python 數據可視化（matplotlib）

時間 2020-06-19

標籤 python 數據可視化 matplotlib 欄目 Python 简体版

原文原文鏈接

matpotlib 官網：https://matplotlib.org/index.htmlhtml

matplotlib 可視化示例：https://matplotlib.org/gallery/index.htmlpython

matplotlib 教程：https://matplotlib.org/tutorials/index.html數組

matplotlib 的官網教程分爲初級（Introductory）、中級（Intermediate）、高級（Advanced）三部分，此外還有專門的章節，如 Colors、Text、Toolkitsapp

一個簡單的示例

import matplotlib.pyplot as plt 
import numpy as np

x = [1,2,3,4] y = [5,4,3,2] y1 = [7,8,5,3] plt.figure() # 建立新圖表 
 plt.subplot(231)  # 將圖表分隔成 2X3 的網格，並在當前圖表上添加子圖
plt.plot(x, y) plt.subplot(232) plt.bar(x=x, height=y) plt.subplot(233) plt.barh(y=x, width=y) plt.subplot(234) plt.bar(x=x, height=y) plt.bar(x=x, height=y1, bottom=y, color='r') plt.subplot(235) plt.boxplot(x=x) plt.subplot(236) plt.scatter(x=x, y=y) plt.show()

簡單的正弦圖和餘弦圖

import matplotlib.pyplot as plt import numpy as np x = np.linspace(start=-np.pi, stop=np.pi, num=32, endpoint=True) y = np.cos(x) y1 = np.sin(x) plt.figure() plt.plot(x, y, 'b.-') plt.plot(x, y1, 'r.-') # 設置圖標的標題
plt.title(label='Functions $\sin$ and $\cos$') # 設置x軸和y軸的範圍
plt.xlim(left=-3.0, right=3.0) plt.ylim(bottom=-1.0, top=1.0) # 設置x軸和y軸的刻度和標記
plt.xticks(ticks=[-np.pi, -np.pi/2, 0, np.pi/2, np.pi], labels=[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$']) plt.yticks(ticks=[-1, 0, +1], labels=[r'$-1$', r'$0$', r'$+1$']) plt.show()

設置座標軸的長度和範圍

matplotlib.pyplot.axis()框架

若是不使用 axis() 或者其餘設置，matplotlib 會自動使用最小值，恰好可讓咱們在一個圖中看到全部的數據點。less

若是設置 axis() 的範圍比數據集合中的最大值小，matplotlib 會按照設置執行，這樣就沒法看到全部的數據點，爲避免這一狀況能夠使用 matplotlib.pyplot.autoscale(enable=True, axis='both', tight=None) 方法，該方法會計算座標軸的最佳大小以適應數據的顯示。dom

matplotlib.pyplot.axes(arg=None, **kwargs) 向相同圖形中添加新的座標軸。ide

matplotlib.pyplot.axhline(y=0, xmin=0, xmax=1, **kwargs) 根據y軸添加水平線函數

matplotlib.pyplot.axvline(x=0, ymin=0, ymax=1, **kwargs) 根據x軸添加垂直線佈局

matplotlib.pyplot.axhspan(ylim, ymax, xmin=0, xmax=1, **kwargs) 生成水平帶

matplotlib.pyplot.axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs) 生成垂直帶

matplotlib.pyplot.grid(b=None, which='major', axis='both', **kwargs) 顯示網格

import matplotlib.pyplot as plt plt.figure() plt.plot([1,2,3,4], [1,2,3,4], 'b.-') plt.axis([0,5,0,5])  # 設置座標軸的範圍
plt.axhline(y=3.5, xmin=0.4, xmax=0.7, color='r') plt.axvline(x=2, ymin=0.5, ymax=0.7, color='y') plt.axhspan(ymin=1, ymax=2, xmin=0.5, xmax=0.6, color='k') plt.axvspan(xmin=3.5, xmax=4, ymin=0.4, ymax=0.6, color='g') plt.grid(b=True, which='major', axis='both') plt.xlabel('x') plt.ylabel('y') plt.title('This is a test') plt.legend(['test line']) plt.show()

改變線的屬性的三種方法

1. 直接在 plot() 函數中社設置線的屬性

2. 獲取 matplotlib.lines.Line2D 實例對象後，調用該對象的方法進行設置。用來改變線條的全部屬性都包含在 matplotlib.lines.Line2D 類中，詳見官網說明。

3. 使用 matplotlib.pyplot.setp() 函數進行設置

import matplotlib.pyplot as plt x = [1,2,3,4,5,6,7,8,9] y = [2,2,3,4,5,5,6,4,3] plt.axis([0, 10, 1, 10]) plt.plot(x, y, 'g.-', linewidth=2.5)  # 1.直接在 plot() 函數中設置線條屬性
line, = plt.plot(x, y, 'g.-')  # plot() 返回一個線條的實例對象的列表(matplotlib.lines.Line2D)，取第一個
line.set_linewidth(2.5)  # 2.調用 Line2D 的相應的set_方法進行設置
plt.setp(line, color='r', linewidth=2.5)  # 3.調用 setp() 函數進行設置
plt.show()

顏色

1.最簡單的方法是使用 matplotlib 提供的字母代碼，共八個，{'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'}， color='b'

2. 能夠直接使用合法的 HTML 顏色的名字，color=‘chartreuse’

2.使用 HTML 十六進制字符串來指定顏色，color='#eeefff'

3.使用歸一化到 [0, 1] 的 RGB 元組，color=(0.3, 0.3, 0.4)

還有其餘的指定顏色的方法，官網的 matplotlib.colors 模塊中有介紹，不過以上的4種方法徹底能夠知足需求。

查詢顏色編碼的網站有不少，推薦取色網站 https://html-color-codes.info/chinese/

import matplotlib.pyplot as plt import numpy as np y = np.arange(0.0, 5.0, 0.2) plt.plot(y, '.-', linewidth=1.5, color='g') plt.plot(y*1.2, '.-', linewidth=1.5, color='chartreuse') plt.plot(y*1.4, '.-', linewidth=1.5, color='#8a2be2') plt.plot(y*1.6, '.-', linewidth=1.5, color=(0.3, 0.3, 0.4)) plt.show()

座標軸和刻度

繼續學習： https://blog.csdn.net/wuzlun/article/details/80053277

在 matplotlib 中，調用 figure() 會顯式的建立一個圖形，表示一個圖形用戶界面窗口。另外，經過調用 plot() 或者相似的方法會隱式的建立圖形。

