How to Extract Tables in PDFs to pandas DataFrames With Python
There are several libraries that can be used to extract tables from PDFs and convert them into pandas DataFrames in Python. Some options include:
pdfplumber: This is a lightweight library that allows you to extract tables from PDFs and convert them into pandas DataFrames. To use it, you will need to install it first using
pip install pdfplumber
. Here is an example of how to extract a table from a PDF and convert it into a DataFrame using pdfplumber:import pdfplumber import pandas as pd # Open the PDF file using pdfplumber with pdfplumber.open("sample.pdf") as pdf: # Iterate through all the pages in the PDF for page in pdf.pages: # Extract the table from the page table = page.extract_table() # Convert the table into a pandas DataFrame df = pd.DataFrame(table[1:], columns=table[0]) # Print the DataFrame print(df)
camelot: This is another library that can be used to extract tables from PDFs and convert them into pandas DataFrames. To use it, you will need to install it first using
pip install camelot-py[cv]
. Here is an example of how to extract a table from a PDF and convert it into a DataFrame using camelot:import camelot import pandas as pd # Extract the tables from the PDF using camelot tables = camelot.read_pdf("sample.pdf") # Iterate through the tables and convert each one into a DataFrame for table in tables: df = table.df # Print the DataFrame print(df)
tabula-py: This is another library that can be used to extract tables from PDFs and convert them into pandas DataFrames. To use it, you will need to install it first using
pip install tabula-py
. Here is an example of how to extract a table from a PDF and convert it into a DataFrame using tabula-py:import tabula import pandas as pd # Read the PDF into a pandas DataFrame using tabula-py df = tabula.read_pdf("sample.pdf", pages="all") # Print the DataFrame print(df)
These are just a few examples of the libraries and techniques that can be used to extract tables from PDFs and convert them into pandas DataFrames in Python. You may need to experiment with different libraries and techniques to find the one that works best for your specific use case.