// Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Guppy is deprecated; use Dorado for all new analyses.
Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation.
Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
Prepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning.
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
| name | pdf-processing |
| description | Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction. |
Use pdfplumber to extract text from PDFs:
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
text = pdf.pages[0].extract_text()
print(text)
Extract tables from PDFs with automatic detection:
import pdfplumber
with pdfplumber.open("report.pdf") as pdf:
page = pdf.pages[0]
tables = page.extract_tables()
for table in tables:
for row in table:
print(row)
Process multi-page documents efficiently:
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
full_text = ""
for page in pdf.pages:
full_text += page.extract_text() + "\n\n"
print(full_text)
For PDF form filling, see FORMS.md for the complete guide including field analysis and validation.
Combine multiple PDF files:
from pypdf import PdfMerger
merger = PdfMerger()
for pdf in ["file1.pdf", "file2.pdf", "file3.pdf"]:
merger.append(pdf)
merger.write("merged.pdf")
merger.close()
Extract specific pages or ranges:
from pypdf import PdfReader, PdfWriter
reader = PdfReader("input.pdf")
writer = PdfWriter()
# Extract pages 2-5
for page_num in range(1, 5):
writer.add_page(reader.pages[page_num])
with open("output.pdf", "wb") as output:
writer.write(output)
Extract and save text:
import pdfplumber
with pdfplumber.open("input.pdf") as pdf:
text = "\n\n".join(page.extract_text() for page in pdf.pages)
with open("output.txt", "w") as f:
f.write(text)
Extract tables to CSV:
import pdfplumber
import csv
with pdfplumber.open("tables.pdf") as pdf:
tables = pdf.pages[0].extract_tables()
with open("output.csv", "w", newline="") as f:
writer = csv.writer(f)
for table in tables:
writer.writerows(table)
Handle common PDF issues:
import pdfplumber
try:
with pdfplumber.open("document.pdf") as pdf:
if len(pdf.pages) == 0:
print("PDF has no pages")
else:
text = pdf.pages[0].extract_text()
if text is None or text.strip() == "":
print("Page contains no extractable text (might be scanned)")
else:
print(text)
except Exception as e:
print(f"Error processing PDF: {e}")