Author: Egor Polusmak. Translated and edited by Alena Sharlo, Yury Kashnitsky, Artem Trunov, Anastasia Manokhina, and Yuanyuan Pao. This material is subject to the terms and conditions of the Creative Commons CC BY-NC-SA 4.0 license. Free use is permitted for any non-commercial purpose.
First, we will set up our environment by importing all necessary libraries. We will also change the display settings to better show plots.
# Matplotlib forms basis for visualization in Python import matplotlib.pyplot as plt # We will use the Seaborn library import seaborn as sns sns.set() # Graphics in SVG format are more sharp and legible %config InlineBackend.figure_format = 'svg' # Increase the default plot size and set the color scheme plt.rcParams['figure.figsize'] = (8, 5) plt.rcParams['image.cmap'] = 'viridis' import pandas as pd
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.
df = pd.read_csv('../../data/video_games_sales.csv').dropna() print(df.shape)
Next, print the summary of the
DataFrame to check data types and to verify everything is non-null.
<class 'pandas.core.frame.DataFrame'> Int64Index: 6825 entries, 0 to 16706 Data columns (total 16 columns): Name 6825 non-null object Platform 6825 non-null object Year_of_Release 6825 non-null float64 Genre 6825 non-null object Publisher 6825 non-null object NA_Sales 6825 non-null float64 EU_Sales 6825 non-null float64 JP_Sales 6825 non-null float64 Other_Sales 6825 non-null float64 Global_Sales 6825 non-null float64 Critic_Score 6825 non-null float64 Critic_Count 6825 non-null float64 User_Score 6825 non-null object User_Count 6825 non-null float64 Developer 6825 non-null object Rating 6825 non-null object dtypes: float64(9), object(7) memory usage: 906.4+ KB
We see that
pandas has loaded some of the numerical features as
object type. We will explicitly convert those columns into
df['User_Score'] = df['User_Score'].astype('float64') df['Year_of_Release'] = df['Year_of_Release'].astype('int64') df['User_Count'] = df['User_Count'].astype('int64') df['Critic_Count'] = df['Critic_Count'].astype('int64')
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.
useful_cols = ['Name', 'Platform', 'Year_of_Release', 'Genre', 'Global_Sales', 'Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Rating' ] df[useful_cols].head()
|2||Mario Kart Wii||Wii||2008||Racing||35.52||82.0||73||8.3||709||E|
|3||Wii Sports Resort||Wii||2009||Sports||32.77||80.0||73||8.0||192||E|
|6||New Super Mario Bros.||DS||2006||Platform||29.80||89.0||65||8.5||431||E|
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
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
df[[x for x in df.columns if 'Sales' in x] + ['Year_of_Release']].groupby('Year_of_Release').sum().plot();
Note that the implementation of the
plot() method in
pandas is based on
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):
df[[x for x in df.columns if 'Sales' in x] + ['Year_of_Release']].groupby('Year_of_Release').sum().plot(kind='bar', rot=45);
Now, let's move on to the
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.
# `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
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.
%config InlineBackend.figure_format = 'svg' sns.distplot(df['Critic_Score']);
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
User_Score features are related.
sns.jointplot(x='Critic_Score', y='User_Score', data=df, kind='scatter');