Python 基于子字符串匹配列

Python 基于子字符串匹配列,python,pandas,join,merge,pattern-matching,Python,Pandas,Join,Merge,Pattern Matching,假设您有三个组(A、B、C),它们的组件概述如下: +-------+-----------+ | Group | Component | +-------+-----------+ | A | 31 | | A | 322 | | A | 323 | | B | 321 | | B | 327 | | B | 33 | | C |

假设您有三个组(A、B、C),它们的组件概述如下:

 +-------+-----------+
 | Group | Component |
 +-------+-----------+
 | A     |        31 |
 | A     |       322 |
 | A     |       323 |
 | B     |       321 |
 | B     |       327 |
 | B     |        33 |
 | C     |        31 |
 | C     |        32 |
 | C     |        33 |
 +-------+-----------+
这可以存储在名为“组”的数据帧或字典中。请注意,组之间存在重叠

我有以下称为“df”的数据帧(实际表更大):

我想以某种方式将'groups'表与'df'连接起来,这样我就有了另外两列,每一列根据发送方/接收方代码是否包含该组件来显示发送方和接收方关联的组。我只想看到两组都是相同的情况。目标表如下所示:

 +--------+----------+--------+--------------+----------------+
 | Sender | Receiver | Value  | Sender_Group | Receiver_Group |
 +--------+----------+--------+--------------+----------------+
 | 312345 |   313452 | 1022.1 | A            | A              |
 | 312345 |   313452 | 1022.1 | C            | C              |
 | 320952 |   327901 | 4921.0 | C            | C              |
 +--------+----------+--------+--------------+----------------+
pat = "|".join(groups.Component.astype('str'))
df.insert(0, 'Sender_Group', df['Sender'].str.extract("(" + pat + ')', expand=False))
df.insert(1, 'Receiver_Group', df['Receiver'].str.extract("(" + pat + ')', expand=False))
new_df = df.query('Sender_Group == Receiver_Group')
请注意,尽管327901与组B关联,但320952并非如此,因此未显示。最终的目标是对每组的值进行汇总

我试过这样的方法:

 +--------+----------+--------+--------------+----------------+
 | Sender | Receiver | Value  | Sender_Group | Receiver_Group |
 +--------+----------+--------+--------------+----------------+
 | 312345 |   313452 | 1022.1 | A            | A              |
 | 312345 |   313452 | 1022.1 | C            | C              |
 | 320952 |   327901 | 4921.0 | C            | C              |
 +--------+----------+--------+--------------+----------------+
pat = "|".join(groups.Component.astype('str'))
df.insert(0, 'Sender_Group', df['Sender'].str.extract("(" + pat + ')', expand=False))
df.insert(1, 'Receiver_Group', df['Receiver'].str.extract("(" + pat + ')', expand=False))
new_df = df.query('Sender_Group == Receiver_Group')

然而,这样做的限制是每个发送方/接收方只能与一个组相关联。我需要一个解决方案,允许他们与多个。有什么想法吗?

您可以使用一列来表示发送者(接收者)所属的所有组,作为
列表。然后可以将此列展开为多行,如前所述

   sender  receiver   value sender_group
0  312345    313452  1022.1       [A, C]
1  320952    327901  4921.0          [C]
   sender  receiver   value sender_group
0  312345    313452  1022.1            A
1  312345    313452  1022.1            C
2  320952    327901  4921.0            C
对于接收器,程序类似

膨胀 有关展开该列的方法,请参阅。此处给出了一个示例:

indices = np.repeat(df.index.values, df['sender_group'].str.len())
df = df.loc[indices]\
    .assign(sender_group=np.concatenate(df['sender_group'].values))\
    .reset_index(drop=True)

   sender  receiver   value sender_group
0  312345    313452  1022.1       [A, C]
1  320952    327901  4921.0          [C]
   sender  receiver   value sender_group
0  312345    313452  1022.1            A
1  312345    313452  1022.1            C
2  320952    327901  4921.0            C

使用的变量:

groups = pd.DataFrame(
    columns=['group', 'component'],
    data=[['A', 31],
          ['A', 322],
          ['A', 323],
          ['B', 321],
          ['B', 327],
          ['B', 33],
          ['C', 31],
          ['C', 32],
          ['C', 33],]
)

df = pd.DataFrame(
    columns=['sender', 'receiver', 'value'],
    data=[[312345, 313452, 1022.1],
          [320952, 327901, 4921.0]]
)