首先看官网的DataFrame.plot( )函数

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, xerr=None,secondary_y=False, sort_columns=False, **kwds)

参数详解如下：

Parameters:x : label or position, default None#指数据框列的标签或位置参数y : label or position, default Nonekind : str‘line’ : line plot (default)#折线图‘bar’ : vertical bar plot#条形图‘barh’ : horizontal bar plot#横向条形图‘hist’ : histogram#柱状图‘box’ : boxplot#箱线图‘kde’ : Kernel Density Estimation plot#Kernel 的密度估计图，主要对柱状图添加Kernel 概率密度线‘density’ : same as ‘kde’‘area’ : area plot#不了解此图‘pie’ : pie plot#饼图‘scatter’ : scatter plot#散点图 需要传入columns方向的索引‘hexbin’ : hexbin plot#不了解此图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#判断图片中是否有子图Make separate subplots for each columnsharex : boolean, default True if ax is None else False#如果有子图，子图共x轴刻度，标签In 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 (optional)#子图的行列布局(rows, columns) for the layout of subplotsfigsize : 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Use index as ticks for x axistitle : string#图片的标题用字符串Title to use for the plotgrid : boolean, default None (matlab style default)#图片是否有网格Axis grid lineslegend : False/True/’reverse’#子图的图例，添加一个subplot图例(默认为True)Place legend on axis subplotsstyle : list or dict#对每列折线图设置线的类型matplotlib line style per columnlogx : boolean, default False#设置x轴刻度是否取对数Use log scaling on x axislogy : boolean, default FalseUse log scaling on y axisloglog : boolean, default False#同时设置x，y轴刻度是否取对数Use log scaling on both x and y axesxticks : sequence#设置x轴刻度值，序列形式（比如列表）Values to use for the xticksyticks : sequence#设置y轴刻度，序列形式（比如列表）Values to use for the yticksxlim : 2-tuple/list#设置坐标轴的范围，列表或元组形式ylim : 2-tuple/listrot : int, default None#设置轴标签（轴刻度）的显示旋转度数Rotation for ticks (xticks for vertical, yticks for horizontal plots)fontsize : int, default None#设置轴刻度的字体大小Font size for xticks and ytickscolormap : str or matplotlib colormap object, default None#设置图的区域颜色Colormap to select colors from. If string, load colormap with that name from matplotlib.colorbar : boolean, optional #图片柱子If True, plot colorbar (only relevant for ‘scatter’ and ‘hexbin’ plots)position : float Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)layout : tuple (optional) #布局(rows, columns) for the layout of the plottable : boolean, Series or DataFrame, default False #如果为正，则选择DataFrame类型的数据并且转换匹配matplotlib的布局。If True, draw a table using the data in the DataFrame and the data will be transposed to meet matplotlib’s default layout. If a Series or DataFrame is passed, use passed data to draw a table.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 andbar plots, and True in area plot. If True, create stacked plot.sort_columns : boolean, default False # 以字母表顺序绘制各列，默认使用前列顺序secondary_y : boolean or sequence, default False ##设置第二个y轴（右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 TrueWhen using a secondary_y axis, automatically mark the column labels with “(right)” in the legendkwds : keywordsOptions to pass to matplotlib plotting methodReturns:axes : matplotlib.AxesSubplot or np.array of them

1、画图图形

import pandas as pd from pandas import DataFrame,Seriesdf = pd.DataFrame(np.random.randn(4,4),index = list("ABCD"),columns=list("OPKL"))dfOut[4]: O P K LA -1.736654 0.327206 -1.000506 1.235681B 1.216879 0.506565 0.889197 -1.478165C 0.091957 -2.677410 -0.973761 0.123733D -1.114622 -0.600751 -0.159181 1.041668

注意一下散点图scatter是需要传入两个Y的columns参数的：

传入x，y参数

同时画多个子图，可以设置 subplot = True

2、注意事项：

- 在画图时，要注意首先定义画图的画布：fig = plt.figure( )

- 然后定义子图ax ，使用 ax= fig.add_subplot( 行，列，位置标)

- 当上述步骤完成后，可以用 ax.plot（）函数或者 df.plot(ax = ax)

- 在jupternotebook 需要用%定义：%matplotlib notebook；如果是在脚本编译器上则不用，但是需要一次性按流程把代码写完；

- 结尾时都注意记录上plt.show()