Views, Copies, and that annoying SettingWithCopyWarning
If you’ve done any work in pandas, you’ve surely seen the SettingWithCopyWarning. This is an explanation of what’s happening and how to fix it.
pandas
Selecting in Pandas using where and mask
This is the fifth post in a series on indexing and selecting in pandas. If you are jumping in the middle and want to get caught up, here’s what has been discussed so far: Basic indexing, selecting by label and location Slicing in pandas Selecting by boolean indexing Selecting by callable Once the basics were …
Indexing and Selecting in Pandas by Callable
In pandas, you can use callables where indexers are accepted. It turns out that can be handy for a pretty common use case.
Indexing and Selecting in Pandas – slicing
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 …
Indexing and Selecting in Pandas (part 1)
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 …
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 …
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 …
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 …
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 …
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 …