Thousand separator - Indian numbering format

India follows a different style of thousand separator. This script converts a number into Indian style thousand comma separated value.

Example

• In Great Britain, 1 million pounds is written as 1,000,000
• In India, 1 million rupees in India is written as 10,00,000

The input can be a positive or negative value in any of the following format:

1. integer
2. float
3. string

Use case 1

Working with single values.

Usage                                   Outcome
===================================     ==========================

number_comma_separation(1234)           '1,2345'
number_comma_separation(2345.678)       '2,345.678'
number_comma_separation('3456.14')      '3,456.14'
number_comma_separation(-145.1256)      '-145.1256'
number_comma_separation(-789456)        '-7,89,456'
number_comma_separation('-456.321')     '-456.321'

Use case 2

Instead of single values, to use this function with pandas series or dataframes, use the .apply() function as show below.

import pandas as pd

# Creating a sample dataframe to work with:

df = pd.DataFrame({'category': ['a', 'b', 'c', 'd', 'e'],
                   'amount': [1234, 14456.78, -357426, '78945.6521', -14825.96]})
print(df)

#   category      amount
#          a        1234
#          b    14456.78
#          c     -357426
#          d  78945.6521
#          e   -14825.96

# Formatting the values in column 'amount' to India style and 
# saving the outcomes to a new column 'amount_formatted' 

df['amount_formatted'] = df['amount'].apply(number_comma_separation)
print(df)

#   category      amount amount_formatted
#          a        1234            1,234
#          b    14456.78        14,456.78
#          c     -357426        -3,57,426
#          d  78945.6521      78,945.6521
#          e   -14825.96       -14,825.96

Use case 3

If the value passed to the function is of any other data type except integer or string or float, the function will notify the user that the value is not of an acceptable data type.

# Passing a dict data type
number_comma_separation({'a': 1})

# Passing a boolean data type
number_comma_separation(True)