5.2. String Functions

Description	Function
Add leading 0’s to str to full the length	str.zfill(5)
Convert to upper case	str.upper()
Convert to lower case	str.lower()
Check if upper case	str.isupper()
Check if lower case	str.islower()
Check if string is only numbers	str.isdigit()
Check if string is only characters	str.isalpha()
Check if string is chars+numbers	str.isalnum()
Check if string is only spaces and not blank	str.isspace()
Remove white spaces from string	str.strip() (l,r also)
Remove 0’s from left of string	str.lstrip(0)
Remove 0’s from right of string	str.rstrip(0)
Remove 0’s from string	str.strip(0)
Remove anything from string	str.strip(‘abc’)
Replace anything from string	str.replace(‘old’,’new’)
Remove anything from string	str.strip(‘abc’)
Check if string starts with abc	str.startswith(‘abc’)
Check if string ends with abc	str.endswith(‘abc’)
Check if string contains ‘abc’	if ‘abc’ in str
Check if string doesn’t contains ‘abc’	if ‘abc’ not in str
Concatenation or making a string dynamic	‘Hi {name}’.format(name=’Sahil’)

5.2.1. Note :

- Space is considered as number in python so will be detected in .isnum()
- Nan is float in pandas
- Left,Right and Mid() functions are not available in python but you can use string slicing to get the same results'
- String functions can be chained as well
    - str.replace().replace()

5.2.2. Example of method chaining

s='ab123abvba'
s.replace('ab','AB').replace('v','V')

'AB123ABVba'

5.2.3. Applying operations on multiple columns

• Lets say you want to convert datatype of multiple columns

• Format to convert datatype of one column

  • df['col'].astype(int)

• Format to convert datatype of multiple columns

  • Store the list of col names in list

    • list=['A','B','C']

  • Iterate through the list and do the operation on single single element of iterable

    •   df[i].astype(int)```