Python groupby结果为空datafram

问题：

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            1
NaN            a             12          12-09   09            1
NaN            b             23          23-87   87            1
NaN            b             23          23-87   87            1
NaN            b             34          34-76   76            1

group_df = df.groupby(['business_group', 'business_unit','cost_center','GL_code','profit_center'],
           as_index=False).count()

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            2
NaN            b             23          23-87   87            2
NaN            c             34          34-76   76            1

groupby对下面的数据，导致数据框为空，不确定如何修复，请帮助，谢谢

数据：

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            1
NaN            a             12          12-09   09            1
NaN            b             23          23-87   87            1
NaN            b             23          23-87   87            1
NaN            b             34          34-76   76            1

group_df = df.groupby(['business_group', 'business_unit','cost_center','GL_code','profit_center'],
           as_index=False).count()

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            2
NaN            b             23          23-87   87            2
NaN            c             34          34-76   76            1

groupby:

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            1
NaN            a             12          12-09   09            1
NaN            b             23          23-87   87            1
NaN            b             23          23-87   87            1
NaN            b             34          34-76   76            1

group_df = df.groupby(['business_group', 'business_unit','cost_center','GL_code','profit_center'],
           as_index=False).count()

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            2
NaN            b             23          23-87   87            2
NaN            c             34          34-76   76            1

预期结果：

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            1
NaN            a             12          12-09   09            1
NaN            b             23          23-87   87            1
NaN            b             23          23-87   87            1
NaN            b             34          34-76   76            1

group_df = df.groupby(['business_group', 'business_unit','cost_center','GL_code','profit_center'],
           as_index=False).count()

business_group business_unit cost_center GL_code profit_center count
NaN            a             12          12-09   09            2
NaN            b             23          23-87   87            2
NaN            c             34          34-76   76            1

收到的结果：

Empty DataFrame
Columns: [business_group, business_unit, cost_center, GL_code, profit_center, count]
Index: []

---

这是因为

business_group

中的

NaN

是空值，默认情况下

groupby（）

将删除所有

NaN

值。您可以将

dropna=False

传递到

groupby（）

：

输出：

   business_group business_unit  cost_center GL_code  profit_center  count
0             NaN             a           12   12-09              9      2
1             NaN             b           23   23-87             87      2
2             NaN             b           34   34-76             76      1

---

这是因为

business_group

中的

NaN

是空值，默认情况下

groupby（）

将删除所有

NaN

值。您可以将

dropna=False

传递到

groupby（）

：

输出：

   business_group business_unit  cost_center GL_code  profit_center  count
0             NaN             a           12   12-09              9      2
1             NaN             b           23   23-87             87      2
2             NaN             b           34   34-76             76      1