Match everything except a complex regex pattern and replace it in Pandas

I have a complex regex pattern to match mixed dates for a csv column in pandas df. I would like to replace everything except the regex pattern match with “” . I have tried pretty much all the negation cases (^ ?! and others). But I keep replacing the regex match with “” (empty string). My Code:

import pandas as pd 
df.read_csv('path')
df=DataFrame(df)
df.columns=['Date'] 
Date=df.Date
df['Date']=df['Date'].str.replace(r'^((b(0?[1-9]|[12]d|30|31)[^wdrn:](0?[1-9]|1[0-2])[^wdrn:](d{4}|d{2})b)|(b(0?[1-9]|1[0-2])[^wdrn:](0?[1-9]|[12]d|30|31)[^wdrn:](d{4}|d{2})b))','')

Some examples of my data:

Date
21/04/2004
[N/F]
6/07/2004
{}
[N/F]
6/10/2004
16/06/2004
{}
21/06/2004
[N/F]
1/03/2018
23/03/17
{}
{}
4/04/2006
19/05/2006
"**3/04/2006/-2/06
2006**"

Expected Output

21/04/2004

6/07/2004


6/10/2004
16/06/2004

21/06/2004

1/03/2018
23/03/17


4/04/2006
19/05/2006
3/04/2006

I would appreciate your help. Many thanks.

Answer

I have simplified your regex a little and am extracting rather than replacing:

Loading your data to a DataFrame:

import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
print(df)

Gives:

                         Date
0                  21/04/2004
1                       [N/F]
2                   6/07/2004
3                          {}
4                       [N/F]
5                   6/10/2004
6                  16/06/2004
7                          {}
8                  21/06/2004
9                       [N/F]
10                  1/03/2018
11                   23/03/17
12                         {}
13                         {}
14                  4/04/2006
15                 19/05/2006
16  **3/04/2006/-2/06n2006**

Now extract anything that can be parsed as a date:

pattern = r'(([1-9]|[12][0-9]|3[01])/(0[1-9]|1[012])/(20[01][0-9]|[0-9]{2}))'
df['extracted_date'] = df['Date'].astype(str).str.extract(pattern)[0]
df = df.fillna('')
print(df)

Which returns:

                         Date extracted_date
0                  21/04/2004     21/04/2004
1                       [N/F]               
2                   6/07/2004      6/07/2004
3                          {}               
4                       [N/F]               
5                   6/10/2004      6/10/2004
6                  16/06/2004     16/06/2004
7                          {}               
8                  21/06/2004     21/06/2004
9                       [N/F]               
10                  1/03/2018      1/03/2018
11                   23/03/17       23/03/17  
12                         {}               
13                         {}               
14                  4/04/2006      4/04/2006
15                 19/05/2006     19/05/2006
16  **3/04/2006/-2/06n2006**      3/04/2006