association and correlation matrix

For ratio scale data it is relatively simple to create and visualize a correlation matrix e.g. as shown below. Ho can I do the same for a data frame that contains also nominal scale data? I would like to see associations (e.g. Cramer's Phi or others) for nominal-nominal, and nominal-ratio pairs and correlation for ratio-ratio pair variables. Something like the matrix the sweetviz pyhton package generates for an automatic exploratory data analysis.

I appreciate each answer.

import seaborn as sns
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(15, 15))  # Sample figsize in inches (width, heights)
hm = sns.heatmap(df[meta_data + summary_ravenscroft].corr(method='spearman'), vmin=-1, vmax=1, annot=True, ax=ax);

# Give a title to the heatmap. Pad defines the distance of the title from the top of the heatmap.
hm.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);

Update 1 what sweetviz does and what I would like to do: "A major source of insight and unique feature of Sweetviz' associations graph and analysis is that it unifies in a single graph (and detail views):

1. Numerical correlation (between numerical features)
2. Uncertainty coefficient (for categorical-categorical)
3. Correlation ratio (for categorical-numerical) Pairwise sample

Squares represent categorical-featured-related variables and circles represent numerical-numerical correlations. Note that the trivial diagonal is left empty, for clarity."