Using requests Module to Search FRED Metadata

notr

FRED data with requests¶

The FRED API is so easy to use that finding what series to read is the hard part. There are hundreds of thousands of series, too many even to list in a simple html table. API calls through the requests module can help.

For pulling individual series, we do not need an API key, so it's simple for anyone to copy a notebook and follow along. For pulling metadata, a key is required. There is a key in the notebook.

So many FRED sequences have non-farm descriptions that it made me wonder what series they have for farms, so we take a look at that.

Imports¶

In [31]:

# imports
import pandas as pd
import requests 
import json 

# CONST
fredKey = "see notebook" # api key for notebook

requests.get¶

The requests.get method takes a string argument. The string has the following components connected with anpersands

• base tags search url and tag to look for
• api key
• file_type=json

The decode method for the content attribute from that output is a json string we'll interrogate.

In [18]:

reqS = "&".join(("""https://api.stlouisfed.org/fred/tags/series?tag_names=%s
api_key=%s
file_type=json""" %("farm", fredKey)).split("\n"))
respS = requests.get(reqS).content.decode()
len(respS)

Out[18]:

573397

The string from the request can be sent to json.loads to obtain a dictionary.

In [32]:

tD = json.loads(respS)
tD.keys()

Out[32]:

dict_keys(['realtime_start', 'realtime_end', 'order_by', 'sort_order', 'count', 'offset', 'limit', 'seriess'])

It's the "seriess" entry we are interested in, and that is a list of dictionaires.

In [33]:

seriesL = tD["seriess"]
len(seriesL)

Out[33]:

838

So there are over 800 series with "farm" in the tags. Just browsing one shows a dictionary.

In [34]:

seriesL[0]

Out[34]:

{'frequency': 'Annual',
 'frequency_short': 'A',
 'group_popularity': 1,
 'id': 'A0282AUSA398NNBR',
 'last_updated': '2012-08-16 11:38:36-05',
 'notes': 'Series Is Presented Here As 3 Variables: (1) Original Data, 1915-46; (2) Original Data 1946-63; (3) Seasonally Adjusted Data, 1939-46. Included In This Series Are: (1) New Housing Units And Additions And Alterations To Existing Units On Places Classified As Farms, According To The 1960 Census Definition; And (2) Other Buildings And Structures Used In Farm Production, Such As Barns, Storage Houses, Smoke Houses, Fences, Wells, Etc. Excluded Are Operations Which Are An Integral Part Of Farming, Such As Plowing, Terracing, And Digging Of Drainage Ditches. For Further Information On Sources, Collection Procedures, And Coverage, See Introductory Notes On Bls-Dept. Of Commerce Series, And Lipsey & Preston, Pp. 263-264. Source: Business & Defense Services Administration And Bureau Of Labor Statistics Publications (See Note).\n\nThis NBER data series a02182a appears on the NBER website in Chapter 2 at http://www.nber.org/databases/macrohistory/contents/chapter02.html.\n\nNBER Indicator: a02182a',
 'observation_end': '1946-01-01',
 'observation_start': '1915-01-01',
 'popularity': 1,
 'realtime_end': '2018-05-28',
 'realtime_start': '2018-05-28',
 'seasonal_adjustment': 'Not Seasonally Adjusted',
 'seasonal_adjustment_short': 'NSA',
 'title': 'Private Farm Construction for United States',
 'units': 'Millions of Current Dollars',
 'units_short': 'Mil. Of Current $'}

FRED Data with pandas_datareader

FRED data with pandas_datareader¶

The Federal Reserve Bank of St. Louis publishes a lot of series and has a simple API. Below we take a really quick look at a couple of unemployment series. Really quick because it's just a couple lines beyond the expected imports.

Imports¶

In [2]:

# imports
import pandas as pd
from datetime import datetime as dt
pd.core.common.is_list_like = pd.api.types.is_list_like # workaround
import pandas_datareader.data as pdd
import matplotlib.pyplot as plt

# CONST
Sdt = dt(1990, 1, 1) # negative infinity date
Edt = dt(2020, 1, 1) # future date 
SW = "HSGS2564W" # Unemployment Rate - High School Graduates, No College, 25 to 64 years, Women
SM = "HSGS2564M" # Unemployment Rate - High School Graduates, No College, 25 to 64 years, Men

Grab data with DataReader¶

Arguments are

• series wanted or a list of series wanted
• constant "fred" to indicate we are reading from FRED. Could have supplied instead "google" for Google Finance, or "ff" for Kenneth French's data library
• earliest datetime wanted
• latest datetime wanted

There are some additional arguments not use here. For more details, help(pdd.DataReader).

