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Olha K., Python Engineer, Data Quality, ML

Radomsko, Poland
Last Updated: 4 Jul 2023
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- 13 years of experience in IT - Proficiency in Python, Pandas. - Data quality for ICC profiles and algorithms for display color calibration - Mathematical modeling (MATLAB, Octave, Comsol, MathCad), software algorithms, numerical methods (algebra, interpolation, regression, nonlinear problems, optimization, ordinary and partial differential equations), machine learning. - Upper-Intermediate English. - Available in two weeks

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Anton, Software Engineer

Last Updated: 19 Feb 2024

Software Engineer with a Computer Science and Software Engineering background and 5 years of experience specializing in data analysis, visualization, and backend systems across retail, mobile, and finance domains. Proven expertise in languages such as Python, SQL, and R, supported by strong knowledge of cloud services like AWS and GCP. Skilled in BI tools and data visualization with Tableau, Looker Studio, and programming libraries Matplotlib, Seaborn, and Folium. Experienced in database management with MySQL, PostgreSQL, and NoSQL databases like Redis and MongoDB. Proficient in Data Engineering practices using Apache Spark and ETL/ELT processes with Apache Airflow. Demonstrates advanced capabilities in Machine Learning and Data Science with extensive use of Pandas, NumPy, and Scikit-learn. Committed to DevOps with experience in Docker, Bash Scripting, and version control systems like Git.

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Simon K., Python Software Engineer with data engineering skills

Hannover, Germany
Last Updated: 6 Jul 2023

- 2+ years of experience with Python as a Data Engineer and Deep/Machine Learning Intern - Experience with Data Vault modeling and AWS cloud services (S3, Lambda, and Batch) - Cloud Services: Sagemaker, Google BigQuery, Google Data Studio, MS Azure Databricks, IBM Spectrum LSF, Slurm - Data Science Frameworks: PyTorch, TensorFlow, PySpark, NumPy, SciPy, scikit-learn, Pandas, Matplotlib, NLTK, OpenCV - Proficient in SQL, Python, Linux, Git, and Bash scripting. - Had experience leading a BI development team and served as a Scrum Master. - Native English - Native German

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Anton, Data Analyst / BI Engineer

Warsaw, Poland
Last Updated: 6 Feb 2024

Data Analyst / BI Engineer with an extensive background in Computer Science and Software Engineering, bringing over 5 years of hands-on experience in high-level programming languages like Python and SQL to the table. Expertise is anchored in building robust data engineering solutions utilizing technologies such as Apache Spark, Apache Airflow, Databricks, and a variety of cloud platforms including AWS and Azure. Proven track record in data migration, optimization, and visualization with tools like Power BI and Tableau, reinforced by a deep understanding of Data Science principles. Adept at both relational and non-relational databases, showcasing remarkable proficiency in PostgreSQL and experience in MongoDB, MSSQL and Cassandra. This individual has made major contributions to cross-domain projects including blockchain, crowd investing, and securities analysis, evidenced by measurable improvements in data processing efficiency and reliability.

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SQL   5 yr.

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Azure Databricks   3 yr.

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Roman V., Senior Data Scientist

Last Updated: 6 Oct 2023

- Software engineer with 6 years of experience in data science and computer vision. - Proficient in Python, C++, and various data science libraries such as NumPy, Pandas, and scikit-learn. - Experienced in developing real-time computer vision algorithms for iOS and Android devices. - Skilled in dataset gathering, neural network training, and model optimization using Inference Triton Server. - Developed and integrated a face reenactment algorithm for photo editing. - Familiar with DevOps and MLOps tools such as AWS, Docker, and Google Cloud. - Holds a Master's degree in Data Science and a Bachelor's degree in Computer Science from Ukrainian Catholic University.

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Python   6 yr.


OpenCV   6 yr.


Pandas   6 yr.

