本篇文章给大家分享的是有关怎么在Python中使用matplotlib和pandas实现画图,小编觉得挺实用的,因此分享给大家学习,希望大家阅读完这篇文章后可以有所收获,话不多说,跟着小编一起来看看吧。
#encoding:utf-8 ''''' Created on 2015年9月11日 @author: ZHOUMEIXU204 ''' # pylab 是 matplotlib 面向对象绘图库的一个接口。它的语法和 Matlab 十分相近 import pandas as pd #from ggplot import * import numpy as np import matplotlib.pyplot as plt df=pd.DataFrame(np.random.randn(1000,4),columns=list('ABCD')) df=df.cumsum() print(plt.figure()) print(df.plot()) print(plt.show()) # print(ggplot(df,aes(x='A',y='B'))+geom_point())
运行效果:
# 画简单的图形 from pylab import * x=np.linspace(-np.pi,np.pi,256,endpoint=True) c,s=np.cos(x),np.sin(x) plot(x,c, color="blue", linewidth=2.5, line, label="cosine") #label用于标签显示问题 plot(x,s,color="red", linewidth=2.5, line, label="sine") show()
运行效果:
#散点图 from pylab import * n = 1024 X = np.random.normal(0,1,n) Y = np.random.normal(0,1,n) scatter(X,Y) show()
运行效果:
#条形图 from pylab import * 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) bar(X, +Y1, facecolor='#9999ff', edgecolor='white') bar(X, -Y2, facecolor='#ff9999', edgecolor='white') for x,y in zip(X,Y1): text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom') ylim(-1.25,+1.25) show()
运行效果:
#饼图 from pylab import * n = 20 Z = np.random.uniform(0,1,n) pie(Z), show()
运行效果:
#画三维图 import numpy as np from mpl_toolkits.mplot3d import Axes3D from pylab import * fig=figure() ax=Axes3D(fig) x=np.arange(-4,4,0.1) y=np.arange(-4,4,0.1) x,y=np.meshgrid(x,y) R=np.sqrt(x**2+y**2) z=np.sin(R) ax.plot_surface(x,y,z,rstride=1,cstride=1,cmap='hot') show()
运行效果:
#用于图像显示的问题 import matplotlib.pyplot as plt import pandas as pd weights_dataframe=pd.DataFrame() plt.figure() plt.plot(weights_dataframe.weights_ij,weights_dataframe.weights_x1,label='weights_x1') plt.plot(weights_dataframe.weights_ij,weights_dataframe.weights_x0,label='weights_x0') plt.plot(weights_dataframe.weights_ij,weights_dataframe.weights_x2,label='weights_x2') plt.legend(loc='upper right') #用于标签显示问题 plt.xlabel(u"迭代次数", fontproperties='SimHei') plt.ylabel(u"参数变化", fontproperties='SimHei') plt.title(u"迭代次数显示", fontproperties='SimHei') #fontproperties='SimHei' 用于可以显示中文 plt.show() import matplotlib.pyplot as plt from numpy.random import random colors = ['b', 'c', 'y', 'm', 'r'] lo = plt.scatter(random(10), random(10), marker='x', color=colors[0]) ll = plt.scatter(random(10), random(10), marker='o', color=colors[0]) l = plt.scatter(random(10), random(10), marker='o', color=colors[1]) a = plt.scatter(random(10), random(10), marker='o', color=colors[2]) h = plt.scatter(random(10), random(10), marker='o', color=colors[3]) hh = plt.scatter(random(10), random(10), marker='o', color=colors[4]) ho = plt.scatter(random(10), random(10), marker='x', color=colors[4]) plt.legend((lo, ll, l, a, h, hh, ho), ('Low Outlier', 'LoLo', 'Lo', 'Average', 'Hi', 'HiHi', 'High Outlier'), scatterpoints=1, loc='lower left', ncol=3, fontsize=8) plt.show()
#pandas中画图 #画累和图 import pandas as pd import numpy as np import matplotlib.pyplot as plt ts=pd.Series(np.random.randn(1000),index=pd.date_range('1/1/2000',periods=1000)) ts=ts.cumsum() ts.plot() plt.show() df=pd.DataFrame(np.random.randn(1000,4),index=ts.index,columns=list('ABCD')) df=df.cumsum() df.plot() plt.show()
import pandas as pd import numpy as np import matplotlib.pyplot as plt #画柱状图 df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd']) df2.plot(kind='bar') #分开并列线束 df2.plot(kind='bar', stacked=True) #四个在同一个里面显示 百分比的形式 df2.plot(kind='barh', stacked=True)#纵向显示 plt.show() df4=pd.DataFrame({'a':np.random.randn(1000)+1,'b':np.random.randn(1000),'c':np.random.randn(1000)-1},columns=list('abc')) df4.plot(kind='hist', alpha=0.5) df4.plot(kind='hist', stacked=True, bins=20) df4['a'].plot(kind='hist', orientation='horizontal',cumulative=True) #cumulative是按顺序排序,加上这个 plt.show() #Area Plot df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd']) df.plot(kind='area') df.plot(kind='area',stacked=False) plt.show()
#散点图 import pandas as pd import numpy as np import matplotlib.pyplot as plt df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd']) df.plot(kind='scatter', x='a', y='b') df.plot(kind='scatter', x='a', y='b',color='DarkBlue', label='Group 1') #饼图 df = pd.DataFrame(3 * np.random.rand(4, 2), index=['a', 'b', 'c', 'd'], columns=['x', 'y']) df.plot(kind='pie', subplots=True, figsize=(8, 4)) df.plot(kind='pie', subplots=True,autopct='%.2f',figsize=(8, 4)) #显示百分比 plt.show() #画矩阵散点图 df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd']) pd.scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde') plt.show()
以上就是怎么在Python中使用matplotlib和pandas实现画图,小编相信有部分知识点可能是我们日常工作会见到或用到的。希望你能通过这篇文章学到更多知识。更多详情敬请关注亿速云行业资讯频道。
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