Python Matplotlib: 7 Advantages and 5 Disadvantages

What are the basics of Python Matplotlib?

What is Matplotlib?

Matplotlib is a graph drawing tool that can be used with the programming language Python. By using this tool, you can visually represent numbers and data. For example, if you graph the changes in grades or temperature, you can see at a glance how much change there has been. The basic usage of Matplotlib is as follows:

• Prepare your dataFirst, gather the data you want to graph. This can be in the form of a list or an array.
• Import Matplotlib: Import the necessary parts to be able to use Matplotlib in your program.
• Draw a graph: Use the data you have prepared to draw bar graphs, line graphs, etc.

As you can see, Matplotlib is a powerful tool for visualizing data and has uses in many fields, from comparing grades in classes in school to showing sales in business.

Usage and features of Matplotlib

Matplotlib is very simple to use, so even beginners can get started right away. First, make sure your Python environment is ready. Next, install Matplotlib. Usually, you use the following command:

pip install matplotlib

Once the installation is complete, you can plot a graph by following the steps below.

1. Prepare your dataFor example, prepare data that records temperatures over a week.
2. Import MatplotlibAdd the following code to the beginning of your program:

   import matplotlib.pyplot as plt

3. Draw a graph: plt.plot()orplt.bar()Use functions such as these to draw graphs.
4. Displaying a graph: lastlyplt.show()Use to display the graph you have drawn on the screen.

As you can see, Matplotlib is intuitive to use, making it easy to visualize data. There are many types of graphs you can draw, so you can choose the one that best suits your needs. For example, histograms are good for showing the distribution of numerical data, and line graphs are good for showing the passage of time.

7 Advantages of Python Matplotlib

Intuitive graph creation

The biggest appeal of Matplotlib is that it allows you to create graphs intuitively. Even if you don't have much programming experience, you can draw graphs by writing simple code. For example, look at the following code.

import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y = [2, 3, 5, 7, 11] plt.plot(x, y) plt.show()

By simply executing this code, a line graph based on the x and y values will be displayed. This simplicity has made Matplotlib popular with many users. In addition, there are a wide variety of functions for drawing graphs, so with a little ingenuity you can create graphs in a variety of styles.

• Simple code: It uses easy-to-understand syntax, so even beginners can use it with ease.
• Various graphs: You can draw a variety of graphs, including bar graphs, scatter plots, and pie charts.

As you can see, Matplotlib is very useful for visualizing data and is easy for anyone to use, which is why it is popular with many people.

A wide range of graph types

Matplotlib can draw various types of graphs. This allows users to choose the graph that best suits their purpose. The following graphs are available:

• bar graph: Good for comparing data across categories, for example comparing sales by month.
• line graph: Great for showing change over time. Use it to show temperature changes or stock price trends.
• Scatter Plot: It is useful for showing the relationship between two variables. For example, it is suitable for investigating the relationship between height and weight.
• histogram: Used to visualize the distribution of data. Useful for showing test score distributions.

As you can see, Matplotlib can draw a wide variety of graphs, so you can choose the best way to display your data. For example, you can choose a bar graph to compare multiple data, or a histogram to see the distribution. The wide range of graph types available is one of Matplotlib's great attractions.

Lots of customization available

Another big advantage of Matplotlib is its high customizability. You can freely change various elements of the graph you draw, such as the color, style, and labels. This allows you to create graphs that suit your preferences. Specific examples of customization are as follows.

• Change color: You can change the color of the lines and bars on your graphs, for example making them red to highlight important data.
• Line Style: You can change the line style, such as solid, dotted, dashed, etc. This makes the graph easier to understand.
• Adding labels: You can also add titles and axis labels to your graphs. For example, adding a title such as "Sales Trends" will make the content clearer.

