matplotlib

今天我们来介绍一下python的一个可视化工具matplotlib
matplotlib
使用matplotlib的方式有很多，但最通常的是Pylab模式的ipython(-ipython –pylab)
matplotlib的api都位于matplotlib.pyplot中，所以一般的引入方式为：
import matplotlib.pyplot as plt

Figure、Subplot
Figure
matplitlib的图像必须在Figure中，Figure创建方式如下：
fig = plt.figure() # 之后会弹出一个Figure窗口

创建好Figure后，并不能直接绘图，需要使用add_subplot创建一个或多个子图，在子图中绘图，创建方式如下：
ax1 = fig.add_subplot(2,2,1) # 将figure分为四块，在第一块创建一个子图;返回AxesSubplot对象

之后我们就可以绘制我们的图像了
plt.plot(randn(50).cumsum(), 'k--') 

Figure和subplot的创建经常使用，所以matplotlib提供了简洁的创建方式：
fig, axes = plt.subplots(2,3) # 创建一个新Figure、并返回一个含有已创建subplot对象的Numpy数组 
ax = axes[0,1] # 引用第一行第二列的subplot对象

该函数的一些参数选项：

调整图像之间的间距
通常情况下，matplotlib会在subplot外围留下一些边距，并在subplot之间留下一些间距。
我们可以利用subplots_adjust()函数来修改间距
subplots_adjust(left=None,bootom=None,right=None,top=None,wspace=None,hspace=None) # 该函数是一个顶级函数；wspace、hspace用来控制宽度和高度的百分比，用作subplot之间的间距
plt.subplots_adjust(wspace=0, hspace=0) # 设置subplots之间的间距为0

颜色、线型和标记
matplotlib的plot()函数接受一组X和Y坐标，和一个表示颜色和线形的字符串
ax.plot(x,y,'g--') # 根据x，y绘制绿色虚线
# ax为一subplot

刻度、标签和图例
设置X轴范围：
plt.xlim() # 返回当前X轴的范围
plt.xlim([0,10]) # 将X轴的范围设为[0, 10]
# 以上方法都只对当前或最近创建的Subplot起作用

设置标题、轴标签、刻度以及刻度标签


设置标题
ax.set_title("My first matplotlib plot") # 设置对应dubplot的标题



设置轴标签
ax.set_xlabel() # 为对应subplot的x轴设置标签
ax.set_ylabel()



设置刻度
设置刻度有两个方法，set_xticks和set_xticklabels，前者告诉maatplotlib将刻度放在对应范围的哪个位置，默认情况下这些刻度就是刻度标签；后者可以将任何其他类型的值作为标签
ax.set_xticks([0,1,2,3,4]) # 将0,1,2,3,4作为x轴刻度标签
ax.set_xticklabels(['one','two','three','four','five']) # 将one,two,three,four,five作为x轴刻度标签
ax.set_yticks()
ax.set_xticklabels()



添加图例

图例是标识图表元素的重要工具。


方式一，在添加subplot时传入label参数

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(randn(1000).cumsum(), 'k--', label='one')
ax.plot(randn(1000).cumsum(), 'k', label='two')
ax.plot(randn(1000).cumsum(), 'k.', label='three')
ax.legend(loc='best') # loc指定图例放置的位置,'best'参数标识系统将图例放置到最不碍事的地方


将图标保存到文件
利用plt.savefig可以将当前图表保存到文件
plt.savefig('figpath.png', dpi=400) # 将当前图表存储为png文件
# dpi参数指定每英寸点数(分辨率)

Pandas中的绘图函数
从上面的例子中我们可以看出，matplotlib实际上是一个比较低级的画图工具，当我们绘制一张图标时，往往要指定他的多个属性，如图例，标签，标题等。而pandas中的绘图函数大大简化了这个过程。
Series与DataFrame数据都可以通过调用plot函数来绘制图表


Series：
s = Series(np.random.randn(10).cumsum(), index=np.arange(0,100,10))
s.plot()








DataFrame：
df = DataFrame(np.random.randn(10,4).cumsum(), columns=['A','B','C','D'],index=np.arange(0,100,10))
# columns参数指定图例名称，index指定x轴标签
# np.random.randn(10,4)共10行4列，产生四条曲线，每条曲线十个数据点
df.plot()






除了matplotlib外，还有许多优秀的画图工具，如chaco、mayavi等
目前对于matplotlib的介绍就是这些，更多的知识可以研究一下pandas官方文档（pandas正在不断地更新中，会提供越来越多的函数），链接如下：
http://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html

收起

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github地址
python中的matplotlib是一种用于创建图表的桌面绘图包（主要是2D方面）。

使用python对matplotlib库操作使得对图形的显现极为方便，下面是用的较多的一些用法。

建议配合Ipython使用，如果通过cmd启动ipython，请使用ipython --pylab启动，方便绘图时的即时操作修改与显示，如果用jupyter notebook,则可使用ipython的魔术操作%matploltlib inline，当然如果不用ipython的话最后为了让图形显示出来请键入：plt.show()
一、综合介绍
1、常规方法作柱形，散点，饼状，折线图

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline

fig = plt.figure(figsize=(10,8))  #建立一个大小为10*8的画板
ax1 = fig.add_subplot(331)  #在画板上添加3*3个画布，位置是第1个
ax2 = fig.add_subplot(3,3,2)
ax3 = fig.add_subplot(3,3,3)
ax4 = fig.add_subplot(334)
ax5 = fig.add_subplot(3,3,5)
ax6 = fig.add_subplot(3,3,6)
ax7 = fig.add_subplot(3,3,7)
ax8 = fig.add_subplot(3,3,8)
ax9 = fig.add_subplot(3,3,9)

ax1.plot(np.random.randn(10))
_ = ax2.scatter(np.random.randn(10),np.arange(10),color='r')  #作散点图
ax3.hist(np.random.randn(20),bins=10,alpha=0.3)  #作柱形图
ax4.bar(np.arange(10),np.random.randn(10))  #做直方图
ax5.pie(np.random.randint(1,15,5),explode=[0,0,0.2,0,0])  #作饼形图

x = np.arange(10)
y = np.random.randn(10)
ax6.plot(x,y,color='green')
ax6.bar(x,y,color='k')

data = DataFrame(np.random.randn(1000,10),
                 columns=['one','two','three','four','five','six','seven','eight','nine','ten'])
data2 = DataFrame(np.random.randint(0,20,(10,2)),columns=['a','b'])
data.plot(x='one',y='two',kind='scatter',ax=ax7)  #针对DataFrame的一些作图
data2.plot(x='a',y='b',kind='bar',ax=ax8,color='red',legend=False)
data2.plot(x='a',y='b',kind='barh',color='m',ax=ax9)
#plt.tight_layout() #避免出现叠影
#plt.show()


