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You’ve probably heard a lot about inflation in the past few months. Let’s take a closer look at the data and learn some Python along the way! In this blog post we’ll be exploring data from the US Bureau of Labor Statistics, in particular the CPI (Consumer Price Index).
Libraries¶
We should always do a quick search to make sure we’re not duplicating previous work. A cursory search reveals this that the BLS provides us with a wonderful Python API: https://www.bls.gov/developers/api_python.htm
Now we could use this API ourselves, but we already know the gist of what we want to do, download the data, analyze with pandas, and then visualize with Python. Surely this is a common enough task that others have explored it already, right?
Fortunately for us, the Python ecosystem is a supportive community of many open-source libraries, and thanks to data journalist Ben Welsh we have access to the CPI library with Python, which is a very nice wrapper around the BLS Python API that will save us a lot of work.
We can install it with pip install cpi.
Let’s explore how to use it, first we’ll import a few more data and visualization libraries:
# IMPORTS
import pandas as pd
import cpi
import seaborn as sns
import matplotlib.pyplot as plt
Often when working with the cpi library, you may get a warnings that the data is stale, which makes sense as the BLS is constantly updating with the latest inflation information, let’s update our data:
# This sometimes take awhile!
cpi.update()
We can get a pandas dataframe for the “All Items” tracked, which contains within it an average inflation of many items, such as food, energy, shelter, new vehicles, etc…
cpi_items_df = cpi.series.get(seasonally_adjusted=False).to_dataframe()
cpi_items_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1422 entries, 0 to 1421 Data columns (total 22 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 year 1422 non-null int64 1 date 1422 non-null object 2 value 1422 non-null float64 3 series_id 1422 non-null object 4 series_title 1422 non-null object 5 series_survey 1422 non-null object 6 series_seasonally_adjusted 1422 non-null bool 7 series_periodicity_id 1422 non-null object 8 series_periodicity_code 1422 non-null object 9 series_periodicity_name 1422 non-null object 10 series_area_id 1422 non-null object 11 series_area_code 1422 non-null object 12 series_area_name 1422 non-null object 13 series_items_id 1422 non-null object 14 series_items_code 1422 non-null object 15 series_items_name 1422 non-null object 16 period_id 1422 non-null object 17 period_code 1422 non-null object 18 period_abbreviation 1422 non-null object 19 period_name 1422 non-null object 20 period_month 1422 non-null int64 21 period_type 1422 non-null object dtypes: bool(1), float64(1), int64(2), object(18) memory usage: 234.8+ KB
Data Cleaning¶
While the data is already extremely clean, it actually has more info that what we need, we just need the monthly periods and this data set has monthly and annual, let’s clean it up and also set a date index.
cpi_items_df = cpi_items_df[cpi_items_df['period_type']=='monthly']
cpi_items_df['date'] = pd.to_datetime(cpi_items_df['date'])
cpi_items_df = cpi_items_df.set_index('date')
cpi_items_df['value'].plot()
It isn’t super useful for us living in the 2020s to view inflation on such a large timescale (unless you want to reminisce about your 5 cent cup of coffee from the 1920s). Let’s zoom in to a more current timeline.
fig = plt.figure(dpi=200)
cpi_items_df['value'].loc['2010':'2023'].plot(figsize=(10,4))
plt.xlabel('Date')
plt.ylabel('CPI Value')
If we were targeting a yearly 2% inflation rate since 2010, let’s explore what this line would look like on the same plot, starting with the 2010 CPI value. We’ll do a very simple method for this (overly simplified, but it should work fine for our exploratoy data analysis), by simply looping through the values and adding the original plus 2% of the original. Then we can plot these values against the real data.