The following code is an example of customizing a chart:

plt.plot(x, y, color='red', linestyle='--', label='Data') plt.title('Customized Chart') plt.xlabel('X-Axis') plt.ylabel('Y-Axis') plt.legend() plt.show()

As you can see, Matplotlib allows you to create your own original graphs. The ability to customize them allows for more effective data visualization, which can be useful in presentations and reports.

Easily visualize your data

Visualizing data is very important for conveying information in an easy-to-understand way. Matplotlib makes it easy to display complex numbers and data as graphs. This makes it easier to see and understand trends and relationships in the data at a glance. For example, it is useful in the following cases:

• Comparison of results: By graphing your school grades, you can see at a glance which subjects you are good at.
• Sales Trends: By plotting your company's monthly sales as a line graph, you can see growth trends.
• Temperature changes: By graphing temperatures over the course of a year, seasonal changes become clearly visible.

There are many benefits to visualization, such as making it easier to find patterns and communicate information to others. This makes Matplotlib very effective when analyzing and reporting data. In particular, visualizing numbers as graphs makes it easier for others to understand the meaning of the data.

For example, consider the following specific scenario:

• If you're comparing the performance of your classmates on a school project, a bar chart can show at a glance which students performed better in which subjects. This type of visualization makes the data easier to interpret.
• In business presentations, displaying sales data as a line graph can clearly show changes over time and effectively communicate information to managers and colleagues.

Works well with Python

Thus, visualizing data with Matplotlib allows us to present information in a visually appealing and easy-to-understand format, which in turn improves the quality of communication by making information more memorable.

Matplotlib works very well with the programming language Python. Python itself has a simple and intuitive syntax, so Matplotlib has the same advantages when used with Python. Matplotlib is often used in data analysis and machine learning projects using Python.

The specific benefits are as follows:

• Consistent grammar: It follows Python syntax, so it's easy to use with other Python code. For example, you can use a data frame to manipulate data and turn it into a graph.
• Linkage with a wide range of libraries: By combining it with other data processing libraries such as NumPy and Pandas, you can easily perform complex data analysis. For example, after organizing data with Pandas, it is very smooth to visualize the data with Matplotlib.
• ACTIVE COMMUNITY: Both Python and Matplotlib have many users, so it is easy to find information when you are stuck. There is official documentation and many tutorials to help you solve problems.

As you can see, Matplotlib's compatibility with Python is a big advantage when using it. Especially when performing data analysis, you can work efficiently and get results quickly.

Supports large-scale data

Matplotlib also has the ability to handle large data sets. In recent years, big data has been gaining attention, and there is a demand to handle huge amounts of data, but Matplotlib can also handle such data. Specifically, the following points can be mentioned:

• Plotting a large number of data pointsMatplotlib can draw graphs containing thousands or even millions of data points, which is useful for visualizing large amounts of data, for example sensor data or logged data.
• Performance optimization: The time it takes to draw a graph has been reduced, allowing for efficient processing.scatterScatter plots with functions can handle large amounts of data efficiently.
• Interactive GraphsWhen used in an interactive environment such as Jupyter Notebook, you can also create graphs with zoom and pan capabilities to help you gain a deeper understanding of your data.

For example, if you have weather data for a year and plot the daily temperatures, Matplotlib can smoothly visualize that data and easily spot seasonal trends. In this way, Matplotlib is a very useful tool even when dealing with large amounts of data.

Free and open source

Matplotlib is open source software and is freely available. This brings great benefits, especially in areas such as education and research. Specific benefits include:

• No cost: No software purchase is required, making it easy for schools and individuals to use, making data visualization possible even in budget-constrained situations.
• Community Support: Because it is open source, developers and users from all over the world participate in improvements and bug fixes. This ensures that the latest features and improvements are always available, giving users peace of mind.
• Ease of learning: Since it is available for free, students and beginners can try it out without hesitation, and it is a good way to improve their programming and data analysis skills.

In fact, many schools and research institutes use Matplotlib to help visualize data in their lessons and projects, and the fact that it is free to use is a major factor that encourages its adoption in the educational field.