2、较为简略方法绘图

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline


fig, axes = plt.subplots(2,3,figsize=(20,10))  #这个可以方便同时建立画板画布
axes[0,1].plot(np.random.randn(10))  #第1行第二个画布绘图
axes[0,2].plot(np.random.randn(10),'g--',marker='o')

arr = np.random.randn(20).cumsum()
axes[1,1].plot(data,linestyle='--',color='red',marker='o')
plt.plot(data,'k--')  #未给定画布则在最后一个画布上绘图
axes[1,0].plot(arr,linestyle='dashed',color='yellow',marker='*')

data = DataFrame(np.random.randn(2,3),columns=['a','b','c'])
data.plot(ax=axes[0,0])  #针对DataFrame可以使用参数给定画布
#plt.show()
#plt.savefig('test.png',dpi=400,bbox_inches='tight',facecolor='m')
#保存到指定路径，dpi-像素，bbox_inches-剪除当前图表周围空白,facecolor-背景颜色


3-1、对一个DataFrame的几个列在不同画板同时作图

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline


data11 = DataFrame(np.random.randn(100,4),columns=list('abcd'))
data11.plot(subplots=True,figsize=(8,5),grid=False,title='my plot',legend=False,layout=(2,2))


3-2、对一个DataFrame的几个列在不同画板同时作图

import numpy as np
from pandas import DataFrame
import matplotlib.pyplot as plt

%matplotlib inline
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

test = DataFrame(np.random.randn(10,10))

plt.subplot2grid((3,3),(0,0))
test.ix[:,0].plot(kind='bar')
plt.ylabel(u'测试 y')
plt.xlabel('test x')


plt.subplot2grid((3,3), (0,1), colspan=2, rowspan=2)
test.ix[:,1].plot(kind='kde',color='red')
plt.grid(b=True, axis='y')

plt.subplot2grid((3,3),(1,0))
test.ix[:,2].plot(kind='barh')

plt.subplot2grid((3,3),(2,0))
plt.scatter(test.ix[:,3],test.ix[:,4])

plt.subplot2grid((3,3),(2,1))
test.ix[:,5].plot(ls='--')

plt.subplot2grid((3,3),(2,2))
test.ix[:,6].plot(lw=2,color='green', alpha=.4,grid=True)

plt.tight_layout();


4、画蜡烛图：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
from pandas import Series, DataFrame
from matplotlib.pylab import date2num
%matplotlib inline

plt.rcParams['figure.autolayout'] = True
plt.rcParams['figure.figsize'] = 25,6
plt.rcParams['grid.alpha'] = .4
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['font.sans-serif'] = ['SimHei']

fig, ax = plt.subplots(1,1,figsize=(12,5))

mpf.candlestick_ohlc(ax=ax,quotes=data2.values[::3],width=.002,colorup='red',colordown='green')
plt.xticks(data2.date[::25],data.date.map(lambda x:x[:5])[::25],rotation=0)
ax.twiny().plot(data3.Open)
plt.tight_layout();


5、matplotlib画热图显示：
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline


df = DataFrame(np.random.randn(10,10))
fig = plt.figure(figsize=(12,5))
ax = fig.add_subplot(111)
axim = ax.imshow(df.values,interpolation='nearest')#cmap=plt.cm.gray_r, #cmap用来显示颜色，可以另行设置
plt.colorbar(axim)
plt.show()


二、对图中的一些参数进行修改
1、对图表添加描述，修改x轴y轴区间

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas import Series, DataFrame
%matplotlib inline

fig = plt.figure(figsize=(12,5))
ax = fig.add_subplot(111)
ax.plot(np.random.randn(1000).cumsum(),linewidth=2,color='red')
plt.axhline(y=0,linewidth=1,color='green',linestyle='-')#设置对比线
plt.axvline(x=500,linewidth=4,color='green',linestyle='--')
#ticks = ax.set_xticks([0,250,500,750,1000])
#labels = ax.set_xticklabels(['one','two','three','four','five'],rotation=30,fontsize='small') ##这两个的效果可综合用下面的一个来描述
plt.xticks([0,250,500,750,1000],['one','two','three','four','five'],rotation=30,fontsize='small')
#ax.set_title('Just for test')
plt.title('Just for test')#作用同上
#ax.set_xlabel('ax x')
plt.xlabel('ax x')#作用同上
#ax.set_ylabel('ax y')
plt.ylabel('ax y')#作用同上
plt.grid()
#plt.xlim([0,900])  #将x轴调整为0-900
#plt.ylim([-60,0])   #将y轴调整为-60-0
#plt.subplots_adjust(left=None,top=None,right=None,bottom=None,wspace=None,hspace=None)#用来修改间距。


2、给不同段的线设置不同的颜色
ser = Series(np.random.randn(24))
ser[:8].plot(color='red')
ser[7:16].plot(color='green')
ser[15:24].plot(color='blue')


3、给DataFrame作图时同时给用来作图的不同列设置颜色
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline


plt.rcParams['figure.figsize'] = 12,5  #设置图像大小
data = DataFrame(np.random.randn(100,2),columns=['one','two'])
plt.plot(data['one'],'g-',data['two'],'r^')  #颜色分别为green,red,linestyle分别为"-"和"^"
plt.show()



