GeoFRED

This library helps to parse out the series titles into topics, locations, and levels of aggregations (i.e. geography types) and returns pandas.DataFrame objects.

It has the following main function groups:

1. Search. Input topics and some optional constraints (e.g. seasonal adjustment, frequency, geography type) and receive a dataframe with the relevant FRED series IDs along with their metadata, including parsed location information.
2. Data. Get a panel dataframe of all relevant data series from FRED. This has the following columns: series ID, date, topic, value, location, aggregation. This can take as input either the dataframe result of a previous "search" call, or the raw topics and constraints that you would otherwise pass to "search".
3. Locations. Input an array of zip codes (as strings), and return a dataframe of the corresponding counties, MSA regions, and state identifies.

Quick Use

You will need a FRED API key, click here.

From source

First, clone the repo and setup a dev environment.

$ pip install --upgrade pip
$ pip install geofred

Next, load the library and use the functions.

import geofred 

API_KEY = "<YOUR_KEY_HERE>"
geofred.key(API_KEY)

#######################################################
# search 
#######################################################

search_terms = ["transportation",  "warehousing"]
filters = {
  "freq": "Monthly", 
  "sa": "NSA", 
}

series_df = geofred.search(search_terms, **filters)

# filter only series for texas
texas_df = series_df[series_df["location"] == "Texas"]

#######################################################
# data
#######################################################

# grab sample of data from texas 
sample_series_df = texas_df[1:3]
df = geofred.data(sample_series_df)

#######################################################
# locations
#######################################################

zips = [60610, 38139, 38103]
geofred.locations(zips)

Interface

Search

• search(topic, **kwargs): -> pandas.DataFrame. Search for a topic with various optional filters and get the relevant FRED series IDs.
  • Input
    • topic: Union[str, List[str]]. A topic (or topics) to search against FRED's database (e.g. "Unemployment Rate")
    • Additional filters currently include:
      • "api_key": your assigned FRED api key.
      • "locations": specific locations you want to
      • "sa"
      • "agg"
      • "freq"
      • "topic"
  • Output
    • A pandas.DataFrame with the following columns:
      • id: the FRED series ID.
      • ...

Data

• data(topics: List[str], **kwargs)
• data(id_df: pandas.DataFrame, **kwargs)

Locations

• locations(zips: List[str])