如何检查熊猫数据帧列中的子字符串是否存在于同一数据帧中另一列的子字符串中？

Question

I have a dataframe with columns like this:

  A                               B
0  - 5923FoxRd                    5923 Fox Rd
1 631 Newhaven Ave                Modesto
2 Saratoga Street, Suite 200      Saratoga Street, Suite 200

I want to create a list with values from A that matches values from B. The list should look like [- 5923FoxRd, Saratoga Street, Suite 200...]. What is the easiest way to do this?

Answer 1

To make a little go a long way, do the following:

1. Create a new series for each column and pass the regex pattern \\W+ to str.replace()
2. use str.lower()
3. create replace lists to normalize drive to dr , avenue to ave , etc.

---

s1 = df['A'].str.replace('\W+', '').str.lower()
s2 = df['B'].str.replace('\W+', '').str.lower()
lst = [*df[s1==s2]['A']]
lst
Out[1]: ['- 5923FoxRd', 'Saratoga Street, Suite 200']

This is what s1 and s2 look like:

print(s1,s2)

0                 5923foxrd
1            631newhavenave
2    saratogastreetsuite200
Name: A, dtype: object

0                 5923foxrd
1                   modesto
2    saratogastreetsuite200
Name: B, dtype: object

---

From there, you might want to create some replace values in order to normalize your data even further like:

to_replace = ['drive', 'avenue', 'street']
replaced = ['dr', 'ave', 'str']

to_replace = ['drive', 'avenue', 'street']
replaced = ['dr', 'ave', 'str']
s1 = df['A'].str.replace('\W+', '').str.lower().replace(to_replace, replaced, regex=True)
s2 = df['B'].str.replace('\W+', '').str.lower().replace(to_replace, replaced, regex=True)
lst = [*df[s1==s2]['A']]
lst
print(s1,s2)
0              5923foxrd
1         631newhavenave
2    saratogastrsuite200
Name: A, dtype: object

0              5923foxrd
1                modesto
2    saratogastrsuite200
Name: B, dtype: object