4、图表添加描述或注释
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline


fig, ax = plt.subplots(1,1)
data = DataFrame(np.random.randn(10,3),columns=['one','two','three'])
data.plot(ax=ax)
plt.legend(loc='upper left')
x = (data[['one']].idxmax()).values[0]
y = data[['one']].max()

ax.annotate('column one Highest point',xy=(x,y),xycoords='data',xytext=(+10,-25),textcoords='offset points',
           fontsize=14,arrowprops=dict(arrowstyle='->',color='k',linewidth=2))
plt.plot([1,1],[0,1],linestyle='--')
plt.scatter(x=1,y=1,color='m')
plt.text(1,0.5,'hello world',fontsize=14)
plt.show()


5、对数据作拟合曲线：
import scipy as sp #需要引入scipy，这个需要额外安装，是一个类似于numpy的科学计算类库
from pandas import Series
from matplotlib import pyplot as plt

ser = Series(np.random.randn(100).cumsum())
x = ser.index  #作为拟合的x
y = ser.values  #作为拟合的y
fp1,residuals,rank,sv,rcond = sp.polyfit(x,y,1,full=True)  #拟合为一阶多项式
fp5 = sp.polyfit(x,y,5)   #拟合为五阶多项式
fp100 = sp.polyfit(x,y,120)   #拟合为一百二十阶多项式

f1 = sp.poly1d(fp1)  #转换为模型函数
f5 = sp.poly1d(fp5)
f100 = sp.poly1d(fp100)

fx1 = sp.linspace(0,x[-1],1000)  #生成x值用来作图
fx5 = sp.linspace(0,x[-1],1000)
fx100 = sp.linspace(0,x[-1],1000)

ser.plot(color='yellow',linewidth=2)
plt.plot(fx1,f1(fx1),linewidth=2,color='red')
plt.plot(fx5,f5(fx5),linewidth=2,color='green')
plt.plot(fx100,f100(fx100),linewidth=2,color='blue')
plt.show()


6、一些样式设计

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline


fig = plt.figure()
fig.suptitle('bold figure suptitle', fontsize=14,fontweight='bold')

ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
ax.set_title('axes title')
ax.set_xlabel('xlabel')
ax.set_ylabel('ylabel')
ax.text(3,8,'boxed italics text in data coords',style='italic',
        bbox={'facecolor':'red','alpha':0.5,'pad':10})
ax.text(2,6,r'an equation:$E=mc^2$',fontsize=15)
ax.text(3,2,unicode('unicode: Institut f\374r Festk\366rperphysik',
                   'latin-1'))
ax.text(0.95,0.01,'colored text in axes coords',
       verticalalignment='bottom',horizontalalignment='right',
       transform=ax.transAxes,
       color='green',fontsize=15)  #其中verticalalignment、horizontalalignment可以分别用va、ha代替
ax.plot([2],[1],'o')
ax.annotate('annotate',xy=(2,1),xytext=(3,4),
           arrowprops=dict(facecolor='black',shrink=.05))
ax.axis([0,10,0,10])
#ax.set_axis_off()  #隐藏坐标系
plt.show()


7、将不同y轴的两个series画在同一个画板上
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline

fig, ax = plt.subplots(1,1,figsize=(8,4))
data = DataFrame(np.random.randn(100,2),columns=['firstCol','secondCol'])
data.firstCol = data.firstCol.map(lambda x:100*x)
data.firstCol.cumsum().plot(ax=ax,color='blue')
data.secondCol.cumsum().plot(ax=ax.twinx(),kind='line',color='red')


8、区域图

data = DataFrame(abs(np.random.randn(20,2)),columns=['one','two'])
data.plot(kind='area',colormap='viridis_r',legend=False)


8.1、区间图
fig, ax = plt.subplots(1,1,figsize=(12,6))

ax.fill_between(range(20),np.random.randn(20),np.random.randn(20),alpha=.5)
plt.grid(True);


mapcolor可设置为: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spectral, spectral_r, spring, spring_r, summer, summer_r, terrain, terrain_r, viridis, viridis_r, winter, winter_r

9、饼状图及参数设计
```
data = DataFrame(abs(np.random.randn(20,2)),columns=['one','two'])
data.plot(subplots=True,kind='pie',legend=False, figsize=(6,3));
```
![这里写图片描述](https://imgconvert.csdnimg.cn/aHR0cDovL2ltZy5ibG9nLmNzZG4ubmV0LzIwMTcwODE1MTY0ODI4OTgx?x-oss-process=image/format,png)
import random
plt.figure(figsize=(5,5))
plt.pie(data.ix[:, 0], autopct='%.2f%%', explode=[0.1, *[0]*19], pctdistance=0.8, labels=[''.join(random.sample(['a', 'b', 'c','d', 'e', 'f'], 2)) for _ in range(20)]);


fig, ax = plt.subplots(figsize=(10, 8), subplot_kw=dict(aspect="equal"))

recipe = ["375 k U.S. Large Cap Blend",
            "300 k U.S. Large Cap Value",
            "75 k U.S. Short-Term Bonds",
            "50 k U.S. Small Cap Blend",
            "55 k U.S. Small Cap Value" ,
            "95 k U.S. Real Estate",
            "250 k Intermediate-Term Bonds"]


data = [float(x.split()[0]) for x in recipe]
ingredients = [" ".join(x.split()[2:]) for x in recipe]

def func(pct, allvals):
    absolute = int(pct/100. * np.sum(allvals))
    return "{:.1f}%\n{:d} k".format(pct, absolute)

explode = np.full(len(data), 0.1)

wedges, texts, autotexts = ax.pie(data, explode=explode, autopct=lambda pct: func(pct, data),
                                 textprops=dict(color="w")
                                 )
ax.legend(wedges,
         ingredients,
         loc="center left",
         bbox_to_anchor=(1, 0, 0.5, 1))

plt.setp(autotexts, size=8, weight="bold");


10、设置轴的线条颜色，隐藏轴边框

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
%matplotlib inline

ser = Series(np.random.randn(30))
fig, ax = plt.subplots(1,1)
ser.plot()
ax.spines['left'].set_color('green')
ax.spines['bottom'].set_color('red')
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
plt.show()


