Difference between revisions of "Data Visualization with Python"
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| − | == Introduction == | + | ==Introduction== |
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. <ref>https://plotly.com/</ref> |
| − | == 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. | ||
| + | |||
| + | ==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:Matplotlib.png|200px|thumb|right|Matplotlib library logo]] | ||
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| + | |||
| + | * '''Matplotlib''': Matplotlib holds a position in the landscape of Pythons data visualization capabilities. Its user friendly interface and adaptability make it an excellent starting point for creating types of plots. Whether its line plots, scatter plots, bar charts or complex visualizations Matplotlib can generate them effortlessly with a few lines of code. [[File:Seaborn.png|200px|thumb|right|Seaborn library logo]] | ||
| − | |||
| − | For | + | * '''Seaborn''': Seaborn is an extension of Matplotlib that focuses on statistical data visualization. It aims to make the creation of informative and visually pleasing graphics easier. With its user interface Seaborn allows users to generate visualizations using concise code. For an overview of this library check [[Introduction to Seaborn]][[File:Plotly.png|200px|thumb|right|Seaborn library logo]] |
| − | |||
| − | + | * '''Plotly''': Python's data visualization capabilities extend beyond static plots. Plotly excels in creating interactive web-based visualizations for dynamic exploration. | |
| − | |||
| + | ==Specific graphics and figures== | ||
| + | If you want to know only the most important command to create specific statistical figures, click on this image. | ||
* [[Scatterplots in Python|Scatterplots]] | * [[Scatterplots in Python|Scatterplots]] | ||
| + | ==References== | ||
| + | # https://matplotlib.org/ | ||
| + | # https://seaborn.pydata.org/ | ||
| + | # https://plotly.com/ | ||
Contributors of this entry are: Abraham and Gustavo Rodriguez | Back to [[Python Landscape]] | Contributors of this entry are: Abraham and Gustavo Rodriguez | Back to [[Python Landscape]] | ||
Latest revision as of 12:22, 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. <ref>https://plotly.com/</ref>
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:
- Matplotlib: Matplotlib holds a position in the landscape of Pythons data visualization capabilities. Its user friendly interface and adaptability make it an excellent starting point for creating types of plots. Whether its line plots, scatter plots, bar charts or complex visualizations Matplotlib can generate them effortlessly with a few lines of code.
- Seaborn: Seaborn is an extension of Matplotlib that focuses on statistical data visualization. It aims to make the creation of informative and visually pleasing graphics easier. With its user interface Seaborn allows users to generate visualizations using concise code. For an overview of this library check Introduction to Seaborn
- Plotly: Python's data visualization capabilities extend beyond static plots. Plotly excels in creating interactive web-based visualizations for dynamic exploration.
Specific graphics and figures
If you want to know only the most important command to create specific statistical figures, click on this image.
References
Contributors of this entry are: Abraham and Gustavo Rodriguez | Back to Python Landscape
