Introduction to Data Visualization with Matplotlib and Seaborn

While it may appear simpler at times to browse through a collection of data points and draw conclusions from them, this approach typically does not produce satisfactory outcomes. Many things might go undiscovered as a result of this method. Furthermore, the majority of real-world data sets are too large for manual analysis. In essence, this is where data visualisation comes into play.

Using visual aids to analyse trends and correlations between variables, data visualisation makes even the most complex data easier to present and understand.

The advantages of data visualisation are as follows.

Simpler way to display compels data.

identifies regions that perform well and poorly.

investigates the connections between the data points.

finds patterns in the data even for larger points.

It’s usually a good idea to keep the following points in mind when creating visuals.

Make sure to use sizes, colours, and forms appropriately while creating visualisations.

Plots and graphs with a coordinate system have more detail.

Understanding the appropriate visualisation for each type of data improves information clarity.

Labels, titles, legends, and pointers are used to seamlessly transmit information to a larger audience.

Python Libraries

Many Python libraries, such as matplotlib, vispy, bokeh, seaborn, pygal, folium, plotly, cufflinks, and networkx, can be used to create visualisations. Matplotlib and Seaborn appear to be the most popular among the many for visualisations at the basic to intermediate levels.

Matplotlib

It is a multi-platform data visualisation toolkit built on NumPy arrays and intended to interact with the larger SciPy stack. It is an incredible Python visualisation framework for 2D plots of arrays. In 2002, John Hunter made its debut. Let’s attempt to comprehend a few of matplotlib’s characteristics and advantages.

Because it is based on Numpy, it is quick and effective to construct.

has experienced numerous enhancements from the open source community since its founding, making it a better library with more sophisticated functionality.

Many people are drawn to well-maintained visualisation output with excellent graphics.

Both simple and complex charts might be created with ease.

From the perspective of users and developers, troubleshooting and debugging are made considerably simpler because of the strong community assistance.

Seaborn

This library was initially designed and developed at Stanford University and is built on top of matplotlib. It is similar to matplotlib in several aspects, but it is superior to it in terms of visualisation and comes with more capabilities.

• Integrated themes improve visual representation 
• 
  Statistical functions that provide more insightful data 
• 
  Improved appearance and integrated plotting 
• 
  Documentation that is helpful and includes practical examples 

Character of Visualisation

There are various chart kinds that we can use to comprehend the relationship, depending on the type and quantity of variables utilised to create the visualisation. Considering the number of variables, we may have

A univariate plot, which shows just one variable
Bivariate plot (needs more than one variable)

For a continuous variable, a univariate plot may be used to comprehend the variable’s spread and distribution, but for a discrete variable, it may provide the count.

Comparably, a bivariate plot for a continuous variable might show a crucial statistic like correlation, and for a continuous versus discrete variable, it could help us understand the distribution of data across various levels of a categorical variable, among other crucial findings. It is also possible to create a bivariate plot between two discrete variables.

Box Plot

A boxplot, sometimes called a box and whisker plot, shows the box and the whisker in the image below. When it comes to gauging the dispersion of the data, this visual representation is excellent. Plots the quartiles, outliers, and median values clearly. Gaining an understanding of data distribution is another crucial component that improves model construction. A box plot is a suggested method for locating outliers in data and taking the appropriate action.

Syntax: seaborn.boxplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orientation=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, fliersize=5, linewidth=None, whis=1.5, ax=None, **kwargs)

Parameters

x, y, hue: Inputs for plotting long-form data. 

data: Dataset for plotting. If x and y are missing, this is considered as wide-form. 

color: Color for all of the elements.

It returns an Axes object with the plot drawn on it.

The data distribution is depicted in the box and whiskers chart. Generally, the chart has five bits of information.

The chart’s minimum is displayed at the very left, at the tip of the left “whisker.”

The extreme left of the box is the first quartile, or Q1 (left whisker).

A line representing the median is displayed in the box’s centre.

Q3, the third quartile, is displayed at the box’s extreme right (right whisker).

On the far right side of the box is the maximum.

A box plot can be displayed for one or more variables, as seen in the illustrations and charts below, which offer excellent insights into our data.

Scatter Plot

In terms of construction, scatter plots, also known as scatter graphs, are bivariate plots that are more similar to line graphs. A scatter plot employs dots to represent individual data points, whereas a line graph plots a continuous function on an X-Y axis. To determine whether two variables are connected, these graphs are quite helpful. A scatter plot might have two or three dimensions.

syntax: seaborn.scatterplot.(x=None, y=None, hue=None, style=None, size=None, data=None, palette=None, hue_order=None, hue_norm=None, sizes=None, size_order=None, size_norm=None, markers=True, style_order=None, x_bins=None, y_bins=None, units=None, estimator=None, ci=95, n_boot=1000, alpha=’auto’, x_jitter=None, y_jitter=None.

Parameters

x, y: Input numeric data variables.

data: A dataframe in which each column is a variable and each row represents an observation.

size: Grouping variable that will produce points with different sizes.Size: A grouping variable that produces points of varying sizes.

style: A grouping variable that generates points with various markers. 

Palette: A grouping variable that generates points with various markers.  

markers: Object that determines how to draw the markers at various levels.

alpha: Proportional opacity of the points.

This function returns an Axes object with the plot drawn on it.

Benefits of scatter plotting 

shows the relationship between the variables
Ideal for large datasets
Finding data clusters is easier.
enhanced depiction of every data point

Histogram

Histograms and bar charts are comparable because they both show counts of data. The proximity of a data distribution to a normal curve can also be determined using a histogram plot. It is crucial that we have data that is regularly distributed, or nearly so, when determining statistical methods. But bar charts are bivariate while histograms are univariate.

A histogram shows the same categorical variables in bins, but a bar graph plots actual counts versus categories. For example, the height of the bar reflects the number of items in that category.

Bins are essential for controlling the data points that fall inside a range while creating a histogram. Although it’s common knowledge that bin sizes should be limited to five to twenty, the actual number of data points present ultimately determines this.

Read also: AI Chatbots: The Future of Conversational Communication

Start Visualizing in Python Now

All things considered, Seaborn and Matplotlib are both useful resources for data scientists. Effective data transmission requires straightforward labelling, titling, and formatting of graphs, which Matplotlib simplifies. Additionally, it offers a large number of the fundamental data visualisation tools, such as bar charts, pie charts, scatter plots, and histograms. 

Because of its robust statistical tooling and stunning visuals, Seaborn is a valuable library to be familiar with. As you can see above, even though they convey the identical information, the plots created with Seaborn are far more attractive than those created with Matplotlib. Furthermore, far more advanced analysis and visualisations are possible with Seaborn’s capabilities. Seaborn may be used to create more complex graphics, such as cluster maps, line plots with confidence intervals, density maps for variables, and much more, even though I’ve only covered how to use it to create heatmaps and pairwise plots. 

Two of the most popular Python visualisation libraries are Matplotlib and Seaborn. Both of these let you easily visualise data to draw conclusions from statistics and use the facts to tell a story. Even though the use cases for each of these libraries significantly overlap, a data scientist with an understanding of both libraries can create stunning visualisations that effectively convey the story of the data being analysed. You too can learn data visualization like this by opting for  Python course in Mumbai, Jaipur, Pune, Dehradun, Delhi, Noida and all other Indian cities.