Por Que Criar Gráficos via Programação em Python se Podemos Usar Power BI, Tableau ou Looker Studio?

Data Science Academy

13 Mar 202306:39

Summary

TLDRThis chapter focuses on data visualization using Netpoint and Plotly, two excellent libraries for creating charts in Python. It discusses the benefits of programmatic chart creation over low-code tools like Power BI and Tableau, highlighting flexibility and customization. The video emphasizes the importance of learning to program charts in Python for seamless integration within data analysis workflows, offering a complete environment for data scientists. It also touches on the advantages and disadvantages of both approaches, guiding viewers to choose the right tool based on their needs.

Takeaways

📊 The chapter aims to introduce data visualization using Netpoint and Plotly libraries, which are excellent for creating charts in Python.
🤔 The discussion addresses the question of why one would program charts in Python when tools like Power BI or Tableau are available for quick visualization.
🔧 Low-code tools like Power BI offer limited flexibility and are best suited for creating basic charts quickly without extensive programming.
🚫 Proprietary tools do not allow users to access or modify the source code, limiting customization options.
💡 Python and R are suggested for their extensive customization capabilities, especially for those who need to create highly specific or customized visualizations.
📈 Plotly and ggplot2 are highlighted as powerful, customizable, and free libraries for data visualization in Python and R, respectively.
🛠️ The ability to modify the source code in Python and R allows for creating high-quality, customizable charts that can even be integrated into commercial solutions.
🔬 Python and R are widely used in data science and provide a comprehensive environment for data analysis, including the creation of visualizations.
🧩 It's beneficial to create visualizations within the same environment used for data analysis to maintain workflow continuity and efficiency.
⚖️ Each tool has its advantages and disadvantages, and the choice depends on the user's needs, whether it's for quick, basic chart creation or in-depth, customized visualizations.

Q & A

What is the main objective of the chapter discussed in the transcript?
-The main objective of the chapter is to introduce data visualization using Netpoint and Plotly libraries, which are excellent for creating charts in Python.
Why is it necessary to create charts via programming in Python when there are tools like Power BI or Tableau?
-While tools like Power BI and Tableau are great for creating basic charts quickly, they offer limited flexibility and customization. Programming in Python allows for greater customization and control over the charts.
What does the term 'low code' refer to in the context of the transcript?
-In the context of the transcript, 'low code' refers to tools that require minimal programming effort, allowing users to create charts with just a few clicks.
What are some of the limitations of proprietary tools like Power BI and Tableau mentioned in the transcript?
-Proprietary tools like Power BI and Tableau have limitations such as lack of access to the source code, inability to modify the tool's standards, and limited customization options.
Why is it beneficial to learn programming for creating charts in Python?
-Learning to program charts in Python is beneficial because it allows for complete customization and control over the charts, and it integrates well with data analysis workflows, allowing for a seamless process from data manipulation to visualization.
What are some advantages of using programming languages like Python and R for data visualization?
-Python and R offer free and highly customizable libraries for data visualization, such as ggplot2 for R, which allows for high-quality statistical charts with extensive customization options.
How does the transcript differentiate between using low code tools and programming for data visualization?
-The transcript differentiates by stating that low code tools are suitable for quick, basic chart creation with less flexibility, while programming offers extensive customization and control, which is essential for complex or specific data visualization needs.
What is the significance of being able to modify the source code when creating custom visualizations?
-The ability to modify the source code is significant as it allows for tailoring the visualization to specific requirements, including the integration of these visualizations into commercial solutions like analytical applications or predictive data applications.
What are some scenarios where using programming for data visualization might be preferred over low code tools?
-Programming for data visualization is preferred when working on a data analysis project where customization and specific visualizations are needed, or when the workflow requires seamless integration of data manipulation and visualization within the same environment.
Why might someone choose to use a low code tool like Power BI over programming for data visualization?
-Someone might choose a low code tool like Power BI for its ease of use and quick chart creation, which is ideal for users who need to resolve business problems with basic charts without the need for extensive customization.
How does the transcript suggest one should approach the choice between low code tools and programming for data visualization?
-The transcript suggests that the choice between low code tools and programming for data visualization should be based on individual needs, with low code tools being suitable for quick, basic chart creation and programming being more appropriate for complex, customized visualizations within a data analysis project.

Outlines

00:00

📊 Introduction to Data Visualization Libraries in Python

This paragraph introduces the topic of the chapter, which is about data visualization using Netpoint and Plotly libraries in Python. The speaker discusses the benefits of programming graphs in Python over using tools like Power BI or Tableau, emphasizing the need for customization and flexibility. The comparison is made between low-code tools, which are easy to use but offer limited flexibility, and programming libraries that allow for extensive customization. The speaker also mentions that they will provide a detailed discussion on why programming graphs in Python is valuable, especially for those who require specific customizations that cannot be achieved with proprietary tools.