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Table of Contents

Hard skills of a Matplotlib Developer

As a Matplotlib Developer, having strong hard skills is crucial for success in the field. Matplotlib is a popular data visualization library in Python, used extensively by data scientists, analysts, and developers to create visually appealing and informative plots and charts. Here are the hard skills required for different levels of expertise as a Matplotlib Developer:


  • Python Programming: Proficiency in Python programming language, including knowledge of data structures, control flow, and object-oriented programming.
  • Matplotlib Basics: Understanding the fundamentals of Matplotlib, including the different types of plots, customization options, and plot formatting.
  • Data Visualization Concepts: Familiarity with basic data visualization concepts, such as understanding data types, selecting appropriate visualizations for different data types, and effectively communicating insights through plots.
  • Data Manipulation: Ability to manipulate and preprocess data using libraries like NumPy and Pandas before creating visualizations with Matplotlib.
  • Problem-Solving: Strong problem-solving skills to troubleshoot and debug issues that may arise while creating visualizations using Matplotlib.


  • Advanced Plot Customization: Proficiency in customizing plots in Matplotlib using various options like colors, markers, annotations, legends, and axes manipulation.
  • Data Visualization Best Practices: Understanding and applying best practices for data visualization, such as selecting appropriate chart types, labeling axes, adding titles, handling missing data, and ensuring visual clarity.
  • Data Exploration: Ability to explore and analyze datasets using Matplotlib to gain insights and identify patterns or trends.
  • Performance Optimization: Knowledge of optimizing plot rendering performance by using efficient coding techniques and leveraging Matplotlib’s internal optimization options.
  • Documentation and Collaboration: Experience in documenting code and collaborating with team members using version control systems like Git.
  • Debugging and Troubleshooting: Proficiency in identifying and resolving complex issues that may arise during the development and implementation of Matplotlib-based visualizations.
  • Unit Testing: Familiarity with writing unit tests to ensure the reliability and functionality of Matplotlib-based visualizations.


  • Advanced Plot Types: Mastery in creating advanced plot types such as subplots, 3D plots, polar plots, contour plots, heatmaps, and geospatial visualizations using Matplotlib.
  • Integration with Other Libraries: Experience in integrating Matplotlib with other libraries like Seaborn, Plotly, and Pandas to enhance the capabilities and aesthetics of visualizations.
  • Interactive Visualizations: Proficiency in creating interactive visualizations using Matplotlib’s interactive backend, enabling user interactions like zooming, panning, and hovering.
  • Performance Optimization: Expertise in optimizing plot rendering performance by implementing advanced caching techniques, leveraging hardware acceleration, and utilizing parallel processing.
  • Code Review and Mentorship: Ability to review code and provide constructive feedback to junior team members, as well as mentoring and guiding them in their learning and development.
  • Visualization Design Principles: Knowledge of design principles and aesthetics to create visually appealing and effective data visualizations that effectively convey insights to the intended audience.
  • Visualization Libraries: Familiarity with other data visualization libraries like D3.js, Tableau, or ggplot, allowing for a broader range of visualization options and flexibility.
  • Big Data Visualization: Experience in visualizing large-scale datasets efficiently, leveraging distributed computing frameworks like Apache Spark.

Expert/Team Lead

  • Custom Plotting Functions: Ability to create custom plotting functions and classes in Matplotlib to encapsulate reusable visualization components and promote code modularity.
  • Performance Tuning: Expertise in fine-tuning the performance of Matplotlib visualizations by profiling and optimizing critical code sections, leveraging GPU acceleration, and implementing advanced caching techniques.
  • Visualization Frameworks: Knowledge of other visualization frameworks, such as Bokeh, Plotly, or Dash, to choose the most suitable tool for specific visualization requirements.
  • Advanced Data Analysis: Proficiency in advanced data analysis techniques, such as statistical modeling, time series analysis, or machine learning, to support data-driven decision-making through visualizations.
  • Leadership and Project Management: Strong leadership skills to lead a team of developers, manage projects, and effectively communicate project requirements and progress to stakeholders.
  • Community Contributions: Active participation in the Matplotlib community, contributing to the development of the library, providing support to other users, and sharing knowledge through tutorials or blog posts.
  • Data Visualization Research: Experience in conducting research on data visualization techniques, staying updated with the latest trends and advancements in the field, and applying them to create innovative visualizations.
  • Domain Knowledge: Deep understanding of specific domains or industries, allowing for the creation of domain-specific visualizations tailored to the unique requirements of those industries.
  • Client Interaction: Experience in liaising with clients, understanding their visualization needs, and effectively translating those requirements into actionable visualizations.
  • Project Estimation and Planning: Ability to estimate project timelines and resource requirements, create project plans, and ensure timely delivery of high-quality Matplotlib-based visualizations.
  • Continuous Learning: Commitment to continuous learning and self-improvement, staying updated with the latest advancements in data visualization, data science, and related technologies.

