pandas 熊猫按另一列中的值对一列进行排序
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pandas sort a column by values in another column
提问by John Shin
I have a dataset that I want to sort and assign rank based on it.
我有一个数据集，我想根据它进行排序和分配排名。
Suppose it has two columns, one is year and the other is the column that I want to sort.
假设它有两列，一列是年份，另一列是我要排序的列。
import pandas as pd
data = {'year': pd.Series([2006, 2006, 2007, 2007]),
'value': pd.Series([5, 10, 4, 1])}
df = pd.DataFrame(data)
I want to sort the column 'value' by each year and then give rank to it. What I would like to have is
我想每年对“值”列进行排序，然后对其进行排名。我想要的是
data2= {'year': pd.Series([2006, 2006, 2007, 2007]),
'value': pd.Series([10, 5, 4, 1]),
'rank': pd.Series([1, 2, 1, 2]}
df2=pd.DataFrame(data2)
>>> df2
rank value year
0 1 10 2006
1 2 5 2006
2 1 4 2007
3 2 1 2007
回答by Alexander
You can use groupbyand then use rank(with ascending=Falseto get the largest values first). You don't need to sort in the groupby, as the result is indexed to the dataframe (slightly faster performance).
您可以使用groupby然后使用rank(withascending=False首先获得最大值)。您不需要在 中排序groupby，因为结果已索引到数据帧（性能稍快）。
df['yearly_rank'] = df.groupby('year', sort=False)['value'].rank(ascending=False)
>>> df.sort_values(['year', 'yearly_rank'])
value year yearly_rank
1 10 2006 1
0 5 2006 2
2 4 2007 1
3 1 2007 2
回答by Parfait
Consider a groupby applyfunction with sort:
考虑一个带有排序的groupby 应用函数：
def rankfct(row):
row['rank'] = row['value'].rank(ascending=False)
return row
df = df.groupby(['year']).apply(rankfct).sort(['year','value'], ascending=[1,0])
