《Python数据可视化之matplotlib实践》 源码 第二篇 精进 第五章（matplotlib数据可视化基础）

《Python数据可视化之matplotlib实践》 源码 第二篇 精进 第五章（matplotlib数据可视化基础）

图 5.1

import matplotlib.pyplot as pltimport numpy as npfrom matplotlib.ticker import AutoMinorLocator, MultipleLocator, FuncFormatterx=np.linspace(0.5, 3.5, 100)y=np.sin(x)fig=plt.figure(figsize=(8, 8))ax=fig.add_subplot(111)ax.xaxis.set_major_locator(MultipleLocator(1.0))ax.yaxis.set_major_locator(MultipleLocator(1.0))ax.xaxis.set_minor_locator(AutoMinorLocator(4))ax.yaxis.set_minor_locator(AutoMinorLocator(4))def minor_tick(x, pos): if not x%1.0: return "" return "%.2f"%xax.xaxis.set_minor_formatter(FuncFormatter(minor_tick))ax.tick_params("y", which='major',length=15, width=2.0, colors='r')ax.tick_params(which='minor', length=5, width=1.0, labelsize=10, labelcolor='0.25')ax.set_xlim(0, 4)ax.set_ylim(0, 2)ax.plot(x, y, c=(0.25, 0.25, 1.00), lw=2, zorder=10)# ax.plot(x, y, c=(0.25, 0.25, 1.00), lw=2, zorder=0)ax.grid(linestyle='-', linewidth=0.5, color='r', zorder=0)# ax.grid(linestyle='-', linewidth=0.5, color='r', zorder=10)# ax.grid(linestyle='--', linewidth=0.5, color='0.25', zorder=0)plt.show()

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图 5.2

import matplotlib.pyplot as pltimport numpy as npfig=plt.figure(facecolor=(1.0, 1.0, 0.9412))ax=fig.add_axes([0.1, 0.4, 0.5, 0.5])for ticklabel in ax.xaxis.get_ticklabels(): ticklabel.set_color("slateblue") ticklabel.set_fontsize(18) ticklabel.set_rotation(30)for ticklabel in ax.yaxis.get_ticklabels(): ticklabel.set_color("lightgreen") ticklabel.set_fontsize(20) ticklabel.set_rotation(2)plt.show()

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图 5.3

import matplotlib.pyplot as pltimport numpy as npfrom calendar import month_name, day_namefrom matplotlib.ticker import FormatStrFormatterfig=plt.figure()ax=fig.add_axes([0.2, 0.2, 0.7, 0.7])x=np.arange(1, 8, 1)y=2*xax.plot(x, y, ls='-', lw=2, color='orange', marker='o', ms=20, mfc='c', mec='r')ax.yaxis.set_major_formatter(FormatStrFormatter(r"$\yen%1.1f$"))plt.xticks(x, day_name[0:7], rotation=20)ax.set_xlim(0, 8)ax.set_ylim(0, 18)plt.show()

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图 5.4

import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0.5, 3.5, 100)y=np.sin(x)fig=plt.figure(figsize=(8, 8))ax=fig.add_subplot(111)ax.plot(x, y, c='b', ls='-', lw=2)ax.annotate("maximum", xy=(np.pi/2, 1.0), xycoords='data', xytext=((np.pi/2)+0.15, 0.8), textcoords="data", weight="bold", color='r', arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='r'))ax.text(2.8, 0.4, "$y=\sin(x)$", fontsize=20, color='b', bbox=dict(facecolor='y', alpha=0.5))plt.show()

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图 5.5

import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0.0, 10, 40)y=np.random.randn(40)plt.plot(x, y, ls='-', lw=2, marker='o', ms=20, mfc='orange', alpha=0.6)plt.grid(ls=':', color='gray', alpha=0.5)plt.text(6, 0, 'Matplotlib', size=30, rotation=30.0, bbox=dict(boxstyle='round', ec='#8968CD', fc='#FFE1FF'))plt.show()

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图 5.6

import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0.0, 10, 40)y=np.random.randn(40)plt.plot(x, y, ls='-', lw=2, marker='o', ms=20, mfc='orange', alpha=0.6)plt.grid(ls=':', color='gray', alpha=0.5)plt.text(1, 2, 'Matplotlib', size=50, alpha=0.5)plt.show()

