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Matplotlib画图

mac2025-12-13  8

文章目录

MatplotlibMatplotlib基本Pandas中的基本图形Matplotlib作图误差条形图饼图等高线图3D柱形图3D直方图

Matplotlib

Matplotlib基本

######################################## #### #### #### Matplotlib #### #### #### ######################################## from __future__ import division import numpy as np import pandas as pd from pandas import DataFrame,Series import os import matplotlib.pyplot as plt ###matplotlib创建图表 plt.plot([1, 2, 3, 2, 3, 2, 2, 1]) plt.show() ##前面的y轴，后面的是x轴 plt.plot([4,3,2,1],[1,2,3,4]) plt.show() x = [1,2,3,4] y = [5,4,3,2] #画多个图 plt.figure() #分成2 * 3个区域，画第1个上面 #线性图 plt.subplot(231) plt.plot(x,y) #筒形图 plt.subplot(232) plt.bar(x,y) #筒形图（和上面x，y相反） plt.subplot(233) plt.bar(x,y) #筒形图（两个y叠加） plt.subplot(234) plt.bar(x,y) y1=[7,8,5,3] plt.bar(x,y1,bottom=y,color='r') plt.subplot(235) plt.boxplot(x) #点图 plt.subplot(236) plt.scatter(x,y) plt.show() plt.show() ####figure与subpolt figure对象 fig = plt.figure() ax1 = fig.add_subplot(2,2,1) ax2 = fig.add_subplot(2,2,2) ax3 = fig.add_subplot(2,2,3) #cumsum() 累加，第一个元素加到第二个，在累加到第三个 k--黑色的曲线 plt.plot(np.random.randn(50).cumsum(),'k--') #在第一个格子里面绘图 直方图 间距（划分成多少快） 颜色的深度 ax1.hist(np.random.randn(100),bins=20,color='r',alpha=1) #散点图 ax2.scatter(np.arange(30),np.arange(30)+np.random.randn(30)*3) plt.show() plt.close() fig,axes = plt.subplots(2,3) axes[0][0].scatter(np.arange(30),np.arange(30)+np.random.randn(30)*3) plt.show() plt.subplots_adjust(None,None,None,None,None,None) fig,axes = plt.subplots(2,2,sharex=True,sharey=True) for i in range(2): for j in range(2): axes[i][j].hist(np.random.randn(500),bins=50,color='r',alpha=1) plt.subplots_adjust(wspace=0,hspace=0) plt.show() #####matplotlib基本设置 #颜色，标记和线型 plt.figure() x = [1, 2, 3, 4] y = [5, 4, 3, 2] # b--颜色和虚线 plt.plot(x,y,'r--') plt.plot(x,y,linestyle="--",color='r') # k = 黑色 o = 标记 -- 虚线 data = np.random.randn(30).cumsum() plt.plot(data, 'ko--') plt.plot(data, 'k--', label='Default') plt.plot(data, 'k-', drawstyle='steps-post', label='step-post') plt.legend(loc='best') plt.show() plt.close("all") ######设置标题、轴标签、刻度以及刻度标签 fig = plt.figure() ax = fig.add_subplot(111) ax.plot(np.random.randn(1000).cumsum()) #设置下标 ax.set_xticks([0, 250, 500, 750, 1000]) # rotation旋转角度，fontsize字体大小 ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'], rotation=30, fontsize='small') #图名字 ax.set_title("My First Matplotlib Plot") #y轴标签 ax.set_ylabel("y") #x轴标签 ax.set_xlabel("x") plt.show() ###添加图例 fig = plt.figure() ax = fig.add_subplot(111) ax.plot(np.random.randn(1000).cumsum(),'k', label='one') ax.plot(np.random.randn(1000).cumsum(),'k--', label='two') ax.plot(np.random.randn(1000).cumsum(),'k.', label='three') #在图上添加label标签 # 如 ——— one # - - two # · · three plt.legend(loc='best') plt.show() #注释以及在subplot上绘图 from datetime import datetime fig = plt.figure() ax = fig.add_subplot(111) #使用第一列当成索引并解析 data = pd.read_csv("week7/spx.csv",index_col=0,parse_dates=True) spx = data['SPX'] spx.plot(ax=ax,style = 'k--') crisis_data = [ (datetime(2007,10,11),'Peak of bull market'), (datetime(2008,3,12),'Bear Stearns Fails'), (datetime(2008,9,15),'Lehman Bankruptcy') ] for date,label in crisis_data: ax.annotate(label,xy=(date,spx.asof(date)+50), xytext=(date,spx.asof(date)+200), arrowprops=dict(facecolor='black'), horizontalalignment='left', verticalalignment='top' ) ax.set_xlim('1/1/2007','1/1/2011') ax.set_title("Imprtana dates in 2008-2009 financial crisis") plt.show()

