如果两个字符串列是另一个数据框中某一列的子字符串，则在Python中合并这些列

Question

假设有两个如下数据框：

df1:

   id                                      address  price
0   1         8563 Parker Ave. Lexington, NC 27292      3
1   2         242 Bellevue Lane Appleton, WI 54911      3
2   3       771 Greenview Rd. Greenfield, IN 46140      5
3   4       93 Hawthorne Street Lakeland, FL 33801      6
4   5  8952 Green Hill Street Gettysburg, PA 17325      3
5   6    7331 S. Sherwood Dr. New Castle, PA 16101      4

df2:

  state            street  quantity
0    PA       S. Sherwood        12
1    IN  Hawthorne Street         3
2    NC       Parker Ave.         7

假设如果df2中的state和street都包含在df2的address列中，则将df2合并到df1中。 在Pandas中如何实现？谢谢。

期望的结果df:

   id                                      address  ...       street quantity
0   1         8563 Parker Ave. Lexington, NC 27292  ...  Parker Ave.     7.00
1   2         242 Bellevue Lane Appleton, WI 54911  ...          NaN      NaN
2   3       771 Greenview Rd. Greenfield, IN 46140  ...          NaN      NaN
3   4       93 Hawthorne Street Lakeland, FL 33801  ...          NaN      NaN
4   5  8952 Green Hill Street Gettysburg, PA 17325  ...          NaN      NaN
5   6    7331 S. Sherwood Dr. New Castle, PA 16101  ...  S. Sherwood    12.00

[6 rows x 6 columns]

我的测试代码：

df2['addr'] = df2['state'].astype(str) + df2['street'].astype(str)

pat = '|'.join(r'\b{}\b'.format(x) for x in df2['addr'])
df1['addr']= df1['address'].str.extract('\('+ pat + ')', expand=False)

df = df1.merge(df2, on='addr', how='left')

输出：

   id                                      address  ...  street_y quantity_y
0   1         8563 Parker Ave. Lexington, NC 27292  ...       NaN        nan
1   2         242 Bellevue Lane Appleton, WI 54911  ...       NaN        nan
2   3       771 Greenview Rd. Greenfield, IN 46140  ...       NaN        nan
3   4       93 Hawthorne Street Lakeland, FL 33801  ...       NaN        nan
4   5  8952 Green Hill Street Gettysburg, PA 17325  ...       NaN        nan
5   6    7331 S. Sherwood Dr. New Castle, PA 16101  ...       NaN        nan

[6 rows x 10 columns]

- ah bon

Answer 1

k="|".join(df2['street'].to_list())
df1=df1.assign(temp=df1['address'].str.findall(k).str.join(', '), temp1=df1['address'].str.split(",").str[-1])
dfnew=pd.merge(df1,df2, how='left', left_on=['temp','temp1'], right_on=['street',"state"])

- wwnde

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谢谢，但是你没有使用 df2['state']？ - ah bon

谢谢，如果address没有逗号进行分割，我们该如何修改您的代码？ - ah bon

Answer 2

尝试：

pat_state = f"({'|'.join(df2['state'])})"
pat_street = f"({'|'.join(df2['street'])})"
df1['street'] = df1['address'].str.extract(pat=pat_street) 
df1['state'] = df1['address'].str.extract(pat=pat_state) 
df1.loc[df1['state'].isna(),'street'] = np.NAN
df1.loc[df1['street'].isna(),'state'] = np.NAN
df1 = df1.merge(df2, left_on=['state','street'], right_on=['state','street'], how ='left')

- Nk03

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谢谢，我会用我的真实数据进行测试，并告诉你。 - ah bon

抱歉，它引发了一个错误：错误：缺少），未终止的子模式。 - ah bon

使用 df2["street"] = df2['street'].str.replace('[^\w\s]','') 去除标点符号后，它可以正常工作。 - ah bon

如果我需要基于3列进行合并呢？ - ah bon