刻度是圖形的一部分，有刻度定位器和刻度格式器組成。刻度有主刻度和次刻度，默認不顯示次刻度。主刻度和次刻度能夠被獨立的指定位置和格式化。

刻度定位器指定刻度所在的位置

刻度格式器指定刻度顯示的樣式。

import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import datetime fig = plt.figure()  # 返回 matplotlib.figure.Figure 類的對象實例
ax = plt.gca()  # 返回 matplotlib.axes.Axes 類的對象實例
start = datetime.datetime(2018, 1, 1)  # 返回 datetime.datetime 類的對象實例
stop = datetime.datetime(2018, 12, 31) delta = datetime.timedelta(days = 1)  # 返回 datetime.datedelta 類的對象實例， 用做時間間隔
dates = mpl.dates.drange(dstart=start, dend=stop, delta=delta)  # 返回一個 numpy.array 數組類型
values = np.random.rand(len(dates))  # 返回一個 numpy.array 數組類型
ax.plot_date(x=dates, y=values, linestyle='-', marker='')  # plot date that contains dates
date_format = mpl.dates.DateFormatter('%Y-%m-%d') ax.xaxis.set_major_formatter(date_format) # 獲取 x 軸，設置格式
fig.autofmt_xdate()  # 主要用於旋轉x軸的刻度
plt.show()

移動座標軸

import matplotlib.pyplot as plt import numpy as np x = np.linspace(start=-np.pi, stop=np.pi, num=64, endpoint=True) y = np.sin(x) plt.plot(x, y, 'b.-') ax = plt.gca() ax.spines['right'].set_color('none')  # 去掉右側座標軸
ax.spines['top'].set_color('none')  # 去掉上側座標軸
ax.spines['bottom'].set_position(('data', 0))  # 移動下側座標軸
ax.spines['left'].set_position(('data', 0))  # 移動左側座標軸 ax.xaxis.set_ticks_position('bottom')  # 設置x軸刻度的位置
ax.yaxis.set_ticks_position('left')  # 設置y軸刻度的位置 plt.show()

圖例和註解

import matplotlib.pyplot as plt import numpy as np y1 = np.random.normal(loc=30, scale=3, size=100) y2 = np.random.normal(loc=20, scale=2, size=100) y3 = np.random.normal(loc=10, scale=3, size=100) plt.plot(y1, 'b.-', label='plot')  # label屬性爲圖線設置標籤
plt.plot(y2, 'g.-', label='2nd plot') plt.plot(y3, 'r.-', label='last plot') plt.legend(bbox_to_anchor=(0.0, 1.02, 1.0, 0.102), loc=3, ncol=3, mode='expand', borderaxespad=0.0)  # 添加並設置圖例
plt.annotate(s='Important value', xy=(55.0, 20.0), xycoords='data', xytext=(0, 38), arrowprops=dict(arrowstyle='->'))  # 添加並設置圖解
plt.show()

繪製直方圖 hist

直方圖被用於可視化數據的分佈估計。直方圖中 bin 表示必定間隔下數據點頻率的垂直矩形，bin 以固定的間隔建立，所以直方圖的總面積等於數據點的數量。

直方圖能夠顯示數據的相對頻率，而不是使用數據的絕對值，此時，總面積就等於1

import matplotlib.pyplot as plt import numpy as np mu = 100 sigma = 15 x = np.random.normal(mu, sigma, 10000)  # 根據正態分佈隨機取值
ax = plt.gca() ax.hist(x, bins=35, rwidth=0.8, color='r') ax.set_xlabel('Values') ax.set_ylabel('Frequency') ax.set_title(r'$\mathrm{Histogram:}\ \mu=%d, \ \sigma=%d$' % (mu, sigma)) plt.show()

繪製條形偏差圖

import matplotlib.pyplot as plt import numpy as np x = np.arange(1, 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()

繪製餅圖

import matplotlib.pyplot as plt plt.figure(1, figsize=(5, 5)) x = [45, 30, 10, 15]  # fractions are either x/sum(x) or x if sum(x) <= 1
labels = ['Spring', 'Summer', 'Autumn', 'Winter'] print(type(labels)) explode = (0, 0, 0.1, 0)  # 各部分的間隔，長度與 x 的長度相同
plt.pie(x, explode=explode, labels=labels, autopct='%1.1f%%', startangle=67) plt.title('Rainy days by season') plt.show()

填充區域

import matplotlib.pyplot as plt import numpy as np x = np.arange(0.0, 2.0, 0.01) y1 = np.sin(2*np.pi*x) y2 = 1.2*np.sin(4*np.pi*x) fig = plt.figure() plt.plot(x, y1, x, y2, color='black') # fill_between()方法使用x爲定位點選取y值（y1,y2）, where參數指定一個條件來填充曲線。
plt.fill_between(x, y1, y2, where=y2>=y1, facecolor='darkblue', interpolate=True) plt.fill_between(x, y1, y2, where=y2<=y1, facecolor='deeppink', interpolate=True) plt.title('filled between') plt.show()

帶彩色標記的散點圖

import matplotlib.pyplot as plt import numpy as np x = np.random.randn(1000) y1 = np.random.randn(len(x)) y2 = 1.2 + np.exp(x) plt.subplot(121) plt.scatter(x, y1, color='indigo', alpha=0.3, edgecolors='white', label='no correl') plt.xlabel('no correlation') plt.xlim([-4, 4]) plt.ylim([-5, 30]) plt.legend() plt.subplot(122) plt.scatter(x, y2, color='green', alpha=0.3, edgecolors='grey', label='correl') plt.xlabel('strong correlation') plt.grid(True) plt.legend() plt.xlim([-4, 4]) plt.ylim([-5, 30]) plt.show()

設置座標軸標籤的透明度和大小

import matplotlib.pyplot as plt import matplotlib.patheffects as pef import numpy as np data = np.random.randn(70) fontsize = 18 title = 'This is figure title' x_label = 'This is x axis label' y_label = 'This is y axis label' offset_xy = (1, -1) rgbRed = (1.0, 0.0, 0.0) alpha = 0.8 plt.plot(data) # 繪圖
title_text_obj = plt.title(title, fontsize=fontsize, verticalalignment='bottom')  # 添加標題，返回 Text 實例對象，該類繼承自 Artist 類
title_text_obj.set_path_effects([pef.withSimplePatchShadow()])  # 爲標題添加陰影效果，默認值offset=(2, -2), shadow_rgbFace=None, alpha=None
xlabel_obj = plt.xlabel(x_label, fontsize=fontsize, alpha=0.5)  # 添加x軸標籤，返回一個 Text 實例對象
ylabel_obj = plt.ylabel(y_label, fontsize=fontsize, alpha=0.5) pe = pef.withSimplePatchShadow(offset=offset_xy, shadow_rgbFace=rgbRed, alpha=alpha)  # 獲取一個withSimplePatchShadow類的實例對象
xlabel_obj.set_path_effects([pe]) ylabel_obj.set_path_effects([pe]) plt.show()

