Working with Time Zones & Daylight Saving Time in pandas ๐
Are you working with datetime data in pandas? Learn how to become “timezone-aware” so that your dataset cooperates with Daylight Saving Time!continue reading.
Are you working with datetime data in pandas? Learn how to become “timezone-aware” so that your dataset cooperates with Daylight Saving Time!continue reading.
Learn how to use Python’s f-strings for substitution and formatting, and then combine those features to solve a real-world pandas problem!continue reading.
Need help with your code? Learn my step-by-step process for asking great Stack Overflow questions that will get answered quickly!continue reading.
Watch me answer 59 of YOUR scikit-learn questions in 90 minutes! Topic include class imbalance, preprocessing, categorical features, data leakage, and more…continue reading.
Learn how to use the “merge” function in pandas so that you can combine multiple datasets into one DataFrame. Includes examples of the four types of joins.continue reading.
In this 28-minute video, you’ll learn how to properly encode your categorical features using scikit-learn’s OneHotEncoder, ColumnTransformer, and Pipeline.continue reading.
There are two ways to select a Series from a DataFrame: “dot notation” and “bracket notation” (square brackets). Find out which one you should use, and why!continue reading.
50+ tricks that will save you time and energy every time you use pandas! New tricks added daily. Up-to-date with the latest version of pandas (0.25)continue reading.
Work faster, write better pandas code, and impress your friends! These are the most useful tricks I’ve learned from 5 years of teaching Python’s pandas library.continue reading.
Learn how to use Python’s pandas library to effectively explore, clean, and visualize your data. Become more fluent at using pandas to answer data science questions.continue reading.
Comparing free services for running an interactive Jupyter Notebook in the cloud: Binder, Kaggle Kernels, Google Colab, Azure Notebooks, CoCalc, Datalore.continue reading.
pandas is a very popular Python library for data analysis, manipulation, and visualization, but it still hasn’t reached version 1.0. What’s next for pandas?continue reading.
pandas is a very popular Python library for data analysis, manipulation, and visualization, but it still hasn’t reached version 1.0. What’s next for pandas?continue reading.
Feature engineering is the process of creating new features so that your Machine Learning model will more accurately predict the value of your target.continue reading.
Feature engineering is the process of creating new features so that your Machine Learning model will more accurately predict the value of your target.continue reading.
In 2015, I created a 4-hour video series called Introduction to machine learning in Python with scikit-learn. In the years since, hundreds of thousands of students have watched these videos,...continue reading.
In 2015, I created a 4-hour video series called Introduction to machine learning in Python with scikit-learn. In the years since, hundreds of thousands of students have watched these videos,...continue reading.
At the PyCon 2018 conference, I presented a tutorial called “Using pandas for Better (and Worse) Data Science”. Through a series of exercises, I demonstrated best practices with pandas to...continue reading.
At the PyCon 2018 conference, I presented a tutorial called “Using pandas for Better (and Worse) Data Science”. Through a series of exercises, I demonstrated best practices with pandas to...continue reading.
Since launching my Python pandas video series in 2016, there have been 10 new releases of the pandas library, including hundreds of new features, bug fixes, and API changes. I...continue reading.