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图 5.7

import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0, 10, 2000)y=np.sin(x)*np.cos(x)fig=plt.figure()ax=fig.add_subplot(111)ax.plot(x, y, ls='-', lw=2)bbox=dict(boxstyle='round', fc='#7EC0EE', ec='#9B30FF')arrowprops=dict(arrowstyle='-|>', color='r', connectionstyle='angle, angleA=0, angleB=90, rad=10')ax.annotate("single point", (5, np.sin(5)*np.cos(5)), xytext=(3, np.sin(3)*np.cos(3)), fontsize=12, color='r', bbox=bbox, arrowprops=arrowprops)ax.grid(ls=":", color='gray', alpha=0.6)plt.show()

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图 5.8

import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0, 10, 2000)y=np.sin(x)fig=plt.figure()ax=fig.add_subplot(111)ax.plot(x, y, ls='-', lw=2)ax.set_ylim(-1.5, 1.5)arrowprops=dict(arrowstyle='-|>', color='r')ax.annotate("", (3*np.pi/2, np.sin(3*np.pi/2)+0.15), xytext=(np.pi/2, np.sin(np.pi/2)+0.15), color='r', arrowprops=arrowprops)ax.arrow(0.0, -0.4, np.pi/2, 1.2, head_width=0.05, head_length=0.1, fc='g', ec='g')ax.grid(ls=':', color='gray', alpha=0.6)plt.show()

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图 5.9

import matplotlib.pyplot as pltimport matplotlib as mplimport numpy as npfrom matplotlib.sankey import Sankeympl.rcParams["font.sans-serif"]=['FangSong']mpl.rcParams['axes.unicode_minus']=Falseflows=[0.2, 0.1, 0.4, 0.3, -0.6, -0.05, -0.15, -0.2]labels=['', '', '', '', 'family', 'trip', 'education', 'sport']orientations=[1, 1, 0, -1, 1, -1, 1, 0]sankey=Sankey()sankey.add(flows=flows, labels=labels, orientations=orientations, color='c', fc='lightgreen', patchlabel='Life Cost', alpha=0.7)diagrams=sankey.finish()diagrams[0].texts[4].set_color('r')diagrams[0].texts[4].set_weight('bold')diagrams[0].text.set_fontsize(20)diagrams[0].text.set_fontweight('bold')plt.title("日常生活的成本开支的流量图")plt.show()

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图 5.10

import matplotlib.pyplot as pltimport matplotlib.patheffects as pesimport numpy as npx=np.linspace(0.5, 3.5, 100)y=np.sin(x)fontsize=23plt.plot(x, y, ls='--', lw=2)title='$y=\sin({x})$'xaxis_label='$x\_axis$'yaxis_label="$y\_axis$"title_text_obj=plt.title(title, fontsize=fontsize, va='bottom')xaxis_label_text_obj=plt.xlabel(xaxis_label, fontsize=fontsize-3, alpha=1.0)yaxis_label_text_obj=plt.ylabel(yaxis_label, fontsize=fontsize-3, alpha=1.0)title_text_obj.set_path_effects([pes.withSimplePatchShadow()])pe=pes.withSimplePatchShadow(offset=(1, -1), shadow_rgbFace='r', alpha=0.3)xaxis_label_text_obj.set_path_effects([pe])yaxis_label_text_obj.set_path_effects([pe])plt.show()

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图 5.11

import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0.5, 3.5, 100)y=np.sin(x)fig=plt.figure(figsize=(8, 8))ax=fig.add_subplot(111)box=dict(facecolor='#6959CD', pad=2, alpha=0.4)ax.plot(x, y, c='b', ls='--', lw=2)title='$y=\sin({x})$'xaxis_label='$x\_axis$'yaxis_label="$y\_axis$"ax.set_xlabel(xaxis_label, fontsize=18, bbox=box)ax.set_ylabel(yaxis_label, fontsize=18, bbox=box)ax.set_title(title, fontsize=23, va='bottom')ax.yaxis.set_label_coords(-0.08, 0.5)ax.xaxis.set_label_coords(1.0, -0.05)ax.grid(ls='-.', lw=1, color='gray', alpha=0.5)plt.show()

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