python 散点图自定义x轴标签/经度180到-180连接

矫情吗；* 2022-11-10 10:51 173阅读 0赞

最近在做一个散点图，横轴为经度，纵轴为速度，即不是纬度，因此不是那种展现空间关系的。

但是数据的经度为70到180，之后-180到-170这样，因此用正常的方法出图会出现，-180和180的散点无法进行连接。

于是使用python修改标签的办法。首先将横轴的数据即经度的-180~180变为0-360

利用以下函数实现

def changto360(data):
        c = data['lon']
        c[c<0]=c+360
        data['lon']=c
        return data

之后采用以下办法自定义横轴标签

plt.xticks([60,90,120,150,180],#,210
                   ['60°E','90°E','120°E','150°E','180°'],#,'150°W'
                   fontsize=12)

以下是所有代码，包含字体的大小的定义和副的刻度值

def changto360(data):
        c = data['lon']
        c[c<0]=c+360
        data['lon']=c
        
        c = data['piomas']
        c[c==-1]=np.nan
        data['piomas']=c
        return data
    
    
    #该程序为了绘制散点图，随着经度变化而变化
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    from matplotlib.ticker import MultipleLocator, FormatStrFormatter
    fig, host = plt.subplots(figsize=(15, 7), dpi=80)#
    fig.subplots_adjust(top=0.8,bottom=0.095,left=0.06,right=0.95)
    
    ship4=pd.read_csv(r"H:\mission\AIS\speed\20_AIS_position_value.csv") #extent_LS.csv
    ship4['datetime'] = pd.to_datetime(ship4['datetime'])
    
    ship1=pd.read_csv(r"H:\mission\AIS\speed\2_AIS_position_value.csv") #extent_LS.csv
    ship1['datetime'] = pd.to_datetime(ship1['datetime'])
    
    ship2=pd.read_csv(r"H:\mission\AIS\speed\2_back_AIS_position_value.csv") #extent_LS.csv
    ship2['datetime'] = pd.to_datetime(ship2['datetime'])
    
    ship3=pd.read_csv(r"H:\mission\AIS\speed\3_AIS_position_value.csv") #extent_LS.csv
    ship3['datetime'] = pd.to_datetime(ship3['datetime'])
    
    ship1=changto360(ship1)
    ship2=changto360(ship2)
    ship3=changto360(ship3)
    ship4=changto360(ship4)
    
    
    
    plt.scatter(ship1['lon'], ship1['piomas'],label="20210105-20210126",alpha=0.5)
    plt.scatter(ship3['lon'], ship3['piomas'],label="20210106-20210128",alpha=0.5)
    plt.scatter(ship4['lon'], ship4['piomas'],label="20211224-20210119",alpha=0.5)
    plt.scatter(ship2['lon'], ship2['piomas'],label="20210127-20210219",alpha=0.5)
    #系统默认为蓝色和橙色
    plt.xticks([60,90,120,150,180],#,210
                   ['60°E','90°E','120°E','150°E','180°'],#,'150°W'
                   fontsize=12)
    xminorLocator = MultipleLocator(10) #将x轴次刻度标签设置为10的倍数
    host.xaxis.set_minor_locator(xminorLocator)
    
    plt.yticks(fontsize=12)
    plt.legend(loc="upper center",bbox_to_anchor=(0.7,1.29),prop={
    'weight' : 'normal',
    'size'   : 15,
    })#在散点图右上角显示名字
    #plt.xticks(ticks, [labels], **kwargs)
    plt.xlabel('longitude(°)',fontdict={'weight': 'normal', 'size': 16})
    #plt.ylabel('ship speed(kn)',fontdict={'weight': 'normal', 'size': 16})
    plt.ylabel('PIOMAS sea ice thickness(m)',fontdict={'weight': 'normal', 'size': 16})
    
    plt.show()
    plt.savefig(r'H:\mission\plot\scatter_piomas_longitude.png', dpi=300)