# Starting value
cpi_items_df['value'].loc['2010':'2023'].iloc[0]
216.687
start = cpi_items_df['value'].loc['2010':'2023'].iloc[0]
periods = len(cpi_items_df['value'].loc['2010':'2023'])//12
def get_target_cpi(previous_cpi):
return previous_cpi + 0.02*(previous_cpi)
target_cpis = [start]
for year in range(0,periods):
target_cpis.append(get_target_cpi(target_cpis[year]))
target_cpis
[216.687, 221.02074000000002, 225.44115480000002, 229.949977896, 234.54897745392, 239.2399570029984, 244.02475614305837, 248.90525126591953, 253.88335629123793, 258.9610234170627, 264.14024388540395, 269.42304876311204, 274.8115097383743]
len(target_cpis)
13
Creating a pandas series for the target CPIs starting in 2010:
dates = pd.date_range('2010-01-01','2023-01-01',periods=periods+1)
target_cpi_series = pd.Series(data =target_cpis, index= dates)
fig = plt.figure(dpi=200)
cpi_items_df['value'].loc['2010':'2023'].plot(figsize=(10,4))
target_cpi_series.plot(ls='--')
plt.xlabel('Date')
plt.ylabel('CPI Value');
Looks like the Fed was actually doing a pretty good job at keeping things steady and the pandemic, stimulus, and supply chain really put strain on the inflation rate. How bad is this compared to the 1970s? Let’s compare…
Inflation in the 1970s¶
# Starting value
cpi_items_df['value'].loc['1969':'1980'].iloc[0]
35.6
start = cpi_items_df['value'].loc['1969':'1980'].iloc[0]
periods = len(cpi_items_df['value'].loc['1969':'1980'])//12
def get_target_cpi(previous_cpi):
return previous_cpi + 0.02*(previous_cpi)
target_cpis = [start]
for year in range(0,periods):
target_cpis.append(get_target_cpi(target_cpis[year]))
target_cpis
[35.6, 36.312000000000005, 37.03824, 37.7790048, 38.534584896000005, 39.305276593920006, 40.091382125798404, 40.89320976831437, 41.71107396368066, 42.54529544295428, 43.39620135181336, 44.26412537884963, 45.14940788642662]
len(target_cpis)
13
Creating a pandas series for the target CPIs starting in 2010:
dates = pd.date_range('1969-01-01','1980-01-01',periods=periods+1)
target_cpi_series = pd.Series(data =target_cpis, index= dates)
fig = plt.figure(dpi=200)
cpi_items_df['value'].loc['1969':'1980'].plot(figsize=(10,4))
target_cpi_series.plot(ls='--')
plt.xlabel('Date')
plt.ylabel('CPI Value');
Wow, things were really bad in the 70s compared to today! Let’s see the change when Paul Volcker “broke the back” of inflation by substantially raising rates in the early 1980s.
Volcker “crushes” inflation:¶
fig = plt.figure(dpi=200)
cpi_items_df['value'].loc['1969':'1990'].plot(figsize=(10,4))
plt.xlabel('Date')
plt.ylabel('CPI Value');
So far we’ve explored the raw CPI values, but we’re probably more interested in terms of percent change as is reported on the news (e.g. inflation came in at 6%). Let’s see the change in rates of inflation after Volcker dramatically hikes rates:
fig = plt.figure(dpi=200)
cpi_items_df['value'].loc['1969':'1990'].pct_change(12).plot(figsize=(10,4))
plt.axhline(0.02,ls='--',c='red')
plt.xlabel('Date')
plt.ylabel('CPI YoY % Change');
Now let’s do a similar plot for the current timeline:
fig = plt.figure(dpi=200)
cpi_items_df['value'].loc['2010':'2023'].pct_change(12).plot(figsize=(10,4))
plt.axhline(0.02,ls='--',c='red')
plt.xlabel('Date')
plt.ylabel('CPI YoY % Change');
Looks like rates may have to rise quite a bit to solve this! We also have to consider supply chain issues and global macroeconomic changes (e.g. conflict in Ukraine) that can effect particular commodities, such as oil.
To conclude, let’s do a similar plot for only a single category: Gasoline (All Types):
gas = cpi.series.get(items="Gasoline (all types)", seasonally_adjusted=False).to_dataframe()
gas = gas[gas['period_type']=='monthly']
gas['date'] = pd.to_datetime(gas['date'])
gas = gas.set_index('date')
fig = plt.figure(dpi=200)
gas['value'].loc['2015':'2023'].pct_change(12).plot(figsize=(10,4))
plt.axhline(0,ls='--',c='red')
plt.xlabel('Date')
plt.ylabel('Gasoline Price YoY % Change');
Clearly there is very high inflation in gasoline, the 2021 values are looking to average somewhere above 4% YoY increases!
We encourage you to explore this library and other series as well!