TOP 12 Tech facts and history of creation and versions about Matplotlib Development

  • Matplotlib is a widely used data visualization library for Python, first released in 2003.
  • It was created by John D. Hunter to provide a MATLAB-like plotting interface in Python.
  • The name “Matplotlib” is a portmanteau of “MATLAB” and “plotting.”
  • Matplotlib was inspired by the plotting capabilities of MATLAB, but aimed to be more flexible and accessible.
  • It is an open-source project, allowing users to contribute to its development and improvement.
  • Matplotlib has a rich set of plotting functions and supports various plot types, including line plots, scatter plots, bar plots, histograms, and more.
  • It provides a wide range of customization options, allowing users to control every aspect of their plots.
  • Matplotlib supports multiple output formats, including interactive plots for use in Jupyter notebooks and static image files for publication.
  • Over the years, Matplotlib has become an essential tool in the scientific and data analysis communities.
  • Matplotlib’s success and popularity have led to its integration with other data analysis libraries, such as NumPy and pandas.
  • Matplotlib has undergone several major updates and improvements since its initial release, introducing new features and enhancing performance.
  • One significant update was the introduction of the object-oriented API, which provides a more intuitive and flexible way to create plots.

Soft skills of a Matplotlib Developer

Soft skills are essential for a Matplotlib Developer to effectively communicate, collaborate, and problem-solve in a team environment. These skills complement technical expertise and contribute to the overall success of a project.


  • Strong communication skills: Ability to effectively convey ideas and information to team members and stakeholders.
  • Attention to detail: Being meticulous in code documentation and adhering to best practices.
  • Time management: Ability to prioritize tasks and meet deadlines.
  • Willingness to learn: Eagerness to acquire new skills and knowledge in order to grow as a developer.
  • Collaboration: Ability to work well in a team, actively participate in discussions, and contribute to the project’s goals.


  • Leadership: Ability to take ownership of tasks, guide junior developers, and provide mentorship.
  • Problem-solving: Aptitude for identifying and resolving complex technical issues efficiently.
  • Critical thinking: Ability to analyze situations, assess alternatives, and make informed decisions.
  • Adaptability: Flexibility to adjust to changing project requirements and technologies.
  • Empathy: Understanding the perspectives of team members and stakeholders, and effectively addressing their concerns.
  • Effective presentation skills: Ability to present technical concepts and solutions to both technical and non-technical audiences.
  • Teamwork: Collaboration with cross-functional teams and fostering a positive team environment.


  • Mentorship: Guiding and mentoring junior and mid-level developers, sharing knowledge and expertise.
  • Project management: Ability to oversee projects, including planning, resource allocation, and risk management.
  • Client management: Building and maintaining strong relationships with clients, understanding their needs, and managing expectations.
  • Strategic thinking: Ability to align technical decisions with long-term project and business objectives.
  • Conflict resolution: Skill in resolving conflicts within the team or with stakeholders in a constructive manner.
  • Influence and negotiation: Ability to advocate for technical solutions and negotiate compromises when necessary.
  • Continuous improvement: Drive to enhance development processes, optimize workflows, and implement best practices.
  • Decision-making: Ability to make informed decisions based on analysis, experience, and consideration of the project’s impact.

