Much of working within the data science industry is learning different programming languages and data science tools. As one of the most popular data science tools, Python is one of the most-used programming languages within the field. It is helpful to learn more about the different Python libraries that are most effective when completing data science projects. In particular, the Matplotlib library is used within the data science industry to communicate the findings of a data analysis project through unique graphics and visualizations. Here are some of the many reasons why you should add this Python library to your data science toolkit!
What is Matplotlib?
Matplotlib is a Python library commonly used for creating two-dimensional graphs and other data visualizations and models. Created in 2003, Matplotlib is an extension of NumPy, another Python programming library that is used to complete mathematical functions. Matplotlib is useful for transforming statistical analyses and operations into visually interesting findings. Similar to other open-source data science tools, the Matplotlib library also has an active community of Python developers and users that regularly make contributions to the library. The Matplotlib blog includes articles and examples of all of the ways that data science students and professionals can use this library in their own projects.
Using Matplotlib for Data Science
As a library that is known for plotting and charting graphs, many of the data science functions that are useful within Matplotlib are related to the process of data visualization or modeling. The following list includes some of the ways this dynamic Python library is used to communicate findings and tell stories with data.
Plotting Charts and Graphs
In order to create data visualizations within the Matplotlib library, it is important to first learn how to plot charts and graphs. Within data science, plotting is the method used to create a graph, i.e. placing the different points or variables on an x-y axis in order to show the relationship between the different data points. Plotting in Matplotlib requires the Plot function in order to create the different types of graphs and visualizations that are referenced in the library. Plotting is useful to data scientists that want to visually explore their data, because it allows you to see the relationships that emerge from within the dataset and develop inferences based on characteristics such as slope and clustering.
After importing Matplotlib into the Python environment of your choice, each type of data visualization within the library corresponds to a specific function or method. For example, data scientists that want to create a histogram can use the “plt.hst()” function in Matplotlib, whereas creating a bar graph requires the use of “plt.bar()” and a pie graph is created with “plt.pie()”. In this sense, the syntax used within Matplotlib is intuitive and accessible to data scientists that are familiar with statistical analysis and traditional mathematical operators. These different functions also make it easier for data scientists to run their data with different types of graphs or graphics in order to determine the most effective and efficient method of communicating their findings.
There is a multitude of data visualizations and graphics that you can create in Matplotlib. The Matplotlib library includes resources for data scientists that are interested in creating different types of graphs, such as line plots, histograms, and scatter plots.
- Line plots are simple graphics displaying the plotting of multiple points on an x-y axis, which are then joined by a line.
- Scatter plots are similar to line plots in that they display the plotting of multiple points, but a line is not included (which is more common among a dataset which has more variability).
- Histograms are charts that are used to show the distribution of data by using bars of different heights stacked next to each other.
In addition to the more traditional graphics, there are several graph options within Matplotlib that add creativity to their functionality. Graphs such as pie charts and box plots offer unique visual characteristics for your audience.
- Pie graphs in particular are recognized and used across industries, making them an essential graphic for communicating findings from a data analysis project based on comparative portions.
- Box plots can also be used in multiple industries and are commonly employed to visualize data that represents a distribution of some kind.
Once you have determined which type of data visualization to use, you can use Matplotlib to add more visual interest to your data analysis.
Images, Animations, and Graphics
Within the Matplotlib library there are also functions for editing different parts of your graphs, such as labeling the axes, working with color, and even animations. Through colormaps which are included in the library, you can find the best combinations of colors to present your graphs or image. Once you have edited a graph or image with this library, you can also use Matplotlib to animate your output. These animations can be used to make any data visualization more interactive, or even to show changes or updates to a data analysis project, as well as to create 2-D and 3-D imagery.
Due to its capabilities in creating visualizations and models, Matplotlib can also be used to create other types of images and graphics that are commonly seen within data science. For example, creating images, such as heatmaps, is useful for data scientists working with data about a particular population, healthcare statistics, or even displaying data about the weather or natural disasters. Once you have created these different images and graphs, Matplotlib output can also be embedded in multiple platforms and programs, making it an excellent library for creating, sharing, and displaying data.
Want to learn more about Matplotlib?
Data Scientists have a variety of Python libraries to choose from when working on data analysis projects, modeling, and visualization. To learn more about these different libraries, take one of Noble Desktop’s data science classes or bootcamps. The Data Science Certificate includes instruction in multiple Python libraries, including how to create data visualizations with Matplotlib. Or enroll in one of the many Python classes near you to take a bootcamp or course in the topic of your choice.