Pandas 合并多列,NaN
Pandas Coalesce Multiple Columns, NaN
我想 coalesce
4 列使用 pandas。我试过这个:
final['join_key'] = final['book'].astype('str') + final['bdr'] + final['cusip'].fillna(final['isin']).fillna(final['Deal'].astype('str')).fillna(final['Id'])
当我使用它时 returns:
+-------+--------+-------+------+------+------------+------------------+
| book | bdr | cusip | isin | Deal | Id | join_key |
+-------+--------+-------+------+------+------------+------------------+
| 17236 | ETFROS | | | | 8012398421 | 17236.0ETFROSnan |
+-------+--------+-------+------+------+------------+------------------+
字段 Id
没有正确附加到我的 join_key
字段。
如有任何帮助,我们将不胜感激。
更新:
+------------+------+------+-----------+--------------+------+------------+----------------------------+
| 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| |
+------------+------+------+-----------+--------------+------+------------+----------------------------+
df['join_key'] = ("20191031|" + df['book'].astype('str') + "|" + df['bdr'] + "|" + df[['cusip', 'isin', 'Deal', 'id']].bfill(1)['cusip'].astype(str))
出于某种原因,此代码未将 Id
作为密钥的一部分。
试试这个:
import pandas as pd
import numpy as np
# setup (ignore)
final = pd.DataFrame({
'book': [17236],
'bdr': ['ETFROS'],
'cusip': [np.nan],
'isin': [np.nan],
'Deal': [np.nan],
'Id': ['8012398421'],
})
# answer
final['join_key'] = final['book'].astype('str') + final['bdr'] + final['cusip'].fillna(final['isin']).fillna(final['Deal']).fillna(final['Id']).astype('str')
输出
book bdr cusip isin Deal Id join_key
0 17236 ETFROS NaN NaN NaN 8012398421 17236ETFROS8012398421
cusip
的最后一个链 fillna
太复杂了。您可以将其更改为 bfill
final['join_key'] = (final['book'].astype('str') +
final['bdr'] +
final[['cusip', 'isin', 'Deal', 'Id']].bfill(1)['cusip'].astype(str))
我想 coalesce
4 列使用 pandas。我试过这个:
final['join_key'] = final['book'].astype('str') + final['bdr'] + final['cusip'].fillna(final['isin']).fillna(final['Deal'].astype('str')).fillna(final['Id'])
当我使用它时 returns:
+-------+--------+-------+------+------+------------+------------------+
| book | bdr | cusip | isin | Deal | Id | join_key |
+-------+--------+-------+------+------+------------+------------------+
| 17236 | ETFROS | | | | 8012398421 | 17236.0ETFROSnan |
+-------+--------+-------+------+------+------------+------------------+
字段 Id
没有正确附加到我的 join_key
字段。
如有任何帮助,我们将不胜感激。
更新:
+------------+------+------+-----------+--------------+------+------------+----------------------------+
| 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| |
+------------+------+------+-----------+--------------+------+------------+----------------------------+
df['join_key'] = ("20191031|" + df['book'].astype('str') + "|" + df['bdr'] + "|" + df[['cusip', 'isin', 'Deal', 'id']].bfill(1)['cusip'].astype(str))
出于某种原因,此代码未将 Id
作为密钥的一部分。
试试这个:
import pandas as pd
import numpy as np
# setup (ignore)
final = pd.DataFrame({
'book': [17236],
'bdr': ['ETFROS'],
'cusip': [np.nan],
'isin': [np.nan],
'Deal': [np.nan],
'Id': ['8012398421'],
})
# answer
final['join_key'] = final['book'].astype('str') + final['bdr'] + final['cusip'].fillna(final['isin']).fillna(final['Deal']).fillna(final['Id']).astype('str')
输出
book bdr cusip isin Deal Id join_key
0 17236 ETFROS NaN NaN NaN 8012398421 17236ETFROS8012398421
cusip
的最后一个链 fillna
太复杂了。您可以将其更改为 bfill
final['join_key'] = (final['book'].astype('str') +
final['bdr'] +
final[['cusip', 'isin', 'Deal', 'Id']].bfill(1)['cusip'].astype(str))