Difference between revisions of "Data Visualization with Python"
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Data visualization is the process of representing data in visual formats like charts, graphs, or maps to convey information | Data visualization is the process of representing data in visual formats like charts, graphs, or maps to convey information | ||
| − | quickly by revealing patterns and trends that may be difficult to discern when data is presented in tables or text formats. By turning complex data into visual elements, data visualization makes analysis more intuitive and accessible for everyone | + | quickly by revealing patterns and trends that may be difficult to discern when data is presented in tables or text formats. By turning complex data into visual elements, data visualization makes analysis more intuitive and accessible for everyone ultimately leading to more informed decision making processes. |
| − | To get into data visualization it is necessary to learn some fundamentals | + | To get into data visualization it is necessary to learn some fundamentals such as [[Data formats|data formats]] and basic [[Introduction to statistical figures|statistical figures]] that are commonly used. |
| − | == Data visualization in Python == | + | == Data visualization with Python == |
| − | Python has become the language for data scientists and analysts due to its simplicity, versatility and robust ecosystem of libraries specifically tailored for data visualization purposes. These libraries offer tools for creating dynamic and interactive | + | In Python, data visualization enhances statistical analysis by enabling the graphical representation of statistical summaries and distributions, such as histograms, box plots, and scatter plots. These visualizations help in assessing the distribution of data, identifying correlations, and detecting anomalies. Advanced visualizations, like heatmaps or pair plots, provide deeper insights into multivariate relationships and the impact of statistical measures such as correlation coefficients or regression lines. |
| − | visualizations that cater to diverse needs. | + | |
| + | For an introduction to the workflow of data visualization, check this entry. | ||
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| + | ===Python Libraries for visualizing data=== | ||
| + | Python has become the language for data scientists and analysts due to its simplicity, versatility and robust ecosystem of libraries specifically tailored for data visualization purposes. These libraries offer tools for creating dynamic and interactive visualizations that cater to diverse needs. Below we present three main libraries: | ||
| + | |||
| + | [[File:Seaborn|thumb]] | ||
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[[Introduction to Seaborn]] | [[Introduction to Seaborn]] | ||
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== Specific graphics and figures == | == Specific graphics and figures == | ||
Revision as of 09:30, 6 September 2024
Contents
Introduction
Data visualization is the process of representing data in visual formats like charts, graphs, or maps to convey information quickly by revealing patterns and trends that may be difficult to discern when data is presented in tables or text formats. By turning complex data into visual elements, data visualization makes analysis more intuitive and accessible for everyone ultimately leading to more informed decision making processes.
To get into data visualization it is necessary to learn some fundamentals such as data formats and basic statistical figures that are commonly used.
Data visualization with Python
In Python, data visualization enhances statistical analysis by enabling the graphical representation of statistical summaries and distributions, such as histograms, box plots, and scatter plots. These visualizations help in assessing the distribution of data, identifying correlations, and detecting anomalies. Advanced visualizations, like heatmaps or pair plots, provide deeper insights into multivariate relationships and the impact of statistical measures such as correlation coefficients or regression lines.
For an introduction to the workflow of data visualization, check this entry.
Python Libraries for visualizing data
Python has become the language for data scientists and analysts due to its simplicity, versatility and robust ecosystem of libraries specifically tailored for data visualization purposes. These libraries offer tools for creating dynamic and interactive visualizations that cater to diverse needs. Below we present three main libraries:
Specific graphics and figures
If you want to know only the most important command to create specific figures, click on this image.
Contributors of this entry are: Abraham and Gustavo Rodriguez | Back to Python Landscape
