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We will also create a figure and an axis using plt.subplots to give our plot a title and labels. To create a scatter plot in Matplotlib, we can use the scatter method.

It can be imported by typing: import matplotlib.pyplot as plt Scatter Plot Matplotlib is specifically suitable for creating basic graphs like line charts, bar charts, histograms, etc. To install Matplotlib, pip, and conda can be used. It is a low-level library with a Matlab-like interface that offers lots of freedom at the cost of having to write more code. Matplotlib is the most popular Python plotting library. Wine_reviews.head() Figure 2: Wine Review dataset head Matplotlib Print(iris.head()) Figure 1: Iris dataset head wine_reviews = pd.read_csv('winemag-data-130k-v2.csv', index_col=0) The Iris and Wine Reviews dataset, which we can both load into memory using pandas read_csv method. In this article, we will use two freely available datasets. In further articles, I will go over interactive plotting tools like Plotly, which is built on D3 and can also be used with JavaScript. This article will focus on the syntax and not on interpreting the graphs, which I will cover in another blog post. In this article, we will learn how to create basic plots using Matplotlib, Pandas visualization, and Seaborn as well as how to use some specific features of each library. plotnine:based on R’s ggplot2, uses Grammar of Graphics.Seaborn:high-level interface, great default styles.Pandas Visualization:easy to use interface, built on Matplotlib.Matplotlib:low level, provides lots of freedom.To get a little overview, here are a few popular plotting libraries: Whether you want to create interactive or highly customized plots, Python has an excellent library for you.

Python offers multiple great graphing libraries packed with lots of different features. Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends, and correlations that might not otherwise be detected can be exposed.