11、翻转x轴，y轴（这个可以跟上面的对比，注意ticks显示）
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import Series,DataFrame
%matplotlib inline

ser = Series(np.random.randn(30))
fig, ax = plt.subplots(1,1)
ser.plot()
ax.spines['left'].set_color('green')
ax.spines['bottom'].set_color('red')
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
plt.gca().invert_yaxis()#翻转y轴，
plt.gca().invert_xaxis();#翻转x轴


12、隐藏x轴y轴
import numpy as np
import pandas as pd
import scipy as sp
from matplotlib import pyplot as plt

%matplotlib inline
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

p1 = sp.polyfit([0,80,90],[-6390,0,-90],3)
f1 = sp.poly1d(p1)

p2 = sp.polyfit([100,30,110],[-6500,f1(30),-6400],3)
f2 = sp.poly1d(p2)
x = range(100)

plt.rcParams['font.size'] = 18
fig, ax = plt.subplots(1,1,figsize=(12,8))
ax.plot(x,f1(x),lw=2)
ax.plot(x,f2(x),lw=2)
ax.plot(x,f1(x)+f2(x)+7000,lw=2)
ax.set_xticks([]);
ax.set_yticks([]);
ax.annotate(u'总效应',xy=(30,f1(30)+f2(30)+7000),xytext=(35,-1000),arrowprops=dict(arrowstyle='->', linewidth=2.5))
ax.annotate(u'规模效应',xy=(45,f1(45)),xytext=(45,-3000),arrowprops=dict(arrowstyle='->', linewidth=2.5))
ax.annotate(u'激励效应',xy=(55,f2(55)),xytext=(45,-6000),arrowprops=dict(arrowstyle='->', linewidth=2.5))
plt.ylabel(u'环境效率')
plt.xlabel(u'经营规模');
plt.xlim([0,95]);


12_1、突出指定坐标值
import numpy as np
import matplotlib.pyplot as plt

plt.plot(np.random.randn(100))
plt.xticks(list(range(100))[::20], list('ABCDE'))
plt.gca().get_xticklabels()[2].set(weight='heavy', color='red');


12_2、设定x轴坐标显示方式
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np


ax = plt.gca()  # 获取当前轴
ax.locator_params(tight=True, nbins=5)  # x显示为5个label值
ax.plot(np.random.normal(10, .1, 100));


import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np


ax = plt.gca()
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(7))  #设置x轴label显示为7的倍数
ax.plot(np.random.normal(10, .1, 100));


import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np


ax = plt.gca()
ax.xaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%2.1f'))
ax.plot(np.random.normal(10, .1, 100));


import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

import datetime

fig = plt.figure(figsize=(12, 5))
ax = plt.gca()
start = datetime.datetime(2012, 1, 1)
stop = datetime.datetime(2012, 12, 31)
delta = datetime.timedelta(days=1)
dates = mpl.dates.drange(start, stop, delta)
values = np.random.rand(len(dates))
ax.plot_date(dates, values, ls='-', marker='o', c='blue', alpha=.4)
#date_format = mpl.dates.DateFormatter("%Y-%m-%d")
#ax.xaxis.set_major_formatter(date_format)
#fig.autofmt_xdate()


import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np

x1 = np.random.normal(30, 3, 100)
x2 = np.random.normal(20, 2, 100)
x3 = np.random.normal(10, 3, 100)

plt.plot(x1, label='plot')
plt.plot(x2, label='2nd plot')
plt.plot(x3, label='last plot')

plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,ncol=3, mode='expand', 
           borderaxespad=0.)
plt.annotate("Import value", (55, 20), xycoords='data', xytext=(5, 38),
             arrowprops=dict(arrowstyle='->'));


import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np


plt.figure(figsize=(12, 6))
x = np.arange(0, 2, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = 1.2 * np.sin(4 * np.pi * x)

# fig = plt.figure()
ax = plt.gca()

ax.plot(x, y1, x, y2, color='k')
ax.fill_between(x, y1, y2, where=y2>=y1, facecolor='darkblue', interpolate=False)
ax.fill_between(x, y1, y2, where=y2<=y1, facecolor='deeppink', interpolate=True);


from matplotlib import patheffects


data = np.random.randn(70)

fontsize = 18
plt.plot(data)

title = 'This is figure title'
x_label = "This is x axis label"
y_label = "This is y axis label"

title_text_obj = plt.title(title, fontsize=fontsize, va='bottom')
title_text_obj.set_path_effects([patheffects.withSimplePatchShadow()])
offset_xy = (1, -1)
rgbRed = (1., 0., 0.)
alpha = .8

pe = patheffects.withSimplePatchShadow(offset=offset_xy, 
                                       shadow_rgbFace=rgbRed,
                                       alpha=alpha)

xlabel_obj = plt.xlabel(x_label, fontsize=fontsize, alpha=.5)
xlabel_obj.set_path_effects([pe])


ylabel_obj = plt.ylabel(y_label, fontsize=fontsize, alpha=.5)
ylabel_obj.set_path_effects([pe]);


13、散点气泡图
from matplotlib import pyplot as plt
%matplotlib inline

for x,y in enumerate(range(10)):
    plt.scatter(x,y,s=100 * x+10, alpha=.3,edgecolors='red',linewidths=3)
    plt.grid(True);


14、箱体图
import random

colors = ['red', 'green', 'lightgreen', 'cyan', 'purple', 'orange', 'blue']

p = plt.boxplot([range(10 * x + 10) for x in range(7)], notch=True, widths=0.5, positions=range(7),)
plt.grid()

for box in p['boxes']:
    box.set_color(random.choice(colors))


15、雷达图
![参考](https://imgconvert.csdnimg.cn/aHR0cHM6Ly93d3cua2VzY2kuY29tL2FwcHMvaG9tZS9wcm9qZWN0LzU5ZjZmODhmYzVmM2Y1MTE5NTJjMzBjOQ?x-oss-process=image/format,png)

import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline


plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

plt.style.use('ggplot')

values = [3.2, 2.1, 3.5, 2.8, 3]
features = ['个人能力', 'QC知识', '解决问题能力', '服务质量意识', '团队精神']