Expert/Team Lead

  • Strategic leadership: Ability to set the technical direction, oversee multiple projects, and provide guidance to the team.
  • Team management: Building and leading high-performing development teams, fostering collaboration and growth.
  • Industry expertise: Deep knowledge of Matplotlib and related technologies, staying up-to-date with the latest trends and advancements.
  • Business acumen: Understanding the business context, aligning technical decisions with organizational goals, and driving innovation.
  • Conflict resolution: Skill in resolving complex conflicts and mediating between different stakeholders.
  • Decision-making: Making critical decisions that have a significant impact on the project, considering both technical and business factors.
  • Communication and presentation: Ability to effectively communicate complex technical concepts to various audiences, including executives and clients.
  • Strategic partnerships: Building and maintaining strategic relationships with external partners, vendors, and open-source communities.
  • Innovation: Driving innovation within the team, exploring new technologies and approaches to enhance the development process.
  • Thought leadership: Establishing oneself as an industry thought leader through speaking engagements, publications, and contributions to the community.
  • Conflict resolution: Skill in resolving complex conflicts and mediating between different stakeholders.

TOP 10 Matplotlib Related Technologies

  • Python

    Python is a widely-used programming language that is highly popular in the field of data visualization. Matplotlib, being a Python library, makes it easy to create high-quality plots and charts for data analysis and presentation.

  • NumPy

    NumPy is a powerful numerical computing library for Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions. Matplotlib integrates seamlessly with NumPy, allowing for efficient data manipulation and visualization.

  • Pandas

    Pandas is a data manipulation library that provides easy-to-use data structures and data analysis tools. It is widely used in data science and analytics. Matplotlib can directly work with Pandas data structures, making it convenient for plotting and visualizing data from dataframes.

  • Seaborn

    Seaborn is a Python data visualization library built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. Seaborn offers a wide range of plots and customization options, making it a popular choice for data visualization tasks.

  • Plotly

    Plotly is an interactive data visualization library that allows for the creation of interactive plots and dashboards. It supports a wide range of plot types and can be used with Python, R, and other programming languages. Matplotlib can export plots to Plotly, enabling interactive exploration of data.

  • Bokeh

    Bokeh is another interactive data visualization library for Python. It focuses on creating interactive visualizations for the web. Bokeh provides a powerful set of tools for building interactive plots and applications. Matplotlib can generate Bokeh-compatible plots, combining the strengths of both libraries.

  • Jupyter Notebook

    Jupyter Notebook is an open-source web application that allows for the creation and sharing of documents containing live code, equations, visualizations, and narrative text. Matplotlib integrates well with Jupyter Notebook, making it easy to generate and display plots within the notebook environment.

How and where is Matplotlib used?

