一.绘制简单图形

1.折线图

import matplotlib.pyplot as plt import numpy as np #单条sin函数 x=np.linspace(0.1, 10, 100) y=np.sin(x) plt.plot(x,y, linestyle='-', linewidth=1,label='Sin() by plot()') plt.legend() plt.show() #多条曲线 a=np.random.random((9,3))*2 y1=a[0:,1] y2=a[0:,2] x=np.arange(1,10) ax = plt.subplot(111)#行数，列数，索引值 width=10 hight=3 ax.axes.set_xlim(-0.5,width+0.2) ax.axes.set_ylim(-0.5,hight+0.2) plotdict = { 'dx': x, 'dy': y1 } ax.plot('dx','dy','bD-',data=plotdict) ax.plot(x,y2,'r^-') plt.show()

2.柱状图

import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt 普通柱状图 mpl.rcParams['font.sans-serif']=['SimHei'] x = ['c', 'a', 'd', 'b'] y = [1, 2, 3, 4] plt.bar(x, y, alpha=0.5, width=0.3, color='yellow', edgecolor='red', label='The First Bar', lw=3) plt.legend(loc='upper left') plt.xticks(np.arange(4), ('A','B', 'C', 'D'), rotation=30) plt.yticks(np.arange(0, 5, 0.4)) plt.ylabel('亏损情况（万元）', fontsize=10) plt.xlabel('部门', fontsize=10) plt.title('一季度各部门亏损情况', fontsize=10) plt.tick_params(axis='both', labelsize=15) plt.show() #堆积柱状图 x = [1,3,5] y = [3,8,9] y1 = [2,6,3] plt.bar(x,y,align="center",color="#66c2a5",tick_label=["A","B","C"],label="title_A") plt.bar(x,y1,align="center",color="#8da0cb",tick_label=["A","B","C"],label="title_B") plt.legend() plt.show() #多数据列柱状图 x = np.arange(3) y = [2,6,3] y1 = [6,10,4] bar_width = 0.4 tick_label = ["A","B","C"] plt.bar(x,y,align="center",color="c",width=bar_width,label="title_A",alpha=0.5) plt.bar(x+bar_width,y1,align="center",color="b",width=bar_width,label="title_B",alpha=0.5) plt.xticks(x+bar_width/2,tick_label) plt.legend() plt.show() #倒影柱状图 n = 12 X = np.arange(n) Y1 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n) Y2 = (1 - X / float(n)) * np.random.uniform(0.5, 1.0, n) plt.bar(X, +Y1, facecolor='#9966ff', edgecolor='white') plt.bar(X, -Y2, facecolor='#ff9966', edgecolor='white') plt.xlim(-.5, n) plt.ylim(-1.25, 1.25) for x, y in zip(X, Y1): plt.text(x, y + 0.05, '%.2f' % y, ha='center', va='bottom') for x, y in zip(X, Y2): plt.text(x, -y - 0.05, '%.2f' % y, ha='center', va='top') plt.show()

3.条形图

import matplotlib.pyplot as plt import numpy as np x = np.arange(4) y = [6,10,4,5] y1 = [2,6,3,8] bar_width = 0.4 tick_label = ["A","B","C","D"] plt.barh(x,y,bar_width,align="center",color="c",label="title_A",alpha=1) plt.barh(x+bar_width,y1,bar_width,align="center",color="b",label="title_B",alpha=1) plt.yticks(x+bar_width/2,tick_label) plt.legend() plt.show()

4.直方图

import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt #普通直方图 mu = 100 sigma = 15 x = mu + sigma * np.random.randn(10000) n, bins, patches = plt.hist(x, 50, normed=1, facecolor='blue', alpha=1) y = mlab.normpdf(bins, mu, sigma) plt.show() #堆积直方图 x1 = np.random.randint(0,100,100) x2 = np.random.randint(0,100,100) x = [x1,x2] colors = ["#fc8d62","#66c2a5"] labels = ["A","B"] bins = range(0,101,10) plt.hist(x,bins=bins,color=colors,histtype="bar",rwidth=10,stacked=True,label=labels,edgecolor = 'k') plt.show()

