Inflating and Converting Currency¶

Background¶

This notebook serves to show a brief and simple example of how to use the convert_currency() and inflate_currency() methods from pyjanitor's finance submodule.

The data for this example notebook come from the United States Department of Agriculture Economic Research Service, and we are specifically going to download the data of nominal food and alcohol expenditures, with taxes and tips, for all purchasers. The data set includes nominal expenditures for 1997-2018, and the expenditures are provided in millions of U.S. dollars for the year in the which the expenditures were made. For example, the expenditure values for 1997 are in units of 1997 U.S. dollars, whereas expenditures for 2018 are in 2018 U.S. dollars.

Getting and Cleaning the Data¶

In [1]:

import pandas as pd
import janitor

In [26]:

url = 'https://www.ers.usda.gov/webdocs/DataFiles/50606/nominal_expenditures.csv?v=9289.4'
# 1) Read in the data from .csv file
# 2) Clean up the column names
# 3) Remove any empty rows or columns
# 4) Melt the dataframe (from wide to long) to obtain "tidy" format
data = (
    pd.read_csv(url, skiprows=4, usecols=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], nrows=22, thousands=',')
    .clean_names()
    .remove_empty()
    .melt(id_vars=['year'], var_name='store_type', value_name='expenditure')
    )
data.head()

Out[26]:

	year	store_type	expenditure
0	1997	grocery_stores	270956.91
1	1998	grocery_stores	273638.88
2	1999	grocery_stores	283782.47
3	2000	grocery_stores	286669.78
4	2001	grocery_stores	296436.72

Use inflate_currency() to Inflate All Values to 2018$¶

In [43]:

from janitor.finance import inflate_currency, convert_currency

# Use split-apply-combine strategy to obtain 2018$ values
# Group the data frame by year
grouped=data.groupby(['year'])
# Apply the inflate_currency() method to each group
# (Note that each group comes with a name; in this case,
#  the name corresponds to the year)
data_constant_dollar = grouped.apply(lambda x: x.inflate_currency(
        column_name='expenditure',
        country='USA',
        currency_year=x.name,
        to_year=2018,
        make_new_column=True))
data_constant_dollar.head()

Out[43]:

	year	store_type	expenditure	expenditure_2018
0	1997	grocery_stores	270956.91	423875.807122
1	1998	grocery_stores	273638.88	421528.097543
2	1999	grocery_stores	283782.47	427793.590858
3	2000	grocery_stores	286669.78	418029.852893
4	2001	grocery_stores	296436.72	420391.299188

Plot Time Series to Observe Currency Inflation¶

In [44]:

# Plot time series of nominal and real (2018$) expenditures for grocery stores
# Note that the 2018 values for both series should be equal
(data_constant_dollar
 .loc[data_constant_dollar['store_type'] == 'grocery_stores', :]
 .set_index('year')
 .drop(columns='store_type')
 .plot())

Out[44]:

<matplotlib.axes._subplots.AxesSubplot at 0x1e48363e198>

Use convert_currency() to Convert USD to British Pounds¶

In [45]:

from datetime import date

# Apply the convert_currency() method to the 'expenditure_2018' column
# Use the exchange rate from Dec. 31, 2018, since our data are in 2018$
data_constant_pounds = (
    data_constant_dollar
    .convert_currency(
        column_name='expenditure_2018',
        from_currency='USD',
        to_currency='GBP',
        historical_date=date(2018,12,31),
        make_new_column=True)
)
data_constant_pounds.head()                    

Out[45]:

	year	store_type	expenditure	expenditure_2018	expenditure_2018_GBP
0	1997	grocery_stores	270956.91	423875.807122	331152.511569
1	1998	grocery_stores	273638.88	421528.097543	329318.366023
2	1999	grocery_stores	283782.47	427793.590858	334213.275836
3	2000	grocery_stores	286669.78	418029.852893	326585.366209
4	2001	grocery_stores	296436.72	420391.299188	328430.243550

Plot Time Series to Observe Currency Conversion¶

In [46]:

# You can see from this plot that GBP were worth more than USD on Dec. 31, 2018
# (by about 1.3x, it appears)
(data_constant_pounds
 .loc[data_constant_pounds['store_type'] == 'grocery_stores', :]
 .set_index('year')
 .drop(columns='store_type')
 .plot())

Out[46]:

<matplotlib.axes._subplots.AxesSubplot at 0x1e4836d32b0>

In [ ]: