Seaborn学习

ゝ一纸荒年。 2023-06-13 12:30 6阅读 0赞

**1 什么是Seaborn**

Seaborn是基于matplotlib的图形可视化python包。它提供了一种高度交互式界面，便于用户能够做出各种有吸引力的统计图表。

Seaborn是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn能做出很具有吸引力的图，而使用matplotlib就能制作具有更多特色的图。应该把Seaborn视为matplotlib的补充，而不是替代物。同时它能高度兼容numpy与pandas数据结构以及scipy与statsmodels等统计模式。

2 条形图

API:

seaborn.barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, orient=None, color=None, palette=None, saturation=0.75, errcolor='.26', errwidth=None, capsize=None, dodge=True, ax=None, **kwargs)

import seaborn as sns
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    
    x = np.arange(8)
    y = np.array([1, 5, 3, 6, 8, 9, 4, 7])
    
    df = pd.DataFrame({"X-axis": x, "Y-axis": y})
    print(df)
    
    sns.barplot("X-axis", "Y-axis", palette="muted", data=df)
    plt.xticks(rotation=45)
    plt.show()

结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70][]

palette改变样式，

例如：palette="RdBu\_r"

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 1][]

**2、属性 相关性的 热图**

import seaborn as sns
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    
    
    def correclation_map(df, columns, figsize=(15, 10)):
        correclation = (df.loc[:, columns]).corr()
        print(correclation)
    
        fig, ax = plt.subplots(figsize=figsize)
        sns.heatmap(correclation, annot=True, ax=ax)
        plt.show()
    
    x = np.arange(4)
    y = np.array([1, 5, 3, 6])
    z = np.array([1, 1, 1, 10])
    D = np.array([1, 10, 110, 10])
    df = pd.DataFrame({"R": x, "P": y, "F1": z, "D": D})
    
    columns = ["R", "P", "F1", "D"]
    correclation_map(df, columns)

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 2][]

3、计算target 相同标签的个数，画成条形图

import seaborn as sns
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    
    y = np.array([1, 5, 3, 6, 3, 6, 9, 10, 6, 5, 1, 1, 1])
    df = pd.DataFrame({"P": y})
    
    sns.countplot(df['P'])
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 3][]

4、箱型图

箱形图（英文：Box plot），又称为盒须图、盒式图、盒状图或箱线图，是一种用作显示一组数据分散情况资料的统计图。因型状如箱子而得名。在各种领域也经常被使用，常见于品质管理，快速识别异常值。

import numpy as np
    import pandas as pd
    import seaborn as sns
    import matplotlib.pyplot as plt
    
    sns.set(style="ticks")
    f, ax = plt.subplots(figsize=(7, 6))
    # ax.set_xscale("log")
    
    distance = np.array([10, 1, 2, 1, 3, 2, 1, 3, 4, 10])
    method = np.array(["WA", "WB", "WA", "WA", "WA", "WA", "WA", "WA", "WA", "WB"])
    
    planets = pd.DataFrame({"distance": distance, "method": method})
    # Plot the orbital period with horizontal boxes
    sns.boxplot(x="distance", y="method", data=planets,
                whis="range", palette="vlag")
    
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 4][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/201911171601087.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/2019111716022277.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20191117165550737.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/20191117165922144.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1_size_16_color_FFFFFF_t_70 4]: https://img-blog.csdnimg.cn/2019111717313379.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzIzOTQ0OTE1,size_16,color_FFFFFF,t_70