Pandas数据帧 - 行和列的多索引？

Question

想象一下这是我的输入数据：

    data = [("France",    "Paris",      "Male",   "1"),
            ("France",    "Paris",      "Female", "6"),
            ("France",    "Nice",       "Male",   "2"),
            ("France",    "Nice",       "Female", "7"),
            ("Germany",   "Berlin",     "Male",   "3"),
            ("Germany",   "Berlin",     "Female", "8"),
            ("Germany",   "Munchen",    "Male",   "4"),
            ("Germany",   "Munchen",    "Female", "9"),
            ("Germany",   "Koln",       "Male",   "5"),
            ("Germany",   "Koln",       "Female", "10")]

我想把它放到像这样的数据帧中：

Country City       Sex
                   Male     Female
France  Paris       1         6
        Nice        2         7
Germany Berlin      3         8
        Munchen     4         9
        Koln        5         10

第一部分很简单：

df = pd.DataFrame(data, columns=["country", "city", "sex", "count"])
df = df.set_index(["country", "city"])

给我输出：

                   sex  count
country city                 
France  Paris      Male     1
        Paris    Female     6
        Nice       Male     2
        Nice     Female     7
Germany Berlin     Male     3
        Berlin   Female     8
        Munchen    Male     4
        Munchen  Female     9
        Koln       Male     5
        Koln     Female    10

因此行是可以的，但现在我想将'sex'列中的值放入列多索引中。 有可能这样做，如果是这样，怎么样？

Answer 1

添加列Sex来list在set_index并调用unstack ：

df = df.set_index(["country", "city",'sex']).unstack()
#data cleaning - remove columns name sex and rename column count
df = df.rename_axis((None, None),axis=1).rename(columns={'count':'Sex'})
print (df)
                   Sex     
                Female Male
country city               
France  Nice         7    2
        Paris        6    1
Germany Berlin       8    3
        Koln        10    5
        Munchen      9    4

Answer 2

使用枢轴取代堆叠的另一种方法（两者几乎意味着相同）即

df.set_index(['country','city']).pivot(columns='sex')

count     
sex             Female Male
country city               
France  Nice         7    2
        Paris        6    1
Germany Berlin       8    3
        Koln        10    5
        Munchen      9    4