N = len(values)

angles = np.linspace(0, 2*np.pi, N, endpoint=False)

values.append(values[0])
angles = np.append(angles, angles[0])

fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, values, 'o-', lw=2)
ax.fill(angles, values, alpha=0.15)
ax.set_thetagrids(angles*180/np.pi, features)
ax.set_ylim(0, 5)
plt.title('活动前后员工状态表现')
plt.grid(True);
#ax.grid(True);


16、3D图
曲面图
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

%matplotlib inline

fig = plt.figure()
ax = Axes3D(fig)

X = np.arange(-2,2,0.1)
Y = np.arange(-2,2,0.1)
X, Y = np.meshgrid(X, Y)

def f(x,y):
    return (1 - y** 5 + x ** 5) * np.exp(-x ** 2 - y ** 2)

ax.plot_surface(X, Y, f(X, Y), rstride=1, cstride=1, color='red', alpha=.4);


改变颜色及仰角
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

fig = plt.figure()
ax = Axes3D(fig)

X = np.arange(-2, 2, 0.1)
Y = np.arange(-2, 2, 0.1)
X, Y = np.meshgrid(X, Y)

def f(x, y):
    return (1-y**5+pow(x, 5)) * np.exp(-pow(x, 2) - pow(y, 2))
ax.plot_surface(X, Y, f(X, Y), rstride=1, cmap=plt.cm.hot)

ax.view_init(elev=10, azim=125)


散点图
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

xs = np.random.randint(30,40,100)
ys = np.random.randint(20,30,100)
zs = np.random.randint(10,20,100)
xs2 = np.random.randint(50,60,100)
ys2 = np.random.randint(30,40,100)
zs2 = np.random.randint(50,70,100)
xs3 = np.random.randint(10,30,100)
ys3 = np.random.randint(40,50,100)
zs3 = np.random.randint(40,50,100)

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(xs, ys, zs)
ax.scatter(xs2, ys2, zs2, c='r', marker='^')
ax.scatter(xs3, ys3, zs3, c='g', marker='*')
ax.set_xlabel('X label')
ax.set_ylabel('Y label')
ax.set_ylabel('Z label');


直方图
import matplotlib.pyplot as plt
import numpy as  np
from mpl_toolkits.mplot3d import Axes3D

x = np.arange(8)
y = np.random.randint(0,10,8)

y2 = y + np.random.randint(0,3,8)
y3 = y2 + np.random.randint(0,3,8)
y4 = y3 + np.random.randint(0,3,8)
y5 = y4 + np.random.randint(0,3,8)

clr = ['red', 'green', 'blue', 'black'] * 2

fig = plt.figure()
ax = Axes3D(fig)
ax.bar(x, y, 0,zdir='y', color=clr)
ax.bar(x, y2, 10,zdir='y', color=clr)
ax.bar(x, y3, 20,zdir='y', color=clr)
ax.bar(x, y4, 30,zdir='y', color=clr)
ax.bar(x, y5, 40,zdir='y', color=clr)

ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Z axis')

ax.view_init(elev=40);


嵌套图
import matplotlib.pyplot as plt
fig = plt.figure()

ax = fig.add_axes([.1, .1, .8, .8])
inner_ax = fig.add_axes([.2, .6, .25, .25])
ax.plot(np.arange(10))
inner_ax.plot(np.arange(10));


others

其他。。。
1、一些参数缩写形式：
_alias_map = {'color': ['c'],
              'linewidth': ['lw'],
              'linestyle': ['ls'],
              'facecolor': ['fc'],
              'edgecolor': ['ec'],
              'markerfacecolor': ['mfc'],
              'markeredgecolor': ['mec'],
              'markeredgewidth': ['mew'],
              'markersize': ['ms'],
             }

2、一些参数含义介绍：

bins    柱形个数

color  颜色，其中c,m,y,k分别代表青，滇红，黄，黑，别的如g–green,b–blue,可以选用RGB值如’#CECECE’等等

plt.grid(True)  #plt.grid() 显示网格

plt.autoscale(tight=True)#plt.autoscale()自动最佳化比例

plt.legend(loc=‘best’)#loc=right

center left

upper right

lower right

best

center

lower left

center right

upper left

upper center

lower center

等等，图例安放位置，一般选用‘best’即可，自动帮你选择最佳位置
 ===============   =============
                Location String   Location Code
                ===============   =============
                'best'            0
                'upper right'     1
                'upper left'      2
                'lower left'      3
                'lower right'     4
                'right'           5
                'center left'     6
                'center right'    7
                'lower center'    8
                'upper center'    9
                'center'          10
                ===============   =============

label=‘。。。’#图例名

style=‘g–’#颜色为绿色，线条风格为‘–’
        ==========  ========
        character   color
        ==========  ========
        'b'         blue
        'g'         green
        'r'         red
        'c'         cyan
        'm'         magenta
        'y'         yellow
        'k'         black
        'w'         white
        ==========  ========


linestyle=’–’,#线条风格
        ================    ===============================
        character           description
        ================    ===============================
        ``'-'``             solid line style
        ``'--'``            dashed line style
        ``'-.'``            dash-dot line style
        ``':'``             dotted line style
        ``'.'``             point marker
        ``','``             pixel marker
        ``'o'``             circle marker
        ``'v'``             triangle_down marker
        ``'^'``             triangle_up marker
        ``'<'``             triangle_left marker
        ``'>'``             triangle_right marker
        ``'1'``             tri_down marker
        ``'2'``             tri_up marker
        ``'3'``             tri_left marker
        ``'4'``             tri_right marker
        ``'s'``             square marker
        ``'p'``             pentagon marker
        ``'*'``             star marker
        ``'h'``             hexagon1 marker
        ``'H'``             hexagon2 marker
        ``'+'``             plus marker
        ``'x'``             x marker
        ``'D'``             diamond marker
        ``'d'``             thin_diamond marker
        ``'|'``             vline marker
        ``'_'``             hline marker
        ================    ===============================

