Best of Matplotlib — 2024

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   Video

   Tech With Tim·2y

   Master Python With This ONE Project!

   This post guides you through building a personal finance tracker in Python, covering syntax, advanced features, and popular modules like Pandas and Matplotlib. The project involves tracking and logging transactions, organizing data, generating summaries of income and expenses, and visualizing the data with graphs. It also explains how to use CSV files for data storage and offers a quick demo followed by step-by-step instructions for implementation.

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2. 2
   Article

   Hacker News·2y

   Spreadsheet UI for Python

   PySheets provides a spreadsheet UI for Python, allowing users to perform exploratory data science, use Pandas, create charts with matplotlib, import Excel sheets, analyze data, and create reports. All the Python code runs in the browser, and PySheets itself is also written in Python. Collaboration, unlimited sheets, community support, and unlimited AI generations.

   43

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3. 3
   Article

   Medium·2y

   Simulating Wildfires in a Forest🔥

   Simulating wildfires involves using a computational model called Cellular Automaton (CA), leveraging Python libraries such as NumPy and Matplotlib. The simulation describes a forest grid with cells representing trees, wildfires, and burned areas. The model applies rules to change cell states over time, incorporating additional elements like temperature, wind, and precipitation to closely mimic real-world wildfire scenarios. The more sophisticated model includes additional states (water, bedrock, grass) and weather conditions to make the simulation more realistic.

   40

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4. 4
   Article

   Machine Learning Mastery·2y

   Beginning Data Science (7-day mini-course)

   This post provides a 7-day mini-course on beginning data science. It covers topics such as tools in data science, target audience, and the lessons covered in the course.

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5. 5
   Article

   Daily Dose of Data Science | Avi Chawla | Substack·2y

   Improve Matplotlib Plot Quality

   Matplotlib plots in Jupyter Notebook can appear dull and blurry when scaled. A useful hack is to render plots as SVGs (Scalable Vector Graphics) instead of the default image format. This ensures high-quality plots that remain sharp even when zoomed. Use either `from matplotlib_inline.backend_inline import set_matplotlib_formats` with `set_matplotlib_formats('svg')` or `%config InlineBackend.figure_format = 'svg'` to implement this improvement in your notebooks.

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   Article

   TigerData (Creators of TimescaleDB)·2y

   Guide to Time-Series Analysis in Python

   Learn how Python can be used for time-series analysis, including loading and analyzing time-series data, plotting with Pyplot, and handling challenges of working with large datasets. Explore the advantages of Python, such as its simplicity, extensive library support, and code reusability. Discover specialized libraries like pandas, Matplotlib, tsfresh, and more for advanced time-series tasks. Gain insights into data cleaning, trend analysis, seasonality detection, forecasting models, and feature extraction for machine learning or deep learning algorithms.

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   Article

   The New Stack·2y

   Visualizing Data in Python With Matplotlib

   Learn to effectively communicate complex datasets using Matplotlib, a versatile Python library for data visualization. This guide covers the basics of creating line plots, bar charts, scatter plots, and subplots, as well as how to customize these visualizations to enhance data comprehension and support data-driven decisions.

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   Article

   Real Python·2y

   Exploring Astrophysics in Python With pandas and Matplotlib – Real Python

   Use Python to explore introductory astrophysics topics with this course. Learn about using pandas for working with dataframes, visualizing information with Matplotlib, and handling scientific units. The course includes 14 lessons, video subtitles, full transcripts, downloadable resources, and Q&A support from Python experts.

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   Article

   freeCodeCamp·2y

   Applied Data Science: A / B Testing

   Learn A/B testing in data science, its impact on business decisions, and how to implement it using Python.

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