向圖表線條添加陰影使用transformation框架

import matplotlib.pyplot as plt import matplotlib.transforms as transforms import numpy as np def setup(layout=None): assert layout is not None fig = plt.figure() ax = fig.add_subplot(layout) return fig, ax def get_signal(): t = np.arange(0.0, 2.5, 0.01) s = np.sin(5*np.pi*t) return t, s def plot_signal(t, s): line, = axes.plot(t, s, linewidth=5, color='magenta')  # magenta：洋紅色
    return line def make_shadow(fig, axes, line, t, s): delta = 2/72.0
    # xt和 yt 是轉換的偏移量，scale_trans 是一個轉換可調用對象，在轉換時和顯示以前對xt和yt進行比例調整
    offset = transforms.ScaledTranslation(xt=delta, yt=-delta, scale_trans=fig.dpi_scale_trans)  # 獲取ScaledTranslation實例對象
    offset_transform = axes.transData + offset # 陰影其實是經過將原先的線條進行座標軸變換後，重現換了一條線，而且經過設置zorder屬性值來控制繪製兩條線的前後順序，後劃的線會覆蓋先劃的線
    axes.plot(t, s, linewidth=5, color='black', transform=offset_transform, zorder=0.5*line.get_zorder())  # zorder值小的會先繪製

if __name__ == '__main__': fig, axes = setup(111)  # 獲取 Figure 和 Axes 實例對象
    t, s = get_signal()  # 獲取繪圖用的x和y的值，二者都是numpy.ndarray類型
    line = plot_signal(t, s)  # 繪圖，返回 LinkedIn2D實例對象
    make_shadow(fig, axes, line, t, s)  # 爲線條添加陰影
    axes.set_title('Shadow effect using an offset transform') plt.show()

向圖表中添加數據表格

import matplotlib.pyplot as plt import numpy as np plt.figure() y = np.random.randn(9) col_labels = ['col1', 'col2', 'col3'] row_labels = ['row1', 'row2', 'row3'] table_values = [[11, 12, 13], [21, 22, 23], [31, 32, 33]] row_colors = ['red', 'gold', 'green'] # 使用table()方法建立了一個帶單元格的表格，並把它添加到當前座標軸中，並返回 Table 實例對象 #注意 表格裏的數據跟繪圖的數據不是關聯在一塊兒的，須要手動同時修改二者，使之對應起來
my_table = plt.table(cellText=table_values, colWidths=[0.1]*3, rowLabels=row_labels, colLabels=col_labels, rowColours=row_colors, loc='upper right') plt.plot(y,'b.-') plt.show()

使用subplots（子區）

子區的基類是matplotlib.axes.SubplotBase，子區是matplotlib.axes.Axes的實例，但提供了helper方法來生成和操做圖表中的一系列Axes。

matplotlib.pyplot.subplot() 位置從1開始計數，返回一個（fig, ax）元組，其中ax能夠是一個座標軸實例，當建立多個子區時，ax是一個座標軸實例的數組。

matplotlib.pyplot.subplot2grid() 位置從0開始計數

matplotlib.pyplot.subplots() 位置從1開始計數

matplotlib.pyplot.subplot_tool()

matplotlib.pyplot.subplots_adjust() 調整子區的佈局，關鍵字參數制定了圖表中子區的座標（left, right, bottom, top），其值是歸一化的圖表大小的值。

import matplotlib.pyplot as plt import numpy as np y = np.random.randn(10) plt.figure() #subplot()位置是基於1的；subplot2grid()位置是基於0的
axes1 = plt.subplot2grid(shape=(3,3), loc=(0,0), colspan=3) axes2 = plt.subplot2grid(shape=(3,3), loc=(1,0), colspan=2) axes3 = plt.subplot2grid(shape=(3,3), loc=(1,2)) axes4 = plt.subplot2grid(shape=(3,3), loc=(2,0)) axes5 = plt.subplot2grid(shape=(3,3), loc=(2,1), colspan=2) axes1.plot(y, 'b.-') axes2.plot(y, 'k.-') axes3.plot(y, 'g.-') axes4.plot(y, 'r.-') axes5.plot(y, 'c.-') all_axes = plt.gcf().axes for ax in all_axes: for ticklabel in ax.get_xticklabels() + ax.get_yticklabels(): ticklabel.set_fontsize(10) plt.suptitle('Demo of subplot2grid') plt.show()

定製化網格

使用matplotlib.pyplot.grid來設置網格的可見度、密度和風格，或者是否顯示網格。

import matplotlib.pyplot as plt from matplotlib.cbook import get_sample_data import numpy as np from mpl_toolkits.axes_grid1 import ImageGrid def get_demo_image(): # 返回一個數據文件，fname是基於 ..\mpl-data\sample_data 的相對路徑。asfileobj爲True時返回文件對象，不然返回文件的絕對路徑
    f = get_sample_data(fname="axes_grid/bivariate_normal.npy", asfileobj=False) #載入文件，返回 ndarray
    z = np.load(f) return z def get_grid(fig=None, layout=None, nrows_ncols=None): assert fig is not None assert layout is not None assert nrows_ncols is not None # 獲取 ImageGrid 類的實例對象
    grid = ImageGrid(fig, layout, nrows_ncols=nrows_ncols, axes_pad=0.05, add_all=True, label_mode='L') return grid def load_images_to_grid(grid, z, *images): min, max = z.min(), z.max()  # 獲取最大值和最小值
    for i, image in enumerate(images): axes = grid[i] axes.imshow(image, origin='lower', vmin=min, vmax=max, interpolation='nearest') if __name__ == '__main__': fig = plt.figure() grid = get_grid(fig, 111, (1, 3)) z = get_demo_image() image1 = z image2 = z[:, :10]  # 切片，取前10列
    image3 = z[:, 10:]  # 切片，取10列及後面的列
 load_images_to_grid(grid, z, image1, image2, image3) plt.show()

建立等高線圖 Contour

使用 matplotlib.pyplot.contour 和 matplotlib.pyplot.contourf 繪製等高線圖，前者繪製等高線，後者繪製填充的等高線。

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)  # 生成座標矩陣 根據傳入的兩個一位數組參數生成兩個數組元素的列表。
Z = process_signals(X, Y) N = np.arange(-1, 1.5, 0.3) CS = plt.contour(Z, N, linewidths=2)  # 繪製等高線
plt.clabel(CS, inline=True, fmt='%1.1f', fontsize=10)  # 向等值線中添加標籤
plt.colorbar(CS)  # 向圖表中添加 colorbar
plt.title('My function: $z=(1-x^2+y^2) e^{-(y^3)/3}$') plt.show()

