The book is out!
After almost a year of work, Effective Pandas 2 is here.
Covers:
- New features of Pandas 2.2!
- PyArrow types!
- .case_when method!
- Chapter on testing Pandas
- Chapter on refactoring Pandas
- Leveraging Cython & Numba
If you are serious about taking
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Hi 👋 if you are interested in:
🐍 Python
🪄 Data Science
🔨 Career Advice
Follow me. ✔
I'm planning on using Twitter to share a lot of content that you won't want to miss. 🎉
Hi 👋 if you are interested in:
🐍 Python
🪄 Data Science
🔨 Career Advice
Follow me. ✔
I'm planning on using Twitter to share a lot of content that you won't want to miss. 🎉
Hello, I'm Matt 👋
I'm a Python and Data Science author and trainer. 🐍
Follow me to learn more about:
🖥 Python
🙈 Data analysis
🎨 Visualization
🤯 Machine Learning
Hello, I'm Matt 👋
I'm a Python and Data Science author and trainer. 🐍
Follow me to learn more about:
🖥 Python
🙈 Data analysis
🎨 Visualization
🤯 Machine Learning
Hi 👋 if you are interested in:
🐍 Python
🪄 Data Science
🔨 Career Advice
Follow me. ✔
I'm planning on using Twitter to share a lot of content that you won't want to miss. 🎉
Hello, I'm Matt 👋
I'm a Python and Data Science author and trainer. 🐍
Follow me to learn more about:
🖥 Python
🙈 Data analysis
🎨 Visualization
🤯 Machine Learning
I often teach about Decorators in Python.
Many know how to use them, but few can write them.
These are tricky because nested functions make our brains hurt.
Here are some hints for grokking them.
1/
Big O notation (pronounced Big-Oh) is a way to discuss how long an algorithm takes to run. It is also called "runtime complexity".
Understanding this is key for technologists.
Let's look at it.
1/
Recently, I've been working on a new course for data analytics. This Python code stands out for its ability to turn raw sales data into insightful visualizations. I want to share why this code is crucial and how it could help transform your data analysis process.
Want to speed up your Pandas code by 10-1000x?
With no code change?
The folks from
@nvidia
have created cuDF pandas accelerator mode. By using this line in Jupyter, you automatically leverage your GPU to run Pandas code:
%load_ext cudf.pandas
From command-line:
python -m
Pandas tip 🐼🤓
Sometimes you want to get all of the values for each category, but there might be a different amount for each category.
This example shows each recorded daily snowfall for every month.
Combine .pivot with .apply! 🤯🐼
Hi 👋, if you are intrigued with:
🐍 Python
🪄 Data Science
🔨 Career Advice
Follow me. ✔
I'm planning on using Twitter to share a lot of content, that you won't want to miss. 🎉
I created a simple example data science project. It includes a makefile that you can copy and use to easily make your own.
It includes jupyter, tests, docker, dash, pipelines, CI, and codespaces.
Last week I taught a course that covered Decorators in Python.
Many know how to use them, but few can write them.
These are tricky because nested functions make our brains hurt.
Here are some hints for grokking them.
1/
Working on a new Pandas course. Meant to train beginners correct first principles from the start.
This will be a live cohort based class where you get to work on your real-world data while learning Pandas! 🤯🐼
Hi 👋 if you are interested in:
🐍 Python
🪄 Data Science
🔨 Career Advice
Follow me. ✔
I'm planning on using Twitter to share a lot of content that you won't want to miss. 🎉
After my Python "training", my students still don't know how to use the %s formatting specifier in a print statement. 🤦
That's because I teach them f-strings. 😜
If you are considering learning Pandas, I'm doing a cohort-based course at the end of this month. This is a great way to learn Pandas in a group-based setting.
Customizing a Plotly plot is no easier (nor harder) than customizing a Matplotlib plot.
Choose the former if you need more interactivity or the latter if you want more to create publication quality plots.
Big O notation (pronounced Big-Oh) is a way to discuss how long an algorithm takes to run. It is also called "runtime complexity".
Understanding this is key for technologists.
Let's look at it.
1/
Love seeing happy readers.
"Harrison’s book presents the most comprehensive collection of “best practices” for cleaning and preparing data using Pandas. WARNING: might turn you into a Pandas snob…" Walter U.
Hi 👋, if you are intrigued with:
🐍 Python
🪄 Data Science
🔨 Career Advice
Follow me. ✔
I'm planning on using Twitter to share a lot of content, that you won't want to miss. 🎉
Pandas tip 🐼🤓
Sometimes you want to get all of the values for each category, but there might be a different amount for each category.
This example shows each recorded daily snowfall for every month.
Combine .pivot with .apply! 🤯🐼
Code is usually written once or twice but read often. You should optimize for readability when you write code. (This includes data scientists writing Pandas or ML code). 🤯