5.饼图

import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['SimHei'] labels = ["A部门","B部门","C部门","D部门"] nums = [0.25,0.15,0.36,0.24] colors = ["#377eb8","#4daf4a","#984ea3","#ff7f00"] explode = (0.1,0.1,0.1,0.1) plt.pie(nums,explode=explode,labels=labels,autopct="%3.1f%%",startangle=45,shadow=True,colors=colors) plt.title("上季度各部门盈利构成") plt.show() #嵌套环形图 labels = ["A","B","C","D","E"] nums1 = [29,19,22,18,12] nums2 = [22,27,18,11,22] colors = ["#e41a1c","#377eb8","#4daf4a","#984ea3","#ff7f00"] w1, t1, a1 = plt.pie(nums1,autopct="%3.1f%%",radius=1,pctdistance=0.85,colors=colors,textprops=dict(color="w"),wedgeprops=dict(width=0.3,edgecolor="w")) w2, t2, a2 = plt.pie(nums2,autopct="%3.1f%%",radius=0.7,pctdistance=0.75,colors=colors,textprops=dict(color="w"),wedgeprops=dict(width=0.3,edgecolor="w")) plt.setp(a1,size=8,weight="bold") plt.setp(a2,size=8,weight="bold") plt.setp(t1,size=8,weight="bold") plt.show()

6.雷达图

import numpy as np import matplotlib.pyplot as plt labels = np.array(['a','b','c','d','e','f']) dataLenth = 6 data = np.array([2,4,3,6,5,8]) angles = np.linspace(0, 2*np.pi, dataLenth, endpoint=False) data = np.concatenate((data, [data[0]])) angles = np.concatenate((angles, [angles[0]])) plt.polar(angles, data, 'bo-', linewidth=2) plt.thetagrids(angles * 180/np.pi, labels) plt.fill(angles, data, facecolor='r', alpha=0.25) plt.ylim(0,10) plt.show()

7.散点图

import numpy as np import matplotlib.pyplot as plt x1 = np.random.randn(20) x2 = np.random.randn(20) plt.figure(1) #plot画散点图，x取值0,1,2...... plt.plot(x1, 'bo', markersize=20) plt.plot(x2, 'ro', ms=10,) plt.show()

8.棉棒图

import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 10, 20) y = np.random.randn(20) plt.stem(x, y, linefmt='-', markerfmt='o', basefmt='-') plt.show()

9.箱线图

import matplotlib.pyplot as plt import numpy as np import pandas as pd df = pd.DataFrame(np.random.rand(8,5),columns=['A','B','C','D','E']) df.boxplot() plt.show()

10.误差棒图

import numpy as np from matplotlib import pyplot as plt from scipy.stats import t X = np.random.randint(5, 15, 15) n = X.size X_mean = np.mean(X) X_std = np.std(X) X_se = X_std / np.sqrt(n) dof = n - 1 alpha = 1.0 - 0.95 conf_interval = t.ppf(1-alpha/2., dof) * X_std*np.sqrt(1.+1./n) fig = plt.gca() plt.errorbar(1, X_mean, yerr=X_std, fmt='-s') plt.errorbar(2, X_mean, yerr=X_se, fmt='-s') plt.errorbar(3, X_mean, yerr=conf_interval, fmt='-s') plt.xlim([0,4]) plt.ylim(X_mean-conf_interval-2, X_mean+conf_interval+2) plt.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="on", right="off", labelleft="on") plt.show() #结合条形图 mean_values = [1, 2, 3] variance = [0.2, 0.4, 0.5] bar_labels = ['bar 1', 'bar 2', 'bar 3'] fig = plt.gca() x_pos = list(range(len(bar_labels))) plt.bar(x_pos, mean_values, yerr=variance, align='center', alpha=0.5) max_y = max(zip(mean_values, variance)) plt.ylim([0, (max_y[0] + max_y[1]) * 1.1]) plt.xticks(x_pos, bar_labels) plt.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="on", right="off", labelleft="on") plt.show()