填充圖標底層區域

繪製一個填充多邊形的基本方式是使用 matplotlib.pyplot.fill()

matplotlib.pyplot.fill_between() 填充 y 軸的值之間的區域

matplotlib.pyplot.fill_betweenx() 填充 x 軸的值之間的區域

import matplotlib.pyplot as plt from math import sqrt x = range(1000) y = [sqrt(i) for i in x] plt.plot(x, y, color='red', linewidth=2) # fill_between()填充y軸之間的區域，區域由（x,y1）和（x,y2）肯定，x和y1的長度必須相等，y2默認爲0
plt.fill_between(x=x[400:800], y1=y[400:800], color='silver') plt.show()

import matplotlib.pyplot as plt import numpy as np x = np.arange(0.0, 2.0, 0.01) y1 = np.sin(np.pi*x) y2 = 1.7*np.sin(4*np.pi*x) fig = plt.figure()  # 返回 Figure 實例對象
 axes1 = fig.add_subplot(211)  # 向圖表中添加 一個 Axes；返回一個Axes實例對象
axes1.plot(x, y1, x, y2, color='grey') axes1.fill_between(x, y1, y2, where=y2<=y1, facecolor='blue', interpolate=True) axes1.fill_between(x, y1, y2, where=y2>=y1, facecolor='gold', interpolate=True) axes1.set_title('Blue where y2 <= y1. Gold-color where y2 >= y1') axes1.set_ylim(-2,2) axes1.grid('on') y2 = np.ma.masked_greater(y2, 1.0)  # 將大於1的值變爲--
axes2 = fig.add_subplot(212, sharex=axes1) axes2.plot(x, y1, x, y2, color='black') axes2.fill_between(x, y1, y2, where=y2<=y1, facecolor='blue', interpolate=True) axes2.fill_between(x, y1, y2, where=y2>=y1, facecolor='gold', interpolate=True) axes2.set_title('Same as above, but mask') axes2.set_ylim(-2, 2) axes2.grid('on') plt.show()

繪製極座標

import matplotlib.pyplot as plt import numpy as np import matplotlib.cm as cm figsize = 7 colormap = lambda r:cm.Set2(r/20.0) N = 18 fig = plt.figure(figsize=(figsize, figsize)) ax = fig.add_axes([0.2, 0.2, 0.7, 0.7], polar=True) theta = np.arange(0.0, 2*np.pi, 2*np.pi/N) radii = 20*np.random.rand(N) width = np.pi/4*np.random.rand(N) bars = ax.bar(theta, radii, width=width, bottom=0.0) for r, bar in zip(radii, bars): bar.set_facecolor(colormap(r)) bar.set_alpha(0.6) plt.show()

建立 3D 柱狀圖

import numpy as np import matplotlib as mpl import matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D mpl.rcParams['font.size'] = 10 fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # 獲取Axes3D實例對象 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(left=xs, height=ys, zs=z, zdir='y', color=color, alpha=0.8) # 繪製柱狀圖 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()

繪製 3D 曲面

import matplotlib.pyplot as plt import numpy as np from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D n_angles = 36 n_radii = 8 radii = np.linspace(start=0.125, stop=1.0, num=n_radii) angles = np.linspace(start=0, stop=2*np.pi, num=n_angles, endpoint=False) angles = np.repeat(a=angles[..., np.newaxis], repeats=n_radii, axis=1) # 重複array中的元素，axis=1表示在行方向上，axis=0表示在列方向上 x = np.append(arr=0, values=(radii*np.cos(angles)).flatten()) y = np.append(arr=0, values=(radii*np.sin(angles)).flatten()) z = np.sin(-x*y) fig = plt.figure() ax = fig.gca(projection='3d') ax.plot_trisurf(x, y, z, cmap=cm.jet, linewidth=0.2, antialiased=True) # 繪製三翼面圖 plt.show()

繪製 3D 直方圖

import matplotlib.pyplot as plt import numpy as np import matplotlib as mpl from mpl_toolkits.mplot3d import Axes3D mpl.rcParams['font.size'] = 10 samples = 25 x = np.random.normal(loc=5, scale=1, size=samples) y = np.random.normal(loc=3, scale=5, size=samples) fig = plt.figure() ax = fig.add_subplot(211, projection='3d') # 獲取Axes3D實例對象 hist, xedges, yedges = np.histogram2d(x=x, y=y, bins=10) # 生成2d的直方圖的數據 elements = (len(xedges)-1)*(len(yedges)-1) xpos, ypos = np.meshgrid(xedges[:-1]+0.25, yedges[:-1]+0.25) xpos = xpos.flatten() ypos = ypos.flatten() zpos = np.zeros(elements) dx = 0.1*np.ones_like(zpos) dy = dx.copy() dz = hist.flatten() ax.bar3d(x=xpos, y=ypos, z=zpos, dx=dx, dy=dy, dz=dz, color='b', alpha=0.4) # 繪製直方圖 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()

建立動畫

matplotlib.animation.Animation 此類用matplotlib建立動畫，僅僅是一個基類，應該被子類化以提供所需的行爲

matplotlib.animation.TimedAnimation(Animation) 該動畫子類支持基於時間的動畫

matplotlib.animation.ArtistAnimation(TimedAnimation)

matplotlib.animation.FuncAnimation(TimedAnimation)

init()函數：經過參數 init_func 傳入 matplotlib.animation.FuncAnimation 構造器中，在繪製下一幀前清空當前幀。

animate()函數：經過參數 func 傳入 matplotlib.animation.FuncAnimation 構造器中，經過 fig 參數傳入想要繪製動畫的圖形窗口，其內部其實是將 fig 傳入到 matplotlib.animatin.FuncAnimation 構造器中，把要繪製圖形的窗口和動畫時間關聯起來。該函數從 frames 參數中獲取參數。

matplotlib.animation.Animation.save()函數：經過繪製每一幀保存一個視頻文件。

import matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np fig = plt.figure() ax = plt.axes(xlim=(0, 2), ylim=(-2, 2)) line, = ax.plot([], [], lw=2) # 返回Line2D實例對象的列表 def init(): line.set_data([], []) return line, def animate(i): # i 的值有frames參數決定 x = np.linspace(start=0, stop=2, num=1000) y = np.sin(2*np.pi*(x-0.01*i))*np.cos(22*np.pi*(x-0.01*i)) line.set_data(x, y) return line, animator = animation.FuncAnimation(fig=fig, func=animate, init_func=init, frames=200, interval=20, blit=True) animator.save(filename='basic_animation.html', fps=30, extra_args=['-vcodec', 'libx264'], writer='html') # 保存動畫 plt.show()

import matplotlib.pyplot as plt import numpy as np import matplotlib.animation as animation fig = plt.figure() ax = fig.add_subplot(111) x = np.arange(0, 2*np.pi, 0.01) line, = ax.plot(x, np.sin(x)) def animate(i): line.set_ydata(np.sin(x+i/10.0)) return line, def init(): line.set_ydata(np.ma.array(x, mask=True)) return line, ani = animation.FuncAnimation(fig=fig, func=animate, frames=np.arange(1, 200), init_func=init, interval=25, blit=True) plt.show()

