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Best Data Structure for this?
1 project | reddit.com/r/learnpython | 17 Jan 2022
If you really want to store it all (labels included) in one data structure, you should look up pandas.
SEC Speed is a myth.
1 project | reddit.com/r/CFB | 15 Jan 2022
Another question you may be asking is: "What about skill players?" Well, what about them? Skill players are defined as players that consistently tote the rock. I was able to filter out skill player's performance in different combine events using pandas. For our purposes, the following positions (as listed on PRF) were considered 'skill players': WR, RB, QB, TE, DB, LB. In included linebackers but if you want to not include them, knock yourself out. It kind of only helps my case that the likes of Roquan Smith and Nakobe Dean don't count for the SEC. When only considering skill players, the SEC ranks 2nd to the Big 12 in 40-yard dash times. In the other combine events for which there is data, the SEC ranks first in none of them.
Open source projects that are good to read to learn best practices?
2 projects | reddit.com/r/cscareerquestions | 14 Jan 2022
5 Useful Pandas Methods You May Not Know Existed (Part 2)
1 project | reddit.com/r/Python | 9 Jan 2022
You glossed over the fact that `.pct_change` isn't actually "percent change" as documented. More fun reading: https://github.com/pandas-dev/pandas/issues/20752
Career change - data analysis
1 project | reddit.com/r/AusFinance | 9 Jan 2022
I suggest pandas might be a great tool for you as you will be able to read write excel / csv files and process them and see how you get on.
Trading Algos - 5 Key Metrics and How to Implement Them in Python
4 projects | dev.to | 8 Jan 2022
Now to implement this one, we'll have to do some manipulation to our account values. Let's use the power of numpy to help us out here (oh and it's also the same in pandas too. We'll be using np.diff to take the returns of our account values and resampling them.
What does it mean to scale your python powered pipeline?
4 projects | dev.to | 3 Jan 2022
Increase code efficiency: Python is designed for ease of use and easy extension, but not performance. As a developer, the onus is on you to do more work so that the application executes less code. Whenever possible use vectorized library functions instead of loops. Python is successful in data science because of the pre-compiled code offered by data-appropriate libraries in the pydata stack such as pandas and numpy.
How do I combine two lists together to form a x y coordinate reference point?
3 projects | reddit.com/r/learnpython | 2 Jan 2022
Top 7 Dev Tools for AI Startups
4 projects | dev.to | 30 Dec 2021
Built on top of Python, pandas is an open source data analysis and manipulation tool, similar to NumPy. While it relies on NumPy arrays for much of its manipulation and computation, pandas makes it easier to visualize and explore data, helping our team make better sense of the large amounts of data we work with on a daily basis.
Appending Data to DataFrames
1 project | reddit.com/r/learnpython | 24 Dec 2021
Dataframes are not meant to be as flexible as lists in terms of extending the data they hold, dataframes are much more "deliberate". Ideally if you're trying to dynamically add data to a dataframe you should first collect all the data and then initialize the dataframe once. Or collect separate dataframes and concat them once. The pandas developers are even thinking about deprecating append (see here)
ETL Library for Python
1 project | reddit.com/r/Python | 27 Sep 2021
"On the simpler side". Do you mean with a graphical interface? Then, orange would be a nice solution. https://orangedatamining.com/
[D] Why Hasn't FOSS Drag-and-Drop ML tools taken off yet?
2 projects | reddit.com/r/MachineLearning | 8 Sep 2021
Currently, I am looking around for modules for Knime and Orange and looked at some of the modules, and realized that it does not have enough tools within their tool kit (e.g. text data analysis, network analysis, image classification).
Orange: Open-source component-based machine learning and data visualization
1 project | news.ycombinator.com | 23 Jun 2021
No-code vs Visual Programming
1 project | reddit.com/r/nocode | 12 Mar 2021
I am using visual programming tools that overlap with the no-code concept such as: KNIME and Orange. To visualize the results, I use connectors with platforms like DataStudio or Google AppSheet.
Informatica per la SCIENZA, per un ignorante in materia.
1 project | reddit.com/r/ItalyInformatica | 28 Feb 2021
What are some alternatives?
Cubes - Light-weight Python OLAP framework for multi-dimensional data analysis
glue - Linked Data Visualizations Across Multiple Files
Dask - Parallel computing with task scheduling
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
NumPy - The fundamental package for scientific computing with Python.
SymPy - A computer algebra system written in pure Python
pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files
blaze - NumPy and Pandas interface to Big Data
SciPy - SciPy library main repository
astropy - Repository for the Astropy core package
RDKit - The official sources for the RDKit library
Bubbles - [NOT MAINTAINED] Bubbles – Python ETL framework