Pandas中的基本图形

#########pandas中的绘图函数 s = Series(np.random.randn(10),index=np.arange(0,100,10)) s.plot() plt.show() df = DataFrame(np.random.randn(10,4).cumsum(0), columns=['A','B','C','D'], index=np.arange(0,100,10)) df.plot() plt.show() fig,axes = plt.subplots(2,1) data = Series(np.random.randn(16),index=list('abcdefghijklmnop')) data.plot(kind='bar',ax=axes[0],color='k',alpha=0.7) data.plot(kind='barh',ax=axes[1],color='k',alpha=0.7) plt.show() df = DataFrame(np.random.randn(6,4), index=['one','tow','three','four','five','six'], columns=pd.Index(['A','B','C','D'],name='Genus')) #df.plot(kind='bar') #plt.figure() #df.plot(kind='barh',stacked=True,alpha=0.5) #plt.show() tips = pd.read_csv('week7/tips.csv') #统计 类似与groupBy party_counts = pd.crosstab(tips.day,tips['size']) #统计比较多的2345 party_counts = party_counts.loc[:,2:5] print(party_counts) #转换成百分比形式 #按照 汇总和进行相除 party_pcts = party_counts.div(party_counts.sum(1).astype(float),axis=0) # print(party_counts.sum(1)) # print(party_pcts) party_pcts.plot(kind='bar',stacked=True) #plt.show() ##直方图和密度图 tips = pd.read_csv('week7/tips.csv') print(tips) tips['tip_pct'] = tips['tip'] / tips['total_bill'] tips['tip_pct'].hist(bins=50) plt.show() #正态分布 comp1 = np.random.normal(0,1,size=200) comp2 = np.random.normal(10,2,size=200) #数值组合 values = Series(np.concatenate([comp1,comp2])) values.hist(bins=100,alpha=0.3,color='k',density=True) values.plot(kind='kde',style='k--') plt.show()

Matplotlib作图

误差条形图

####误差条形图 x = np.arange(0,10,1) y = np.log(x) xe = 0.1 * np.abs(np.random.randn(len(y))) plt.bar(x,y,yerr=xe,width=0.4,align='center',color='cyan',ecolor='r',label="experiment #1") plt.xlabel('# measurement') plt.ylabel("Measured values") plt.title("Measurements") plt.legend(loc='upper left') plt.show()

饼图

###饼图 plt.figure(1,figsize=(8,8)) labels = 'Spring','Summer','Autumn','Winter' values = [15,16,16,28] explode =[0.1,0.1,0.1,0.1] plt.pie(values, explode=explode, #炸裂 labels=labels, #标签 autopct='%1.1f%%', #标准格式 startangle=0) #旋转多少度 plt.title("Rainy days by season") plt.show()

等高线图

import matplotlib as mpl def process_signals(x,y): return (1 - (x**2 + y**2)) * np.exp(-y ** 3 / 3) x = np.arange(-1.5,1.5,0.1) y = np.arange(-1.5,1.5,0.1) X, Y = np.meshgrid(x, y) process_signals(X,Y) Z = process_signals(X,Y) N = np.arange(-1,1.5,0.3) CS = plt.contour(Z, N, linewidths = 2,cmap = mpl.cm.jet) plt.clabel(CS,inline=True,fmt="%1.1f",fontsize=10) plt.colorbar(CS) plt.show()

3D柱形图

###3D柱形图 import matplotlib as mpl import matplotlib.dates as mdates from mpl_toolkits.mplot3d import Axes3D mpl.rcParams['font.size'] = 10 fig = plt.figure() ax = fig.add_subplot(111,projection='3d') for z in [2011,2012,2013,2014]: xs = range(1,13) ys = 1000 * np.random.rand(12) color = plt.cm.Set2(np.random.choice(range(plt.cm.Set2.N))) ax.bar(xs, ys, zs=z, zdir='y', color=color, alpha=0.8) ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(xs)) ax.yaxis.set_major_locator(mpl.ticker.FixedLocator(ys)) ax.set_xlabel('Month') ax.set_ylabel('Year') ax.set_zlabel('Sales Net [usd]') plt.show()

3D直方图

#3d直方图 mpl.rcParams['font.size'] = 10 samples = 25 x = np.random.normal(5, 1, samples) y = np.random.normal(3, .5, samples) fig = plt.figure() ax = fig.add_subplot(211, projection='3d') hist, xedges, yedges = np.histogram2d(x, y, bins=10) elements = (len(xedges) - 1) * (len(yedges) - 1) xpos, ypos = np.meshgrid(xedges[:-1]+.25, yedges[:-1]+.25) xpos = xpos.flatten() ypos = ypos.flatten() zpos = np.zeros(elements) dx = .1 * np.ones_like(zpos) dy = dx.copy() dz = hist.flatten() ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', alpha=0.4) ax.set_xlabel('X Axis') ax.set_ylabel('Y Axis') ax.set_zlabel('Z Axis') ax2 = fig.add_subplot(212) ax2.scatter(x, y) ax2.set_xlabel('X Axis') ax2.set_ylabel('Y Axis') plt.show()

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