指數圖

from matplotlib import pyplot as plt import numpy as np x = np.linspace(start=1, stop=10) y = [10 ** el for el in x] z = [2 * el for el in x] fig = plt.figure(figsize=(10, 8))  # 返回一個Figure對象實例
 ax1 = fig.add_subplot(2, 2, 1)  # 返回一個SubplotBase對象實例， 該類是Axes的子類
ax1.plot(x, y, marker='.', color='blue')  # Plot y versus x as lines and/or markers
ax1.set_yscale('log')  # Set the y-axis scale
ax1.set_title(r'1. Logarithmic plot of $ {10}^{x} $')  # Set a title for the axes.
ax1.set_xticks(np.linspace(start=0, stop=11, num=12, endpoint=True))  # Set the x ticks with list of ticks
ax1.set_ylabel(r'$ {y} = {10}^{x} $')  # Set the label for the y-axis
ax1.grid(b=True, which='both', axis='both') ax2 = fig.add_subplot(2, 2, 2) ax2.plot(x, y, marker='.', color='red') ax2.set_yscale('linear') ax2.set_title(r'2. Linear plot of $ {10}^{x} $') ax2.set_xticks(np.linspace(start=0, stop=11, num=12, endpoint=True)) ax2.set_ylabel(r'$ {y} = {10}^{x} $') ax2.grid(b=True, which='both', axis='both') print(ax2.get_xticks()) ax3 = fig.add_subplot(2, 2, 3) ax3.plot(x, z, marker='.', color='green') ax3.set_yscale('log') ax3.set_title(r'3. Logarithmic plot of $ {2}*{x} $') ax3.set_xticks(np.linspace(start=0, stop=11, num=12, endpoint=True)) ax3.set_ylabel(r'$ {y} = {2}*{x} $') ax3.grid(b=True, which='both', axis='both') ax4 = fig.add_subplot(2, 2, 4) ax4.plot(x, z, marker='.', color='magenta') ax4.set_yscale('linear') ax4.set_title(r'4. Linear plot of $ {2}*{x} $') ax4.set_xticks(np.linspace(start=0, stop=11, num=12, endpoint=True)) ax4.set_ylabel(r'$ {y} = {2}*{x} $') ax4.grid(b=True, which='both', axis='both') plt.show()

頻譜圖

頻譜圖是一個隨時間變化的譜表現，它顯示了信號的頻譜強度隨時間的變化。頻譜圖是把聲音或者其餘信號的頻譜以可視化的方式呈現出來。

一般，頻譜圖的佈局以下：x軸表示時間，y軸表示頻率，第三個維度是頻率-時間對的幅值，經過顏色表示。

import numpy as np import matplotlib.pyplot as plt def _get_mask(t, t1, t2, lvl_pos, lvl_neg): if t1 >= t2: raise ValueError("t1 must be less than t2") return np.where(np.logical_and(t>t1, t<t2), lvl_pos, lvl_neg) def generate_signal(t): sin1 = np.sin(2*np.pi*100*t) sin2 = 2*np.sin(2*np.pi*200*t) masks = _get_mask(t, 2, 4, 1.0, 0.0) + _get_mask(t, 14, 15, 1.0, 0.0) sin2 = sin2*masks noise = 0.02*np.random.randn(len(t)) final_signal = sin1 + sin2 + noise return final_signal if __name__ == '__main__': step = 0.001 sampling_freq = 1000 t = np.arange(0.0, 20.0, step) y = generate_signal(t) fig = plt.figure() ax211 = fig.add_subplot(211) ax211.plot(t, y) ax211.set_xlim(left=0, right=20)  # 設置x軸的範圍
    ax212 = fig.add_subplot(212) ax212.specgram(y, NFFT=1024, noverlap=900, Fs=sampling_freq, cmap=plt.cm.gist_heat)  # 繪製頻譜圖
    ax212.set_xlim(left=0, right=20) plt.show()

火柴桿圖

一個二維的火柴桿圖（stem plot）把數據顯示爲沿x軸的基線延伸的線條。圓圈（默認值）或者其餘標記表示每一個杆的結束，其y軸表示了數據值。

import matplotlib.pyplot as plt import numpy as np x = np.linspace(0, 20, 50) y = np.sin(x+1)+np.cos(x**2) bottom = -0.1 label = "delta" markerline, stemlines, baseline, = plt.stem(x, y, bottom=bottom, label=label)  # 繪製火柴桿圖，返回一個元組，包含markerline, stemlines, baseline
plt.setp(markerline, color='red', marker='o')  # 設置markerline的屬性
plt.setp(stemlines, color='blue', linestyle=':')  # 設置stemlines的屬性
plt.setp(baseline, color='grey', linewidth=2, linestyle='-')  # 設置baseline的屬性 
plt.legend(loc='upper right') plt.show()

矢量場流線圖

import matplotlib.pyplot as plt import numpy as np Y, X = np.mgrid[0:5:100j, 0:5:100j] U = X V = Y plt.streamplot(X, Y, U, V) # 繪製矢量場流線圖
plt.show()

多顏色的散點圖

import matplotlib.pyplot as plt import numpy as np red_yellow_green = ['#d73027', '#f46d43', '#fdae61', '#fee08b', '#ffffbf', '#d9ef8b', '#a6d96a', '#66bd63', '#1a9850'] sample_size = 300 fig = plt.figure(figsize=(8, 5)) ax = fig.gca() for i in range(9): y = np.random.normal(size=sample_size).cumsum() x = np.arange(sample_size) ax.scatter(x, y, label=str(i), linewidth=0.1, edgecolors='grey', facecolor=red_yellow_green[i]) ax.legend(loc='upper right') ax.set_xlim(left=-10, right=350) plt.show()

import matplotlib.pyplot as plt import numpy as np d = [ 1.04, 1.04, 1.16, 1.22, 1.46, 2.34, 1.16, 1.12, 1.24, 1.30, 1.44, 1.22, 1.26, 1.34, 1.26, 1.40, 1.52, 2.56, 1.36, 1.30, 1.20, 1.12, 1.12, 1.12, 1.06, 1.06, 1.00, 1.02, 1.04, 1.02, 1.06, 1.02, 1.04, 0.98, 0.98, 0.98, 1.00, 1.02, 1.02, 1.00, 1.02, 0.96, 0.94, 0.94, 0.94, 0.96, 0.86, 0.92, 0.98, 1.08, 1.04, 0.74, 0.98, 1.02, 1.02, 1.12, 1.34, 2.02, 1.68, 1.12, 1.38, 1.14, 1.16, 1.22, 1.10, 1.14, 1.16, 1.28, 1.44, 2.58, 1.30, 1.20, 1.16, 1.06, 1.06, 1.08, 1.00, 1.00, 0.92, 1.00, 1.02, 1.00, 1.06, 1.10, 1.14, 1.08, 1.00, 1.04, 1.10, 1.06, 1.06, 1.06, 1.02, 1.04, 0.96, 0.96, 0.96, 0.92, 0.84, 0.88, 0.90, 1.00, 1.08, 0.80, 0.90, 0.98, 1.00, 1.10, 1.24, 1.66, 1.94, 1.02, 1.06, 1.08, 1.10, 1.30, 1.10, 1.12, 1.20, 1.16, 1.26, 1.42, 2.18, 1.26, 1.06, 1.00, 1.04, 1.00, 0.98, 0.94, 0.88, 0.98, 0.96, 0.92, 0.94, 0.96, 0.96, 0.94, 0.90, 0.92, 0.96, 0.96, 0.96, 0.98, 0.90, 0.90, 0.88, 0.88, 0.88, 0.90, 0.78, 0.84, 0.86, 0.92, 1.00, 0.68, 0.82, 0.90, 0.88, 0.98, 1.08, 1.36, 2.04, 0.98, 0.96, 1.02, 1.20, 0.98, 1.00, 1.08, 0.98, 1.02, 1.14, 1.28, 2.04, 1.16, 1.04, 0.96, 0.98, 0.92, 0.86, 0.88, 0.82, 0.92, 0.90, 0.86, 0.84, 0.86, 0.90, 0.84, 0.82, 0.82, 0.86, 0.86, 0.84, 0.84, 0.82, 0.80, 0.78, 0.78, 0.76, 0.74, 0.68, 0.74, 0.80, 0.80, 0.90, 0.60, 0.72, 0.80, 0.82, 0.86, 0.94, 1.24, 1.92, 0.92, 1.12, 0.90, 0.90, 0.94, 0.90, 0.90, 0.94, 0.98, 1.08, 1.24, 2.04, 1.04, 0.94, 0.86, 0.86, 0.86, 0.82, 0.84, 0.76, 0.80, 0.80, 0.80, 0.78, 0.80, 0.82, 0.76, 0.76, 0.76, 0.76, 0.78, 0.78, 0.76, 0.76, 0.72, 0.74, 0.70, 0.68, 0.72, 0.70, 0.64, 0.70, 0.72, 0.74, 0.64, 0.62, 0.74, 0.80, 0.82, 0.88, 1.02, 1.66, 0.94, 0.94, 0.96, 1.00, 1.16, 1.02, 1.04, 1.06, 1.02, 1.10, 1.22, 1.94, 1.18, 1.12, 1.06, 1.06, 1.04, 1.02, 0.94, 0.94, 0.98, 0.96, 0.96, 0.98, 1.00, 0.96, 0.92, 0.90, 0.86, 0.82, 0.90, 0.84, 0.84, 0.82, 0.80, 0.80, 0.76, 0.80, 0.82, 0.80, 0.72, 0.72, 0.76, 0.80, 0.76, 0.70, 0.74, 0.82, 0.84, 0.88, 0.98, 1.44, 0.96, 0.88, 0.92, 1.08, 0.90, 0.92, 0.96, 0.94, 1.04, 1.08, 1.14, 1.66, 1.08, 0.96, 0.90, 0.86, 0.84, 0.86, 0.82, 0.84, 0.82, 0.84, 0.84, 0.84, 0.84, 0.82, 0.86, 0.82, 0.82, 0.86, 0.90, 0.84, 0.82, 0.78, 0.80, 0.78, 0.74, 0.78, 0.76, 0.76, 0.70, 0.72, 0.76, 0.72, 0.70, 0.64] d1 = np.random.random(365) assert len(d) == len(d1) fig = plt.figure(figsize=(14, 3)) ax151 = fig.add_subplot(151) ax151.plot(d) ax151.set_title("d1") ax152 = fig.add_subplot(152) ax152.scatter(d, d1) ax152.set_title('No correlateion') ax152.grid(True) ax153 = fig.add_subplot(153) ax153.scatter(d1, d1, alpha=1)  # alpha控制透明度， 0是透明，1是不透明
ax153.set_title('Ideal positive correlation') ax153.grid(True) ax154 = fig.add_subplot(154) ax154.scatter(d1, d1*-1, alpha=1) ax154.set_title('Ideal negative correlation') ax154.grid(True) ax155 = fig.add_subplot(155) ax155.scatter(d1, d1+d, alpha=0.5) ax155.set_title('Non ideal positive correlation') ax155.grid(True) plt.tight_layout() plt.show()

散點圖和直方圖

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable def scatterhist(x, y, figsize=(8, 8)): _, scatter_axes = plt.subplots(figsize=figsize)  # Create a figure and a set of subplots. 返回一個Figure對象實例和Axes對象實例。
    scatter_axes.scatter(x, y, alpha=0.5)  # 繪製散點圖
    scatter_axes.set_xlim(left=0.5, right=2) scatter_axes.set_ylim(bottom=-0.2, top=1.2) # scatter_axes.set_aspect(aspect='equal') # 設置軸刻度的外觀
    divider = make_axes_locatable(scatter_axes)  # 
    axes_hist_x = divider.append_axes(position="top", sharex=scatter_axes, size=1, pad=0.1)  # create an axes at the given position with the same height (or width) of the main axes.
    axes_hist_y = divider.append_axes(position="right", sharey=scatter_axes, size=1, pad=0.1)  # 返回一個Axes對象實例
    binwidth = 0.25 xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))]) bincap = int(xymax/binwidth)*binwidth bins = np.arange(-bincap, bincap, binwidth) nx, binsx, _ = axes_hist_x.hist(x, bins=bins, edgecolor='black', histtype='bar', orientation='vertical')  # 繪製直方圖，
    ny, binsy, _ = axes_hist_y.hist(y, bins=bins, edgecolor='black', histtype='bar', orientation='horizontal') tickstep = 50 ticksmax = np.max([np.max(nx), np.max(ny)]) xyticks = np.arange(0, ticksmax+tickstep, tickstep) for t1 in axes_hist_x.get_xticklabels(): t1.set_visible(False) axes_hist_x.set_yticks(xyticks) for t1 in axes_hist_y.get_yticklabels(): t1.set_visible(False) axes_hist_y.set_xticks(xyticks) plt.show() if __name__ == '__main__': d = [ 1.04, 1.04, 1.16, 1.22, 1.46, 2.34, 1.16, 1.12, 1.24, 1.30, 1.44, 1.22, 1.26, 1.34, 1.26, 1.40, 1.52, 2.56, 1.36, 1.30, 1.20, 1.12, 1.12, 1.12, 1.06, 1.06, 1.00, 1.02, 1.04, 1.02, 1.06, 1.02, 1.04, 0.98, 0.98, 0.98, 1.00, 1.02, 1.02, 1.00, 1.02, 0.96, 0.94, 0.94, 0.94, 0.96, 0.86, 0.92, 0.98, 1.08, 1.04, 0.74, 0.98, 1.02, 1.02, 1.12, 1.34, 2.02, 1.68, 1.12, 1.38, 1.14, 1.16, 1.22, 1.10, 1.14, 1.16, 1.28, 1.44, 2.58, 1.30, 1.20, 1.16, 1.06, 1.06, 1.08, 1.00, 1.00, 0.92, 1.00, 1.02, 1.00, 1.06, 1.10, 1.14, 1.08, 1.00, 1.04, 1.10, 1.06, 1.06, 1.06, 1.02, 1.04, 0.96, 0.96, 0.96, 0.92, 0.84, 0.88, 0.90, 1.00, 1.08, 0.80, 0.90, 0.98, 1.00, 1.10, 1.24, 1.66, 1.94, 1.02, 1.06, 1.08, 1.10, 1.30, 1.10, 1.12, 1.20, 1.16, 1.26, 1.42, 2.18, 1.26, 1.06, 1.00, 1.04, 1.00, 0.98, 0.94, 0.88, 0.98, 0.96, 0.92, 0.94, 0.96, 0.96, 0.94, 0.90, 0.92, 0.96, 0.96, 0.96, 0.98, 0.90, 0.90, 0.88, 0.88, 0.88, 0.90, 0.78, 0.84, 0.86, 0.92, 1.00, 0.68, 0.82, 0.90, 0.88, 0.98, 1.08, 1.36, 2.04, 0.98, 0.96, 1.02, 1.20, 0.98, 1.00, 1.08, 0.98, 1.02, 1.14, 1.28, 2.04, 1.16, 1.04, 0.96, 0.98, 0.92, 0.86, 0.88, 0.82, 0.92, 0.90, 0.86, 0.84, 0.86, 0.90, 0.84, 0.82, 0.82, 0.86, 0.86, 0.84, 0.84, 0.82, 0.80, 0.78, 0.78, 0.76, 0.74, 0.68, 0.74, 0.80, 0.80, 0.90, 0.60, 0.72, 0.80, 0.82, 0.86, 0.94, 1.24, 1.92, 0.92, 1.12, 0.90, 0.90, 0.94, 0.90, 0.90, 0.94, 0.98, 1.08, 1.24, 2.04, 1.04, 0.94, 0.86, 0.86, 0.86, 0.82, 0.84, 0.76, 0.80, 0.80, 0.80, 0.78, 0.80, 0.82, 0.76, 0.76, 0.76, 0.76, 0.78, 0.78, 0.76, 0.76, 0.72, 0.74, 0.70, 0.68, 0.72, 0.70, 0.64, 0.70, 0.72, 0.74, 0.64, 0.62, 0.74, 0.80, 0.82, 0.88, 1.02, 1.66, 0.94, 0.94, 0.96, 1.00, 1.16, 1.02, 1.04, 1.06, 1.02, 1.10, 1.22, 1.94, 1.18, 1.12, 1.06, 1.06, 1.04, 1.02, 0.94, 0.94, 0.98, 0.96, 0.96, 0.98, 1.00, 0.96, 0.92, 0.90, 0.86, 0.82, 0.90, 0.84, 0.84, 0.82, 0.80, 0.80, 0.76, 0.80, 0.82, 0.80, 0.72, 0.72, 0.76, 0.80, 0.76, 0.70, 0.74, 0.82, 0.84, 0.88, 0.98, 1.44, 0.96, 0.88, 0.92, 1.08, 0.90, 0.92, 0.96, 0.94, 1.04, 1.08, 1.14, 1.66, 1.08, 0.96, 0.90, 0.86, 0.84, 0.86, 0.82, 0.84, 0.82, 0.84, 0.84, 0.84, 0.84, 0.82, 0.86, 0.82, 0.82, 0.86, 0.90, 0.84, 0.82, 0.78, 0.80, 0.78, 0.74, 0.78, 0.76, 0.76, 0.70, 0.72, 0.76, 0.72, 0.70, 0.64] d1 = np.random.random(365) assert len(d) == len(d1) scatterhist(d, d1)

自相關圖形

自相關表示在一個給定的時間序列或一個連續的時間間隔上其自身的延遲版本之間的類似度。它發生在一個時間序列研究中，指在一個給定的時間週期內的錯誤在將來的時間週期上繼續存在。

import matplotlib.pyplot as plt import numpy as np d = [ 1.04, 1.04, 1.16, 1.22, 1.46, 2.34, 1.16, 1.12, 1.24, 1.30, 1.44, 1.22, 1.26, 1.34, 1.26, 1.40, 1.52, 2.56, 1.36, 1.30, 1.20, 1.12, 1.12, 1.12, 1.06, 1.06, 1.00, 1.02, 1.04, 1.02, 1.06, 1.02, 1.04, 0.98, 0.98, 0.98, 1.00, 1.02, 1.02, 1.00, 1.02, 0.96, 0.94, 0.94, 0.94, 0.96, 0.86, 0.92, 0.98, 1.08, 1.04, 0.74, 0.98, 1.02, 1.02, 1.12, 1.34, 2.02, 1.68, 1.12, 1.38, 1.14, 1.16, 1.22, 1.10, 1.14, 1.16, 1.28, 1.44, 2.58, 1.30, 1.20, 1.16, 1.06, 1.06, 1.08, 1.00, 1.00, 0.92, 1.00, 1.02, 1.00, 1.06, 1.10, 1.14, 1.08, 1.00, 1.04, 1.10, 1.06, 1.06, 1.06, 1.02, 1.04, 0.96, 0.96, 0.96, 0.92, 0.84, 0.88, 0.90, 1.00, 1.08, 0.80, 0.90, 0.98, 1.00, 1.10, 1.24, 1.66, 1.94, 1.02, 1.06, 1.08, 1.10, 1.30, 1.10, 1.12, 1.20, 1.16, 1.26, 1.42, 2.18, 1.26, 1.06, 1.00, 1.04, 1.00, 0.98, 0.94, 0.88, 0.98, 0.96, 0.92, 0.94, 0.96, 0.96, 0.94, 0.90, 0.92, 0.96, 0.96, 0.96, 0.98, 0.90, 0.90, 0.88, 0.88, 0.88, 0.90, 0.78, 0.84, 0.86, 0.92, 1.00, 0.68, 0.82, 0.90, 0.88, 0.98, 1.08, 1.36, 2.04, 0.98, 0.96, 1.02, 1.20, 0.98, 1.00, 1.08, 0.98, 1.02, 1.14, 1.28, 2.04, 1.16, 1.04, 0.96, 0.98, 0.92, 0.86, 0.88, 0.82, 0.92, 0.90, 0.86, 0.84, 0.86, 0.90, 0.84, 0.82, 0.82, 0.86, 0.86, 0.84, 0.84, 0.82, 0.80, 0.78, 0.78, 0.76, 0.74, 0.68, 0.74, 0.80, 0.80, 0.90, 0.60, 0.72, 0.80, 0.82, 0.86, 0.94, 1.24, 1.92, 0.92, 1.12, 0.90, 0.90, 0.94, 0.90, 0.90, 0.94, 0.98, 1.08, 1.24, 2.04, 1.04, 0.94, 0.86, 0.86, 0.86, 0.82, 0.84, 0.76, 0.80, 0.80, 0.80, 0.78, 0.80, 0.82, 0.76, 0.76, 0.76, 0.76, 0.78, 0.78, 0.76, 0.76, 0.72, 0.74, 0.70, 0.68, 0.72, 0.70, 0.64, 0.70, 0.72, 0.74, 0.64, 0.62, 0.74, 0.80, 0.82, 0.88, 1.02, 1.66, 0.94, 0.94, 0.96, 1.00, 1.16, 1.02, 1.04, 1.06, 1.02, 1.10, 1.22, 1.94, 1.18, 1.12, 1.06, 1.06, 1.04, 1.02, 0.94, 0.94, 0.98, 0.96, 0.96, 0.98, 1.00, 0.96, 0.92, 0.90, 0.86, 0.82, 0.90, 0.84, 0.84, 0.82, 0.80, 0.80, 0.76, 0.80, 0.82, 0.80, 0.72, 0.72, 0.76, 0.80, 0.76, 0.70, 0.74, 0.82, 0.84, 0.88, 0.98, 1.44, 0.96, 0.88, 0.92, 1.08, 0.90, 0.92, 0.96, 0.94, 1.04, 1.08, 1.14, 1.66, 1.08, 0.96, 0.90, 0.86, 0.84, 0.86, 0.82, 0.84, 0.82, 0.84, 0.84, 0.84, 0.84, 0.82, 0.86, 0.82, 0.82, 0.86, 0.90, 0.84, 0.82, 0.78, 0.80, 0.78, 0.74, 0.78, 0.76, 0.76, 0.70, 0.72, 0.76, 0.72, 0.70, 0.64] total = sum(d) av = total/len(d) z = [i-av for i in d] fig = plt.figure() ax1 = fig.add_subplot(221) ax1.plot(d) ax1.set_title('Google Trends data for "flowers"') ax2 = fig.add_subplot(222) ax2.acorr(z, usevlines=True, maxlags=None, normed=True, lw=2)  # Plot the autocorrelation of x.
ax2.grid(True) ax2.set_title('Autocorrelation') d1 = np.random.random(365) assert len(d) == len(d1) total = sum(d1) av = total/len(d1) z = [i-av for i in d1] ax3 = fig.add_subplot(223) ax3.plot(d1) ax3.set_title('Random data') ax4 = fig.add_subplot(224) ax4.acorr(z, usevlines=True, maxlags=None, normed=True, lw=2) ax4.set_title('Autocorrelation of random data') ax4.grid(True) plt.tight_layout() plt.show()

繪製風杆圖（barbs）

風杆是風速和風向的一種表現形式，主要由氣象學家使用。

import matplotlib.pyplot as plt import numpy as np x = np.linspace(-10, 20, 8) y = np.linspace(0, 20, 8) X, Y = np.meshgrid(x, y) U, V = X+25, Y-35 fig = plt.figure() ax1 = fig.add_subplot(121) ax1.barbs(X, Y, U, V, flagcolor='green', alpha=0.75)  # Plot a 2-D field of barbs.
ax1.grid(True, color='gray') ax2 = fig.add_subplot(122) ax2.quiver(X, Y, U, V, facecolor='red', alpha=0.75)  # Plot a 2-D field of arrows.
ax2.grid(True, color='grey') plt.show()

箱線圖

import matplotlib.pyplot as plt PROCESSES = {"A":[12, 15, 23, 24, 30, 31, 33, 36, 50, 73], "B":[6, 22, 26, 33, 35, 47, 54, 55, 62, 63], "C":[2, 3, 6, 8, 13, 14, 19, 23, 60, 69], "D":[1, 22, 36, 37, 45, 47, 48, 51, 52, 69]} DATA = PROCESSES.values() LABELS = PROCESSES.keys() fig = plt.figure() ax = fig.gca() ax.boxplot(DATA, notch=False, widths=0.5)  # 繪製箱線圖
ax.set_xticklabels(LABELS) for spine in ax.spines.values():  # 獲取四個邊框
 spine.set_visible(True) ax.xaxis.set_ticks_position('none')  # 設置x軸的刻度位置
ax.yaxis.set_ticks_position('left')  # 設置y軸的刻度位置
ax.set_ylabel("Errors observed over defined period.") ax.set_xlabel("Process observed over defined period.") plt.show()

import matplotlib.pyplot as plt from matplotlib.path import Path import matplotlib.patches as patches fig = plt.figure() ax = fig.add_subplot(111) coords = [(1.0, 0.0), (0.0, 1.0), (0.0, 2.0), (1.0, 3.0), (2.0, 3.0), (3.0, 2.0), (3.0, 1.0), (2.0, 0.0), (0.0, 0.0),] line_cmds = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY,] path = Path(coords, line_cmds) patch = patches.PathPatch(path, lw=1, facecolor='#A1D99B', edgecolor='black') ax.add_patch(patch) # Add a Patch p to the list of axes patches
ax.text(x=1.1, y=1.4, s='Python', fontsize=24) ax.set_xlim(-1, 4) ax.set_ylim(-1, 4) plt.show()