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Pandas: Extract word mention someone in tweets using @ from the specified column of a given DataFrame

Pandas: String and Regular Expression Exercise-26 with Solution

Write a Pandas program to extract word mention someone in tweets using @ from the specified column of a given DataFrame.

Sample Solution:

Python Code :

import pandas as pd
import re as re
pd.set_option('display.max_columns', 10)
df = pd.DataFrame({
    'tweets': ['@Obama says goodbye','Retweets for @cash','A political endorsement in @Indonesia', '1 dog = many #retweets', 'Just a simple #egg']
    })
print("Original DataFrame:")
print(df)
def find_at_word(text):
    word=re.findall(r'(?<[email protected])\w+',text)
    return " ".join(word)

df['at_word']=df['tweets'].apply(lambda x: find_at_word(x))
print("\Extracting @word from dataframe columns:")
print(df)

Sample Output:

Original DataFrame:
                                  tweets
0                    @Obama says goodbye
1                     Retweets for @cash
2  A political endorsement in @Indonesia
3                 1 dog = many #retweets
4                     Just a simple #egg
\Extracting @word from dataframe columns:
                                  tweets    at_word
0                    @Obama says goodbye      Obama
1                     Retweets for @cash       cash
2  A political endorsement in @Indonesia  Indonesia
3                 1 dog = many #retweets           
4                     Just a simple #egg

Python Code Editor:


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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)