在具有重复数据的数据框中，按年份对 pandas 进行分组，按销售额列进行排名 [英] pandas group by year, rank by sales column, in a dataframe with duplicate data
问题描述
我想创建一个年度排名(因此，在2012年，经理B为1.在2011年，经理B又为1).我在熊猫排名函数上苦苦挣扎了一段时间，不想诉诸于for循环.
I would like to create a rank on year (so in year 2012, Manager B is 1. In 2011, Manager B is 1 again). I struggled with the pandas rank function for awhile and DO NOT want to resort to a for loop.
s = pd.DataFrame([['2012','A',3],['2012','B',8],['2011','A',20],['2011','B',30]], columns=['Year','Manager','Return'])
Out[1]:
Year Manager Return
0 2012 A 3
1 2012 B 8
2 2011 A 20
3 2011 B 30
我遇到的问题是附加代码(以前不认为这是相关的):
The issue I'm having is with the additional code (didn't think this would be relevant before):
s = pd.DataFrame([['2012', 'A', 3], ['2012', 'B', 8], ['2011', 'A', 20], ['2011', 'B', 30]], columns=['Year', 'Manager', 'Return'])
b = pd.DataFrame([['2012', 'A', 3], ['2012', 'B', 8], ['2011', 'A', 20], ['2011', 'B', 30]], columns=['Year', 'Manager', 'Return'])
s = s.append(b)
s['Rank'] = s.groupby(['Year'])['Return'].rank(ascending=False)
raise Exception('Reindexing only valid with uniquely valued Index '
Exception: Reindexing only valid with uniquely valued Index objects
有什么想法吗?
这是我正在使用的实际数据结构.
重新索引时遇到麻烦.
Any ideas?
This is the real data structure I am using.
Been having trouble re-indexing..
推荐答案
听起来您想按Year分组，然后按降序对Returns进行排名.
It sounds like you want to group by the Year, then rank the Returns in descending order.
import pandas as pd
s = pd.DataFrame([['2012', 'A', 3], ['2012', 'B', 8], ['2011', 'A', 20], ['2011', 'B', 30]],
columns=['Year', 'Manager', 'Return'])
s['Rank'] = s.groupby(['Year'])['Return'].rank(ascending=False)
print(s)
收益
Year Manager Return Rank
0 2012 A 3 2
1 2012 B 8 1
2 2011 A 20 2
3 2011 B 30 1
要解决OP的修订问题:错误消息
To address the OP's revised question: The error message
ValueError: cannot reindex from a duplicate axis
尝试对索引中具有重复值的DataFrame进行groupby/rank时发生
.您可以通过在附加后将s构造为具有唯一的索引值来避免此问题:
occurs when trying to groupby/rank on a DataFrame with duplicate values in the index. You can avoid the problem by constructing s to have unique index values after appending:
s = pd.DataFrame([['2012', 'A', 3], ['2012', 'B', 8], ['2011', 'A', 20], ['2011', 'B', 30]], columns=['Year', 'Manager', 'Return'])
b = pd.DataFrame([['2012', 'A', 3], ['2012', 'B', 8], ['2011', 'A', 20], ['2011', 'B', 30]], columns=['Year', 'Manager', 'Return'])
s = s.append(b, ignore_index=True)
收益
Year Manager Return
0 2012 A 3
1 2012 B 8
2 2011 A 20
3 2011 B 30
4 2012 A 3
5 2012 B 8
6 2011 A 20
7 2011 B 30
如果您已经使用来添加新行
If you've already appended new rows using
s = s.append(b)
然后使用reset_index创建唯一索引:
then use reset_index to create a unique index:
s = s.reset_index(drop=True)
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