Search PDFs, Turn Tables into DataFrames
This tutorial will show you how to:
- Search for PDFs online directly in Fused
- Turn tables in PDFs into DataFrames
Left: Original PDF. Right: DataFrame of PDF in Workbench
Try out a hosted app for yourself:
Getting Started​
As of writing this Search in the AI Assistant is still under experimental flag so turn it on:
- Go to the Preferences
- Turn on "AI Dataset Search"
Searching for PDFs​
Now that you have AI Search turned on, you can search for PDFs directly in Fused:
Search for any PDFs from Census data that would provide me health insurance coverage & type by age.
I want the data to be available in a table format.
The AI Assistant will provide a list of links to PDFs that are as close to your request as possible:
In its current stage we recommend you explore the PDFs for yourself to see which one matches your request best.
The first link, pointing to Census .gov data seems to match our request. Opening it and going to the Appendix we find what we're looking for:
Turning PDF Tables in DataFrames​
Next:
- Use the Datalab API to turn a PDF into a JSON
- Write a UDF that turns the JSON into a DataFrame
Using a preexisting UDF​
We built a UDF on top of the Datalab API that you can directly use:
# in a notebook
import fused
df = fused.run(
"fsh_1IH3QxpoAIEz9qtUChfqtS",
pdf_url="https://www2.census.gov/library/publications/2024/demo/p60-284.pdf",
raw_table_idx=0
)
print(df)
This will not return the table we're looking for. This is a simple UDF that returns each table found in the JSON from DataLab as a DataFrame
The print message will however show us all the available tables found:
--- Raw Table 0 ------------------------------------------------
Shape: (14, 5)
Unnamed: 0 2022 2022.1 2023 2023.1
Coverage type NaN Margin of NaN Margin of
NaN Number error1 (±) Number error1 (±)
Total 330000 130 331700 145
Any health plan 304000 746 305200 704
Any private plan2, 3 216500 1399 216800 1294
------------------------------------------------------------
--- Raw Table 1 ------------------------------------------------
Shape: (35, 10)
Unnamed: 0 Total Total.1 Total.2 Total.3 Total.4 Total.5 Total.6 Total.7 Unnamed: 9
NaN NaN Any health insurance Any health insurance Any health insurance Any health insurance Any health insurance Any health insurance NaN NaN
Characteristic NaN NaN NaN NaN Private health NaN Public health Uninsured4 Uninsured4
NaN NaN NaN NaN NaN insurance2 NaN insurance3 Uninsured4 Uninsured4
NaN NaN NaN Margin of NaN Margin of NaN Margin of NaN Margin of
NaN Number Percent error1 (±) Percent error1 (±) Percent error1 (±) Percent error1 (±)
------------------------------------------------------------
--- Raw Table 2 ------------------------------------------------
Shape: (35, 11)
Unnamed: 0 Total Total.1 Total.2 Total.3 Total.4 Total.5 Total.6 Total.7 Unnamed: 9 Unnamed: 10
NaN NaN NaN NaN NaN Any health insurance NaN NaN NaN NaN NaN
Characteristic NaN NaN NaN NaN Private health Public health Public health Uninsured4 Uninsured4 NaN
NaN NaN NaN NaN NaN insurance2 NaN insurance3 insurance3 NaN NaN
NaN NaN NaN Margin of NaN Margin of NaN Margin of NaN Margin of NaN
NaN Number Percent error1 (±) Percent error1 (±) Percent error1 (±) Percent error1 (±) NaN
------------------------------------------------------------
--- Raw Table 3 ------------------------------------------------
Shape: (29, 11)
Unnamed: 0 Total Total.1 Total.2 Total.3 Total.4 Total.5 Total.6 Total.7 Unnamed: 9 Unnamed: 10
NaN NaN Any health insurance Any health insurance Any health insurance Any health insurance Any health insurance Any health insurance NaN NaN NaN
Characteristic NaN NaN NaN NaN Private health Public health Public health Uninsured4 Uninsured4 NaN
NaN NaN NaN NaN NaN insurance2 NaN insurance3 insurance3 NaN NaN
NaN NaN NaN Margin of NaN Margin of NaN Margin of NaN Margin of NaN
NaN Number Percent error1 (±) Percent error1 (±) Percent error1 (±) Percent error1 (±) NaN
------------------------------------------------------------
--- Raw Table 4 ------------------------------------------------
Shape: (30, 11)
Unnamed: 0 Total Total.1 Total.2 Total.3 Total.4 Total.5 Total.6 Total.7 Total.8 Unnamed: 10
NaN Any health insurance Any health insurance Any health insurance Any health insurance Any health insurance Any health insurance NaN NaN NaN NaN
NaN NaN NaN NaN NaN Private health NaN Public health Uninsured4 Uninsured4 NaN
Characteristic NaN NaN NaN NaN insurance2 NaN insurance3 insurance3 NaN NaN
NaN NaN NaN Margin of NaN Margin of NaN Margin of NaN Margin of NaN
NaN Number Percent error1 (±) Percent error1 (±) Percent error1 (±) Percent error1 (±) NaN
------------------------------------------------------------
It looks like the table 3 matches what we're looking for:
import fused
df = fused.run(
"fsh_1IH3QxpoAIEz9qtUChfqtS",
pdf_url="https://www2.census.gov/library/publications/2024/demo/p60-284.pdf",
raw_table_idx=3 # Table 3, one matching our request
)
print(df)
Which returns the following DataFrame:
You may notice the table has some formatting issues. These can be fixed relatively easily by asking the AI Assistant to fix them.
Build it yourself​
You can make a copy of the UDF from the UDF Catalog
You will need to:
- Setup an account on DataLab
- Get an API Key
- Add
datalabas a secret in the Preferences
And now you get edit this UDF for yourself:
Next Steps​
Now that you have a DataFrame you can explore how to build on top:
- Ask AI to visualize the data
- See how to turn any of your UDFs into an API endpoint
- Let anyone talk to this data through MCP Servers