11.堆积折线图

import numpy as np import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y1 = [1, 3, 4, 2, 7] y2 = [3, 4, 1, 6, 5] y3 = [1, 3, 5, 7, 9] y = np.vstack([y1, y2, y3]) labels = ["Fibonacci ", "Evens", "Odds"] fig, ax = plt.subplots() ax.stackplot(x, y1, y2, y3, labels=labels) ax.legend(loc='upper left') plt.show()

12.间断条形图

import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.broken_barh([(110, 30), (150, 10)], (10, 9), facecolors='blue') ax.broken_barh([(10, 50), (100, 20), (130, 10)], (20, 9), facecolors=('red', 'cyan', 'green')) ax.set_ylim(5, 35) ax.set_xlim(0, 180) ax.set_yticks([15, 25]) ax.set_yticklabels(['A', 'B']) ax.grid(True) plt.show()

13.阶梯图

import numpy as np from numpy import ma import matplotlib.pyplot as plt x = np.arange(1, 7, 0.4) y = np.sin(x) + 2.5 plt.step(x, y, label='pre') y -= 0.5 plt.step(x, y, where='mid', label='mid') y -= 0.5 plt.step(x, y, where='post', label='post') plt.legend() plt.xlim(0, 7) plt.ylim(0, 4) plt.show()

二.高级图形

1.对数图

from matplotlib import pyplot as plt import numpy as np x = np.linspace(1, 10) y = [10 ** el for el in x] fig, ax = plt.subplots() ax.set_yscale('linear') #对数标度是一条直线 #ax.set_yscale('log') ax.plot(x, y, color='blue') ax.grid(True) plt.show()

2.频谱图

import wave import matplotlib.pyplot as plt import numpy as np import os f = wave.open('C:\\Users\\Administrator\\.spyder-py3\\V\\test.wav','rb') params = f.getparams() nchannels, sampwidth, framerate, nframes = params[:4] strData = f.readframes(nframes) waveData = np.fromstring(strData,dtype=np.int16) waveData = waveData*1.0/(max(abs(waveData))) waveData = np.reshape(waveData,[nframes,nchannels]).T f.close() plt.specgram(waveData[0],Fs = framerate, scale_by_freq = True, sides = 'default') plt.ylabel('Frequency(Hz)') plt.xlabel('Time(s)') plt.show()

3.矢量场流线图

import numpy as np import matplotlib.pyplot as plt w = 3 Y, X = np.mgrid[-w:w:100j, -w:w:100j] U = -1 - X**2 + Y V = 1 + X - Y**2 speed = np.sqrt(U*U + V*V) fig, ax = plt.subplots() ax.streamplot(X, Y, U, V, density=[0.5, 1]) ax.set_title('Varying Density') plt.show()

4.变量间互相关图

import matplotlib.pyplot as plt import numpy as np np.random.seed(0) x, y = np.random.randn(2, 100) fig = plt.figure() ax1 = fig.add_subplot(211) ax1.xcorr(x, y, usevlines=True, maxlags=50, normed=True, lw=2) ax1.grid(True) ax1.axhline(0, color='black', lw=2) ax2 = fig.add_subplot(212, sharex=ax1) ax2.acorr(x, usevlines=True, normed=True, maxlags=50, lw=2) ax2.grid(True) ax2.axhline(0, color='black', lw=2) plt.show()

