解决绘图中乱码问题

plt.rcParams['font.sans-serif']=['Simhei'] # 解决中文乱码问题 plt.rcParams['axes.unicode_minus']=False # 解决坐标轴刻度负号乱码

Matplotlib中的绘图类型

导入基本模块

import matplotlib.pyplot as plt import numpy as np np.random.seed(1026)

散点图

x = np.linspace(0,2*np.pi,50) y = np.random.random(50) # y = np.sin(x) plt.scatter(x,y,'o');

彩色映射散点图

x = np.random.randint(1,100,100) y = np.random.rand(100) size = np.random.rand(100) * 500 colour = np.random.rand(100) plt.rc('figure', figsize=(10, 6)) plt.scatter(x, y, size, colour, # marker=r'$\clubsuit$' alpha=.7, linewidths=7 ) plt.colorbar() # 显示色条 plt.show()

柱状图、条形图

plt.bar(x,height,width=0.8,bottom=None,*,align='center',data=None, **kwargs) size = 5 a = np.random.random(size) b = np.random.random(size) c = np.random.random(size) d = np.random.random(size) x = np.arange(size) total_width, n = 0.6, 3 # 有多少个类型，只需更改n即可 width = total_width / n x = x - (total_width - width) / 2 plt.bar(x, a, width=width, label='a') plt.bar(x + width, b, width=width, label='b') plt.bar(x + 2 * width, c, width=width, label='c') plt.legend() plt.show()

堆积柱状图

N = 5 menMeans = (20, 35, 30, 35, 27) womenMeans = (25, 32, 34, 20, 25) ind = np.arange(N) width = 0.35 p1 = plt.bar(ind, menMeans, width, color='#d62728', label='Men') p2 = plt.bar(ind, womenMeans, width, bottom=menMeans, label='Wowen') plt.ylabel('Scores') plt.title('Scores by group and gender') plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5')) plt.yticks(np.arange(0, 81, 10)) # plt.legend((p1[0], p2[0]), ('Men', 'Women')) plt.legend() plt.show()

带误差的堆积柱状图

N = 5 menMeans = (20, 35, 30, 35, 27) womenMeans = (25, 32, 34, 20, 25) menStd = (2, 3, 4, 1, 2) womenStd = (3, 5, 2, 3, 3) ind = np.arange(N) width = 0.35 p1 = plt.bar(ind, menMeans, width, color='#d62728', yerr=menStd) p2 = plt.bar(ind, womenMeans, width, bottom=menMeans, yerr=womenStd) plt.ylabel('Scores') plt.title('Scores by group and gender') plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5')) plt.yticks(np.arange(0, 81, 10)) plt.legend((p1[0], p2[0]), ('Men', 'Women')) # plt.legend() plt.show()

直方图

plt.hist(x, bins=None, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, hold=None, data=None, **kwargs)

参数说明

参数说明x数据bins组数histtype{‘bar’, ‘barstacked’, ‘step’, ‘stepfilled’}align{‘left’, ‘mid’, ‘right’}orientation{‘horizontal’, ‘vertical’}rwidthbar间距 x = np.random.randn(100) plt.hist(x, 20, orientation='horizontal'); from scipy.stats import norm import matplotlib.pyplot as plt mu = 100 # 均值 sigma = 15 # 标准差 x = mu + sigma * np.random.randn(1000) n, bins, patches = plt.hist(x, 50, normed=1) y = norm.pdf(bins, mu, sigma) # 添加拟合曲线 plt.plot(bins, y, '--') plt.xlabel('Smarts') plt.ylabel('Probability density') plt.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$') plt.tight_layout(); # 调整间距以防止ylabel被覆盖 plt.tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None) # 参数 # pad：子图边框和figure边框的距离，用空白填补 # h_pad、w_pad：子图的纵横边框之间的距离，用空白填补

问题注解：If you read documentation of matplotlib.mlab.normpdf, this function is deprycated and you should use scipy.stats.norm.pdf instead.

饼图

labels = 'Frogs', 'Hogs', 'Dogs', 'Logs' sizes = [15, 30, 45, 10] explode = (0, 0.1, 0, 0) # 突出'Hogs' fig1, ax1 = plt.subplots() ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) ax1.axis('equal'); # 相等的纵横比确保饼图是圆的

箱线图、小提琴图

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 4)) axes[0].violinplot(data, # vert=False, # showmeans=True, showmedians=True ) axes[0].set_title('Violin plot') axes[1].boxplot(data, sym='r', meanline=True, showmeans=False) axes[1].set_title('Box plot') # adding horizontal grid lines for ax in axes: ax.yaxis.grid(True) ax.set_ylim(0,300000) ax.set_xlabel('不同群组') ax.set_ylabel('观测') ax.set_xticks([y + 1 for y in range(len(data))]) ax.set_xticklabels(['Assets', 'Revenue', 'Spend', 'Margin', 'Pay']) # add x-tick labels 换做一种方式设置标签 # plt.setp(axes, xticks=[y + 1 for y in range(len(data))], # xticklabels=['Assets', 'Revenue', 'Spend', 'Margin', 'Pay']);

添加文本、箭头、注解等

x = np.linspace(0, 10, 1000) y = np.sin(x) plt.plot(x,y,label="$sin(x)$",color="red",linewidth=3) plt.text(2,0,'文本在(2,0)这个点', family='Simhei', fontsize=10) plt.arrow(4, -0.5, 0.5, 0.5) # 4, -0.5, 4.5, 0 plt.annotate("我是注解", xy=(0.5, 0.5), xytext=(1, -0.25),arrowprops=dict(arrowstyle="->")) plt.show()

定制化图表样式

plt.style.available ['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']

定制化图表样式

plt.style.use('classic')

定制化临时图表样式

with plt.style.context(('seaborn-ticks')): plt.plot(np.sin(np.linspace(0, 2 * np.pi）

热力图

import numpy as np from pandas import DataFrame import matplotlib.pyplot as plt Index= ['aaa', 'bbb', 'ccc', 'ddd', 'eee'] Cols = ['A', 'B', 'C', 'D'] df = DataFrame(abs(np.random.randn(5, 4)), index=Index, columns=Cols) plt.pcolor(df) plt.yticks(np.arange(0.5, len(df.index), 1), df.index) plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns) plt.show()

Seaborn中一行命令即可画出精美的图片

import seaborn as sns sns.heatmap(df, annot=True);

参考文档

数据可视化之matplotlib绘图基础一 Matplotlib中文文档