DataFrames.jl
Pandas
DataFrames.jl | Pandas | |
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9 | 397 | |
1,696 | 42,039 | |
1.0% | 0.7% | |
7.0 | 10.0 | |
17 days ago | about 22 hours ago | |
Julia | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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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.
DataFrames.jl
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Julia's latency: Past, present and future
I don't think we've seen the final state of it though. v1.9 really gives people the proper tools for solving latency problems. Before, invalidations hurt a little, but it was also kind of a wash because LLVM code didn't precompile, so you could spend time setting up a Snoopprecompile and fix some invalidations and end up LLVM bound saving 1 second out of 20. But with package images it's almost always better to fix precompilation. The only thing hampering time much now is the `using` time went up, but as mentioned in the Reddit post there's a lot of ideas for what to do there. The other thing is package extensions, which cut down the amount of code to load. There's tons of PRs floating around the ecosystem turning things into extensions, and thus cutting down the overall code that is actually ran and loaded.
This means that in a few months, people will start to see some major tangible benefits from following the compilation improvement practices laid out here and https://sciml.ai/news/2022/09/21/compile_time/. I think then you'll have a lot more people start to take all of these new tools seriously and it will be standard to incorporate them into packages. Right now they are still kind of niche things for packages with known TTFX problems, but I think come v1.9 you'll see every major package use all of these methods.
> Different packages are affected differently
I think this is one of the pieces that's effected by this. I don't think "Julia has become optimised for running Plots.jl" is quite correct. Julia's compilation and runtime is much more optimized for well-inferred code, which Plots.jl is not. However, the compiler developers have been using Plots.jl as a test case for all of these new tools, and therefore its SnoopPrecompile and invalidations have gotten some dramatic improvements because those required ecosystem changes I mentioned are being done by the compiler team for this specific package. Plots.jl had things like precompilation snooping way back before there was even a package for it, the earliest I know of was around v1.0. Meanwhile, DataFrames.jl only setup its precompilation snooping 7 months ago (https://github.com/JuliaData/DataFrames.jl/pull/3182), which I would presume was just in time for the v1.8 mark on your plot and is one of the big reasons for having a sudden drop (which continues into v1.9 because of package images).
What I mean to say then is that, I think all packages will get the improvements we've seen from Plots.jl, but package authors will need to update their packages in order for that to happen. Some packages have already done this, many have not.
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IJulia: The Julia Notebook
IJulia also supports viewing and manipulating tables. To create a table, first install the DataFrames.jl package by running the following command in a new cell:
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
It were just a few percents of all possible manipulations that you can do with data using DataFrames.jl library. Read more about it in the documentation.
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Unleashing the Power of Julia: Top 5 Must-Have Packages
DataFrames.jl
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Automate the boring stuff with Julia?
DataFrames.jl and XLSX.jl for JSON, CSV, and XLSX files
- What would it take to recreate dplyr in Python?
- Dataframes.jl version 1.0: Tools for working with tabular data in Julia
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Teaching Python
Julia also has the CSV.jl library for reading/writing csv files, the DataFrames.jl library for manipulating data like pandas, and Images.jl for image processing/analysis. However, since Julia is so much newer than Python, the Julia libraries are almost never as feature rich as their Python counterparts.
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Polars (Rust DataFrame library) join algorithm fastest in db-benchmark
Looks like it's single threaded according to this open issue: https://github.com/JuliaData/DataFrames.jl/issues/2626
Pandas
- PDEP-13: The Pandas Logical Type System
- PHP Doesn't Suck Anymore
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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.
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Tables.jl - An interface for tables in Julia
tensorflow - An Open Source Machine Learning Framework for Everyone
DataFramesMeta.jl - Metaprogramming tools for DataFrames
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Plots.jl - Powerful convenience for Julia visualizations and data analysis
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
MPI.jl - MPI wrappers for Julia
Keras - Deep Learning for humans
Curry.jl - Currying for Julia
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration