pandas pandas.DataFrame.plot( )绘图函数

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

Parameters:

参数	方法及用法
x	label or position, default None#指数据框列的标签或位置参数
y	label or position, default None
kind	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 column
sharex	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 invisible
layout :	tuple (optional)#子图的行列布局 (rows, columns) for the layout of subplots
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 Use index as ticks for x axis
title	string#图片的标题用字符串Title to use for the plot
grid	boolean, default None (matlab style default)#图片是否有网格 Axis grid lines
legend	False/True/’reverse’#子图的图例，添加一个subplot图例(默认为True) Place legend on axis subplots
style	list or dict#对每列折线图设置线的类型 matplotlib line style per column
logx	boolean, default False#设置x轴刻度是否取对数 Use log scaling on x axis
logy	boolean, default False,Use log scaling on y axis
loglog	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 axes
xticks	sequence#设置x轴刻度值，序列形式（比如列表） Values to use for the xticks
yticks	sequence#设置y轴刻度，序列形式（比如列表） Values to use for the yticks
xlim	2-tuple/list#设置坐标轴的范围，列表或元组形式
ylim	2-tuple/list
rot	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 yticks
colormap	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	floatSpecify 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 plot
table	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 str See 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.
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-axis
mark_right	boolean, default True When using a secondary_y axis, automatically mark the column labels with “(right)” in the legend
kwds	keywords Options to pass to matplotlib plotting method Returns:axes : matplotlib.AxesSubplot or np.array of them