Python 多重数据帧中的多重赋值
假设我有一个多索引数据帧,如下所示Python 多重数据帧中的多重赋值,python,pandas,Python,Pandas,假设我有一个多索引数据帧,如下所示 In [221]: df Out[221]: first bar baz second one two one two A -1.089798 2.053026 0.470218 1.440740 B 0.488875 0.428836 1.413451 -0.683677 C -0.243064 -0.069446 -0.9
In [221]: df
Out[221]:
first bar baz
second one two one two
A -1.089798 2.053026 0.470218 1.440740
B 0.488875 0.428836 1.413451 -0.683677
C -0.243064 -0.069446 -0.911166 0.47837
我想在每个一级栏中添加第三列和第四列,'bar'和'baz'
我一直在尝试使用:
df[['bar','baz'],['third','forth']]=prices_df.apply(
lambda row: pd.Series(get_bond_metrics(row))
, axis=1)
但这不是在多索引数据帧中进行多个赋值的正确方法
谢谢一种方法是通过
pd.concat
,将现有数据框与所需列的新数据框(由MultiIndex.from_product
创建,它给出了两个列表的组合)和您的值(即
df
first bar baz
second one two one two
0 -0.122485 0.943154 1.253930 -0.955231
1 -0.293157 -1.167648 -0.864481 1.251452
values = np.random.randn(2,4) # here goes your values
df2 = pd.DataFrame(values, columns=pd.MultiIndex.from_product([['bar','baz'],['third','forth']]))
# Column wise concatenation followed by sorting of index for better view
ndf = pd.concat([df,df2],axis = 1).sort_index(level='first',axis=1,sort_remaining=False)
输出:
first bar baz \
second one two third forth one two third
0 -0.122485 0.943154 -0.419076 0.667690 1.253930 -0.955231 -0.858656
1 -0.293157 -1.167648 0.516346 -0.907558 -0.864481 1.251452 0.429894
first
second forth
0 0.237544
1 -0.521049
我喜欢你的答案,但我希望新值是dataframe旧列的函数。有办法吗?非常感谢。