Matplotlib and Seaborn act as the backbone of data visualization through Python. Matplotlib: It is a Python library used for plotting graphs with the help of other libraries like Numpy and Pandas. It is a powerful tool for visualizing data in Python. It is used for creating statistical inferences and plotting 2D graphs of arrays.

Histogram binning and kernel density overlays, standard in Seaborn, must be explicitly constructed in Matplotlib with additional steps. Seaborn handles grouped categorical plots, ANOVA-style visuals, and faceting with built-in wrappers, streamlining comparison across multiple variables or categories.

Again, the seaborn visual is more aesthetically pleasing. Also note, using the “kde=True” parameter, you can superimpose a line curve that follows the distribution of the histogram. This is especially convenient in statistics when working with normal distributions.

In this article, we will explore and implement multiple histograms on same plot. Plotting multiple Histograms On Same Plot is built by splitting the data into periods, or "bins," and then showing the number or density of readings within each bin as bars.