seaborn从入门到精通01-seaborn介绍与load_dataset(“tips“)出现超时解决方案

seaborn从入门到精通01-seaborn介绍

参考

seaborn官方

seaborn官方介绍

seaborn可视化入门

【宝藏级】全网最全的Seaborn详细教程-数据分析必备手册（2万字总结）

Seaborn常见绘图总结

总结

本文主要是seaborn从入门到精通系列第1篇，本文介绍了seaborn的官方简介，同时介绍了较好的参考文档置于博客前面，读者可以重点查看参考链接。本系列的目的是可以完整的完成seaborn从入门到精通。重点参考连接

seaborn介绍

官方介绍

Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures. Seaborn是一个用Python制作统计图形的库。它构建在matplotlib之上，并与pandas数据结构紧密集成。 Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Seaborn帮助您探索和理解您的数据。它的绘图功能对包含整个数据集的数据框架和数组进行操作，并在内部执行必要的语义映射和统计聚合以生成信息丰富的绘图。它的面向数据集的声明性API让您可以专注于图表的不同元素的含义，而不是如何绘制它们的细节。

seaborn入门流程

# Import seaborn
import seaborn as sns

# Apply the default theme
sns.set_theme()

# Load an example dataset 需要
# tips = sns.load_dataset("tips")
tips = sns.load_dataset("tips",cache=True,data_home=r'.\seaborn-data')

# Create a visualization
sns.relplot(
    data=tips,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)

如果加载数据时出现问题，可以参考博客

seaborn从入门到精通-seaborn在load_dataset(“tips“)出现超时的错误

# Import seaborn
import seaborn as sns

Seaborn is the only library we need to import for this simple example. By convention, it is imported with the shorthand sns. 对于这个简单的示例，我们需要导入的库只有Seaborn。按照惯例，它与简写sns一起导入。 Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot. 在幕后，seaborn使用matplotlib绘制它的情节。对于交互式工作，建议在matplotlib模式下使用Jupyter/IPython接口，否则当您想要查看绘图时，必须调用matplotlib.pyplot.show()。

# Apply the default theme
sns.set_theme()

This uses the matplotlib rcParam system and will affect how all matplotlib plots look, even if you don’t make them with seaborn. Beyond the default theme, there are several other options, and you can independently control the style and scaling of the plot to quickly translate your work between presentation contexts (e.g., making a version of your figure that will have readable fonts when projected during a talk). If you like the matplotlib defaults or prefer a different theme, you can skip this step and still use the seaborn plotting functions. 这将使用matplotlib rcParam系统，并将影响所有matplotlib图的外观，即使您没有使用seaborn创建它们。除了默认主题之外，还有其他几个选项，您可以独立控制图形的样式和缩放，以便在不同的演示上下文之间快速转换您的工作(例如，制作一个在演讲期间投影时具有可读字体的图形版本)。如果您喜欢matplotlib默认值或喜欢不同的主题，您可以跳过此步骤，仍然使用seaborn绘图函数。

# Load an example dataset
#tips = sns.load_dataset("tips")
tips = sns.load_dataset("tips",cache=True,data_home=r'.\seaborn-data')

Most code in the docs will use the load_dataset() function to get quick access to an example dataset. There’s nothing special about these datasets: they are just pandas dataframes, and we could have loaded them with pandas.read_csv() or built them by hand. Most of the examples in the documentation will specify data using pandas dataframes, but seaborn is very flexible about the data structures that it accepts. 文档中的大多数代码将使用load_dataset()函数来快速访问示例数据集。这些数据集没有什么特别之处:它们只是pandas数据框架，我们可以用pandas.read_csv()加载它们，也可以手工构建它们。文档中的大多数示例都将使用pandas数据框架指定数据，但是seaborn对于它所接受的数据结构非常灵活。

# Create a visualization
sns.relplot(
    data=tips,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)

This plot shows the relationship between five variables in the tips dataset using a single call to the seaborn function relplot().

这个图通过对seaborn函数relplot()的一次调用显示了tips数据集中五个变量之间的关系。

Notice how we provided only the names of the variables and their roles in the plot. Unlike when using matplotlib directly, it wasn’t necessary to specify attributes of the plot elements in terms of the color values or marker codes.

请注意，我们如何仅提供变量的名称及其在图中的角色。与直接使用matplotlib不同，不需要根据颜色值或标记代码指定绘图元素的属性。

Behind the scenes, seaborn handled the translation from values in the dataframe to arguments that matplotlib understands. This declarative approach lets you stay focused on the questions that you want to answer, rather than on the details of how to control matplotlib.

在幕后，seaborn处理从数据框架中的值到matplotlib能够理解的参数的转换。这种声明性方法使您能够将注意力集中在想要回答的问题上，而不是集中在如何控制matplotlib的细节上。