Jupyter notebooks are a great way to explore data using Python (and other languages as well). Having a visual representation of your code and output, along with documentation and formatting in one view can be extremely helpful. However, there are some things that are just much better to do in a console session. In this … Continue reading Connecting to your notebook kernel using Jupyter console
Overview of I/O tools in Pandas
Pandas has a lot of functionality, but before you can explore or use it, you'll most likely want to access some data from an external source. You'll also likely want to store results for use later or be able to export results to other tools or to share with others. Pandas has a lot of … Continue reading Overview of I/O tools in Pandas
Removing duplicate data in Pandas
It can be very common when dealing with time series data to end up with duplicate data. This can happen for a variety of reasons, and I've encountered it more than one time when and tried different approaches to eliminate the duplicate values. There's a gem of a solution on Stack Overflow and I thought … Continue reading Removing duplicate data in Pandas
Converting types in Pandas
Pandas is great for dealing with both numerical and text data. In most projects you'll need to clean up and verify your data before analysing or using it for anything useful. Data might be delivered in databases, csv or other formats of data file, web scraping results, or even manually entered. Once you have loaded … Continue reading Converting types in Pandas
Use pyenv and virtual environments to manage Python complexity
In my earlier post, I wrote about how pyenv is a great tool for running multiple versions of Python on the same host. It makes it simple to install multiple versions of Python on your workstation or server and control which version executes in a shell. But as a Python developer, the Python version is … Continue reading Use pyenv and virtual environments to manage Python complexity
You can easily and sensibly run multiple versions of Python with pyenv
Python 3.9 just came out recently, and I thought it would make sense to check out some of the new features (dict union operators, string remove prefix and suffix, etc.). Of course, doing this requires a Python 3.9 environment. Since new versions of Python may break existing code, I don't want to update my entire … Continue reading You can easily and sensibly run multiple versions of Python with pyenv
Python: how to use multiprocessing to finish work faster
It's very common in data science work to have some function or a set of functions that you run in a loop to process or analyze data. When you see a loop that's performing an expensive operation, you should immediately think of (at least) two ways to speed things up. The first is vectorization, which … Continue reading Python: how to use multiprocessing to finish work faster
Basic Pandas: Moving a DataFrame column
Sometimes we want to change the ordering of a DataFrame's columns. Maybe you are saving your data and require the columns to be in a certain order, or you may have a large number of columns and want a few of them to be visible whenever viewing your DataFrame in an interactive session, so you … Continue reading Basic Pandas: Moving a DataFrame column
Basic Pandas: Renaming a DataFrame column
A very common need in working with pandas DataFrames is to rename a column. Maybe the columns were supplied by a data source like a CSV file and they need cleanup. Or maybe you just changed your mind during an interactive session. Let's look at how you can do this, because there's more than one … Continue reading Basic Pandas: Renaming a DataFrame column
Basic Pandas: How to add a column to a DataFrame
Pandas is one of my favorite Python libraries, and I use it every day. A very common action is to add a column to a DataFrame. This is a pretty basic task. I'm going to look at a few examples to better show what is happening when we add a column, and how we need … Continue reading Basic Pandas: How to add a column to a DataFrame