Fundamentals of Python for Financial Econometrics

Chapter 16: Obtaining Economic and Financial Data

How can a live data source be used without making the analysis disappear when the network or provider changes?

Loading notebook...Retrieve data from authoritative APIsRecord series IDs, units, frequencies, and retrieval datesCache legally redistributable snapshotsBuild graceful fallbacks and error handling

Notebook lab guide

Related dataset

Ceteris Lab teaching sample

Estimated time

25 to 40 min

Packages

pandas, numpy

Expected output

Printed Python results that can be compared with the lesson explanation.

Learning goals

Load and inspect Ceteris Lab teaching sample.
Run the Python cells connected to Obtaining Economic and Financial Data.
Interpret the output using Retrieve data from authoritative APIs and Record series IDs, units, frequencies, and retrieval dates.

Common errors

File not found: check that wage_sample.csv is installed or use the course data folder.
Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/wage_sample.csv")
df.head()

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