Case NameCase Description
Data VisualizationMatplotlib is widely used for data visualization in various fields such as scientific research, finance, marketing, and more. It offers a wide range of plotting options, including line plots, scatter plots, bar plots, histograms, and more. With its extensive customization options, Matplotlib allows users to create visually appealing and informative graphs, aiding in data analysis and decision-making.
Exploratory Data AnalysisMatplotlib is often employed for exploratory data analysis tasks. It enables users to quickly generate visualizations to explore datasets, identify patterns, detect outliers, and gain insights into the underlying data distribution. By visualizing the data using Matplotlib, analysts can make informed decisions and formulate hypotheses for further analysis.
Time Series AnalysisMatplotlib provides powerful tools for analyzing and visualizing time series data. It allows users to plot time series data with different frequencies, apply smoothing techniques, and visualize trends, seasonality, and anomalies. Time series analysis using Matplotlib is widely used in finance, economics, weather forecasting, and many other domains.
Geospatial Data VisualizationMatplotlib can be used to create maps and visualize geospatial data. It supports various map projections and allows users to plot geographical features, such as points, lines, polygons, and heatmaps. Geospatial data visualization with Matplotlib is valuable in fields like geography, urban planning, environmental science, and geology.
Statistical AnalysisMatplotlib integrates well with popular statistical libraries like NumPy and pandas, making it a valuable tool for statistical analysis. It enables users to create box plots, violin plots, probability density plots, and other statistical visualizations. These visualizations aid in understanding data distributions, comparing groups, and identifying statistical relationships.
Publication-Quality PlotsMatplotlib provides extensive customization options to create publication-quality plots. It allows users to adjust every aspect of the plot, including colors, fonts, labels, legends, and annotations. Matplotlib’s flexibility in creating visually appealing and professional-looking plots makes it a preferred choice for researchers, scientists, and journalists.
Interactive Data VisualizationMatplotlib can be combined with interactive libraries like Bokeh, Plotly, or PyQt to create interactive data visualizations. These interactive plots enable users to explore the data, zoom in on specific regions, hover over data points for additional information, and interact with the plot dynamically. Interactive data visualization using Matplotlib enhances the user experience and facilitates data exploration.
Machine Learning VisualizationMatplotlib is often used to visualize machine learning algorithms and results. It allows users to plot decision boundaries, feature importance, confusion matrices, and learning curves. Visualizing machine learning models using Matplotlib helps in understanding their performance, identifying areas of improvement, and communicating results to stakeholders.
Dashboard CreationMatplotlib can be used to create interactive dashboards and reports. By combining Matplotlib with web frameworks like Flask or Django, users can create dynamic dashboards that update in real-time, allowing stakeholders to interactively explore the data. Dashboards created with Matplotlib are useful for data-driven decision-making and presenting insights to a wider audience.
AnimationMatplotlib offers animation capabilities to create dynamic and visually engaging plots. Users can animate plots to showcase changes over time, simulate physical processes, or present data in a more engaging way. Animation with Matplotlib is commonly used in fields like physics, biology, and education to visually demonstrate complex concepts.

Pros & cons of Matplotlib

6 Pros of Matplotlib

  • Matplotlib is a widely-used data visualization library in Python that provides a high-level interface for creating static, animated, and interactive visualizations.
  • It offers a wide range of plot types, including line plots, scatter plots, bar plots, histograms, and more, allowing you to effectively represent different types of data.
  • Matplotlib provides extensive customization options, allowing you to control every aspect of your plots, such as colors, labels, titles, axes, and annotations. This level of customization ensures that your visualizations match your specific requirements and design preferences.
  • It has a large and active user community, which means there are plenty of resources, tutorials, and examples available online. This makes it easier to learn and troubleshoot any issues you may encounter while using Matplotlib.
  • Matplotlib integrates well with other libraries in the scientific Python ecosystem, such as NumPy and Pandas, making it a powerful tool for data analysis and exploratory data visualization.
  • It supports various output formats, including image files (PNG, JPEG, etc.), PDF, SVG, and interactive formats like HTML and notebook widgets. This flexibility allows you to easily share and export your visualizations in different formats.

6 Cons of Matplotlib

  • Matplotlib’s default plot aesthetics are not always visually appealing, requiring additional customization to achieve visually pleasing and modern-looking plots.
  • The learning curve for Matplotlib can be steep for beginners due to its extensive customization options and complex syntax. It may take some time and practice to become proficient in creating advanced and complex visualizations.
  • Creating interactive visualizations with Matplotlib can be challenging compared to other libraries specifically designed for interactivity, such as Plotly or Bokeh.
  • Matplotlib’s performance can sometimes be slower compared to other plotting libraries, especially when dealing with large datasets or complex visualizations.
  • The documentation for Matplotlib can be overwhelming for new users, as it covers a wide range of functionalities and options. Finding specific information or understanding certain concepts may require some extra effort.
  • While Matplotlib is highly customizable, the process of customizing plots can be time-consuming, especially when dealing with intricate visualizations that require fine-grained adjustments.

