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pandas
From Pandas to Scikit-Learn — A New Exciting Workflow
Ted will present more on this topic at ODSC East 2019 this May in his presentation, “Integrating Pandas with Scikit-Learn, an Exciting New Workflow“ This article is available as a Jupyter Notebook on Google’s Colaboratory (open in playground mode to run and edit) and at the Machine Learning Github repository for the Dunder Data Organization.... Read more
Handling Missing Data in Python/Pandas
Key Takeaways: It’s important to describe missing data and the challenges it poses. You need to clarify a confusing terminology that further adds to the field’s complexity. You should take the time to review methods for handling missing data. You need to learn how to apply robust multiple imputation methods... Read more
All the Best Parts of Pandas for Data Science
Pandas has been hailed by many in the data science community as the missing link between Python and analysis, a tool that can be leveraged in order to dramatically reduce overhead in data science projects, increase understandability and speed up workflows.   Pandas comes loaded with a wide range of... Read more
Convert Pandas Categorical Data for SciKit-Learn
As you encounter various data elements you should come across categorical data. Some individuals simply discard this data in their analysis or do not bring it into their models. That is certainly an option, however many times the categorical data represents information that we would typically want to bring in to these... Read more
The ODSC team was delighted to present the second Outstanding Data Science Project Award to ‘Pandas’ at ODSC West on November 3rd.    Why ODSC is gives these awards… Most data scientists/developers use an open source language, tool, software or platform daily. All of these resources available because their contributors... Read more
Pandas & Seaborn – A guide to handle & visualize data elegantly
Here at Tryolabs we love Python almost as much as we love machine learning problems. These kind of problems always involve working with large amounts of data which is key to understand before applying any machine learning technique. To understand the data, we need to manipulate it, clean it, make... Read more
Building a Scraper Using Browser Automation
Learning to scrape websites for data is essential to becoming a great data scientist. If the data you want to work with isn’t readily available, there’s always a solution, and collecting the data yourself is one of them. There are several ways to go about this—some websites have API platforms... Read more
Logistic Regression with Python
Logistic regression was once the most popular machine learning algorithm, but the advent of more accurate algorithms for classification such as support vector machines, random forest, and neural networks has induced some machine learning engineers to view logistic regression as obsolete. Though it may have been overshadowed by more advanced... Read more
Creating Multiple Visualizations in a Single Python Notebook
For a data scientist without an eye for design, creating visualizations from scratch might be a difficult task. But as is the case with most problems, a solution awaits thanks to Python. Those drawn to using Python for data analysis have been spoiled, as more advanced libraries have made previously... Read more
Good, Fast, Cheap: How to do Data Science with Missing Data
When doing any sort of data science problem, we will inevitably run into missing data. Let’s say we’re interviewing 100 people and are recording their answers on a piece of paper in front of us. Specifically, one of our questions asks about income. Consider a few examples of missing data:... Read more