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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Next: Write a Pandas program to convert a specified character column in title case in 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)
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