Cases when Matplotlib does not work

  1. Case: Incompatible Python versions
  2. Matplotlib may not work properly if you are using an incompatible version of Python. Matplotlib is designed to work with specific versions of Python, and using an incompatible version can lead to unexpected behavior and errors. It is important to ensure that you are using a compatible version of Python when working with Matplotlib. You can check the Matplotlib documentation to find out the supported Python versions.

  3. Case: Missing or outdated dependencies
  4. Matplotlib relies on various dependencies, such as NumPy and SciPy, to function correctly. If these dependencies are missing or outdated, it can cause Matplotlib to not work as expected. It is crucial to make sure that you have all the necessary dependencies installed and up to date when using Matplotlib. You can use package managers like pip or conda to install or update the required dependencies.

  5. Case: Incorrect installation
  6. If Matplotlib is not installed correctly, it can result in issues and errors. This can happen if there are conflicts with existing installations or if the installation process is interrupted. It is recommended to follow the official installation instructions provided by Matplotlib to ensure a proper installation. Additionally, verifying the installation by importing Matplotlib and checking for any error messages can help identify installation issues.

  7. Case: Backend configuration
  8. Matplotlib supports different backends for rendering its plots, such as Agg, TkAgg, and QtAgg. If the backend is not configured correctly or if there are conflicts with the system environment, it can prevent Matplotlib from working properly. It is important to configure the backend appropriately based on your specific requirements and system setup. The Matplotlib documentation provides detailed information on how to set up and configure the backend.

  9. Case: Hardware or system limitations
  10. In some cases, Matplotlib may not work due to hardware or system limitations. For example, if your system does not have sufficient memory or processing power, it can lead to performance issues or crashes when using Matplotlib. It is recommended to ensure that your hardware meets the minimum requirements for running Matplotlib and that your system is not overloaded with other resource-intensive tasks.

What are top Matplotlib instruments and tools?

  • Matplotlib: Matplotlib is a powerful data visualization library widely used in the Python ecosystem. It was initially released in 2003 and has since become one of the most popular plotting libraries. Matplotlib provides a wide range of chart types, including line plots, bar plots, scatter plots, histograms, and more. It offers extensive customization options to control every aspect of the plot, such as colors, labels, annotations, and axes. Matplotlib is known for its flexibility and ability to produce publication-quality plots.
  • Seaborn: Seaborn is a Python data visualization library built on top of Matplotlib. It was developed to create visually appealing statistical graphics and works well with Pandas data structures. Seaborn simplifies the process of creating complex visualizations by providing high-level functions for tasks like plotting distributions, regression models, and categorical data. It also offers built-in themes and color palettes to enhance the aesthetics of the plots.
  • Plotly: Plotly is an interactive data visualization library that allows for the creation of interactive, web-based plots. It supports a wide range of chart types, including line plots, scatter plots, bar plots, 3D plots, and more. Plotly can be used in Python, R, and JavaScript, making it a versatile tool for data visualization across different programming languages. It also provides an online platform for sharing and collaborating on plots.
  • ggplot: ggplot is a Python implementation of the popular ggplot2 library in R. It follows the grammar of graphics approach, which allows for the creation of highly customizable and expressive plots. ggplot provides a consistent API for generating a wide range of charts, including scatter plots, line plots, bar plots, and more. It also supports themes and scales to control the visual appearance of the plots.
  • Bokeh: Bokeh is a Python library for creating interactive visualizations in web browsers. It focuses on providing interactive and responsive plots that can handle large datasets. Bokeh supports various types of plots, such as line plots, scatter plots, bar plots, and heatmaps. It offers interactive features like zooming, panning, and hovering over data points. Bokeh can also be integrated with other libraries like NumPy and Pandas for data processing.
  • Altair: Altair is a declarative statistical visualization library for Python. It follows a concise and intuitive syntax based on Vega-Lite, a high-level grammar of interactive graphics. Altair allows for the creation of a wide range of plots, including scatter plots, line plots, bar plots, and more. It emphasizes the concept of declarative visualization, where the user specifies the desired visual encoding and the library handles the rest.

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