### pandas_exercises - Practice your pandas skills!

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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.

https://github.com/guipsamora/pandas_exercises

 Tags pandas exercise practice tutorial data-analysis Implementation Jupyter Notebook License Public Platform

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

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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.

## pycon-2019-tutorial - Data Science Best Practices with pandas

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This tutorial was presented by Kevin Markham at PyCon on May 2, 2019. Watch the complete tutorial video on YouTube. The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, the size and complexity of the pandas library makes it challenging to discover the best way to accomplish any given task.

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

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

## xarray - N-D labeled arrays and datasets in Python

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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.

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

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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.

## data-forge-ts - The JavaScript data transformation and analysis toolkit inspired by Pandas and LINQ.

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The JavaScript data transformation and analysis toolkit inspired by Pandas and LINQ. Implemented in TypeScript, used in JavaScript ES5+ or TypeScript.

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

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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.

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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.

## PythonMachineLearning - Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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

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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.

## eland - Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

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Eland is a Python Elasticsearch client for exploring and analyzing data in Elasticsearch with a familiar Pandas-compatible API. Where possible the package uses existing Python APIs and data structures to make it easy to switch between numpy, pandas, scikit-learn to their Elasticsearch powered equivalents. In general, the data resides in Elasticsearch and not in memory, which allows Eland to access large datasets stored in Elasticsearch.

## pandapower - Convenient Power System Modelling and Analysis based on PYPOWER and pandas

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pandapower is an easy to use network calculation program aimed to automate the analysis and optimization of power systems. It uses the data analysis library pandas and is compatible with the commonly used MATPOWER / PYPOWER case format. pandapower allows using different solvers including an improved Newton-Raphson power flow implementation, all PYPOWER solvers, and the PowerModels.jl library. To get realistic load profile data and grid models across all voltage levels that are ready to be used in pandapower, have a look at the SimBench project website or on GitHub.

## py - Repository to store sample python programs for python learning

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Repository to store sample python programs for python learning

## django-rest-pandas - ðŸ“ŠðŸ“ˆ Serves up Pandas dataframes via the Django REST Framework for use in client-side (i

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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.

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

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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.

## sparklingpandas - Sparkling Pandas

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SparklingPandas aims to make it easy to use the distributed computing power of PySpark to scale your data analysis with Pandas. SparklingPandas builds on Spark's DataFrame class to give you a polished, pythonic, and Pandas-like API. See SparklingPandas.com.

## winerama-recommender-tutorial - A wine recommender system tutorial using Python technologies such as Django, Pandas, or Scikit-learn, and others such as Bootstrap

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This repository contains the code for a wine reviews and recommendations web application, in different stages as git tags. The idea is that you can follow the tutorials through the tags listed below, and learn the different concepts explained in them. The tutorials include instructions on how to deploy the web using a Koding account. However, Koding recently moved from solo to team accounts and the link provided to my Koding account deployment of the tutorial result is not working anymore. The tutorial can still be followed with no problem at all. The following tutorials will guide you through each of the previous Git tags while learning different concepts of data product development with Python.

## pycon-pandas-tutorial - PyCon 2015 Pandas tutorial materials

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The first instance of this tutorial was delivered at PyCon 2015 in Montréal, but I hope that many other people will be able to benefit from it over the next few years — both on occasions on which I myself get to deliver it, and also when other instructors are able to do so. To make it useful to as many people as possible, I hereby release it under the MIT license (see the accompanying LICENSE.txt file) and I have tried to make sure that this repository contains all of the scripts needed to download and set up the data set that we used.

## pdpipe - Easy pipelines for pandas DataFrames.

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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.

## evidently - Interactive reports to analyze machine learning models during validation or production monitoring

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Interactive reports and JSON profiles to analyze, monitor and debug machine learning models. Evidently helps evaluate machine learning models during validation and monitor them in production. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. You can use visual reports for ad hoc analysis, debugging and team sharing, and JSON profiles to integrate Evidently in prediction pipelines or with other visualization tools.

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