How to implement visual boxplot in python

This article mainly introduces how to implement visual box plots in python. The editor thinks it is quite good. Now I will share it with you and give it a reference. Let’s follow the editor to take a look.

Data description

Parameter introduction

    plt.boxplot(x, notch=None, sym=None, vert=None,   
                 whis=None, positions=None, widths=None,   
                 patch_artist=None, meanline=None, showmeans=None,   
                 showcaps=None, showbox=None, showfliers=None,   
                 boxprops=None, labels=None, flierprops=None,   
                 medianprops=None, meanprops=None,   
                 capprops=None, whiskerprops=None)

x: Specify the data to be drawn as a box plot;
notch: Whether to display the boxplot in the form of a notch, the default is not notch;
sym: Specify the shape of the abnormal point, the default is + sign display;
vert: Whether the boxplot needs to be placed vertically, the default is vertical Placement;
whis: Specify the distance between the upper and lower whiskers and the upper and lower quartiles, the default is 1.5 times the interquartile range;
positions: Specify the position of the box plot, the default is [0,1,2… ];
widths: Specify the width of the boxplot, the default is 0.5;
patch_artist: Whether to fill the color of the box;
meanline: Whether to express the mean in the form of a line, the default is to use points;
showmeans: Whether to display the mean, not displayed by default;
showcaps: Whether to display the two lines at the top and end of the box plot, displayed by default;
showbox: Whether to display the box of the box plot, displayed by default;
showfliers: Whether to display outliers, displayed by default;
boxprops: Set the properties of the box, such as border color, fill color, etc.;
labels: Add labels to the box plot, similar to the function of the legend;
filerprops: Set the properties of outliers, such as the shape, size, fill color, etc. of outliers;
medianprops: Set the properties of the median, such as line type, thickness, etc.;
meanprops: Set the mean properties, such as point size, color, etc.;
capprops: Set the properties of the top and end lines of the box plot, such as color, thickness, etc.;
whiskerprops: Set the properties of the whiskers, such as color, thickness, line Type, etc.;

Code implementation

    # 导入第三方模块  
    import pandas as pd  
    import matplotlib.pyplot as plt  
      
    # 读取Titanic数据集  
    titanic = pd.read_csv('titanic_train.csv')  
    # 检查年龄是否有缺失  
    any(titanic.Age.isnull())  
    # 不妨删除含有缺失年龄的观察  
    titanic.dropna(subset=['Age'], inplace=True)  
      
    # 设置图形的显示风格  
    plt.style.use('ggplot')  
      
    # 设置中文和负号正常显示  
    plt.rcParams['font.sans-serif'] = 'Microsoft YaHei'  
    plt.rcParams['axes.unicode_minus'] = False  
      
    # 绘图：整体乘客的年龄箱线图  
    plt.boxplot(x = titanic.Age, # 指定绘图数据  
                patch_artist=True, # 要求用自定义颜色填充盒形图，默认白色填充  
                showmeans=True, # 以点的形式显示均值  
                boxprops = {'color':'black','facecolor':'#9999ff'}, # 设置箱体属性，填充色和边框色  
                flierprops = {'marker':'o','markerfacecolor':'red','color':'black'}, # 设置异常值属性，点的形状、填充色和边框色  
                meanprops = {'marker':'D','markerfacecolor':'indianred'}, # 设置均值点的属性，点的形状、填充色  
                medianprops = {'linestyle':'--','color':'orange'}) # 设置中位数线的属性，线的类型和颜色  
    # 设置y轴的范围  
    plt.ylim(0,85)  
      
    # 去除箱线图的上边框与右边框的刻度标签  
    plt.tick_params(top='off', right='off')  
    # 显示图形  
    plt.show()

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