Question

例如，我有这个数据框：

 d = {'data_1' : pd.Series([1., 2., 3., 4., 1], index=['a', 'b', 'c', 'd', 'class']),
  'data_2' : pd.Series([5., 6., 7., 8., 0], index=['a', 'b', 'c', 'd', 'class']),
  'data_3' : pd.Series([9., 10., 11., 12., 1], index=['a', 'b', 'c', 'd', 'class'])}
df = pd.DataFrame(d)

以便输出我得到：

    data_1  data_2  data_3
a   1.0      5.0    9.0
b   2.0      6.0    10.0
c   3.0      7.0    11.0
cls 1.0      0.0    1.0

我想要做的是根据“ cls ”行中报告的值创建过滤器，并根据两个可能的类将数据框拆分为两个

    data_1  data_3
a   1.0      9.0
b   2.0      10.0
c   3.0      11.0
cls 1.0      1.0

和

        data_2
a        5.0
b        6.0    
c        7.0    
cls      0.0

Answer 1

将boolean indexing与[PXButton] [PXUIField(DisplayName = "Generate")] public virtual void GenerateInv() { InventoryItemExt ext = Base.Item.Cache.GetExtension<InventoryItemExt>(Base.Item.Current); Base.Item.Current.InventoryCD = ext.UsrInvId; Base.Item.Cache.Insert(Base.Item.Current); }一起使用 - 首先按条件选择所有列，然后按loc选择所选行：

index

行df1 = df.loc[:, df.loc['class'] == 1] df2 = df.loc[:, df.loc['class'] == 0]的所有唯一值也可能创建dicionary：

class

s = df.loc['class']
dfs = {k:df[v.index] for k, v in s.groupby(s)}
print (dfs)

{0.0:        data_2
a         5.0
b         6.0
c         7.0
d         8.0
class     0.0, 1.0:        data_1  data_3
a         1.0     9.0
b         2.0    10.0
c         3.0    11.0
d         4.0    12.0
class     1.0     1.0}

Answer 2

动态地执行

In [889]: s = df.loc['class']

In [890]: dfs = {k: df[s[s==v].index] for k, v in  enumerate(s.unique())}

# or dfs = {v: df[s[s==v].index] for v in s.unique()} -- for value based keys

In [891]: dfs[0]
Out[891]:
       data_1  data_3
a         1.0     9.0
b         2.0    10.0
c         3.0    11.0
d         4.0    12.0
class     1.0     1.0

In [892]: dfs[1]
Out[892]:
       data_2
a         5.0
b         6.0
c         7.0
d         8.0
class     0.0

pandas：基于行值的列示例

2 个答案: