I previously wrote about how you can use VisiData to quickly scrape HTML tabular data. Claude Code with Opus 4.5 is another excellent data exploration sidekick. This is what ‘agentic’ looks like in practice.
Using the same Federal Reserve list of the largest commercial banks, I started a Claude Code session and prompted:
Can you go to the page at https://www.federalreserve.gov/releases/lbr/current/ and discover the table of large commercial banks; then give me a bar graph of the top 10 by Consol Assets. Use uv for any dependencies you need. You probably want to use pandas read_html() to get the tables and then matplotlib with nice styling to create the viz
It spun for a bit and then produced some Python. Sometimes agents will try to run this kind of thing as a tidy here document, but not always. So I like to do this sort of thing in a scratch directory that it can toss files in. This time, it tossed a bank_chart.py, which it ran itself like this:
uv run \
--with pandas \
--with matplotlib \
--with lxml \
--with html5lib \
--with requests \
python bank_chart.pyOn first run, it realized the Fed has opinions about the User-Agent header (VisiData also hit this issue), so it did the thing everyone does when scripting HTTP calls and made it look like a browser:
The Fed website is blocking the pandas request. I need to use a proper user agent header to fetch the content. Let me use requests to download the HTML first, then parse it with pandas.
--- a/bank_chart.py
+++ b/bank_chart.py
@@ -1,6 +1,15 @@
import pandas as pd
import matplotlib.pyplot as plt
+import requests
+from io import StringIO
-# Fetch the table from the Federal Reserve page
+# Fetch the table from the Federal Reserve page with proper headers
url = "https://www.federalreserve.gov/releases/lbr/current/"
-tables = pd.read_html(url)
+headers = {
+ 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36'
+}
+response = requests.get(url, headers=headers)
+response.raise_for_status()
+
+tables = pd.read_html(StringIO(response.text))It then ran again, successfully, and produced the following graph, both as a PNG and PDF:
The whole thing took about two minutes from prompt to viz.
I think a lot of people still view tools like Claude Code as AI that builds software for people who sell software. But we need to think about them as general purpose agents that help us get work done.
Not bad for a single prompt.