swifter
xgboost_ray
| swifter | xgboost_ray | |
|---|---|---|
| 3 | 1 | |
| 2,464 | 133 | |
| - | 0.8% | |
| 5.5 | 5.8 | |
| about 1 month ago | 2 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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swifter
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Tidyverse equivalent in Python?
With concat, merge, melt, and pivot_table, that may cover everything I have ever needed. There may be more efficient ways at times, but swifter promises to do that for you, maybe it is true.
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[D] A hacky work-around for slow linear algebra operations on pyspark.
Since you already have a working python script, you can try swifter with minimal effort to see if it brings about a significant speedup before digging further.
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What Is The Best Performance Fix You Ever
With few lines of code? Swifter for quicker pandas apply and then there's numba. With concurrent.futures, it'll be a bit more lines of code.
xgboost_ray
What are some alternatives?
modin - Modin: Scale your Pandas workflows by changing a single line of code
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Dask - Parallel computing with task scheduling
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
pandera - A light-weight, flexible, and expressive statistical data testing library
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
siuba - Python library for using dplyr like syntax with pandas and SQL
xarray - N-D labeled arrays and datasets in Python
distributed-compute-on-aws-with-cross-regional-dask - Perform I/O intensive workloads on high-volume data sparsely located across multiple AWS regions through the use of Dask.