pandas -是否可以使用两种不同的聚合方式聚合两列? [英] Pandas - possible to aggregate two columns using two different aggregations?
问题描述
我正在加载一个csv文件,该文件包含以下列: 日期,textA,textB,numberA,numberB
I'm loading a csv file, which has the following columns: date, textA, textB, numberA, numberB
我想按以下列进行分组:日期,textA和textB-但要对numberA应用"sum",但对numberB应用"min".
I want to group by the columns: date, textA and textB - but want to apply "sum" to numberA, but "min" to numberB.
data = pd.read_table("file.csv", sep=",", thousands=',')
grouped = data.groupby(["date", "textA", "textB"], as_index=False)
...但是我看不到如何将两个不同的聚合函数应用于两个不同的列?
IE. sum(numberA), min(numberB)
...but I cannot see how to then apply two different aggregate functions, to two different columns?
I.e. sum(numberA), min(numberB)
推荐答案
agg
方法可以接受一个dict,在这种情况下,键指示要应用该功能的列:
The agg
method can accept a dict, in which case the keys indicate the column to which the function is applied:
grouped.agg({'numberA':'sum', 'numberB':'min'})
例如,
For example,
import numpy as np
import pandas as pd
df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three',
'two', 'two', 'one', 'three'],
'number A': np.arange(8),
'number B': np.arange(8) * 2})
grouped = df.groupby('A')
print(grouped.agg({
'number A': 'sum',
'number B': 'min'}))
收益
number B number A
A
bar 2 9
foo 0 19
这也表明Pandas可以处理列名中的空格.我不确定问题的根源是什么,但是文字空间应该不会造成问题.如果您想进一步调查,
This also shows that Pandas can handle spaces in column names. I'm not sure what the origin of the problem was, but literal spaces should not have posed a problem. If you wish to investigate this further,
print(df.columns)
而无需重新分配列名,将向我们显示名称的repr
.列名中也许有一个很难看的字符,看起来像一个空格(或其他字符),但实际上是一个u'\xa0'
(NO-BREAK SPACE).
without reassigning the column names, will show show us the repr
of the names. Maybe there was a hard-to-see character in the column name that looked like a space (or some other character) but was actually a u'\xa0'
(NO-BREAK SPACE), for example.
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