statsmodels
Dask
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statsmodels | Dask | |
---|---|---|
8 | 32 | |
9,534 | 11,999 | |
2.1% | 1.6% | |
9.4 | 9.6 | |
7 days ago | 1 day ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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statsmodels
- statsmodels Release Candidate 0.14.0rc0 tagged
- How to generate Errors using Scipy Minimize with Powell Method
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[P] statsmodels.tsa.holtwinters.ExponentialSmoothing results in NaN forecasts and parameters when fitting on entire dataset using known parameters from training model.
I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post.
- Statsmodels 0.13.3 released with Python 3.11 support
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First Year UG here, can someone offer any coding advice?
The method they use for computing the parameter covariance (in the code here, around line 330) involves some linear algebra, as they use the Moore-Penrose pseudo-inverse of the outputs.
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How do you usually build your models?
Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for
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Advice required to choose appropriate software for an assignment
Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels
- [C] I have an MS in Statistics - how can I get better at coding?
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
SciPy - SciPy library main repository
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Numba - NumPy aware dynamic Python compiler using LLVM
PyMC - Bayesian Modeling and Probabilistic Programming in Python
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
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
NetworkX - Network Analysis in Python
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
NumPy - The fundamental package for scientific computing with Python.
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python