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Pandas: Remove the html tags within the specified column of a given DataFrame

Pandas: String and Regular Expression Exercise-41 with Solution

Write a Pandas program to remove the html tags within the specified column of a given DataFrame.

Sample Solution:

Python Code :

import pandas as pd
import re as re
df = pd.DataFrame({
    'company_code': ['Abcd','EFGF', 'zefsalf', 'sdfslew', 'zekfsdf'],
    'date_of_sale': ['12/05/2002','16/02/1999','05/09/1998','12/02/2022','15/09/1997'],
    'address': ['9910 Surrey <b>Avenue</b>','92 N. Bishop Avenue','9910 <br>Golden Star Avenue', '102 Dunbar <i></i>St.', '17 West Livingston Court']
})
print("Original DataFrame:")
print(df)
def remove_tags(string):
    result = re.sub('<.*?>','',string)
    return result
df['with_out_tags']=df['address'].apply(lambda cw : remove_tags(cw))
print("\nSentences without tags':")
print(df)

Sample Output:

Original DataFrame:
  company_code             ...                                   address
0         Abcd             ...                 9910 Surrey Avenue
1         EFGF             ...                       92 N. Bishop Avenue
2      zefsalf             ...               9910 
Golden Star Avenue 3 sdfslew ... 102 Dunbar St. 4 zekfsdf ... 17 West Livingston Court [5 rows x 3 columns] Sentences without tags': company_code ... with_out_tags 0 Abcd ... 9910 Surrey Avenue 1 EFGF ... 92 N. Bishop Avenue 2 zefsalf ... 9910 Golden Star Avenue 3 sdfslew ... 102 Dunbar St. 4 zekfsdf ... 17 West Livingston Court [5 rows x 4 columns]

Python Code Editor:


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Previous: Write a Pandas program to extract words starting with capital words from a given column of a given DataFrame.

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Python: Tips of the Day

Python: Cache results with decorators

There is a great way to cache functions with decorators in Python. Caching will help save time and precious resources when there is an expensive function at hand.

Implementation is easy, just import lru_cache from functools library and decorate your function using @lru_cache.

from functools import lru_cache

@lru_cache(maxsize=None)
def fibo(a):
    if a <= 1:
        return a
    else:
        return fibo(a-1) + fibo(a-2)

for i in range(20):
    print(fibo(i), end="|")

print("\n\n", fibo.cache_info())

Output:

0|1|1|2|3|5|8|13|21|34|55|89|144|233|377|610|987|1597|2584|4181|

 CacheInfo(hits=36, misses=20, maxsize=None, currsize=20)