学习pandas下的dataframe画图参数

深碍√TFBOYSˉ_ 2022-06-14 11:36 150阅读 0赞

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`学习pandas数据框的绘图，轻松搞定各种图画法。`

`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)

**Parameters:**

data : DataFrame

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

> Allows plotting of one column versus another

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
>  *  ‘hexbin’ : hexbin plot\#不了解此图

ax : matplotlib axes object, default None\#一个图片切成不同片段，子图对象

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

> 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

> 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)

> (rows, columns) for the layout of the plot

table : boolean, Series or DataFrame, default False

> 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

> Sort column names to determine plot ordering

secondary\_y : boolean or sequence, default False

> 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*

***下面从http://pandas.pydata.org/pandas-docs/version/0.13.1/visualization.html的实例分析***

***%matplotlib inlineimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltplt.rc('figure', figsize=(5, 3))\#设置图片大小ts = pd.Series(np.random.randn(1000), index=pd.date\_range('1/1/2000', periods=1000))ts = ts.cumsum()ts.plot()***

***![Center][]***

***plt.figure(); ts.plot(style='k--', label='Series'); plt.legend()\#创建个新图片，在新图片上画ts的折线图，并添加图例***

***![Center 1][]***

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1.  df =pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD')) 
2.  df = df.cumsum() 
3.  plt.figure(); df.plot(); plt.legend(loc='best')

![Center 2][]

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1.  df.plot(subplots=True, figsize=(6, 6)); plt.legend(loc='best')\#对数据框相同索引分列分别作图

![Center 3][]

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1.  plt.figure(); 
2.  ts = pd.Series(np.random.randn(1000), index=pd.date\_range('1/1/2000', periods=1000)) 
3.  ts = np.exp(ts.cumsum()) 
4.  ts.plot(logy=True) \#对y轴进行log(y)放缩，图中y轴刻度依然是y的真实值，而不是log(y)

![Center 4][]

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1.  plt.figure() 
2.  df3 = pd.DataFrame(np.random.randn(1000, 2), columns=\['B', 'C'\]).cumsum() 
3.  df3\['A'\] = pd.Series(list(range(len(df)))) 
4.  df3.plot(x='A', y='B')\#x,y分别设置x轴，y轴的列标签或列的位置

![Center 5][]

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1.  plt.figure() 
2.  df.A.plot() 
3.  df.B.plot(secondary\_y=True, style='g')\#设置第二个y轴（右y轴）

![Center 6][]

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1.  plt.figure() 
2.  ax = df.plot(secondary\_y=\['A', 'B'\])\#设置2个列轴，分别对各个列轴画折线图。ax（axes）可以理解为子图，也可以理解成对黑板进行切分，每一个板块就是一个axes
3.  ax.set\_ylabel('CD scale') 
4.  ax.right\_ax.set\_ylabel('AB scale') 
5.  ax.legend(loc=2)\#设置图例的位置
6.  plt.legend(loc=1)

![Center 7][]

[view plain]: http://blog.csdn.net/sinat_24395003/article/details/60364345#
[Plotting with Error Bars]: http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization-errorbars
[Center]: /images/20220614/62b7b71d37c94921b1049d10f89da202.png
[Center 1]: /images/20220614/ae752e2287b546ecb29f98b5d397cb5e.png
[Center 2]: /images/20220614/9a2ef3d4e39e483c993a83753492f982.png
[Center 3]: /images/20220614/9464295b67654ec7b739722312fa2207.png
[Center 4]: /images/20220614/e9e690d9c01a4ff8a490c0f30e084b3c.png
[Center 5]: /images/20220614/de5ec2d0fc7b4a88b64e5477d39ba962.png
[Center 6]: /images/20220614/d011441efc714e0ea85fe7221b524f2f.png
[Center 7]: /images/20220614/aa31a7e856af46538877c348608ee34b.png