FRED Categories¶

The pyfredapi.category module has functions for requesting data about FRED Categories.

Each FRED category is associated a unique integer identifier. For example:

• Money, Banking and Finance is category 32991
• Population, Employment, & Labor Markets is category 10
• Prices is category 32455

Categories are organized in a hierarchical structure. All categories are children of the root category (category_id = 0).

FRED series are assigned to categories. This allows users to query the category API to find all series associated with a given category. This tutorial will demonstrate how you can use the pyfredapi.category module to search through the FRED category hierarchy and find series associated with U.S unemployment.

Setup¶

In [1]:

import operator

import matplotlib.pyplot as plt
import seaborn as sns
from rich.pretty import pprint

import pyfredapi as pf

import operator import matplotlib.pyplot as plt import seaborn as sns from rich.pretty import pprint import pyfredapi as pf

get_series_categories¶

When starting an analysis, you will often have a series in mind, but probably don't know the full scope of related series you're interested in. The get_series_categories function allows you to find the category or categories associated with a given series.

As an example, if you're interested in analyzing unemployment you can start with the series id UNRATE and see what category the unemployment series is associated with. Below we see that UNRATE is associated with category 32447, which is the Unemployment Rate category.

In [ ]:

pprint(pf.get_series_categories(series_id="UNRATE"))

pprint(pf.get_series_categories(series_id="UNRATE"))

get_category_series¶

Next, with this information we can use the get_category_series function to find all series associated with the Unemployment Rate category. This will return a list of SeriesInfo objects containing all series in the Unemployment Rate category.

The example code below shows that there are 820 series in the Unemployment Rate category.

In [3]:

all_unemployment_series = pf.get_category_series(category_id=32447)

all_unemployment_series = pf.get_category_series(category_id=32447)

In [ ]:

print(f"Number of series in Unemployment category: {len(all_unemployment_series)}")

print(f"Number of series in Unemployment category: {len(all_unemployment_series)}")

Now we have all the unemployment related series, but 820 series is a lot to look through. Let's narrow it down to just seasonally adjusted series with a popularity >= 20.

In [5]:

seasonally_adjusted_unemployment_series = [
    series_info
    for series_info in all_unemployment_series.values()
    if series_info.seasonal_adjustment == "Seasonally Adjusted"
    and series_info.popularity >= 20
]

seasonally_adjusted_unemployment_series = [ series_info for series_info in all_unemployment_series.values() if series_info.seasonal_adjustment == "Seasonally Adjusted" and series_info.popularity >= 20 ]

Let's also sort the series by popularity. Then print the the series id and title.

In [6]:

seasonally_adjusted_unemployment_series = sorted(
    seasonally_adjusted_unemployment_series,
    key=operator.attrgetter("popularity"),
    reverse=True,
)

seasonally_adjusted_unemployment_series = sorted( seasonally_adjusted_unemployment_series, key=operator.attrgetter("popularity"), reverse=True, )

In [ ]:

len(seasonally_adjusted_unemployment_series)

len(seasonally_adjusted_unemployment_series)

In [ ]:

for series_info in seasonally_adjusted_unemployment_series:
    print(f"{series_info.id}: {series_info.title}")

for series_info in seasonally_adjusted_unemployment_series: print(f"{series_info.id}: {series_info.title}")

Move to SeriesCollection¶

Great! Now we have the 7 most popular unemployment series to work with. Next we can construct a SeriesCollection object to download the data for these series.

In [9]:

series_id = [series.id for series in seasonally_adjusted_unemployment_series]

series_id = [series.id for series in seasonally_adjusted_unemployment_series]

In [10]:

series_names = {
    series_info.id: series_info.title
    for series_info in seasonally_adjusted_unemployment_series
}

series_names = { series_info.id: series_info.title for series_info in seasonally_adjusted_unemployment_series }

In [ ]:

unemployment_sc = pf.SeriesCollection(series_id=series_id, rename=series_names)

unemployment_sc = pf.SeriesCollection(series_id=series_id, rename=series_names)

Plot the series¶

Now that we have the data, we can plot the series.

In [12]:

long_df = unemployment_sc.merge_long()
min_date = min(long_df["date"].dt.date)
max_date = max(long_df["date"].dt.date)

long_df = unemployment_sc.merge_long() min_date = min(long_df["date"].dt.date) max_date = max(long_df["date"].dt.date)

Bar Chart¶

In [ ]:

sns.set(rc={"figure.figsize": (11.7, 8.27)})
ax = sns.barplot(
    data=long_df[long_df["date"] == max(long_df["date"])],
    x="series",
    y="value",
    hue="series",
)
plt.xticks(rotation=290)
ax.set(
    xlabel="Series",
    xticklabels=[],
    ylabel="Unemployment Rate",
    title=f"U.S. Unemployment Rate, Seasonally Adjusted, {max_date}",
)
sns.move_legend(ax, "best")

sns.set(rc={"figure.figsize": (11.7, 8.27)}) ax = sns.barplot( data=long_df[long_df["date"] == max(long_df["date"])], x="series", y="value", hue="series", ) plt.xticks(rotation=290) ax.set( xlabel="Series", xticklabels=[], ylabel="Unemployment Rate", title=f"U.S. Unemployment Rate, Seasonally Adjusted, {max_date}", ) sns.move_legend(ax, "best")

Time series plot¶

In [ ]:

sns.set(rc={"figure.figsize": (11.7, 8.27)})
ax = sns.lineplot(data=long_df, x="date", y="value", hue="series")
ax.set(
    xlabel="Date",
    ylabel="Unemployment Rate",
    title=f"U.S. Unemployment Rate, Seasonally Adjusted, {min_date} - {max_date}",
)
sns.move_legend(ax, "best")

sns.set(rc={"figure.figsize": (11.7, 8.27)}) ax = sns.lineplot(data=long_df, x="date", y="value", hue="series") ax.set( xlabel="Date", ylabel="Unemployment Rate", title=f"U.S. Unemployment Rate, Seasonally Adjusted, {min_date} - {max_date}", ) sns.move_legend(ax, "best")