pandas :使用groupby来获取每个数据类别的均值 [英] Pandas: using groupby to get mean for each data category
本文介绍了 pandas :使用groupby来获取每个数据类别的均值的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我有一个看起来像这样的数据框:
I have a dataframe that looks like this:
>>> df[['data','category']]
Out[47]:
data category
0 4610 2
15 4610 2
22 5307 7
23 5307 7
25 5307 7
... ... ...
数据和类别都是数字,因此我能够做到这一点:
Both data and category are numeric so I'm able to do this:
>>> df[['data','category']].mean()
Out[48]:
data 5894.677985
category 13.805886
dtype: float64
我正在尝试获取每个类别的均值.它看起来很简单,但是当我这样做时:
And i'm trying to get the mean for each category. It looks straight forward but when I do this:
>>> df[['data','category']].groupby('category').mean()
或
>>> df.groupby('category')['data'].mean()
它返回如下错误:
DataError: No numeric types to aggregate
如果我将以上两个功能都替换为.count()
,则没有错误.
There's no error if I replace both functions above with .count()
.
我做错了什么?获取每个类别平均值的正确方法是什么?
What do I do wrongly? What's the correct way to get the mean of each category?
推荐答案
可以执行df.dtypes吗?在下面的示例中,输入类型为Int,因为它可以正常工作.
Can you do a df.dtypes ? In the example below type is Int as it works fine.
import pandas as pd
##group by 1 columns
df = pd.DataFrame({' data': [4610, 4611, 4612, 4613], 'Category': [2, 2, 7, 7]})
print df.groupby('Category'). mean()
##Mutiple columns to group by
df1 = pd.DataFrame({' data': [4610, 4611, 4612, 4613], 'Category': [2, 2, 7, 7], 'Category2' : ['A','B','A','B']})
key=['Category','Category2']
print df1.groupby( key).mean()
Category Category2
2 A 4610
B 4611
7 A 4612
B 4613
这篇关于 pandas :使用groupby来获取每个数据类别的均值的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!
查看全文