drawstyle=‘steps_post’#线条类型

marker=‘o’#标识类型

rotation=30#顺时针旋转30，一般用于x轴显示

fontsize=‘small’#文字显示大小

kind=‘bar’#图表显示类型，有’barh’,‘scatter’,'kde’等等

stacked=True#是否堆积，用于kind='bar’或’barh’时

alpha=0.2#显示透明度

plt.subplots(2,2,sharex=True,sharey=True)#同一x轴y轴

plt.adjust(left=None,bottom=None,right=None,top=None,wspace=None,hspace=None)#修改间距

plt.xlim([0,20])#修改当前x轴绘制范围

plt.ylim([0,20])#修改当前y轴绘制范围

facecolor#图像的背景色

edgecolor#图像的背景色

legend=False#当以dataframe作图时会自动默认列名为图标，如不想显示则设置为False
#plt.title('alpha>beta')
#plt.title(r'$\alpha >\beta$')
#plt.title(r'$\alpha_i>\beta_i$')
plt.title(r'$\sum_{i=0}^\infty x_i$')

3、matplotlib默认设置：

可以通过plt.rcParams动态修改使用。
### MATPLOTLIBRC FORMAT
# This is a sample matplotlib configuration file - you can find a copy
# of it on your system in
# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it
# there, please note that it will be overridden in your next install.
# If you want to keep a permanent local copy that will not be
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux
# like systems) and C:\Documents and Settings\yourname\.matplotlib
# (win32 systems).
#
# This file is best viewed in a editor which supports python mode
# syntax highlighting. Blank lines, or lines starting with a comment
# symbol, are ignored, as are trailing comments. Other lines must
# have the format
# key : val # optional comment
#
# Colors: for the color values below, you can either use - a
# matplotlib color string, such as r, k, or b - an rgb tuple, such as
# (1.0, 0.5, 0.0) - a hex string, such as ff00ff or #ff00ff - a scalar
# grayscale intensity such as 0.75 - a legal html color name, eg red,
# blue, darkslategray
#### CONFIGURATION BEGINS HERE
# the default backend; one of GTK GTKAgg GTKCairo CocoaAgg FltkAgg
# MacOSX QtAgg Qt4Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG Template
# You can also deploy your own backend outside of matplotlib by
# referring to the module name (which must be in the PYTHONPATH) as
# ’module://my_backend’
backend : GTKAgg
# If you are using the Qt4Agg backend, you can choose here
# to use the PyQt4 bindings or the newer PySide bindings to
# the underlying Qt4 toolkit.
#backend.qt4 : PyQt4 # PyQt4 | PySide
# Note that this can be overridden by the environment variable
24 Chapter 5. 
# QT_API used by Enthought Tool Suite (ETS); valid values are
# "pyqt" and "pyside". The "pyqt" setting has the side effect of
# forcing the use of Version 2 API for QString and QVariant.
# if you are runing pyplot inside a GUI and your backend choice
# conflicts, we will automatically try to find a compatible one for
# you if backend_fallback is True
#backend_fallback: True
#interactive : False
#toolbar : toolbar2 # None | classic | toolbar2
#timezone : UTC # a pytz timezone string, eg US/Central or Europe/Paris
# Where your matplotlib data lives if you installed to a non-default
# location. This is where the matplotlib fonts, bitmaps, etc reside
#datapath : /home/jdhunter/mpldata
### LINES
# See http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.lines for more
# information on line properties.
#lines.linewidth : 1.0 # line width in points
#lines.linestyle : - # solid line
#lines.color : blue
#lines.marker : None # the default marker
#lines.markeredgewidth : 0.5 # the line width around the marker symbol
#lines.markersize : 6 # markersize, in points
#lines.dash_joinstyle : miter # miter|round|bevel
#lines.dash_capstyle : butt # butt|round|projecting
#lines.solid_joinstyle : miter # miter|round|bevel
#lines.solid_capstyle : projecting # butt|round|projecting
#lines.antialiased : True # render lines in antialised (no jaggies)
### PATCHES
# Patches are graphical objects that fill 2D space, like polygons or
# circles. See
# http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.patches
# information on patch properties
#patch.linewidth : 1.0 # edge width in points
#patch.facecolor : blue
#patch.edgecolor : black
#patch.antialiased : True # render patches in antialised (no jaggies)
### FONT
#
# font properties used by text.Text. See
# http://matplotlib.sourceforge.net/api/font_manager_api.html for more
# information on font properties. The 6 font properties used for font
# matching are given below with their default values.
#
# The font.family property has five values: ’serif’ (e.g. Times),
# ’sans-serif’ (e.g. Helvetica), ’cursive’ (e.g. Zapf-Chancery),
# ’fantasy’ (e.g. Western), and ’monospace’ (e.g. Courier). Each of
5.2. Dynamic rc settings 25Matplotlib, Release 1.1.0
# these font families has a default list of font names in decreasing
# order of priority associated with them.
#
# The font.style property has three values: normal (or roman), italic
# or oblique. The oblique style will be used for italic, if it is not
# present.
#
# The font.variant property has two values: normal or small-caps. For
# TrueType fonts, which are scalable fonts, small-caps is equivalent
# to using a font size of ’smaller’, or about 83% of the current font
# size.
#
# The font.weight property has effectively 13 values: normal, bold,
# bolder, lighter, 100, 200, 300, ..., 900. Normal is the same as
# 400, and bold is 700. bolder and lighter are relative values with
# respect to the current weight.
#
# The font.stretch property has 11 values: ultra-condensed,
# extra-condensed, condensed, semi-condensed, normal, semi-expanded,
# expanded, extra-expanded, ultra-expanded, wider, and narrower. This
# property is not currently implemented.
#
# The font.size property is the default font size for text, given in pts.
# 12pt is the standard value.
#
#font.family : sans-serif
#font.style : normal
#font.variant : normal
#font.weight : medium
#font.stretch : normal
# note that font.size controls default text sizes. To configure
# special text sizes tick labels, axes, labels, title, etc, see the rc
# settings for axes and ticks. Special text sizes can be defined
# relative to font.size, using the following values: xx-small, x-small,
# small, medium, large, x-large, xx-large, larger, or smaller
#font.size : 12.0
#font.serif : Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L,
#font.sans-serif : Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, He
#font.cursive : Apple Chancery, Textile, Zapf Chancery, Sand, cursive
#font.fantasy : Comic Sans MS, Chicago, Charcoal, Impact, Western, fantasy
#font.monospace : Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono L, Courier New,
### TEXT
# text properties used by text.Text. See
# http://matplotlib.sourceforge.net/api/artist_api.html#module-matplotlib.text for more
# information on text properties
#text.color : black
### LaTeX customizations. See http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex
#text.usetex : False # use latex for all text handling. The following fonts
# are supported through the usual rc parameter settings:
# new century schoolbook, bookman, times, palatino,
26 Chapter 5. 
