简单的matplotlib用法

要引入的库:

import matplotlib.pyplot as plt import pandas as pd import numpy as np from numpy.random import randn

一个画板多个图，默认最后在一个添加，也可署名(或者用坐标表示):

fig = plt.figure() ax1 = fig.add_subplot(2,2,1)#表示在figure中建立2*2个坐标系，ax1位于第一个坐标中 ax2 = fig.add_subplot(2,2,2) ax3 = fig.add_subplot(2,2,3) ax1.hist(randn(100),bins=20,color='k',alpha=0.3)#在ax1上作图 ax2.scatter(np.arange(30),np.arange(30)+3*randn(30))#在ax2上作图 plt.plot(randn(50).cumsum(),"k--") plt.show()

刻度、标签和标题:

fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(randn(1000).cumsum()) ticks = ax.set_xticks([0, 250, 500, 750, 1000]) labels = ax.set_xticklabels(["one", "two", "three", "four", "five"], rotation=30, fontsize="small") ax.set_title("my first matplotlib plot") ax.set_xlabel('Stages') plt.show()

图例:

fig = plt.figure() ax = fig.add_subplot(1,1,1) ax.plot(randn(1000).cumsum(), label="one") # 创建label标签 ax.plot(randn(1000).cumsum(), label="two") ax.plot(randn(1000).cumsum(), label="three") ax.legend(loc="best") # loc是用来说明图例的放置位置 plt.show()

在图表中添加图形:

fig = plt.figure() ax = fig.add_subplot(1, 1, 1) rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3) cric = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3) pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]], color='g', alpha=0.5) ax.add_patch(rect) ax.add_patch(cric) ax.add_patch(pgon) plt.show()

Pandas作图:

s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10)) s.plot() plt.show() df =pd.DataFrame(np.random.randn(10, 4).cumsum(0), columns=['A', 'B', 'C', 'D'], index=np.arange(0, 100, 10)) df.plot() plt.show()

柱状图(Seris):

fig, axes = plt.subplots(2, 1) data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop')) data.plot(kind='bar', ax=axes[0], color='k', alpha=0.7) # 竖直 data.plot(kind='barh', ax=axes[1], color='k', alpha=0.7) # 水平 plt.show()

柱状图(DataFrame)，每一行的值分为一组:

fig, axes = plt.subplots(2, 1) df = pd.DataFrame(np.random.rand(6, 4), index=['one', 'two', 'three', 'four', 'five', 'six'], columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus')) df.plot(kind='bar', ax=axes[0], stacked=False, alpha=0.5) # 不合在一起 df.plot(kind='barh', ax=axes[1], stacked=True, alpha=0.5) # 合在一起 plt.show()

直方图和密度图:

# numpy.random.normal(loc=0.0, scale=1.0, size=None),loc:均值;scale:标准差;size:输出的shape，默认为None comp1 = np.random.normal(0, 1, size=200) # N(0, 1) comp2 = np.random.normal(10, 2, size=200) # N(10, 4) values = pd.Series(np.concatenate([comp1, comp2])) #concatenate函数相当于组合，axis=1表示水平组合，axis=0为垂直组合 values.hist(bins=100, alpha=0.3, color='k', normed=True) # normed=True 是进行拟合的关键 values.plot(kind='kde', style='k--') # kde 表示密度图 plt.show()