Shulou(Shulou.com)06/01 Report--

Today, I will talk to you about how to use seaborn. Many people may not know much about it. In order to make you understand better, the editor has summarized the following contents for you. I hope you can get something from this article.

Seaborn is developed based on matplotlib, provides a higher level of interface, and makes the visualization more expressive.

Here's how to get started with the seaborn library. Import it and the pyplot module first:

Import matplotlib.pyplot as plt

Import seaborn as sns

It contains some classic data sets, such as tips, titanic, iris, etc., which are imported in turn:

Tips = sns.load_dataset ("tips")

Titanic = sns.load_dataset ("titanic")

Iris = sns.load_dataset ("iris")

Take titanic as an example, draw a factorplot diagram to show the survived ratio of sex (male and female) and different classes (1, 2, 3):

Sns.factorplot (x = "pclass", y = "survived", hue= "sex", data=titanic)

You can also customize pointplot diagrams and adjust markers,linestyles and other parameters:

Sns.pointplot (x = "class", y = "survived", hue= "sex", data=titanic

Palette= {"male": "g", "female": "m"}, markers= ["^", "o"], linestyles= ["-", "-"])

Barplot chart of survival ratio of different sexes in different classes:

Sns.barplot (x = "sex", y = "survived", hue= "class", data=titanic)

Statistics of the occurrence frequency countplot chart of different values of deck enumeration values:

Sns.countplot (x = "deck", data=titanic, palette= "Greens_d")

The box chart observes the law of data distribution:

Sns.boxplot (x = "alive", y = "age", hue= "adult_male", data=titanic)

For the use of seaborn, there is a cheetsheet diagram, as shown below:

After reading the above, do you have any further understanding of how to use seaborn? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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