# zapf chancery, charter, serif, sans-serif, helvetica,
# avant garde, courier, monospace, computer modern roman,
# computer modern sans serif, computer modern typewriter
# If another font is desired which can loaded using the
# LaTeX \usepackage command, please inquire at the
# matplotlib mailing list
#text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for handling
# unicode strings.
#text.latex.preamble : # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
# AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
# IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
# preamble is a comma separated list of LaTeX statements
# that are included in the LaTeX document preamble.
# An example:
# text.latex.preamble : \usepackage{bm},\usepackage{euler}
# The following packages are always loaded with usetex, so
# beware of package collisions: color, geometry, graphicx,
# type1cm, textcomp. Adobe Postscript (PSSNFS) font packages
# may also be loaded, depending on your font settings
#text.dvipnghack : None # some versions of dvipng don’t handle alpha
# channel properly. Use True to correct
# and flush ~/.matplotlib/tex.cache
# before testing and False to force
# correction off. None will try and
# guess based on your dvipng version
#text.hinting : True # If True, text will be hinted, otherwise not. This only
# affects the Agg backend.
# The following settings allow you to select the fonts in math mode.
# They map from a TeX font name to a fontconfig font pattern.
# These settings are only used if mathtext.fontset is ’custom’.
# Note that this "custom" mode is unsupported and may go away in the
# future.
#mathtext.cal : cursive
#mathtext.rm : serif
#mathtext.tt : monospace
#mathtext.it : serif:italic
#mathtext.bf : serif:bold
#mathtext.sf : sans
#mathtext.fontset : cm # Should be ’cm’ (Computer Modern), ’stix’,
# ’stixsans’ or ’custom’
#mathtext.fallback_to_cm : True # When True, use symbols from the Computer Modern
# fonts when a symbol can not be found in one of
# the custom math fonts.
#mathtext.default : it # The default font to use for math.
# Can be any of the LaTeX font names, including
# the special name "regular" for the same font
# used in regular text.
### AXES
# default face and edge color, default tick sizes,
# default fontsizes for ticklabels, and so on. See
# http://matplotlib.sourceforge.net/api/axes_api.html#module-matplotlib.axes
#axes.hold : True # whether to clear the axes by default on
#axes.facecolor : white # axes background color
#axes.edgecolor : black # axes edge color
#axes.linewidth : 1.0 # edge linewidth
#axes.grid : False # display grid or not
#axes.titlesize : large # fontsize of the axes title
#axes.labelsize : medium # fontsize of the x any y labels
#axes.labelweight : normal # weight of the x and y labels
#axes.labelcolor : black
#axes.axisbelow : False # whether axis gridlines and ticks are below
# the axes elements (lines, text, etc)
#axes.formatter.limits : -7, 7 # use scientific notation if log10
# of the axis range is smaller than the
# first or larger than the second
#axes.formatter.use_locale : False # When True, format tick labels
# according to the user’s locale.
# For example, use ’,’ as a decimal
# separator in the fr_FR locale.
#axes.unicode_minus : True # use unicode for the minus symbol
# rather than hypen. See
# http://en.wikipedia.org/wiki/Plus_sign
#axes.color_cycle : b, g, r, c, m, y, k # color cycle for plot lines
# as list of string colorspecs:
# single letter, long name, or
# web-style hex
#polaraxes.grid : True # display grid on polar axes
#axes3d.grid : True # display grid on 3d axes
### TICKS
# see http://matplotlib.sourceforge.net/api/axis_api.html#matplotlib.axis.Tick
#xtick.major.size : 4 # major tick size in points
#xtick.minor.size : 2 # minor tick size in points
#xtick.major.pad : 4 # distance to major tick label in points
#xtick.minor.pad : 4 # distance to the minor tick label in points
#xtick.color : k # color of the tick labels
#xtick.labelsize : medium # fontsize of the tick labels
#xtick.direction : in # direction: in or out
#ytick.major.size : 4 # major tick size in points
#ytick.minor.size : 2 # minor tick size in points
#ytick.major.pad : 4 # distance to major tick label in points
#ytick.minor.pad : 4 # distance to the minor tick label in points
#ytick.color : k # color of the tick labels
#ytick.labelsize : medium # fontsize of the tick labels
#ytick.direction : in # direction: in or out
### GRIDS
#grid.color : black # grid color
#grid.linestyle : : # dotted
#grid.linewidth : 0.5 # in points
### Legend
#legend.fancybox : False # if True, use a rounded box for the
# legend, else a rectangle
#legend.isaxes : True
#legend.numpoints : 2 # the number of points in the legend line
#legend.fontsize : large
#legend.pad : 0.0 # deprecated; the fractional whitespace inside the legend border
#legend.borderpad : 0.5 # border whitspace in fontsize units
#legend.markerscale : 1.0 # the relative size of legend markers vs. original
# the following dimensions are in axes coords
#legend.labelsep : 0.010 # deprecated; the vertical space between the legend entries
#legend.labelspacing : 0.5 # the vertical space between the legend entries in fraction of fontsize
#legend.handlelen : 0.05 # deprecated; the length of the legend lines
#legend.handlelength : 2. # the length of the legend lines in fraction of fontsize
#legend.handleheight : 0.7 # the height of the legend handle in fraction of fontsize
#legend.handletextsep : 0.02 # deprecated; the space between the legend line and legend text
#legend.handletextpad : 0.8 # the space between the legend line and legend text in fr
#legend.axespad : 0.02 # deprecated; the border between the axes and legend edge
#legend.borderaxespad : 0.5 # the border between the axes and legend edge in fraction of fontsize
#legend.columnspacing : 2. # the border between the axes and legend edge in fraction of fontsize
#legend.shadow : False
#legend.frameon : True # whether or not to draw a frame around legend
### FIGURE
# See http://matplotlib.sourceforge.net/api/figure_api.html#matplotlib.figure.Figure
#figure.figsize : 8, 6 # figure size in inches
#figure.dpi : 80 # figure dots per inch
#figure.facecolor : 0.75 # figure facecolor; 0.75 is scalar gray
#figure.edgecolor : white # figure edgecolor
# The figure subplot parameters. All dimensions are fraction of the
# figure width or height
#figure.subplot.left : 0.125 # the left side of the subplots of the figure
#figure.subplot.right : 0.9 # the right side of the subplots of the figure
#figure.subplot.bottom : 0.1 # the bottom of the subplots of the figure
#figure.subplot.top : 0.9 # the top of the subplots of the figure
#figure.subplot.wspace : 0.2 # the amount of width reserved for blank space between subplots
#figure.subplot.hspace : 0.2 # the amount of height reserved for white space between subplots
### IMAGES
#image.aspect : equal # equal | auto | a number
#image.interpolation : bilinear # see help(imshow) for options
#image.cmap : jet # gray | jet etc...
#image.lut : 256 # the size of the colormap lookup table
#image.origin : upper # lower | upper
#image.resample : False
### CONTOUR PLOTS
#contour.negative_linestyle : dashed # dashed | solid