In [3]:

# grab data
noCollegeDF = pdd.DataReader((SW, SM), "fred", Sdt, Edt)
noCollegeDF.head()

Out[3]:

	HSGS2564W	HSGS2564M
DATE
2000-01-01	3.8	3.7
2000-02-01	3.7	4.1
2000-03-01	3.5	3.8
2000-04-01	3.0	3.5
2000-05-01	3.5	2.9

Plot¶

In [4]:

%matplotlib inline  
fig, ax = plt.subplots() 
noCollegeDF.plot(ax=ax)
plt.title("Unemployment for HS Grads with No College W and M")

Out[4]:

Text(0.5,1,'Unemployment for HS Grads with No College W and M')

World Bank Data with pandas_datareader

The World Bank publishes lots of data. They make data available for download as csv, xml, or xls files. Additionally, they make the data available through API calls, which is the civilized route. Tariq Khokhar wrote an excellent survey of libraries for accessing World Bank data in Python, R, Ruby, and Stata. We'll use one of the libraries discussed, pandas_datareader. This will be a quick look at per capita GDP trends for a few countries using pandas_datareader. In another blog post, we'll look at the same exercise using the csv files to show how much harder it is.

World Bank Data

Scanning the list of indicators, we see the link to "GDP per capita, PPP...", and the text for the link gives the indicator code we are looking for, "NY.GDP.PCAP.PP.CD". Additionally we can note from the chart that the populated data starts in 1990. Following the link, we get the option to download the series as csv, xml, or xls, and can download a file (now named API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_9908727.zip, but it could be something else another day) to use in a later blog.

Installing pandas_datareader

A pandas_datareader can be installed using conda:

$ conda install -c anaconda pandas-datareader

Using pandas_datareader

The following can be downloaded as a notebook. The chart created in the notebook can be seen below.

world bank data with pandas_datareader¶

Downloaded ppp data csv. Workaround neded for version incompatability in datareader. See discussion</a href>.

In [33]:

# imports
import os
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like # workaround
from pandas_datareader import wb as WB

# CONST
indCode = "NY.GDP.PCAP.PP.CD" # code for  per capita ppp gdp

Downloading with pandas_datareader¶

Arguments are

• indicator= Code for series to grab
• country= "all" or list of 2 byte country codes. Defailt is to "CA MX US".split(). We should prefer to filter in Python rather than in the pull.
• start= Desired start year. Better to filter in Python so set early.
• stop= Desired stop year. Better to filter in Python so set late.

In [50]:

# grab all data for 
pppDF = WB.download(indicator=indCode, country="all", start=1980, end=2020)
pppDF.head()

Out[50]:

		NY.GDP.PCAP.PP.CD
country	year
Arab World	2017	NaN
	2016	16726.722185
	2015	16302.363760
	2014	15934.202070
	2013	15548.200905

Reshaping the data¶

• Removing rows with null entries.
• Reseting indexes because you never want indexes.
• Giving a normal whitespace free name to the series.
• Sorting just to make table display read better.
• Making numeric copy of the string year.

In [51]:

pppDF = pppDF.dropna().reset_index()
pppDF.columns = "country year GDPpc".split()
pppDF.sort_values("year country".split(), inplace=True)
pppDF["yearN"] = pppDF.year.apply(pd.to_numeric)
pppDF.head()

Out[51]:

	country	year	GDPpc	yearN
1263	Albania	1990	2722.280344	1990
1290	Algeria	1990	6616.408352	1990
1317	Angola	1990	2840.200763	1990
1344	Antigua and Barbuda	1990	10587.593409	1990
26	Arab World	1990	6759.785391	1990

Filtering for top African economies¶

The spelling of Egypt is a little unfortunate. Here we need exact matches.

In [52]:

# top Africa economies
aL = "Nigeria|South Africa|Egypt, Arab Rep.|Morocco|Ethiopia".split("|")
africaDF = pppDF[pppDF.country.isin(aL)]
africaDF.head()

Out[52]:

	country	year	GDPpc	yearN
2571	Egypt, Arab Rep.	1990	3819.286370	1990
2694	Ethiopia	1990	421.378824	1990
4261	Morocco	1990	2528.458556	1990
4514	Nigeria	1990	1965.827996	1990
5256	South Africa	1990	6267.091465	1990

plotting¶

In [54]:

# the next line is needed to diaplay the plot in the notebook
%matplotlib inline  
import matplotlib.pyplot as plt
fig, ax = plt.subplots() 
africaDF.groupby("country").plot(x="yearN", y="GDPpc", ax=ax)
ax.legend("Egypt|Ethiopia|Morocco|Nigeria|South Africa".split("|"))
fig.savefig("africaGDPpc.png")