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Article summary:

1. Types of charts: Distribution, Correlation, Evolution, Spatial, Part of a Whole and Ranking.

2. Popular Python libraries for data visualization: Matplotlib, Seaborn and Plotly.

3. These libraries allow users to create visually attractive graphs with few lines of code.

Article analysis:

The article is generally reliable in terms of the information it provides about the types of charts available in Python and the popular libraries used for data visualization. The article does not make any unsupported claims or present any one-sided reporting; instead, it provides an overview of the different types of charts available in Python as well as the popular libraries used for data visualization.

However, there are some points that could be improved upon in terms of trustworthiness and reliability. For example, while the article does provide an overview of the different types of charts available in Python, it does not provide any detailed information about how to use them or how to interpret them correctly. Additionally, while the article mentions three popular libraries for data visualization (Matplotlib, Seaborn and Plotly), it does not mention any other alternatives that may be available or discuss their relative merits compared to these three options. Furthermore, while the article does mention that these libraries allow users to create visually attractive graphs with few lines of code, it does not provide any examples or further details on how this can be achieved.

In conclusion, while the article is generally reliable in terms of providing an overview of the different types of charts available in Python as well as popular libraries used for data visualization, there are some areas where more detail could be provided to improve its trustworthiness and reliability.