﻿ Pandas: Check whether only lower case or upper case is present in a given column of a DataFrame - w3resource

# Pandas: Check whether only lower case or upper case is present in a given column of a DataFrame

## Pandas: String and Regular Expression Exercise-12 with Solution

Write a Pandas program to check whether only lower case or upper case is present in a given column of a DataFrame.

Sample Solution:

Python Code :

``````import pandas as pd
df = pd.DataFrame({
'company_code': ['ABCD','EFGF', 'hhhh', 'abcd', 'EAWQaaa'],
'date_of_sale ': ['12/05/2002','16/02/1999','25/09/1998','12/02/2022','15/09/1997'],
'sale_amount': [12348.5, 233331.2, 22.5, 2566552.0, 23.0]})

print("Original DataFrame:")
print(df)
print("\nIs lower (company_code)?")
df['company_code_ul_cases'] = list(map(lambda x: x.islower(), df['company_code']))
print(df)
print("\nIs Upper (company_code)?")
df['company_code_ul_cases'] = list(map(lambda x: x.isupper(), df['company_code']))
print(df)
``````

Sample Output:

```Original DataFrame:
company_code date_of_sale   sale_amount
0         ABCD    12/05/2002      12348.5
1         EFGF    16/02/1999     233331.2
2         hhhh    25/09/1998         22.5
3         abcd    12/02/2022    2566552.0
4      EAWQaaa    15/09/1997         23.0

Is lower (company_code)?
company_code          ...          company_code_ul_cases
0         ABCD          ...                          False
1         EFGF          ...                          False
2         hhhh          ...                           True
3         abcd          ...                           True
4      EAWQaaa          ...                          False

[5 rows x 4 columns]

Is Upper (company_code)?
company_code          ...          company_code_ul_cases
0         ABCD          ...                           True
1         EFGF          ...                           True
2         hhhh          ...                          False
3         abcd          ...                          False
4      EAWQaaa          ...                          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)```