#Binned scatter plot python code#

The binned scatterplot is a very powerful tool that provides a flexible and parsimonious way of visualizing and summarizing conditional means (and not only) in large datasets. non-parametric) approach that could inform us on the relationship between firm age and sales. However, it would be really cool if there was a more flexible (i.e. Within the linear regression framework, one approach could be to add extra terms such as polynomials ( age^2) or categorical features (e.g. However, this pattern might be very non-linear. What should we conclude? It seems that sales increase over age, on average. The coefficient for log_age is still positive and statistically significant, but its magnitude has halved. smf.ols('log_sales ~ log_age + online + products', df).fit().summary().tables Let’s add the binary indicator for online-only firms and the variable counting the number of products to the regression. With linear regression, we can condition the analysis on covariates. We need to control for this variable in order to avoid Simpson’s Paradox and, more generally, bias. However, maybe this relationship is different for online-only firms and the rest of the sample. From none of the graphs above we could have guessed such a strong positive relationship. It seems that all previous visualizations were very misleading. The regression coefficient for log_age is positive and statistically significant (i.e. Let’s zoom-in on the bottom-left corner, on observations what have age |t| Maybe we could remove outliers and zoom-in on the area where most of the data is located. It looks like the distributions of age and sales are both very skewed and, therefore, most of the action is concentrated in a very small subspace. s = sns.jointplot(x='age', y='sales', data=df, kind='hex', ) Let’s try the hexplot, which is basically a histogram of the data, where the bins are hexagons, in the 2-dimensional space. The default option is the scatterplot, but one can also choose to add a regression line ( reg), change the plot to a histogram ( hist), a hexplot ( hex), or a kernel density estimate ( kde). jointplot plots the joint distribution of two variables, together with the marginal distributions along the axis.

There are multiple solutions in Python to visualize the density of a 2-dimensional distribution. What can we do when we have an extremely dense scatterplot? One solution could be to plot the density of the observations, instead of the observations themselves. We are now going to explore some plausible tweaks and alternatives. If we had to guess, we could say that the relationship looks negative ( sales decrease with age), but it would be a very uninformed guess. We have a lot of observations, therefore, it is very difficult to visualize them all. sns.scatterplot(x='age', y='sales', data=df) Let’s start with a simple scatterplot of sales over age. Suppose we are interested in understanding the relationship between age and sales.

products: the number of products that the firm offers.

online: whether the firm is only active online.

sales: the monthly sales from last month.

%config InlineBackend.figure_format = 'retina'ĭf = dgp_marketplace().generate_data(N=10_000)

#Binned scatter plot python code#

You can find the code for the data generating process here. Let’s load the data and have a look at it.

Our dataset consists in a snapshot of the firms active on the marketplace. Suppose we are an online marketplace where multiple firms offer goods that consumer can efficiently browse, compare and buy. Binned scatterplots are not only a great visualization tool, but they can also be used to do inference on the conditional distribution of the dependent variable.

In this blog post, I am going to review a very powerful alternative to the scatterplot to visualize correlations between two variables: the binned scatterplot. However, when we have a lot of data and/or when the data is skewed, scatterplots can be too noisy to be informative. It’s a very intuitive visualization tool that allows us to directly look at the data. When we want to visualize the relationship between two continuous variables, the go-to plot is the scatterplot.