5 Disadvantages of Python Matplotlib

It can be difficult for beginners

Matplotlib is a very powerful tool, but beginners may find it difficult to use. In particular, it can be a little difficult to customize graphs or create complex graphs. Specific difficulties include the following:

• Grammar Complexity: It's easy to draw a basic graph, but there are so many options and customizations that it can be hard to know what changes to make and how to make them.
• Difficult to understand the errorSometimes you may get an error message that is hard to understand, especially if you are new to programming, and it can be hard to pinpoint what went wrong.
• There is a lot of documentation: Because it has so many features, the official documentation is huge. It can be hard to find the information you need.

For example, if you want to use a specific color or style, it can take time to find that information, which is why Matplotlib can seem a bit intimidating for beginners.

Graphs may be slow to display

Matplotlib is a great tool for handling lots of data, but sometimes it takes a long time to draw a graph. This problem can be especially noticeable when drawing large data sets or complex graphs. Specific problems are as follows:

• Delayed drawing time: When drawing a graph with millions of data points, it may take a long time to draw. This may cause waiting time and reduce work efficiency.
• Difficulty in real-time updates: When real-time updates of data are required, Matplotlib's drawing speed can become a bottleneck. For example, when graphing data from a sensor on the fly, other libraries may be more suitable.

In such cases, it may be a good idea to consider other libraries instead of Matplotlib, but please note that Matplotlib's drawing speed issues depend on the specific situation, so you may get different results depending on the scale and complexity of the data you use.

Limited interactivity

Matplotlib is very useful for drawing basic graphs, but its ability to create interactive graphs is limited, and you may need to use another library to achieve advanced interactions, especially when you want to view data details in real time or allow the user to manipulate the data.

• Basic Operation RestrictionsMatplotlib itself provides basic interaction capabilities such as zooming in and panning, but does not provide advanced features such as detailed data filtering and selection by default.
• The Need for an Interactive Front EndFor a more interactive experience, you should consider other libraries such as Plotly or Bokeh, which are specialized for data visualization in web-based applications and allow you to dynamically update charts in response to user actions.

Therefore, for projects that require interactive data visualization, it is important to consider that Matplotlib alone may not be sufficient.

Can be difficult to customize

Matplotlib offers many customization options, but this variety can also be a challenge, especially when you need to tinker with the details to achieve a particular look or style.

• Many options, but difficult to understandWith so many properties and methods available, it can be hard to know which option will bring you closer to the style you want.
• Trial and error required: Creating the ideal graph can be a time-consuming process that requires a lot of trial and error. For example, when adjusting colors, font sizes, label placement, and other small settings, you may need to adjust them multiple times to get the results you want.

As such, while there is a high degree of freedom in customization, the accompanying complexity means that some users may find it difficult to master.

Can be less intuitive than other libraries

Matplotlib is versatile, but it is less intuitive than other visualization libraries. Users who are not familiar with data visualization may find it difficult to use.

• Procedural styleMatplotlib is designed in a procedural style, which can be difficult to use for users who prefer an object-oriented style.
• There are many steps to get results: The code for drawing a graph can be relatively verbose and require many steps to achieve the desired result. For example, there can be a lot of data preparation and configuration required to draw a simple graph.

For this reason, especially if you are new to visualization, you may find other libraries (such as Seaborn or Plotly) more intuitive.

summary

Matplotlib is a very powerful tool for visualizing data in Python. It can easily draw basic graphs and has many customization options, so it can meet a wide range of needs. It is also widely used in the fields of data science and machine learning because it is compatible with Python and can be used in combination with many data analysis libraries.

However, it has a learning curve for beginners, is limited in terms of drawing speed and interactivity, can be difficult to customize, and is less intuitive than other libraries.

Ultimately, it is important to decide whether or not to choose Matplotlib depending on the purpose of use and the characteristics of the data. It is very useful for drawing simple graphs, but if more advanced visualization is required, you should consider using it in conjunction with other tools.