以下是我要执行的操作的示例:
import io
import pandas as pd
data = io.StringIO('''Fruit,Color,Count,Price
Apple,Red,3,$1.29
Apple,Green,9,$0.99
Pear,Red,25,$2.59
Pear,Green,26,$2.79
Lime,Green,99,$0.39
''')
df_unindexed = pd.read_csv(data)
df = df_unindexed.set_index(['Fruit', 'Color'])
输出:
Out[5]:
Count Price
Fruit Color
Apple Red 3 $1.29
Green 9 $0.99
Pear Red 25 $2.59
Green 26 $2.79
Lime Green 99 $0.39
现在让我说我想计算“颜色”级别的键数:
L = []
for i in pd.unique(df.index.get_level_values(0)):
L.append(range(df.xs(i).shape[0]))
list(np.concatenate(L))
然后,将结果列表[0,1,0,1,0]
添加为新列:
df['Bob'] = list(np.concatenate(L))
如此:
Count Price Bob
Fruit Color
Apple Red 3 $1.29 0
Green 9 $0.99 1
Pear Red 25 $2.59 0
Green 26 $2.79 1
Lime Green 99 $0.39 0
我的问题:
如何将Bob
列作为与Color
相同级别的索引?这就是我想要的:
Count Price
Fruit Color Bob
Apple Red 0 3 $1.29
Green 1 9 $0.99
Pear Red 0 25 $2.59
Green 1 26 $2.79
Lime Green 0 99 $0.39
答案 0 :(得分:5)
您要寻找cumcount
吗?如果是这样,您可以放弃循环并向量化您的解决方案。
df = df.set_index(df.groupby(level=0).cumcount(), append=True)
print(df)
Count Price
Fruit Color
Apple Red 0 3 $1.29
Green 1 9 $0.99
Pear Red 0 25 $2.59
Green 1 26 $2.79
Lime Green 0 99 $0.39
或者,如果您希望一口气做到这一点,
df_unindexed = pd.read_csv(data)
df = df_unindexed.set_index(['Fruit', 'Color', df.groupby('Fruit').cumcount()])
print(df)
Count Price
Fruit Color
Apple Green 0 9 $0.99
Red 1 3 $1.29
Lime Green 0 99 $0.39
Pear Green 1 26 $2.79
Red 0 25 $2.59
要重命名索引,请使用rename_axis
:
df = df.rename_axis(['Fruit', 'Color', 'Bob'])
print(df)
Count Price
Fruit Color Bob
Apple Red 0 3 $1.29
Green 1 9 $0.99
Pear Red 0 25 $2.59
Green 1 26 $2.79
Lime Green 0 99 $0.39
答案 1 :(得分:2)
IIUC,使用append
的{{1}}参数:
set_index