。但是有一个限制，那就是需要为每个分位数创建一个新函数。如果分位数的数量变大，这可能是一个非常非 Python 的练习。更好的方法是使用函数来创建函数，并适当地重命名该函数。
def rename(newname):
def decorator(f):
f.__name__ = newname
return f
return decorator
def q_at(y):
@rename(f'q{y:0.2f}')
def q(x):
return x.quantile(y)
return q
f = {'number': ['median', 'std', q_at(0.25) ,q_at(0.75)]}
df1 = df.groupby('x').agg(f)
df1
Out[]:
number
median std q0.25 q0.75
x
0 52500 17969.882211 40000 61250
1 43000 16337.584481 35750 55000
The rename decorator renames the function so that the pandas agg function can deal with the reuse of the quantile function returned (otherwise all quantiles results end up in columns that are named q).
重命名装饰器重命名函数，以便pandas agg 函数可以处理返回的分位数函数的重用（否则所有分位数结果都以名为q 的列结束）。
