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        具有多个分布的海运距离图/离散图

        时间:2024-08-21

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                  本文介绍了具有多个分布的海运距离图/离散图的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!

                  问题描述

                  我正在使用海运绘制分布图。我想在同一张图上用不同的颜色绘制多个分布:

                  下面是我开始绘制分布图的方式:

                  import numpy as np
                  import pandas as pd
                  from sklearn.datasets import load_iris
                  iris = load_iris()
                  iris = pd.DataFrame(data= np.c_[iris['data'], iris['target']],columns= iris['feature_names'] + ['target'])
                  
                     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target
                  0                5.1               3.5                1.4               0.2     0.0
                  1                4.9               3.0                1.4               0.2     0.0
                  2                4.7               3.2                1.3               0.2     0.0
                  3                4.6               3.1                1.5               0.2     0.0
                  4                5.0               3.6                1.4               0.2     0.0
                  
                  sns.distplot(iris[['sepal length (cm)']], hist=False, rug=True);
                  

                  'target'列包含3个值:0、1、2。

                  我希望看到一个萼片长度分布图,其中target ==0target ==1target ==2共有3个分布图。

                  推荐答案

                  重要的是按target012的值对数据帧进行排序。

                  import numpy as np
                  import pandas as pd
                  from sklearn.datasets import load_iris
                  import seaborn as sns
                  
                  iris = load_iris()
                  iris = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
                                      columns=iris['feature_names'] + ['target'])
                  
                  # Sort the dataframe by target
                  target_0 = iris.loc[iris['target'] == 0]
                  target_1 = iris.loc[iris['target'] == 1]
                  target_2 = iris.loc[iris['target'] == 2]
                  
                  sns.distplot(target_0[['sepal length (cm)']], hist=False, rug=True)
                  sns.distplot(target_1[['sepal length (cm)']], hist=False, rug=True)
                  sns.distplot(target_2[['sepal length (cm)']], hist=False, rug=True)
                  
                  plt.show()
                  

                  输出如下:

                  如果您不知道target可能有多少值,请在target列中找到唯一的值,然后对数据帧进行切片并相应地添加到绘图中。

                  import numpy as np
                  import pandas as pd
                  from sklearn.datasets import load_iris
                  import seaborn as sns
                  
                  iris = load_iris()
                  iris = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
                                      columns=iris['feature_names'] + ['target'])
                  
                  unique_vals = iris['target'].unique()  # [0, 1, 2]
                  
                  # Sort the dataframe by target
                  # Use a list comprehension to create list of sliced dataframes
                  targets = [iris.loc[iris['target'] == val] for val in unique_vals]
                  
                  # Iterate through list and plot the sliced dataframe
                  for target in targets:
                      sns.distplot(target[['sepal length (cm)']], hist=False, rug=True)
                  

                  这篇关于具有多个分布的海运距离图/离散图的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持跟版网!

                  上一篇:海运-根据色调名称更改条形图颜色 下一篇:使用 pandas 交叉表创建条形图

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