ggplot2
Pandas
ggplot2 | Pandas | |
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62 | 395 | |
6,328 | 41,983 | |
0.5% | 0.6% | |
9.4 | 10.0 | |
6 days ago | 4 days ago | |
R | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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ggplot2
- ggplot2
- Ask HN: How do you build diagrams for the web?
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Visualizing shapefiles in R with sf and ggplot2!
ggplot2
- Ask HN: What plotting tools should I invest in learning?
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Relative frequency of letters in five-letter English words (Wordle aid) [OC]
I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package.
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[OC] U.S. News & World Report Best Colleges: 2002 to 2023
Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2.
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Facts about Aaron Boone's Ejections as Manager
I used the ggplot2 package in R to create these figures.
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Fueling Innovation and Collaborative Storytelling
This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and visualization tools to survive on Mars. He uses Python's Pandas to clean and organize data, and he uses R's ggplot2 to create visualizations of his data. These tools allow him to make sense of the vast amounts of data and help him to make critical decisions about his survival.
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[OC] Visualizing Financial Market Returns Across Many Asset Classes via Heatmaps
Sorry about the slow reply, but the auto-moderator seems to be deleting my comments (for some unknown reason). I will try once more: the geom_tile function in ggplot2.
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[OC] Forbes List of Highest-Earning Musicians: 1987 to 2021
Visual cues are a much better idea, thanks! Unfortunately, I don't know how to do that in ggplot2, either (I created these figures in R).
Pandas
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
What are some alternatives?
Altair - Declarative statistical visualization library for Python
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
tmap - R package for thematic maps
tensorflow - An Open Source Machine Learning Framework for Everyone
vega - A visualization grammar.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
dplyr - dplyr: A grammar of data manipulation
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
worldfootballR - A wrapper for extracting world football (soccer) data from FBref, Transfermark, Understat and fotmob
Keras - Deep Learning for humans
glue - Glue strings to data in R. Small, fast, dependency free interpreted string literals.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration