Python的科学计算包-Matplotlib
先来看个简单的例子1 2 3 4 import matplotlib.pyplot as plt plt.plot([1, 2, 3, 2, 3, 2, 2, 1]) plt.show()
1 2 3 4 import matplotlib.pyplot as plt plt.plot([4, 3, 2, 1], [1, 2, 3, 4]) plt.show()
1 2 3 4 5 6 7 8 9 import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [5, 4, 3, 2] # 二维平面分成2x3 plt.subplot(2, 3, 1) plt.plot(x, y) plt.show()
1 2 3 4 5 6 7 8 import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [5, 4, 3, 2] plt.subplot(232) plt.bar(x, y) plt.show()
1 2 3 4 5 6 7 8 import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [5, 4, 3, 2] plt.subplot(233) plt.barh(x, y) plt.show()
1 2 3 4 5 6 7 8 import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [5, 4, 3, 2] plt.subplot(234) plt.bar(x, y) plt.show()
1 2 3 4 5 6 7 8 import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [5, 4, 3, 2] y1 = [7, 8, 5, 3] plt.bar(x, y1, bottom=y, color='r') plt.show()
1 2 3 4 5 6 7 import matplotlib.pyplot as plt x = [1, 2, 3, 4] plt.subplot(235) plt.boxplot(x) plt.show()
1 2 3 4 5 6 7 import matplotlib.pyplot as plt x = [1, 2, 3, 4] plt.subplot(236) plt.scatter(x, y) plt.show()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [5, 4, 3, 2] # 二维平面分成 2行3列，此图在第一个位置 plt.subplot(2, 3, 1) # 斜线 plt.plot(x, y) # 二维平面分成 2行3列，此图在第二个位置 plt.subplot(232) # 垂直柱状图 plt.bar(x, y) # 二维平面分成 2行3列，此图在第三个位置 plt.subplot(233) # 水平柱状图 plt.barh(x, y) # 二维平面分成 2行3列，此图在第四个位置 plt.subplot(234) # 水平柱状图 plt.bar(x, y) # 二维平面分成 2行3列，此图在第四个位置与上一个相叠加 y1 = [7, 8, 5, 3] # 水平柱状图 plt.bar(x, y1, bottom=y, color='r') # 二维平面分成 2行3列，此图在第五个位置 plt.subplot(235) # 盒子图，或箱型图 plt.boxplot(x) # 二维平面分成 2行3列，此图在第六个位置 plt.subplot(236) # 分散图 plt.scatter(x, y) # 绘制 plt.show()
figure对象，就是一个窗口
1 2 3 4 5 6 figure2 = plt.figure() figure2.suptitle(u"This is an example Figure！很简单的呀") ax1 = figure2.add_subplot(2, 2, 1) ax2 = figure2.add_subplot(2, 2, 2) ax3 = figure2.add_subplot(2, 2, 3) plt.show()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 import matplotlib.pyplot as plt figure2 = plt.figure() figure2.suptitle(u"This title is not supported Mandarin.") ax1 = figure2.add_subplot(2, 2, 1) ax2 = figure2.add_subplot(2, 2, 2) ax3 = figure2.add_subplot(2, 2, 3) from numpy.random import randn # 因为最后一个图是第三个，所以这个绘图会在最后一个（也就是第三个图）绘制散点图 plt.plot(randn(50).cumsum(), 'k--') figure2.show() plt.show()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 # coding: utf-8 import matplotlib.pyplot as plt import numpy as np figure2 = plt.figure() ax1 = figure2.add_subplot(2, 2, 1) ax2 = figure2.add_subplot(2, 2, 2) ax3 = figure2.add_subplot(2, 2, 3) from numpy.random import randn # 因为最后一个图是第三个，所以这个绘图会在最后一个（也就是第三个图）绘制散点图 plt.plot(randn(50).cumsum(), 'k--') # 也可以对指定的图进行绘制 ax1.hist(randn(100), bins=20, color='k', alpha=0.3) ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30)) plt.show()
如果需要通过代码关闭窗口，则
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # coding: utf-8 import matplotlib.pyplot as plt # 我们创建的一个2行3列的矩阵图后，通过返回值，我们可以设置更多的参数等 fig, axes = plt.subplots(2, 3) print fig print "-------" print axes ''' Figure(640x480) ------- [[<matplotlib.axes._subplots.AxesSubplot object at 0x1080157d0> <matplotlib.axes._subplots.AxesSubplot object at 0x109599390> <matplotlib.axes._subplots.AxesSubplot object at 0x1095d9c90>] [<matplotlib.axes._subplots.AxesSubplot object at 0x109628650> <matplotlib.axes._subplots.AxesSubplot object at 0x10965ec90> <matplotlib.axes._subplots.AxesSubplot object at 0x1096ad850>]] ''' plt.show()
pyplot.subplots的参数选项
nrows subplot的行数
ncols subplot的列数
sharex 所有subplot应该使用相同的x轴刻度（调节xlim将会影响所有subplot）
sharey 所有subplot应该使用相同的y轴刻度（调节ylim将会影响所有subplot）
subplot_kw 用于创建各subplot的关键字字典
**fig_kw 创建figure时的其他关键字，如：plt.subplots(2, 2, figsize=(8, 6))
而且它的基本设置有
颜色、标记和线型
刻度、标签
注释
图标文件的保存
Matplotlib配置
来看个调整参数的figure1 2 3 4 5 6 7 8 9 10 11 12 13 # coding: utf-8 from numpy.random import randn import matplotlib.pyplot as plt plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=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(randn(500), bins=50, color='k', alpha=0.5) plt.subplots_adjust(wspace=0, hspace=0) plt.show()
1 2 3 4 5 6 7 8 import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [5, 4, 3, 2] # 线的风格是：-- ，线的颜色是：green plt.plot(x, y, linestyle='--', color='g') plt.show()
1 2 3 4 5 6 7 8 9 10 import matplotlib.pyplot as plt # 我们把这个样式合并的写法 # 连接线的风格是：-- ，标记点是：o，线的颜色是：k，表示黑色， plt.plot(randn(30).cumsum(), 'ko--') # 如果这样写，效果也是一样的，样式分开写 # plt.plot(randn(30).cumsum(), color='k', linestyle='dashed', marker='o') plt.show()
根据读取数据设置线的颜色，线的风格1 2 3 4 5 6 7 8 9 10 11 # coding: utf-8 from numpy.random import randn import matplotlib.pyplot as plt data = randn(30).cumsum() plt.plot(data, linestyle='--', label='Default', color='g') plt.plot(data, linestyle='-', drawstyle='steps-post', label='steps-post', color='r') plt.legend(loc='best') plt.show()
设置标题、轴标签、刻度以及刻度标签1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 # coding: utf-8 from numpy.random import randn import matplotlib.pyplot as plt fig = plt.figure() # 添加一个图在figure上 ax = fig.add_subplot(1, 1, 1) ax.set_title('My Matplotlib Plot') ax.set_xlabel('Stages') # 添加数据点是随机1000以内 ax.plot(randn(1000).cumsum()) # 设置刻度在0到1000 ticks = ax.set_xticks([0, 250, 500, 750, 1000]) # 设置刻度标签 labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'], rotation=30, fontsize='small') # 绘制 plt.show()
添加图例 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # coding: utf-8 from numpy.random import randn import matplotlib.pyplot as plt fig = plt.figure() # 在figure窗口，添加一个图 ax = fig.add_subplot(1, 1, 1) # 值为随机1000以内，添加蓝色场景，标签为：one，线样式：默认 ax.plot(randn(1000).cumsum(), 'b', label='one') # 值为随机1000以内，添加绿色场景，标签为：two，线样式：-- ax.plot(randn(1000).cumsum(), 'g--', label='two') # 值为随机1000以内，添加红色场景，标签为：two，线样式：. ax.plot(randn(1000).cumsum(), 'r.', label='three') # 让系统在最佳位置添加图例，位置会随着数据不同而显示在不同的位置 ax.legend(loc='best') plt.show()
注释以及在subplot上绘图 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 import pandas as pd import matplotlib.pyplot as plt from datetime import datetime fig = plt.figure() ax = fig.add_subplot(1, 1, 1) data = pd.read_csv('matplotlib/spx.csv', index_col=0, parse_dates=True) spx = data['SPX'] print spx ''' 1990-02-01 328.79 1990-02-02 330.92 1990-02-05 331.85 ...... Name: SPX, Length: 5472, dtype: float64 ''' spx.plot(ax=ax, style='k--') # 需要标记出来的日期和label crisis_data = [ (datetime(2007, 10, 11), 'Peak of bull market'), (datetime(2008, 3, 12), 'Bear Stearns Fails'), (datetime(2008, 9, 15), 'Lehman Bankruptcy'), ] # 通过遍历对crisis_data里匹配到的数据打上标签，并且对这三个标签设置箭头 for date, label in crisis_data: ax.annotate(label, xy=(date, spx.asof(date) + 50), xytext=(date, spx.asof(date) + 200), arrowprops= dict(facecolor='blue'), horizontalalignment='left', verticalalignment='top') # 设置x方向的值，起始位置到结束位置 ax.set_xlim(['1/1/2007', '1/1/2011']) # 设置y方向的值，起始位置到结束位置 ax.set_ylim([600, 1800]) ax.set_title('Important dates in 2008-2009 financial crisis') plt.show()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 import matplotlib.pyplot as plt # 添加一个窗口 fig = plt.figure() # 添加一个场景 ax = fig.add_subplot(1, 1, 1) # 添加矩形 rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3) # 添加圆 circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3) # 添加三角形 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(circ) ax.add_patch(pgon) plt.show()
