@pf.register_dataframe_method
def inflate_currency(df: pd.DataFrame, column_name: str=None, country: str=None, currency_year: int=None, to_year: int=None, make_new_column: bool=False) -> pd.DataFrame:
    '\n    Inflates a column of monetary values from one year to another, based on\n    the currency\'s country.\n\n    The provided country can be any economy name or code from the World Bank\n    [list of economies]\n    (https://databank.worldbank.org/data/download/site-content/CLASS.xls).\n\n    **Note**: This method mutates the original DataFrame.\n\n    Method chaining usage example:\n\n    >>> import pandas as pd\n    >>> import janitor.finance\n    >>> df = pd.DataFrame({"profit":[100.10, 200.20, 300.30, 400.40, 500.50]})\n    >>> df\n       profit\n    0   100.1\n    1   200.2\n    2   300.3\n    3   400.4\n    4   500.5\n    >>> df.inflate_currency(\n    ...    column_name=\'profit\',\n    ...    country=\'USA\',\n    ...    currency_year=2015,\n    ...    to_year=2018,\n    ...    make_new_column=True\n    ... )\n       profit  profit_2018\n    0   100.1   106.050596\n    1   200.2   212.101191\n    2   300.3   318.151787\n    3   400.4   424.202382\n    4   500.5   530.252978\n\n\n    :param df: A pandas DataFrame.\n    :param column_name: Name of the column containing monetary\n        values to inflate.\n    :param country: The country associated with the currency being inflated.\n        May be any economy or code from the World Bank [List of economies]\n        (https://databank.worldbank.org/data/download/site-content/CLASS.xls).\n    :param currency_year: The currency year to inflate from.\n        The year should be 1960 or later.\n    :param to_year: The currency year to inflate to.\n        The year should be 1960 or later.\n    :param make_new_column: Generates new column for inflated currency if\n        True, otherwise, inflates currency in place.\n    :returns: The dataframe with inflated currency column.\n    '
    inflator = _inflate_currency(country, currency_year, to_year)
    if make_new_column:
        new_column_name = ((column_name + '_') + str(to_year))
        df[new_column_name] = (df[column_name] * inflator)
    else:
        df[column_name] = (df[column_name] * inflator)
    return df
Inflates a column of monetary values from one year to another, based on
the currency's country.

The provided country can be any economy name or code from the World Bank
[list of economies]
(https://databank.worldbank.org/data/download/site-content/CLASS.xls).

**Note**: This method mutates the original DataFrame.

Method chaining usage example:

>>> import pandas as pd
>>> import janitor.finance
>>> df = pd.DataFrame({"profit":[100.10, 200.20, 300.30, 400.40, 500.50]})
>>> df
   profit
0   100.1
1   200.2
2   300.3
3   400.4
4   500.5
>>> df.inflate_currency(
...    column_name='profit',
...    country='USA',
...    currency_year=2015,
...    to_year=2018,
...    make_new_column=True
... )
   profit  profit_2018
0   100.1   106.050596
1   200.2   212.101191
2   300.3   318.151787
3   400.4   424.202382
4   500.5   530.252978


:param df: A pandas DataFrame.
:param column_name: Name of the column containing monetary
    values to inflate.
:param country: The country associated with the currency being inflated.
    May be any economy or code from the World Bank [List of economies]
    (https://databank.worldbank.org/data/download/site-content/CLASS.xls).
:param currency_year: The currency year to inflate from.
    The year should be 1960 or later.
:param to_year: The currency year to inflate to.
    The year should be 1960 or later.
:param make_new_column: Generates new column for inflated currency if
    True, otherwise, inflates currency in place.
:returns: The dataframe with inflated currency column.
@pf.register_dataframe_method
def inflate_currency(df: pd.DataFrame, column_name: str=None, country: str=None, currency_year: int=None, to_year: int=None, make_new_column: bool=False) -> pd.DataFrame:
    '\n    Inflates a column of monetary values from one year to another, based on\n    the currency\'s country.\n\n    The provided country can be any economy name or code from the World Bank\n    [list of economies]\n    (https://databank.worldbank.org/data/download/site-content/CLASS.xls).\n\n    **Note**: This method mutates the original DataFrame.\n\n    Method chaining usage example:\n\n    >>> import pandas as pd\n    >>> import janitor.finance\n    >>> df = pd.DataFrame({"profit":[100.10, 200.20, 300.30, 400.40, 500.50]})\n    >>> df\n       profit\n    0   100.1\n    1   200.2\n    2   300.3\n    3   400.4\n    4   500.5\n    >>> df.inflate_currency(\n    ...    column_name=\'profit\',\n    ...    country=\'USA\',\n    ...    currency_year=2015,\n    ...    to_year=2018,\n    ...    make_new_column=True\n    ... )\n       profit  profit_2018\n    0   100.1   106.050596\n    1   200.2   212.101191\n    2   300.3   318.151787\n    3   400.4   424.202382\n    4   500.5   530.252978\n\n\n    :param df: A pandas DataFrame.\n    :param column_name: Name of the column containing monetary\n        values to inflate.\n    :param country: The country associated with the currency being inflated.\n        May be any economy or code from the World Bank [List of economies]\n        (https://databank.worldbank.org/data/download/site-content/CLASS.xls).\n    :param currency_year: The currency year to inflate from.\n        The year should be 1960 or later.\n    :param to_year: The currency year to inflate to.\n        The year should be 1960 or later.\n    :param make_new_column: Generates new column for inflated currency if\n        True, otherwise, inflates currency in place.\n    :returns: The dataframe with inflated currency column.\n    '
    inflator = _inflate_currency(country, currency_year, to_year)
    if make_new_column:
        new_column_name = ((column_name + '_') + str(to_year))
        df[new_column_name] = (df[column_name] * inflator)
    else:
        df[column_name] = (df[column_name] * inflator)
    return df