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The Python pandas software library can extract tables from HTML webpages via its read_html() function. More challenging is table extraction from PDFs or scanned images, where there usually is no table-specific machine readable markup. [1] Systems that extract data from tables in scientific PDFs have been described. [2] [3]
Library to create and manipulate PDF, RTF, HTML files in Java, C#, and other .NET languages. JasperReports: GNU LGPL: Open-source Java reporting tool that can write to screen, printer, or into PDF, HTML, Microsoft Excel, RTF, ODT, comma-separated values and XML files. libHaru: ZLIB/LIBPNG: Open-source, cross-platform C library to generate PDF ...
Sumatra PDF is a free and open-source document viewer that supports many document formats including: Portable Document Format (PDF), Microsoft Compiled HTML Help (CHM), DjVu, EPUB, FictionBook (FB2), MOBI, PRC, Open XML Paper Specification (OpenXPS, OXPS, XPS), and Comic Book Archive file (CB7, CBR, CBT, CBZ). [3]
Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a user to act as though the index is an array-like sequence of integers, regardless of how it is actually defined. [9]: 110–113 Pandas supports hierarchical indices with multiple values per data point.
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A web browser is a type of file viewer that renders HTML markup into a human-friendly presentation. Although HTML is stored in plain text files, viewing an HTML file in a browser and in a text editor produces significantly different results. Web browsers may also view multimedia files such as images, videos, preformatted documents, interactive ...
Note how the use of A[i][j] with multi-step indexing as in C, as opposed to a neutral notation like A(i,j) as in Fortran, almost inevitably implies row-major order for syntactic reasons, so to speak, because it can be rewritten as (A[i])[j], and the A[i] row part can even be assigned to an intermediate variable that is then indexed in a separate expression.
The library NumPy can be used for manipulating arrays, SciPy for scientific and mathematical analysis, Pandas for analyzing table data, Scikit-learn for various machine learning tasks, NLTK and spaCy for natural language processing, OpenCV for computer vision, and Matplotlib for data visualization. [3]