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Top 23 Python Science and Data analysis Projects
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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
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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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
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I guess it is a rite of passage to rewrite it. I'm doing it for SciPy too together with Propack in [1]. Somebody already mentioned your repo. Thank you for your efforts.
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That's interesting. You should consider yourself lucky to have met Wolfram employees, as they are obviously vastly outnumbered by users of Mathematica.
I have not met any developers for either of these products but I know that SymPy has a huge list of contributors for a project of its size. See: https://github.com/sympy/sympy/blob/master/AUTHORS
You may not be hearing about SymPy users because SymPy is not a monolithic product. It is a library. If you know mathematicians big into using Python, they are probably aware of SymPy as it is the main attraction when it comes to symbolic computation in Python.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Project mention: Show HN: Use an "eraser" to clean data on flight without breaking your workflow | news.ycombinator.com | 2024-03-15
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Project mention: Ask HN: What Underrated Open Source Project Deserves More Recognition? | news.ycombinator.com | 2024-03-07
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Astropy [0] lives at the heart of most work. It has a Python interface, often backed by Fortran and C++ extension modules. If you use Astropy, you're indirectly using libraries like ERFA [6] and cfitsio [7] which are in C/Fortran.
I personally end up doing a lot of work that uses the HEALPix sky tesselation, so I use healpy [2] as well.
Openorb is perhaps a good example of a pure-Fortran package that I use quite. frequently for orbit propagation [3].
In C, there's Rebound [4] (for N-body simulations) and ASSIST [5] (which extends Rebound to use JPL's pre-calculated positions of major perturbers, and expands the force model to account for general relativity).
There are many more, these are just ones that come to mind from frequent usage in the last few months.
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Project mention: Blaze: Fast query execution engine for Apache Spark | news.ycombinator.com | 2023-10-19
Unfortunate name overlap with an under-loved PyData project: https://blaze.pydata.org
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fugue
A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
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bcbio-nextgen
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
Project mention: Deep Sleep May Be the Best Defense Against Alzheimer’s | news.ycombinator.com | 2023-05-22Re WGS there are a lot of well established tool chains that are FLOSS (eg https://github.com/bcbio/bcbio-nextgen). You could run alignment and variant calling on a beefy workstation. A laptop would potentially work. Easy to test this with publicly available raw data. Another option: The sequencing provider often will run alignment and some default variant calling for you. Annotating and analysing these variants can be done on pretty much any computer, all with open source software. A SNP chip is even easier to deal with as the computational requirements are less.
Interpreting the results is a more manual process. Really depends on what you are interested in.
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Python Science and Data analysis related posts
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A note from our sponsor - SaaSHub
www.saashub.com | 18 Mar 2024
Index
What are some of the best open-source Science and Data analysis projects in Python? This list will help you:
Project | Stars | |
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1 | Pandas | 41,573 |
2 | NumPy | 26,009 |
3 | NetworkX | 14,004 |
4 | SciPy | 12,300 |
5 | SymPy | 11,940 |
6 | Dask | 11,885 |
7 | statsmodels | 9,412 |
8 | Numba | 9,309 |
9 | pygwalker | 9,270 |
10 | PyMC | 8,072 |
11 | orange | 4,551 |
12 | astropy | 4,148 |
13 | Biopython | 4,098 |
14 | blaze | 3,181 |
15 | fugue | 1,844 |
16 | Cubes | 1,490 |
17 | bcbio-nextgen | 968 |
18 | Neupy | 738 |
19 | NIPY | 726 |
20 | bccb | 590 |
21 | Bubbles | 448 |
22 | PyDy | 346 |
23 | harold | 170 |