Animal 911 Calls Extraction from Rainforest Cafe Report
This PDF is a service call report covering 911 incidents at the Rainforest Cafe in Niagara Falls, NY. We're hunting for animals! The data is formatted as a spreadsheet within the PDF, and challenges include varied column widths, borderless tables, and large swaths of missing data.
table_result = guide.extract_table(pages, header="first")
df = table_result.to_df()
df.head()first_table = base.extract_table().to_df()
first_table.head() guides = Guides(page)
guides.vertical = base.vertical
guides.horizontal.from_content(page.find_all('text:starts-with(NF-)'))
single_df = guides.extract_table().to_df(header=columns)
dataframes.append(single_df)
print("We made", len(dataframes), "dataframes")Arabic Election Results Table Extraction from Mednine PDF
This PDF has a data table showing election results from the Tunisian region of Mednine. Challenges include spanning header cells and rotated headers. It has Arabic script.
df = flow.extract_table().to_df(header=None)
dfimport pandas as pd
dataframes = pdf.pages.apply(
lambda page: page.extract_table().to_df(header=None)
)
print("Found", len(dataframes), "tables")
Complex Extraction of Law Enforcement Complaints
This PDF contains a set of complaint records from a local law enforcement agency. Challenges include its relational data structure, unusual formatting common in the region, and redactions that disrupt automatic parsing.
.merge()
.expand(top=5, bottom=7)
.extract_table()
.to_df(header=['Type of Complaint', 'Description', 'Complaint Disposition'])
)(
table
.extract_table(verticals=guides.vertical)
.to_df(header=['Type of Complaint', 'Description', 'Complaint Disposition'])
)(
table
.extract_table(verticals=guides.vertical)
.to_df(header=['Name', 'ID No.', 'Rank', 'Division', 'Officer Disposition', 'Action Taken', 'Body Cam'])
)Extracting Economic Data from Brazil's Central Bank PDF
This PDF is the weekly “Focus” report from Brazil’s central bank with economic projections and statistics. Challenges include commas instead of decimal points, images showing projection changes, and tables without border lines that merge during extraction.
sections[0]
.expand(top=-50, right=0)
.extract_table('stream')
.to_df(header=False)
.dropna(axis=0, how='all')
) section
.expand(top=-50, right=0)
.extract_table('stream')
.to_df(header=False)
.dropna(axis=0, how='all')
.assign(
year=section.find('text[size~=10]:regex(\d\d\d\d)').extract_text(),df_2025 = table.expand(top=-5).extract_table('stream').to_df(header=False)
df_2025Extracting State Agency Call Center Wait Times from FOIA PDF
This PDF contains data on wait times at a state agency call center. The main focus is on the data on the first two pages, which matches other states' submission formats. The later pages provide granular breakdowns over several years. Challenges include it being heavily pixelated, making it hard to read numbers and text, with inconsistent and unreadable charts.
df = (
guide
.extract_table()
.to_df(
header=['value', 'amount', 'comments']
)
)Natural PDF basics with text and tables
Learn the fundamentals of Natural PDF - opening PDFs, extracting text with layout preservation, selecting elements by criteria, spatial navigation, and managing exclusion zones. Perfect starting point for PDF data extraction.
table = page.extract_table()
if table:
df = table.to_df()
print(df.head())OCR and AI magic
Master OCR techniques with Natural PDF - from basic text recognition to advanced LLM-powered corrections. Learn to extract text from image-based PDFs, handle tables without proper boundaries, and leverage AI for accuracy improvements.
df = guides.extract_table().to_df()
dfWorking with page structure
Extract text from complex multi-column layouts while maintaining proper reading order. Learn techniques for handling academic papers, newsletters, and documents with intricate column structures using Natural PDF's layout detection features.
regions[0].extract_table().to_df()# Combine them if we want
import pandas as pd
dfs = regions.apply(lambda region: region.extract_table().to_df())
merged = pd.concat(dfs, ignore_index=True)
mergedguides.extract_table().to_df()