### Agg rendering
### Warning: experimental, 2008/10/10
#agg.path.chunksize : 0 # 0 to disable; values in the range
# 10000 to 100000 can improve speed slightly
# and prevent an Agg rendering failure
# when plotting very large data sets,
# especially if they are very gappy.
# It may cause minor artifacts, though.
# A value of 20000 is probably a good
# starting point.
### SAVING FIGURES
#path.simplify : True # When True, simplify paths by removing "invisible"
# points to reduce file size and increase rendering
# speed
#path.simplify_threshold : 0.1 # The threshold of similarity below which
# vertices will be removed in the simplification
# process
#path.snap : True # When True, rectilinear axis-aligned paths will be snapped to
# the nearest pixel when certain criteria are met. When False,
# paths will never be snapped.
# the default savefig params can be different from the display params
# Eg, you may want a higher resolution, or to make the figure
# background white
#savefig.dpi : 100 # figure dots per inch
#savefig.facecolor : white # figure facecolor when saving
#savefig.edgecolor : white # figure edgecolor when saving
#savefig.extension : auto # what extension to use for savefig(’foo’), or ’auto’
#cairo.format : png # png, ps, pdf, svg
# tk backend params
#tk.window_focus : False # Maintain shell focus for TkAgg
# ps backend params
#ps.papersize : letter # auto, letter, legal, ledger, A0-A10, B0-B10
#ps.useafm : False # use of afm fonts, results in small files
#ps.usedistiller : False # can be: None, ghostscript or xpdf
# Experimental: may produce smaller files.
# xpdf intended for production of publication quality files,
# but requires ghostscript, xpdf and ps2eps
#ps.distiller.res : 6000 # dpi
#ps.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType)
# pdf backend params
#pdf.compression : 6 # integer from 0 to 9
# 0 disables compression (good for debugging)
#pdf.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType)
# svg backend params
#svg.image_inline : True # write raster image data directly into the svg file
#svg.image_noscale : False # suppress scaling of raster data embedded in SVG
#svg.fonttype : ’path’ # How to handle SVG fonts:
# ’none’: Assume fonts are installed on the machine where the SVG will be viewed.
# ’path’: Embed characters as paths -- supported by most SVG renderers
# ’svgfont’: Embed characters as SVG fonts -- supported only by Chrome,
# Opera and Safari
# docstring params
#docstring.hardcopy = False # set this when you want to generate hardcopy docstring
# Set the verbose flags. This controls how much information
# matplotlib gives you at runtime and where it goes. The verbosity
# levels are: silent, helpful, debug, debug-annoying. Any level is
# inclusive of all the levels below it. If your setting is "debug",
# you’ll get all the debug and helpful messages. When submitting
# problems to the mailing-list, please set verbose to "helpful" or "debug"
# and paste the output into your report.
#
# The "fileo" gives the destination for any calls to verbose.report.
# These objects can a filename, or a filehandle like sys.stdout.
#
# You can override the rc default verbosity from the command line by
# giving the flags --verbose-LEVEL where LEVEL is one of the legal
# levels, eg --verbose-helpful.
#
# You can access the verbose instance in your code
# from matplotlib import verbose.
#verbose.level : silent # one of silent, helpful, debug, debug-annoying
#verbose.fileo : sys.stdout # a log filename, sys.stdout or sys.stderr
# Event keys to interact with figures/plots via keyboard.
# Customize these settings according to your needs.
# Leave the field(s) empty if you don’t need a key-map. (i.e., fullscreen : ’’)
#keymap.fullscreen : f # toggling
#keymap.home : h, r, home # home or reset mnemonic
#keymap.back : left, c, backspace # forward / backward keys to enable
#keymap.forward : right, v # left handed quick navigation
#keymap.pan : p # pan mnemonic
#keymap.zoom : o # zoom mnemonic
#keymap.save : s # saving current figure
#keymap.grid : g # switching on/off a grid in current axes
#keymap.yscale : l # toggle scaling of y-axes (’log’/’linear’)
#keymap.xscale : L, k # toggle scaling of x-axes (’log’/’linear’)
#keymap.all_axes : a # enable all axes
# Control downloading of example data. Various examples download some
# data from the Matplotlib git repository to avoid distributing extra
# files, but sometimes you want to avoid that. In that case set
# examples.download to False and examples.directory to the directory
# where you have a checkout of https://github.com/matplotlib/sample_data
#examples.download : True # False to bypass downloading mechanism
#examples.directory : ’’ # directory to look in if download is false

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