05:01

🔧 The Advantages of Programming Graphs in Python

The second paragraph delves into the practical advantages of using Python for data visualization. The speaker explains that while tools like Power BI and Tableau are excellent for quickly creating basic graphs, they lack the flexibility needed for more complex or customized visualizations. The paragraph highlights the importance of being able to work within a single environment, such as Jupyter Notebook, for data analysis and visualization. It also touches on the limitations of proprietary tools, which do not allow access to the source code, thus restricting the ability to make significant customizations. The speaker concludes by encouraging the audience to learn programming for graphs in Python, as it offers a comprehensive environment for data analysis and visualization without the need to switch between different tools.

Mindmap

Keywords

💡Data Visualization

Data visualization refers to the graphical representation of information and data. It is a crucial aspect of data analysis that helps in understanding complex data sets by presenting them in a more digestible visual format. In the context of the video, the speaker discusses the use of Netpoint and Lib as libraries for creating data visualizations in Python, emphasizing the importance of visualizing data to better understand and communicate insights derived from data analysis.

💡Netpoint

Netpoint is a library mentioned in the script for creating data visualizations. It is part of the discussion around programming-based data visualization tools in Python. The speaker highlights Netpoint as one of the options for those who require more customization and flexibility in their data visualizations compared to low-code or no-code tools.

💡Lib

Lib, short for 'library', is a collection of pre-written code that can be used in a program. In the script, 'Lib' is used in conjunction with Netpoint to refer to the library for data visualization in Python. The speaker discusses the benefits of using such libraries for creating customized and flexible data visualizations.

💡Python

Python is a high-level programming language known for its readability and versatility. It is widely used in data analysis, machine learning, and web development. The video script mentions Python as the programming language in which Netpoint and Lib are used for data visualization, indicating its role in enabling detailed and customized graphical representations of data.

💡Low-code Tools

Low-code tools are software applications that allow users to create applications with minimal hand-coding. They are designed to accelerate the development process by providing pre-built components and a visual interface. The script contrasts low-code tools like Power BI and Tableau with programming-based visualization, noting that while they offer speed and ease of use, they may lack the flexibility and customization available through programming.

💡Power BI

Power BI is a business analytics service by Microsoft that enables users to visualize data, create reports, and gain business insights. In the video script, Power BI is mentioned as an example of a low-code tool that is excellent for quickly creating basic charts but may not offer the same level of customization as programming-based visualizations.

💡Tableau

Tableau is a data visualization software that allows users to create a variety of interactive and static data visualizations. The script refers to Tableau as a tool that was previously known as 'Google Data Studio' and is now recognized as a powerful but proprietary tool for data visualization, similar to Power BI.

💡Customization

Customization in the context of data visualization refers to the ability to tailor the appearance and functionality of visualizations to specific needs or preferences. The speaker argues for the importance of customization when using programming languages like Python for data visualization, as it allows for unique and specific representations that may not be possible with low-code or no-code tools.

💡ggplot2

ggplot2 is a plotting system for the R programming language, based on the grammar of graphics. It is mentioned in the script as an excellent library for creating high-quality statistical graphics that are fully customizable and flexible. The speaker compares ggplot2 with Python libraries, emphasizing the importance of learning programming for data visualization to maintain consistency within the data analysis environment.

💡Proprietary Tools

Proprietary tools are software applications that are owned by a company and are subject to its licensing terms. The script discusses proprietary tools like Power BI and Tableau, noting that while they are powerful, they do not allow users to modify the source code or customize the tool beyond certain limits, which contrasts with the flexibility offered by programming-based visualization.

💡Data Analysis

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. The video script emphasizes the importance of data analysis in the context of using programming languages for visualization. It suggests that learning to create visualizations within the same environment used for data analysis (like Python or R) can streamline the workflow and enhance the analytical process.

Highlights

The chapter aims to introduce data visualization with Netpoint and Lib that Ciborne, two excellent libraries for creating charts in Python.

Netpoint and Lib that Ciborne require programming to build charts.

Why program charts in Python when tools like Power BI or Tableau exist?

Low-code tools like Power BI and Tableau offer quick chart creation but limited flexibility.

Power BI and Tableau are proprietary tools with no access to the source code for customization.

Python and R offer free and fully customizable libraries like ggplot2 for high-quality statistical charts.

ggplot2 allows for extensive customization and high-resolution statistical graphics.

Python and R are widely used in data science, providing a complete environment for data analysis including chart creation.

It's efficient to create charts within the same environment used for data analysis, such as Jupyter Notebook.

Programming charts in Python is beneficial for in-depth data analysis within a single workflow.

Customization is key when creating specific charts for data analysis projects.

Low-code tools are suitable for quick, basic chart creation without extensive programming.

For in-depth projects requiring specific chart customization, programming is more effective than using low-code tools.

The chapter will cover programming charts using Netpoint, Lib, and Ciborne in Python.

The choice between low-code tools and programming depends on the needs and the specific requirements of the project.

Each tool has its advantages and disadvantages, and the decision should be made based on the project's requirements.

The chapter will provide practical examples of creating charts programmatically with Netpoint, Lib, and Ciborne.