在 pandas pivot_table 函数中重新排序数据
reorder data in pandas pivot_table function
我有一个示例数据框
region headquarter sales base %growth month_year
X Los Angeles 1000 2000 30 202101
X Florida City 2000 2000 20 202101
X Los Angeles 5000 6000 70 202001
X Florida City 4000 4500 45 202001
我正在尝试使用
来旋转数据
data = data.pivot_table(columns=['month_year'], values=['sales', 'base', '%growth'], index=['region', 'headquarter'])
print(data)
> %growth base sales
| | 202001 202101 202001 202101 202001 202101
-----------------------------------------------------------------------
region | headquarter |
x | Los Angeles | 70 30 6000 2000 5000 1000
| Florida City | 45 20 4000 2000 4500 2000
值的顺序与我在上面代码段中提到的顺序不一致。
如何将我的数据重组为(也通过重复行标签)
> 202001 202101
Sales base %growth Sales base %growth
region headquarter
X Los Angeles 5000 6000 70 1000 2000 30
X Florida City 4500 4000 45 2000 2000 20
使用DataFrame.swaplevel
with DataFrame.reindex
:
mux = pd.MultiIndex.from_product([data['month_year'].unique(), ['Sales','base','%growth']])
data = data.pivot_table(columns=['month_year'],
values=['Sales', 'base', '%growth'],
index=['headquarter']).swaplevel(1, 0, axis=1).reindex(mux, axis=1)
print(data)
202101 202001
Sales base %growth Sales base %growth
headquarter
Florida City 2000 2000 20 4000 4500 45
Los Angeles 1000 2000 30 5000 6000 70
编辑:
mux = pd.MultiIndex.from_product([data['month_year'].unique(), ['sales','base','%growth']])
data = data.pivot_table(columns=['month_year'],
values=['sales', 'base', '%growth'],
index=['region', 'headquarter']).swaplevel(1, 0, axis=1).reindex(mux, axis=1)
print (data)
202101 202001
sales base %growth sales base %growth
region headquarter
X Florida City 2000 2000 20 4000 4500 45
Los Angeles 1000 2000 30 5000 6000 70
我有一个示例数据框
region headquarter sales base %growth month_year
X Los Angeles 1000 2000 30 202101
X Florida City 2000 2000 20 202101
X Los Angeles 5000 6000 70 202001
X Florida City 4000 4500 45 202001
我正在尝试使用
来旋转数据data = data.pivot_table(columns=['month_year'], values=['sales', 'base', '%growth'], index=['region', 'headquarter'])
print(data)
> %growth base sales
| | 202001 202101 202001 202101 202001 202101
-----------------------------------------------------------------------
region | headquarter |
x | Los Angeles | 70 30 6000 2000 5000 1000
| Florida City | 45 20 4000 2000 4500 2000
值的顺序与我在上面代码段中提到的顺序不一致。
如何将我的数据重组为(也通过重复行标签)
> 202001 202101
Sales base %growth Sales base %growth
region headquarter
X Los Angeles 5000 6000 70 1000 2000 30
X Florida City 4500 4000 45 2000 2000 20
使用DataFrame.swaplevel
with DataFrame.reindex
:
mux = pd.MultiIndex.from_product([data['month_year'].unique(), ['Sales','base','%growth']])
data = data.pivot_table(columns=['month_year'],
values=['Sales', 'base', '%growth'],
index=['headquarter']).swaplevel(1, 0, axis=1).reindex(mux, axis=1)
print(data)
202101 202001
Sales base %growth Sales base %growth
headquarter
Florida City 2000 2000 20 4000 4500 45
Los Angeles 1000 2000 30 5000 6000 70
编辑:
mux = pd.MultiIndex.from_product([data['month_year'].unique(), ['sales','base','%growth']])
data = data.pivot_table(columns=['month_year'],
values=['sales', 'base', '%growth'],
index=['region', 'headquarter']).swaplevel(1, 0, axis=1).reindex(mux, axis=1)
print (data)
202101 202001
sales base %growth sales base %growth
region headquarter
X Florida City 2000 2000 20 4000 4500 45
Los Angeles 1000 2000 30 5000 6000 70