import pandas as pdimport numpy  as npdf= pd.DataFrame(np.random.rand(10, 4), columns=['A','B','C','D'])

df.plot.bar()

df.plot.bar(stacked=True)

df.plot.barh()

df.plot.barh(stacked=True)

df.plot.area(alpha = 0.9)

df.plot.area(stacked=True,alpha = 0.9)

df.plot.kde()

df.plot.density()

df = pd.DataFrame({'A': np.random.randn(1000) + 1,                   'B': np.random.randn(1000),                   'C': np.random.randn(1000) - 1},                  columns=['A', 'B', 'C']) df.plot.hist(bins=200)

df.plot.hist(stacked=True, bins=20)

df= pd.DataFrame(np.random.rand(1000, 4), columns=['A','B','C','D'])df.diff().hist(color='k', alpha=0.7, bins=50)

df= pd.DataFrame(np.random.rand(100, 4), columns=['A','B','C','D'])df.plot.box()

df.plot.box(vert=False)

df.plot.scatter(x='A',y='B')

df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])df['b'] = df['b'] + np.arange(1000)df.plot.hexbin(x='a', y='b', gridsize=25)

series = pd.Series(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], name='series')series.plot.pie(figsize=(6, 6))

series.plot.pie(labels=['AA', 'BB', 'CC', 'DD'], colors=['r', 'g', 'b', 'c'],autopct='%.2f', fontsize=20, figsize=(6, 6))

from pandas.plotting import scatter_matrixdf = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde')

data = pd.read_csv('C:/Users/wuzhengxiang/Desktop/iris.csv')pd.plotting.andrews_curves(data , 'Name')

andrews_curves(data, 'Name', colormap='winter')

from pandas.plotting import parallel_coordinatesparallel_coordinates(data, 'Name', colormap='gist_rainbow')

from pandas.plotting import lag_plotdf= pd.Series(0.1 * np.random.rand(1000) +        0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))lag_plot(df)

df= pd.DataFrame(np.random.rand(12, 4), columns=['A','B','C','D'])df.plot()

df.plot(subplots=True,layout=(2, 2), figsize=(15, 8))

df= pd.DataFrame(np.random.rand(1000, 4), columns=['A','B','C','D'])df.plot()

df.plot(subplots=True,layout=(2, 2), figsize=(15, 8))

s = pd.Series(np.random.uniform(size=100))pd.plotting.bootstrap_plot(s)

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, position=0.5, table=False, yerr=None,xerr=None, stacked=True/False, sort_columns=False, secondary_y=False, mark_right=True, **kwds)

x : label or position, default None#指数据列的标签或位置参数y : label, position or list of label, positions, default Nonekind : str#绘图类型‘line’ : line plot (default)#折线图‘bar’ : vertical bar plot#条形图。stacked为True时为堆叠的柱状图‘barh’ : horizontal bar plot#横向条形图‘hist’ : histogram#直方图（数值频率分布）‘box’ : boxplot#箱型图‘kde’ : Kernel Density Estimation plot#密度图，主要对柱状图添加Kernel 概率密度线‘density’ : same as ‘kde’‘area’ : area plot#与x轴所围区域图（面积图）。Stacked=True时，每列必须全部为正或负值，stacked=False时，对数据没有要求‘pie’ : pie plot#饼图。数值必须为正值，需指定Y轴或者subplots=True‘scatter’ : scatter plot#散点图。需指定X轴Y轴‘hexbin’ : hexbin plot#蜂巢图。需指定X轴Y轴‘hexbin’ : hexbin plot#蜂巢图。需指定X轴Y轴ax : matplotlib axes object, default None#**子图(axes, 也可以理解成坐标轴) 要在其上进行绘制的matplotlib subplot对象。如果没有设置，则使用当前matplotlib subplot**其中，变量和函数通过改变figure和axes中的元素（例如：title,label,点和线等等）一起描述figure和axes，也就是在画布上绘图。subplots : boolean, default False#是否对列分别作子图sharex : boolean, default True if ax is None else False#如果ax为None，则默认为True，否则为FalseIn case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and sharex=True will alter all x axis labels for all axis in a figure!sharey : boolean, default False#如果有子图，子图共y轴刻度，标签In case subplots=True, share y axis and set some y axis labels to invisiblelayout : tuple (rows, columns) for the layout of subplots#子图的行列布局figsize : a tuple (width, height) in inches#图片尺寸大小use_index : boolean, default True#默认用索引做x轴title : string#图片的标题用字符串Title to use for the plotgrid : boolean, default None#图片是否有网格legend : False/True/’reverse’#子图的图例 (默认为True)style : list or dict#对每列折线图设置线的类型logx : boolean, default False#设置x轴刻度是否取对数logy : boolean, default Falseloglog : boolean, default False#同时设置x，y轴刻度是否取对数xticks : sequence#设置x轴刻度值，序列形式（比如列表）yticks : sequence#设置y轴刻度，序列形式（比如列表）xlim : float/2-tuple/list#设置坐标轴的范围。数值（最小值），列表或元组（区间范围）ylim : float/2-tuple/listrot : int, default None#设置轴标签（轴刻度）的显示旋转度数  fontsize : int, default None#设置轴刻度的字体大小colormap : str or matplotlib colormap object, default None#设置图的区域颜色colorbar : boolean, optional  #柱子颜色If True, plot colorbar (only relevant for ‘scatter’ and ‘hexbin’ plots)position : float  #条形图的对齐方式，取值范围[0,1]，即左下端到右上端默认0.5（中间对齐） layout : tuple (optional)  #布局。layout=(2, 3)两行三列，layout=(2, -1)两行自适应列数Eg. df.plot(subplots=True, layout=(2, -1), sharex=False)table : boolean, Series or DataFrame, default False  #图下添加表。如果为True，则使用DataFrame中的数据绘制表格，并且数据将被转置以满足matplotlib的默认布局。。yerr : DataFrame, Series, array-like, dict and strSee Plotting with Error Bars for detail.xerr : same types as yerr.stacked : boolean, default False in line and bar plots, and True in area plot. If True, create stacked plot. #前面有介绍sort_columns : boolean, default False  #对列名称进行排序以确定绘图顺序secondary_y : boolean or sequence, default False  #设置第二个y轴（右辅助y轴）Whether to plot on the secondary y-axis If a list/tuple, which columns to plot on secondary y-axismark_right : boolean, default True