Python:Pivot groupby对象
我的数据框看起来像这样Python:Pivot groupby对象,python,pandas,Python,Pandas,我的数据框看起来像这样 Name,Comp,Dept,Salary,Allowance Sam,Google,Sales,10000,4500 Sandra,Google,Sales,2300,3450 Allan,Google,Sales,2400,1000 Helen,Google,Mktg,3456,700 Rick,Google,Mktg,5412,352 Farrow,Apple,Sales,9786,190 Isaac,Apple,Sales,4500,230 Tim,Apple,M
Name,Comp,Dept,Salary,Allowance
Sam,Google,Sales,10000,4500
Sandra,Google,Sales,2300,3450
Allan,Google,Sales,2400,1000
Helen,Google,Mktg,3456,700
Rick,Google,Mktg,5412,352
Farrow,Apple,Sales,9786,190
Isaac,Apple,Sales,4500,230
Tim,Apple,Mktg,4500,500
Ben,Apple,Mktg,3490,450
Julie,Apple,Mktg,4590,750
我想得到以下格式的2个输出
输出1:-这是两家公司按部门划分的平均工资
Dept Google Apple
Sales 4900 7143
Mktg 4434 4193
输出2-这是部门为“苹果”和“谷歌”支付的最高工资值
Dept Google Apple
Sales 10000 9786
Mktg 5412 4590
到目前为止,我已经做到了
data.groupby(['Dept','Comp'])['Salary'].mean().order(ascending=False)
Dept Comp
Sales Apple 7143.000000
Google 4900.000000
Mktg Google 4434.000000
Apple 4193.333333
Name: Salary, dtype: float64
如何旋转结果以获得上述格式?使用
取消堆栈
In [111]: means = df.groupby(['Comp', 'Dept'])['Salary'].mean()
In [112]: means.unstack(0).sort(ascending=False)
Out[112]:
Comp Apple Google
Dept
Sales 7143.000000 4900
Mktg 4193.333333 4434
[2 rows x 2 columns]
In [110]: df.groupby(['Comp', 'Dept'])['Salary'].max().unstack(0).sort(ascending=False)
Out[110]:
Comp Apple Google
Dept
Sales 9786 10000
Mktg 4590 5412
[2 rows x 2 columns]