图标的保存 1 2 3 4 # 保存到指定路径 # 保存时plt.show()不要调用，保存后可以调用plt.show() fig.savefig('/Users/victorzhang/Desktop/figpath.png') fig.savefig('/Users/victorzhang/Desktop/figpath1.png', dpi=400, bbox_inches='tight')
保存到内存1 2 3 4 5 6 from io import BytesIO buffer = BytesIO() plt.savefig(buffer) plot_data = buffer.getvalue() print plot_data
plot的一些设置 1 2 3 4 5 6 7 plt.rc('figure', figsize=(10, 10)) font_options = { "family": "Monospace", "weight": "bold", "size": "20" } plt.rc('font', **font_options)
纯matplotlib代码编写图形需要设置很多参数，比较麻烦，所以我们有了pandas来一起构建
pandas的绘图函数
label
ax
style
alpha
kind
logy
use_index
rot
xticks
yticks
xlim
ylim
grid
subplots
sharex
sharey
figsize
title
legend 添加图例（默认为True）
sort_columns
1 2 3 4 5 6 7 8 9 10 import numpy as np import matplotlib.pyplot as plt from numpy.random import randn from pandas import DataFrame, Series s = Series(randn(10).cumsum(), index=np.arange(0, 100, 10)) s.plot() plt.show()
1 2 3 4 5 6 7 8 9 10 11 import numpy as np import matplotlib.pyplot as plt from numpy.random import randn from pandas import DataFrame, Series df = 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 1 2 3 4 5 6 7 8 9 10 import matplotlib.pyplot as plt from numpy.random import randn from pandas import DataFrame, Series fig, axes = plt.subplots(2, 1) data = Series(randn(16), index=list('abcdedghijklmnop')) 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()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 import numpy as np import pandas as pd import matplotlib.pyplot as plt from pandas import DataFrame, Series df = DataFrame(np.random.rand(6, 4), index=['one', 'two', 'three', 'four', 'five', 'six'], columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus')) print df ''' Genus A B C D one 0.603944 0.634066 0.400164 0.305856 two 0.860118 0.090741 0.159745 0.439690 three 0.083134 0.789508 0.602428 0.197421 four 0.039622 0.458276 0.239215 0.297469 five 0.170553 0.455839 0.901505 0.080372 six 0.681694 0.628299 0.657260 0.644590 ''' df.plot(kind='bar') plt.show()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 import numpy as np import pandas as pd import matplotlib.pyplot as plt from pandas import DataFrame, Series df = DataFrame(np.random.rand(6, 4), index=['one', 'two', 'three', 'four', 'five', 'six'], columns=pd.Index(['A', 'B', 'C', 'D'], name='Genus')) print df # 每一个类型都有四段，因为stacked=True，所以最后后堆积在一起 df.plot(kind='barh', stacked=True, alpha=0.5) plt.show()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 # coding: utf-8 import pandas as pd import matplotlib.pyplot as plt tips = pd.read_csv('matplotlib/tips.csv') # 交叉表 party_counts = pd.crosstab(tips.day, tips['size']) print party_counts ''' size 1 2 3 4 5 6 day Fri 1 16 1 1 0 0 Sat 2 53 18 13 1 0 Sun 0 39 15 18 3 1 Thur 1 48 4 5 1 3 ''' party_counts = party_counts.ix[:, 2:5] print party_counts ''' size 2 3 4 5 day Fri 16 1 1 0 Sat 53 18 13 1 Sun 39 15 18 3 Thur 48 4 5 1 ''' party_pcts = party_counts.div(party_counts.sum(1).astype(float), axis=0) print party_pcts ''' size 2 3 4 5 day Fri 0.888889 0.055556 0.055556 0.000000 Sat 0.623529 0.211765 0.152941 0.011765 Sun 0.520000 0.200000 0.240000 0.040000 Thur 0.827586 0.068966 0.086207 0.017241 ''' party_pcts.plot(kind='bar', stacked=True) plt.show()
直方图和密度图 两个分散在不同的plot的图1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # coding: utf-8 import pandas as pd import matplotlib.pyplot as plt fig, axes = plt.subplots(2, 1) tips = pd.read_csv('matplotlib/tips.csv') tips['tip_pct'] = tips['tip'] / tips['total_bill'] # 直方图 tips['tip_pct'].hist(bins=50, ax=axes[0]) # 密度图 tips['tip_pct'].plot(kind='kde', ax=axes[1]) plt.show()
