Showing 7 open source projects for "plotting csv data"

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  • 1
    Book2_Beauty-of-Data-Visualization

    Book2_Beauty-of-Data-Visualization

    Machine Learning, Criticism and Correction

    Book2_Beauty-of-Data-Visualization is an open educational project that teaches the principles and techniques of effective data visualization using Python and modern plotting libraries. The repository focuses on both the technical and aesthetic aspects of visual analytics, helping learners understand how to communicate data clearly and persuasively. It includes practical examples that demonstrate how different chart types reveal patterns, trends, and distributions in real datasets. ...
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  • 2
    Scientific Visualization

    Scientific Visualization

    An open access book on scientific visualization using python

    The Scientific Visualization book is a freely available open-access textbook that introduces how to produce effective scientific visualizations using Python, focusing especially on leveraging the popular plotting library Matplotlib (and related tools). It goes beyond simple plotting tutorials and emphasizes design principles: how to choose colors, layout subplots, annotate graphs, and present data in a way that is both accurate and visually compelling. As such, it serves as a guide for researchers, data scientists, and academic authors who need to create publication-quality figures or explanatory graphics, rather than quick exploratory plots. ...
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  • 3
    US Job Market Visualizer

    US Job Market Visualizer

    A research tool for visually exploring Bureau of Labor Statistics

    US Job Market Visualizer is a research tool for interactively exploring Occupational Outlook Handbook data from the U.S. Bureau of Labor Statistics. It organizes information for 342 occupations into a treemap where area represents employment and color can represent different metrics. Users can compare projected growth, median pay, education requirements, and estimated digital AI exposure. The repository includes tools for scraping BLS pages, converting them to structured data, and building...
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  • 4

    openSkyMatch

    Matches OpenScience Observatories images with astronomical catalogs

    openSkyMatch is a collection of Linux shell and Python scripts designed for the OpenScience Observatories program. It automates the identification and matching of detected celestial objects in locally captured FITS images with entries in large-scale sky catalogs, notably Pan-STARRS1 DR2 (II/389/ps1_dr2). The toolkit supports data preprocessing, coordinate correlation, and catalog-based validation of astronomical detections. All tools are open-source and optimized for reproducibility and...
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  • 5
    Haircut_MF

    Haircut_MF

    Fetches the historical Haircut data (%) of Mutual Funds and Bonds

    ...It is based on previous closing price (NAV). This program is used to identify the real value of an equity / share / mutual fund / Bonds / SGB with date. This program fetches the historical Haircut data (%) of Mutual Funds / bonds listed in NSE at a selected date. Enter the Date (in the format: dd-mm-yyyy) to get Haircut data for equities listed in NSE. It lists the haircut data (%) against the mutual funds on the given date. At the same time .csv file consists of NSE symbol of the equity, previous closing price and haircut value (%) will be downloaded with a filename of the given date. ...
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  • 6
    SimpleDSO
    This program written in Python is used for capture screenshot or raw data from a UNI-T digital storage oscilloscope.
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  • 7
    Matplotlib tutorial

    Matplotlib tutorial

    Matplotlib tutorial for beginner

    The Matplotlib tutorial repository is designed as a hands-on learning resource to help users — especially Python beginners — get started with Matplotlib for creating plots and charts. It provides a sequence of example scripts and notebooks that cover fundamental plotting tasks: line graphs, histograms, scatter plots, bar charts, customizing axes, labels, legends, and styling. This makes it ideal for someone learning data analysis or exploratory data visualization for the first time and needing concrete, runnable examples rather than abstract explanations. As the tutorial builds progressively, learners can gradually advance from simple static plots to more complex visualizations, learning how to control figure size, add multiple subplots, adjust plot aesthetics, and handle different data types. ...
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