﻿ Pandas: Convert a specified character column in upper/lower cases in a given DataFrame - w3resource

# Pandas: Convert a specified character column in upper/lower cases in a given DataFrame

## Pandas: String and Regular Expression Exercise-19 with Solution

Write a Pandas program to convert a specified character column in upper/lower cases in a given DataFrame.

Sample Solution:

Python Code :

``````import pandas as pd
df = pd.DataFrame({
'company_code': ['Abcd','EFGF', 'zefsalf', 'sdfslew', 'zekfsdf'],
'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]
})

df1 = pd.DataFrame({
'company_code': ['Abcd','EFGF', 'zefsalf', 'sdfslew', 'zekfsdf'],
'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("\nUpper cases in comapny_code:")
df['upper_company_code'] = list(map(lambda x: x.upper(), df['company_code']))
print(df)
print("\nLower cases in comapny_code:")
df1['lower_company_code'] = list(map(lambda x: x.lower(), df1['company_code']))
print(df1)
``````

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      zefsalf   25/09/1998         22.5
3      sdfslew   12/02/2022    2566552.0
4      zekfsdf   15/09/1997         23.0

Upper cases in comapny_code:
company_code date_of_sale  sale_amount upper_company_code
0         Abcd   12/05/2002      12348.5               ABCD
1         EFGF   16/02/1999     233331.2               EFGF
2      zefsalf   25/09/1998         22.5            ZEFSALF
3      sdfslew   12/02/2022    2566552.0            SDFSLEW
4      zekfsdf   15/09/1997         23.0            ZEKFSDF

Lower cases in comapny_code:
company_code date_of_sale  sale_amount lower_company_code
0         Abcd   12/05/2002      12348.5               abcd
1         EFGF   16/02/1999     233331.2               efgf
2      zefsalf   25/09/1998         22.5            zefsalf
3      sdfslew   12/02/2022    2566552.0            sdfslew
4      zekfsdf   15/09/1997         23.0            zekfsdf
```

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)```