Getting a firm understanding of NaNs in your dataset ensures you don’t draw wrong conclusions from an incomplete dataset. In this blog post I show how you can list the amount of NaNs per column, per row, and per group. First, let’s create some dummy data, and add some NaNs.…
Without going into detail, here’s something I truly hate in R: replacing multiple values. In Python’s pandas, it’s really easy. In this blog post I try several methods: list comprehension, apply(), replace() and map(). First, let’s create some dummy data. First, let’s try with list comprehension. The get() function tries…
Here’s another post about seemingly convenient things in pandas. Let’s produce a DataFrame from partially matched columns from another DataFrame. Let’s create some dummy data. Let’s say we are trying to select the columns that contain the world ‘color’. Here’s a solution I found on the web. By converting the…
Having R as my data science lingua franca, some things in pandas feel less intuitive. As I’m slowly transitioning to pandas, I’ll be posting a lot of examples over the next couple of weeks. Here’s the first one: concatenating columns values of a pandas DataFrame into another (new) column. First…
In this blog post I decompose a time series of monthly data using the pandas and statsmodels package in Python. You can find the data that I use in this blog post in my github repo. It is a monthly average of daily car counts on different hubs on the…
Pandas is the go-to package for anything data science in Python. However, if you’re used to R and the convenience of dplyr or data.table, pandas can be confusing, now and then. For example, the following error is a real newb issue. ValueError: You are trying to merge on object and…
