pandas plot参数

封装matplotlib的plot函数

• pandas.plot

  DataFrame.plot(x=None, y=None, kind=‘line’, ax=None, subplots=False, sharex=None, sharey=False, layout=None, figsize=None, use_index=True, title=None, grid=None, legend=True, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, secondary_y=False, sort_columns=False, **kwds)

  • kind : str

    • ‘line’ : line plot (default)

    • ‘bar’ : vertical bar plot

    • ‘barh’ : horizontal bar plot

    • ‘hist’ : histogram

    • ‘box’ : boxplot

    • ‘kde’ : Kernel Density Estimation plot

    • ‘density’ : same as ‘kde’

    • ‘area’ : area plot

    • ‘pie’ : pie plot

    • ‘scatter’ : scatter plot

    • ‘hexbin’ : hexbin plot

  • alpha
    点的不透明度，当点的透明度很高时，单个点的颜色很浅。这样点越密集，对应区域颜色越深。通过颜色很浅就可以就看一看出数据的几种区域。alpha=0，无色，整个绘图区域无图，类似于[R, G, B, alpha]四通道中的alpha通道

    housing_copy.plot(kind="scatter", x='longitude', y='latitude', alpha=0.1)
    

    

    housing_copy.plot(kind="scatter", x='longitude', y='latitude', alpha=1)
    

    

  • s :注意此参数为kind="scatter"下才有的，否则会报错 unknown property
    各个点的大小，数值越大，对应点越大

    import matplotlib.pyplot as plt
    allDf = pd.DataFrame({
        'x':[0,1,2,4,7,6],
        'y':[0,3,2,4,5,7],
        's':[0,1,2,3,4,5],
        'c':['red','green','blue','red','green','blue']
    },index = ['p1','p2','p3','p4','p5','p6'])
    
    print(allDf) 
    
    allDf.plot(x='x', y='y', kind="scatter",s=allDf['s']*10 , label='s')
    plt.legend()
    

    

  • c : 此参数也是kind="scatter"下才能用的，为每一点赋予颜色。
    建议用以下语句，只改变c就好。保留cmap和colorbar

      allDf = pd.DataFrame({
      		    'x':[0,1,2,4,7,6],
      		    'y':[0,3,2,4,5,7],
      		    's':[0,1,2,3,4,5],
      		    'c':[1,20, 5, 15, 25, 30]
      		    },index = ['p1','p2','p3','p4','p5','p6'])
      allDf.plot(x='x', y='y', kind="scatter",c='c',cmap=plt.get_cmap("jet"), colorbar=True)
    

    
    利用c和s两个参数在图像上显示housing,其中点大小表示人口密度，颜色表示房屋价格多少。

      housing.plot(kind="scatter", x="longitude", y="latitude", alpha=0.3,
     								s=housing["population"]/100, label="population",
     								c="median_house_value", cmap=plt.get_cmap("jet"), colorbar=True,
     								)
     plt.legend()