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Pandas: Extract numbers less than 100 from the specified column of a given DataFrame

Pandas: String and Regular Expression Exercise-34 with Solution

Write a Pandas program to extract numbers less than 100 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({
    'company_code': ['c0001','c0002','c0003', 'c0003', 'c0004'],
    'address': ['72 Surrey Ave.11','92 N. Bishop Ave.','9910 Golden Star St.', '102 Dunbar St.', '17 West Livingston Court']
    })
print("Original DataFrame:")
print(df)

def test_num_less(n):
    nums = []
    for i in n.split():
        result = re.findall(r'\b(0*(?:[1-9][0-9]?|100))\b',i)
        nums.append(result)
        all_num=[",".join(x) for x in nums if x != []]
    return " ".join(all_num)

df['num_less'] = df['address'].apply(lambda x : test_num_less(x))
print("\nNumber less than 100:")
print(df)

Sample Output:

Original DataFrame:
  company_code                   address
0        c0001          72 Surrey Ave.11
1        c0002         92 N. Bishop Ave.
2        c0003      9910 Golden Star St.
3        c0003            102 Dunbar St.
4        c0004  17 West Livingston Court

Number greater than 940:
  company_code                   address num_less
0        c0001          72 Surrey Ave.11    72 11
1        c0002         92 N. Bishop Ave.       92
2        c0003      9910 Golden Star St.         
3        c0003            102 Dunbar St.         
4        c0004  17 West Livingston Court       17

Python Code Editor:


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Previous: Write a Pandas program to extract numbers greater than 940 from the specified column of a given DataFrame.
Next: Write a Pandas program to check whether two given words present in a specified 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)