Compare 1 independent of many dependent variables using a horizontal sea pair

Question

The pairplot function from the seashore allows you to build paired relationships in a data set.

According to the documentation (highlighted added):

By default, this function will create a grid of axes, so that each variable in the data will be divided along the Y axis along one row and along the X axis through one column. The diagonal axes are processed differently, drawing a graph to show the one-dimensional data distribution for the variable in this column.

You can also show a subset of variables or display various variables in rows and columns.

I could only find one example of a subset of different variables for rows and columns, here (this is the 6th graph when building pair relations with PairGrid and pair () section). As you can see, it draws many independent variables (x_vars) against the same single dependent variable (y_vars), and the results are pretty nice.

I am trying to do the same building a separate independent variable for many dependent ones.

 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns ages = np.random.gamma(6,3, size=50) data = pd.DataFrame({"age": ages, "weight": 80*ages**2/(ages**2+10**2)*np.random.normal(1,0.2,size=ages.shape), "height": 1.80*ages**5/(ages**5+12**5)*np.random.normal(1,0.2,size=ages.shape), "happiness": (1-ages*0.01*np.random.normal(1,0.3,size=ages.shape))}) pp = sns.pairplot(data=data, x_vars=['age'], y_vars=['weight', 'height', 'happiness']) 

The problem is that the subheadings are vertical, and I could not find a way to change it.

I know that then the structure of the tile would not be as neat as the Y axis should be marked on each subheading. Also, I know that I could generate graphs doing it manually with something like this:

 fig, axes = plt.subplots(ncols=3) for i, yvar in enumerate(['weight', 'height', 'happiness']): axes[i].scatter(data['age'],data[yvar]) 

However, I am learning to use the sea island, and I find the interface very convenient, so I wonder if there is a way. In addition, this example is quite simple, but for more complex datasets with nautical pens, there are many more things for you that will make the raw-matplotlib approach much more complex pretty quickly (a shade to start)

Answer 1

You can achieve what you think is looking for a replacement for the variable names passed to the x_vars and y_vars parameters . Therefore, revising the sns.pairplot code part of your code:

 pp = sns.pairplot(data=data, y_vars=['age'], x_vars=['weight', 'height', 'happiness']) 

Note that all I did here is swap x_vars for y_vars. Graphs should now be displayed horizontally:

Now the x axis will be unique for each graph with a common y axis defined by the age column.