weightedcalcs - Pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more

  •        56

weightedcalcs is a pandas-based Python library for calculating weighted means, medians, standard deviations, and more. See this notebook to see examples of other calculations, including grouped calculations.

https://github.com/jsvine/weightedcalcs

Tags
Implementation
License
Platform

   




Related Projects

statistical-analysis-python-tutorial - Statistical Data Analysis in Python

  •    HTML

Chris Fonnesbeck is an Assistant Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia. This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Much of the work involved in analyzing data resides in importing, cleaning and transforming data in preparation for analysis. Therefore, the first half of the course is comprised of a 2-part overview of basic and intermediate Pandas usage that will show how to effectively manipulate datasets in memory. This includes tasks like indexing, alignment, join/merge methods, date/time types, and handling of missing data. Next, we will cover plotting and visualization using Pandas and Matplotlib, focusing on creating effective visual representations of your data, while avoiding common pitfalls. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. This will include fitting your data to probability distributions, estimating relationships among variables using linear and non-linear models, and a brief introduction to bootstrapping methods. Each section of the tutorial will involve hands-on manipulation and analysis of sample datasets, to be provided to attendees in advance.

Zipline - A Pythonic Algorithmic Trading Library

  •    Python

Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

modin - Modin: Speed up your Pandas workflows by changing a single line of code

  •    Python

Modin uses Ray to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical. To use Modin, you do not need to know how many cores your system has and you do not need to specify how to distribute the data. In fact, you can continue using your previous pandas notebooks while experiencing a considerable speedup from Modin, even on a single machine. Once you’ve changed your import statement, you’re ready to use Modin just like you would pandas.

xarray - N-D labeled arrays and datasets in Python

  •    Python

xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences: xarray.Dataset is an in-memory representation of a netCDF file.

100-pandas-puzzles - 100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

  •    Jupyter

Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power. Since pandas is a large library with many different specialist features and functions, these excercises focus mainly on the fundamentals of manipulating data (indexing, grouping, aggregating, cleaning), making use of the core DataFrame and Series objects. Many of the excerises here are straightforward in that the solutions require no more than a few lines of code (in pandas or NumPy - don't go using pure Python!). Choosing the right methods and following best practices is the underlying goal.


pandas-datareader - Extract data from a wide range of Internet sources into a pandas DataFrame.

  •    HTML

Up to date remote data access for pandas, works for multiple versions of pandas. As of v0.6.0 Yahoo!, Google Options, Google Quotes and EDGAR have been immediately deprecated due to large changes in their API and no stable replacement.

django-rest-pandas - 📊📈 Serves up Pandas dataframes via the Django REST Framework for use in client-side (i

  •    Python

Django REST Pandas (DRP) provides a simple way to generate and serve pandas DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats) for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. This capability can often be leveraged by sending users to the same URL that your visualization code uses internally to load the data.

Seaborn - Statistical data visualization using matplotlib

  •    Python

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.Online documentation is available at seaborn.pydata.org. Installation requires numpy, scipy, pandas, and matplotlib. Some functions will optionally use statsmodels if it is installed.

fecon235 - Computational tools for financial economics

  •    Jupyter

This is a free open source project for software tools in financial economics. We develop code for research notebooks which are executable scripts capable of statistical computations, as well as, collection of raw data in real-time. This serves to verify theoretical ideas and practical methods interactively. Economic and financial data, both historical and the most current.

dsp - Metis Data Science Bootcamp - Official Prework Repository

  •    Jupyter

Review the computer requirements on hardware needed for the bootcamp. Completing the pre-work is essential to obtaining the foundational knowledge necessary to succeed in the Metis data science bootcamp. Each student should expect to spend 60+ hours of tutorials to become familiar with software installation, editors, command line, Python (numpy, pandas, etc.), linear algebra and statistics.

vaex - Lazy Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second

  •    Python

Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

pandas-cookbook - Recipes for using Python's pandas library

  •    Jupyter

pandas is a Python library for doing data analysis. It's really fast and lets you do exploratory work incredibly quickly. The goal of this cookbook is to give you some concrete examples for getting started with pandas. The docs are really comprehensive. However, I've often had people tell me that they have some trouble getting started, so these are examples with real-world data, and all the bugs and weirdness that entails.

pandas-videos - Jupyter notebook and datasets from the pandas Q&A video series

  •    Jupyter

Read about the series, and view all of the videos on one page: Easier data analysis in Python with pandas.

pdpipe - Easy pipelines for pandas DataFrames.

  •    Python

Easy pipelines for pandas DataFrames. Some pipeline stages require scikit-learn; they will simply not be loaded if scikit-learn is not found on the system, and pdpipe will issue a warning. To use them you must also install scikit-learn.

blaze - NumPy and Pandas interface to Big Data

  •    Python

Blaze translates a subset of modified NumPy and Pandas-like syntax to databases and other computing systems. Blaze allows Python users a familiar interface to query data living in other data storage systems. We point blaze to a simple dataset in a foreign database (PostgreSQL). Instantly we see results as we would see them in a Pandas DataFrame.

effective-pandas - Source code for my collection of articles on using pandas.

  •    Jupyter

A collection of notebooks behind my series on writing idiomatic pandas.

pandas_exercises - Practice your pandas skills!

  •    Jupyter

Fed up with a ton of tutorials but no easy way to find exercises I decided to create a repo just with exercises to practice pandas. Don't get me wrong, tutorials are great resources, but to learn is to do. So unless you practice you won't learn. My suggestion is that you learn a topic in a tutorial or video and then do exercises. Learn one more topic and do exercises. If you got the answer wrong, don't go directly to the solution with code.

pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data

  •    Python

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. Binary installers for the latest released version are available at the Python package index and on conda.