﻿ Pandas: Check whether alpha numeric values present in a given column of a DataFrame - w3resource

Pandas: Check whether alpha numeric values present in a given column of a DataFrame

Pandas: String and Regular Expression Exercise-9 with Solution

Write a Pandas program to check whether alpha numeric values present in a given column of a DataFrame.

Note: isalnum() function returns True if all characters in the string are alphanumeric and there is at least one character, False otherwise.

Sample Solution:

Python Code :

``````import pandas as pd
df = pd.DataFrame({
'name_code': ['Company','Company a001','Company 123', '1234', 'Company 12'],
'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("\nWhether all characters in the string are alphanumeric?")
df['name_code_is_alphanumeric'] = list(map(lambda x: x.isalnum(), df['name_code']))
print(df)
``````

Sample Output:

```Original DataFrame:
name_code date_of_birth    age
0       Company     12/05/2002  18.5
1  Company a001     16/02/1999  21.2
2   Company 123     25/09/1998  22.5
3          1234     12/02/2022  22.0
4    Company 12     15/09/1997  23.0

Whether all characters in the string are alphanumeric?
name_code            ...            name_code_is_alphanumeric
0       Company            ...                                 True
1  Company a001            ...                                False
2   Company 123            ...                                False
3          1234            ...                                 True
4    Company 12            ...                                False

[5 rows x 4 columns]
```

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)```