h3-py
cheatsheets
| h3-py | cheatsheets | |
|---|---|---|
| 1 | 126 | |
| 763 | 7,259 | |
| 1.6% | 0.4% | |
| 6.6 | 7.0 | |
| 1 day ago | 14 days ago | |
| Python | Python | |
| Apache License 2.0 | BSD 2-clause "Simplified" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
h3-py
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Command-line data analytics made easy with SPyQL
Another advantage of SPyQL is that we can leverage the Python ecosystem. Let's try to do some more geographical statistics. Let's count towers by H3 cell (resolution 5) for Europe. First, we need to install the H3 lib:
cheatsheets
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Matplotlib - A Python 2D plotting library.
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How to retrieve and analyze crypto order book data using Python and a cryptocurrency API
Data visualization: utilizing Python's Matplotlib for visualizing order book information.
- Matplotlib
- Ask HN: What plotting tools should I invest in learning?
- Help with an array
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Getting visual studio code to work with imported library
Name: matplotlib Version: 3.7.1 Summary: Python plotting package Home-page: https://matplotlib.org Author: John D. Hunter, Michael Droettboom Author-email: [email protected] License: PSFLocation: /home/huinker/.local/lib/python3.10/site-packages
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PSA: You don't need fancy stuff to do good work.
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses without relying on expensive or proprietary software.
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What else should I complete before applying for a data analyst role?
programming language: basic python, pandas, matplotlib -- you'll probably do these in school, but if not https://cs50.harvard.edu/python/2022/ https://matplotlib.org/
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[OC] Analyzing 15,963 Job Listings to Uncover the Top Skills for Data Analysts (update)
Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.
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[OC] Data Analyst Skills in need based on 15,963 job listings
Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.
What are some alternatives?
leafmap - A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment
finplot - Performant and effortless finance plotting for Python
clean-architecture - Example project showing off clean/hexagonal architecture concepts in Python
manim - A community-maintained Python framework for creating mathematical animations.
geemap - A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
Fast-F1 - FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry
BlenderGIS - Blender addons to make the bridge between Blender and geographic data
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
felicette - Satellite imagery for dummies.
Keras - Deep Learning for humans
spyql - Query data on the command line with SQL-like SELECTs powered by Python expressions
geogebra - GeoGebra apps (mirror)