层次化索引MultiIndex

mask=pd.MultiIndex.from_arrays([[‘Ohio‘, ‘Ohio‘, ‘Colorado‘], [‘Green‘,‘bed‘, ‘Green‘]],
                               names=(‘state‘, ‘color‘))

df = pd.DataFrame(np.arange(12).reshape((4, 3)),index=[[‘a‘, ‘a‘, ‘b‘, ‘b‘], [1, 2, 1, 2]],
                  columns=mask)


df.index.names=[‘key1‘,‘key2‘]
df.columns.names=[‘state‘,‘color‘]

print("\n源数据：\n",df)


print("\n对源数据index排序：\n",df.sort_index(level=1))
print("\n交换级别：\n")
print(df.swaplevel(‘key1‘,‘key2‘))
print("\n交换级别后排序：\n")
print(df.swaplevel(‘key1‘,‘key2‘).sort_index(level=0))

#df.set_index( )函数的用法
df=pd.DataFrame({‘a‘:range(7),‘b‘:range(7,0,-1),‘c‘:[‘one‘,‘one‘,‘one‘,‘two‘,‘two‘,‘two‘,‘two‘],
                 ‘d‘:[0,1,2,0,1,2,3]})


print("源数据：\n",df)

df2=df.set_index([‘c‘,‘d‘])
df3=df.set_index([‘c‘,‘d‘],drop=False)


print("\ndf.set_index([‘c‘,‘d‘]层次化索引,df2:\n",df2)
print("\ndf.set_index([‘c‘,‘d‘],drop=False层次化索引,drop,df3:\n",df3)