Topic 2. Visual data analysis in Python

Part 2. Overview of Seaborn, Matplotlib and Plotly libraries

First, we will set up our environment by importing all necessary libraries. We will also change the display settings to better show plots.

In [1]:

# Disable warnings in Anacondaimportwarningswarnings.filterwarnings('ignore')# Matplotlib forms basis for visualization in Pythonimportmatplotlib.pyplotasplt# We will use the Seaborn libraryimportseabornassnssns.set()# Graphics in SVG format are more sharp and legible%config InlineBackend.figure_format = 'svg'
# Increase the default plot size and set the color schemeplt.rcParams['figure.figsize']=8,5plt.rcParams['image.cmap']='viridis'importpandasaspd

Now, let’s load the dataset that we will be using into a DataFrame. I have picked a dataset on video game sales and ratings from Kaggle Datasets.
Some of the games in this dataset lack ratings; so, let’s filter for only those examples that have all of their values present.

The resulting DataFrame contains 6825 examples and 16 columns. Let’s look at the first few entries with the head() method to check that everything has been parsed correctly. To make it more convenient, I have listed only the variables that we will use in this notebook.

Before we turn to Seaborn and Plotly, let’s discuss the simplest and often most convenient way to visualize data from a DataFrame: using its own plot() method.

As an example, we will create a plot of video game sales by country and year. First, let’s keep only the columns we need. Then, we will calculate the total sales by year and call the plot() method on the resulting DataFrame.

Note that the implementation of the plot() method in pandas is based on matplotlib.

Using the kind parameter, you can change the type of the plot to, for example, a bar chart. matplotlib is generally quite flexible for customizing plots. You can change almost everything in the chart, but you may need to dig into the documentation to find the corresponding parameters. For example, the parameter rot is responsible for the rotation angle of ticks on the x-axis (for vertical plots):

Now, let's move on to the Seaborn library. seaborn is essentially a higher-level API based on the matplotlib library. Among other things, it differs from the latter in that it contains more adequate default settings for plotting. By adding import seaborn as sns; sns.set() in your code, the images of your plots will become much nicer. Also, this library contains a set of complex tools for visualization that would otherwise (i.e. when using bare matplotlib) require quite a large amount of code.

Let's take a look at the first of such complex plots, a pairwise relationships plot, which creates a matrix of scatter plots by default. This kind of plot helps us visualize the relationship between different variables in a single output.

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# `pairplot()` may become very slow with the SVG format%config InlineBackend.figure_format = 'png'
sns.pairplot(df[['Global_Sales','Critic_Score','Critic_Count','User_Score','User_Count']]);

As you can see, the distribution histograms lie on the diagonal of the matrix. The remaining charts are scatter plots for the corresponding pairs of features.

It is also possible to plot a distribution of observations with seaborn's distplot(). For example, let's look at the distribution of critics' ratings: Critic_Score. By default, the plot displays a histogram and the kernel density estimate.

To look more closely at the relationship between two numerical variables, you can use joint plot, which is a cross between a scatter plot and histogram. Let's see how the Critic_Score and User_Score features are related.