三.调整坐标轴和刻度

1.设置坐标轴刻度

from matplotlib.ticker import AutoMinorLocator, MultipleLocator, FormatStrFormatter ax.set_xlim(0,5) ax.set_ylim(-1.5,1.5) ax.xaxis.set_major_locator(MultipleLocator(1))#刻度线每1个单位一个 ax.yaxis.set_major_locator(MultipleLocator(1)) ax.xaxis.set_minor_locator(AutoMinorLocator(2))#无数刻度线把有数刻度线之间分割成2段 ax.yaxis.set_minor_locator(AutoMinorLocator(5))

2.设置标签文本

ax.xaxis.set_minor_formatter(FormatStrFormatter('%5.1f')) ax.yaxis.set_minor_formatter(FormatStrFormatter('%5.1f')) ax.tick_params(which='minor',length=5,width=1,labelsize=8,labelcolor='r') ax.tick_params('y',which='major',length=8,width=1,labelsize=10,labelcolor='b')

3.绘制网格线

ax.grid(ls=':',lw=0.8,color='b')

4.移动坐标轴位置

ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') ax.spines['bottom'].set_position(('data',0)) ax.spines['left'].set_position(('data',0)) ax.xaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left')

四.添加标题、图例和注释

1.设置标题

plt.title('余弦函数在[-5, 5]的图象', family='kaiti', size=20, color='b', loc='right')

2.设置图例

y1=np.sin(x) cosine, = ax.plot(x,y,lw=2) sine, = ax.plot(x,y1,lw=4,ls='--')plt.title('正弦函数和余弦函数在[-5, 5]的图象', family='kaiti', size=20, color='b', loc='left') plt.legend([sine, cosine], elements, loc='best', prop={'family':'kaiti','size':12})

3.添加注释

ax.annotate('y=sin(x)', xy=(2.5,0.6), xytext=(3.5,0.6), arrowprops=dict(arrowstyle='->', facecolor='black')) ax.text(-2.1, 0.65, 'y=cos(x)', color='b', bbox=dict(facecolor='black', alpha=0.2))

五.设置线形和文本字体以及颜色

1.设置线形

linestyles=['-','--','-.',':'] linemarkernames=['. 点型', 'o 圆圈', 'v 向下的三角形', '^ 向上的三角形', '< 向左的三角形', '> 向右的三角形', 's 正方形', 'p 五边形', '* 星型', '+ +型', 'x x型', '| 竖线', '_ 横线'] linemarkerstyles=['.','o','v','^','<','>','s','p','*','+','x','|','_']

2.设置文本和字体属性

families=['arial','tahoma','verdana'] sizes=['xx-small','x-small','small','medium','large','x-large','xx-large'] styles=['normal','italic','oblique'] variants=['normal','small-caps'] weights=['light','normal','medium','roman','semibold','demibold','demi','bold','heavy','extra bold','black']

3.颜色

import scipy.misc import matplotlib as mpl import matplotlib.pyplot as plt #原图 plt.imshow(scipy.misc.ascent()) plt.show() #white颜色映射 plt.imshow(scipy.misc.ascent(), cmap=mpl.cm.winter) plt.show() #RdYlBu颜色映射 plt.imshow(scipy.misc.ascent(), cmap=mpl.cm.RdYlBu) plt.show() #Accent颜色映射 plt.imshow(scipy.misc.ascent(), cmap=mpl.cm.Accent) plt.show() #灰度映射并添加颜色标尺 plt.imshow(scipy.misc.ascent(), cmap=mpl.cm.gray) plt.colorbar() plt.show()

4.划分画布

import scipy.misc import matplotlib.pyplot as plt fig, ax=plt.subplots(2,2) ax[0,0].imshow(scipy.misc.ascent()) ax[0,1].imshow(scipy.misc.ascent(), cmap=mpl.cm.winter) ax[1,0].imshow(scipy.misc.ascent(), cmap=mpl.cm.RdYlBu) ax[1,1].imshow(scipy.misc.ascent(), cmap=mpl.cm.Accent) plt.show()

来源：《python数据分析与可视化》