Question

我正在尝试在join_key中执行以下逻辑。 date + book + bdr + COALECSE(cusip,isin,deal,id)

+------------+------+------+-----------+--------------+------+------------+----------------------------+
|  endOfDay  | book | bdr  |   cusip   |     isin     | Deal |     Id     |          join_key          |
+------------+------+------+-----------+--------------+------+------------+----------------------------+
| 31/10/2019 |   15 | ITOR | 371494AM7 | US371494AM77 |  161 | 8013210731 | 20191031|15|ITOR|371494AM7 |
| 31/10/2019 |   15 | ITOR |           |              |      | 8011898573 | 20191031|15|ITOR|          |
| 31/10/2019 |   15 | ITOR |           |              |      | 8011898742 | 20191031|15|ITOR|          |
| 31/10/2019 |   15 | ITOR |           |              |      | 8011899418 | 20191031|15|ITOR|          |
+------------+------+------+-----------+--------------+------+------------+----------------------------+

我正在尝试使用：

df['join_key'] = ("20191031|" + df['book'].astype('str') + "|" + df['bdr'] + "|" + df[['cusip', 'isin', 'Deal', 'Id']].bfill(1)['cusip'].astype(str))

也尝试过：

df['position_join_key'] = "20191031|" + df['book'].astype('str') + "|" + df['bdr'] + "|" + df['cusip'].fillna(df['isin']).fillna(df['Deal']).fillna(df['Id']).astype('str')

由于某种原因，此代码未将Id作为密钥的一部分。

例如，在第二行中，我应该得到20191031|15|ITOR|8011898573。

如果它有帮助，也来自我使用na_filter = False

的csv

示例输入：

+------------+------+------+-----------+-------------+------+------------+
|  endOfDay  | book | bdr  |   cusip   |    isin     | Deal |     Id     |
+------------+------+------+-----------+-------------+------+------------+
| 31/10/2019 |   15 | ITOR | 371494AM7 |             |  161 | 8013210731 |
| 31/10/2019 |   15 | ITOR |           | 3.16248E+11 |      | 8011898573 |
| 31/10/2019 |   15 | ITOR |           |             |  352 | 8011898742 |
| 31/10/2019 |   15 | ITOR |           |             |      | 8011899418 |
+------------+------+------+-----------+-------------+------+------------+

示例输出：

+----------------------------+
|          join_key          |
+----------------------------+
| 43769|15|ITOR|371494AM7    |
| 43769|15|ITOR|316247735264 |
| 43769|15|ITOR|352          |
| 43769|15|ITOR|8011899418   |
+----------------------------+

Answer 1

我们可以通过以下一般方式解决您的问题：

首先，我们创建一个名为temp的临时列，该列是回填的值。
我们在您的bdr列之后插入该列
我们将您的日期列转换为datetime
我们可以'|'.join的前4列并创建join_key

通知：步骤3我添加内容是为了使您的代码通用，因此我们不会像您自己那样对20191031进行硬编码。

s = df[['cusip', 'isin', 'Deal', 'Id']].replace('', np.NaN).bfill(axis=1).iloc[:, 0]
df.insert(3, 'temp', s)

df['endOfDay'] = pd.to_datetime(df['endOfDay']).dt.strftime('%Y%m%d')

df['join_key'] = df.iloc[:, :4].apply(lambda x: '|'.join(x.astype(str).to_numpy()), axis=1)
df = df.drop(columns='temp')

   endOfDay  book   bdr      cusip          isin Deal          Id                     join_key
0  20191031    15  ITOR  371494AM7  US371494AM77  161  8013210731   20191031|15|ITOR|371494AM7
1  20191031    15  ITOR                                8011898573  20191031|15|ITOR|8011898573
2  20191031    15  ITOR                                8011898742  20191031|15|ITOR|8011898742
3  20191031    15  ITOR                                8011899418  20191031|15|ITOR|8011899418

熊猫，fillna / bfill联合和合并领域

1 个答案: