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Showing 4 open source projects for "python data analysis"
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Failed Payment Recovery for Subscription BusinessesFlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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Next-Gen Encryption for Post-Quantum Security | CLEAR by Quantum KnightCLEAR by Quantum Knight is a FIPS-140-3 validated encryption SDK engineered for enterprises requiring top-tier security. Offering robust post-quantum cryptography, CLEAR secures files, streaming media, databases, and networks with ease across over 30 modern platforms. Its compact design, smaller than a single smartphone image, ensures maximum efficiency and low energy consumption.
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g2 is an easy to use, portable and powerful 2D graphics library. It provides a comprehensive set of functions for simultaneous generation of graphical output on different types of devices.
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This library is a Fortran 95 gnuplot interface for some Unix like OS'es. This provides some routines that enables direct access of a child gnuplot session from a Fortran 95 program. You will need a copy of fortranposix to make this work.
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The fst2v5d package converts data stored in the Canadian RPN Standard File (FST) format into Vis5D format. FST files, commonly used to store meteorological data, are scanned by fst2v5d to generate both 4D fields and independent topography input files.
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geoscipy
Python-accessible toolkit for Geoscience & Remote Sensing Applications
GeoSciPy is a set of C and Python functions that provide an API for all aspects of Geoscience and Remote Sensing Applications. Functions include reading/writing of many data formats, images, vectors, ..., and processing of images, vectors, ... including map projections, ... The library is written C, using HDF5 as the file format for storage of all remotely-sensed data and ancillary data, as well as processed data. -
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