Pandas – the data analysis and manipulation tool.
This is the third post in the series on indexing and selecting data in pandas. If you haven’t read the others yet, see the first post that covers the basics of selecting based on index or relative numerical indexing, and the second post, that talks about slicing. In this post, I’m going to talk about boolean …
Slicing data in pandas This is second in the series on indexing and selecting data in pandas. If you haven’t read it yet, see the first post that covers the basics of selecting based on index or relative numerical indexing. In this post, I’m going to review slicing, which is a core Python topic, but has …
The topic of indexing and selecting data in pandas is core to using pandas, but it can be quite confusing. One reason for that is because over the years pandas has grown organically based on user requests so there are multiple way to select data out of a pandas DataFrame or Series. Reading through the documentation can be …
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 …
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 …
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 …
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 …
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 …
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 …
Basic Pandas: How to add a column to a DataFrame Read More »
I recently saw a question about pandas.DataFrame.apply and realized that when I first started using Pandas I would often attempt to solve problems with apply when a vectorized solution was what I should have been using instead. Let’s say that you have an existing function to calculate the present value of an investment that takes …