两个在一起的plot的图1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 # coding: utf-8 import numpy as np import matplotlib.pyplot as plt from pandas import DataFrame, Series compl1 = np.random.normal(0, 1, size=200) compl2 = np.random.normal(10, 2, size=200) values = Series(np.concatenate([compl1, compl2])) print values ''' 0 -1.080911 1 -0.522424 2 -1.225437 3 0.755538 ...... Length: 400, dtype: float64 ''' # 直方图 values.hist(bins=100, alpha=0.3, color='k', normed=True) # 密度图 values.plot(kind='kde', style='k--') plt.show()
散点图 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 # coding: utf-8 import numpy as np import pandas as pd import matplotlib.pyplot as plt macro = pd.read_csv('matplotlib/macrodata.csv') data = macro[['cpi', 'm1', 'tbilrate', 'unemp']] trans_data = np.log(data).diff().dropna() print trans_data[-5:] ''' cpi m1 tbilrate unemp 198 -0.007904 0.045361 -0.396881 0.105361 199 -0.021979 0.066753 -2.277267 0.139762 200 0.002340 0.010286 0.606136 0.160343 201 0.008419 0.037461 -0.200671 0.127339 202 0.008894 0.012202 -0.405465 0.042560 ''' plt.scatter(trans_data['m1'], trans_data['unemp']) plt.title('Changes in log %s vs. log %s' % ('m1', 'unemp')) plt.show()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 # coding: utf-8 import numpy as np import pandas as pd import matplotlib.pyplot as plt macro = pd.read_csv('matplotlib/macrodata.csv') data = macro[['cpi', 'm1', 'tbilrate', 'unemp']] trans_data = np.log(data).diff().dropna() print trans_data[-5:] ''' cpi m1 tbilrate unemp 198 -0.007904 0.045361 -0.396881 0.105361 199 -0.021979 0.066753 -2.277267 0.139762 200 0.002340 0.010286 0.606136 0.160343 201 0.008419 0.037461 -0.200671 0.127339 202 0.008894 0.012202 -0.405465 0.042560 ''' pd.plotting.scatter_matrix(trans_data, diagonal='kde', color='k', alpha=0.3) plt.show()
误差条形图 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # coding: utf-8 import matplotlib.pyplot as plt import numpy as np 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', ecolor='r', color='cyan', label='experiment #1') plt.xlabel('# measurement') plt.ylabel('Measured values') plt.title('Measurements') plt.legend(loc='upper left') plt.show()
饼图 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # coding: utf-8 import matplotlib.pyplot as plt plt.figure(1, figsize=(8, 8)) ax = plt.axes([0.1, 0.1, 0.8, 0.8]) labels = 'Spring', 'Summer', 'Autumn', 'Winter' values = [15, 16, 16, 18] explode = [0.1, 0.1, 0.1, 0.1] plt.pie(values, explode=explode, labels=labels, autopct='%1.1f%%', startangle=67) plt.title('Rainy days by season') plt.show()
等高线图 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # coding: utf-8 import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np 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) 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.title('My Function: $z=(1-x^2+y^2) e^{-(y^3) / 3}$') plt.show()
3D图标
3D柱状图 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 # coding: utf-8 import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np 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 = xrange(1, 13) ys = 1000 * np.random.rand(12) color = plt.cm.Set2(np.random.choice(xrange(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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 # coding: utf-8 import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import Axes3D 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()
