Pandas: Count of occurrence of a specified substring in a DataFrame column
Pandas: String and Regular Expression Exercise-6 with Solution
Write a Pandas program to count of occurrence of a specified substring in a DataFrame column.
Sample Solution:
Python Code :
import pandas as pd
df = pd.DataFrame({
'name_code': ['c001','c002','c022', 'c2002', 'c2222'],
'date_of_birth ': ['12/05/2002','16/02/1999','25/09/1998','12/02/2022','15/09/1997'],
'age': [18.5, 21.2, 22.5, 22, 23]
})
print("Original DataFrame:")
print(df)
print("\nCount occurrence of 2 in date_of_birth column:")
df['count'] = list(map(lambda x: x.count("2"), df['name_code']))
print(df)
Sample Output:
Original DataFrame: name_code date_of_birth age 0 c001 12/05/2002 18.5 1 c002 16/02/1999 21.2 2 c022 25/09/1998 22.5 3 c2002 12/02/2022 22.0 4 c2222 15/09/1997 23.0 Count occurrence of 2 in date_of_birth column: name_code date_of_birth age count 0 c001 12/05/2002 18.5 0 1 c002 16/02/1999 21.2 1 2 c022 25/09/1998 22.5 2 3 c2002 12/02/2022 22.0 2 4 c2222 15/09/1997 23.0 4
Python Code Editor:
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Previous: Write a Pandas program to capitalize all the string values of specified columns of a given DataFrame.
Next: Write a Pandas program to find the index of a given substring of a DataFrame column.
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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)
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