最近又用到了matplotlib 中畫圖的函數。總結幾個常用的函數的作用于區別。

from matplotlib import pyplot as plt

1.figure()

函數定義matplotlib.pyplot.figure(num=None,?figsize=None,?dpi=None,?facecolor=None,?edgecolor=None,?frameon=True,?FigureClass=<class 'matplotlib.figure.Figure'>,?clear=False,?**kwargs)

plt.figure()創建一個畫布。

主要講一個參數num：相當于給畫布定義一個id，如果給出了num，之前沒有使用，則創建一個新的畫布；如果之前使用了這個num，那么返回那個畫布的引用，在之前的畫布上繼續作圖。如果沒有給出num, 則每次創建一塊新的畫布。

import numpy as np from matplotlib import pyplot as plt from scipy.interpolate import interp1dx=np.linspace(0,10*np.pi,num=20) y=np.sin(x) yn=np.cos(x) f1=interp1d(x,y,kind='linear')#線性插值 f2=interp1d(x,y,kind='cubic')#三次樣條插值 x_pred=np.linspace(0,10*np.pi,num=1000) y1=f1(x_pred) y2=f2(x_pred) plt.figure(1) plt.plot(x_pred,y1,'r',label='linear') plt.plot(x_pred,y2,'b--',label='cubic') plt.legend() # plt.show() plt.figure(2) plt.plot(x,yn,label='new') plt.legend() plt.show()

import numpy as np from matplotlib import pyplot as plt from scipy.interpolate import interp1dx=np.linspace(0,10*np.pi,num=20) y=np.sin(x) yn=np.cos(x) f1=interp1d(x,y,kind='linear')#線性插值 f2=interp1d(x,y,kind='cubic')#三次樣條插值 x_pred=np.linspace(0,10*np.pi,num=1000) y1=f1(x_pred) y2=f2(x_pred) plt.figure(1) plt.plot(x_pred,y1,'r',label='linear') plt.plot(x_pred,y2,'b--',label='cubic') plt.legend() # plt.show() plt.figure(1) plt.plot(x,yn,label='new') plt.legend() plt.show()

?2 add_subplot()

add_subplot(*args,?**kwargs)

向圖中加入子圖的軸。返回子圖的坐標軸axes

import numpy as np from matplotlib import pyplot as plt from scipy.interpolate import interp1dx=np.linspace(0,10*np.pi,num=20) y=np.sin(x) yn=np.cos(x) f1=interp1d(x,y,kind='linear')#線性插值 f2=interp1d(x,y,kind='cubic')#三次樣條插值 x_pred=np.linspace(0,10*np.pi,num=1000) y1=f1(x_pred) y2=f2(x_pred) fig = plt.figure() fig.add_subplot(221) plt.plot(x_pred,y1,'r',label='linear') fig.add_subplot(222) plt.plot(x_pred,y2,'b--',label='cubic')plt.show()

?

?通過ax設置各種圖的參數。

import matplotlib.pyplot as pltfig = plt.figure() fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold') ax = fig.add_subplot(111) fig.subplots_adjust(top=0.85) ax.set_title('axes title') ax.set_xlabel('xlabel') ax.set_ylabel('ylabel') ax.text(3, 8, 'boxed italics text in data coords', style='italic',bbox={'facecolor':'red', 'alpha':0.5, 'pad':10}) ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15) # ax.text(3, 2, unicode('unicode: Institut f\374r Festk\366rperphysik', 'latin-1')) ax.text(0.95, 0.01, 'colored text in axes coords',verticalalignment='bottom', horizontalalignment='right',transform=ax.transAxes,color='green', fontsize=15) ax.plot([2,3,4], [1,2,5], 'o') ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),arrowprops=dict(facecolor='black', shrink=0.05)) ax.axis([0, 10, 0, 11]) plt.show()

3.subplot()

matplotlib.pyplot.subplot(*args,?**kwargs):當前圖中加子圖

plt.subplot(221)# equivalent but more general ax1=plt.subplot(2, 2, 1) import numpy as np import matplotlib.pyplot as plt# Fixing random state for reproducibility np.random.seed(19680801)x = np.random.rand(10) y = np.random.rand(10) z = np.sqrt(x**2 + y**2)plt.subplot(321) plt.scatter(x, y, s=80, c=z, marker=">")plt.subplot(322) plt.scatter(x, y, s=80, c=z, marker=(5, 0))verts = np.array([[-1, -1], [1, -1], [1, 1], [-1, -1]]) plt.subplot(323) plt.scatter(x, y, s=80, c=z, marker=verts)plt.subplot(324) plt.scatter(x, y, s=80, c=z, marker=(5, 1))plt.subplot(325) plt.scatter(x, y, s=80, c=z, marker='+')plt.subplot(326) plt.scatter(x, y, s=80, c=z, marker=(5, 2))plt.show()

?4. subplots()

matplotlib.pyplot.subplots(nrows=1,?ncols=1,?sharex=False,?sharey=False,?squeeze=True,?subplot_kw=None,?gridspec_kw=None,?**fig_kw) :創建一個圖形和一組子圖。

nrows, ncols?:?int, optional, default: 1 子圖網絡的行列數。.

examples:

#First create some toy data: x = np.linspace(0, 2*np.pi, 400) y = np.sin(x**2)#Creates just a figure and only one subplot fig, ax = plt.subplots() ax.plot(x, y) ax.set_title('Simple plot')#Creates two subplots and unpacks the output array immediately f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) ax1.plot(x, y) ax1.set_title('Sharing Y axis') ax2.scatter(x, y)#Creates four polar axes, and accesses them through the returned array fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True)) axes[0, 0].plot(x, y) axes[1, 1].scatter(x, y

返回： fig, ax

import matplotlib.pyplot as pltdata = {'apples': 10, 'oranges': 15, 'lemons': 5, 'limes': 20} names = list(data.keys()) values = list(data.values())fig, axs = plt.subplots(1, 3, figsize=(9, 3), sharey=True) axs[0].bar(names, values) axs[1].scatter(names, values) axs[2].plot(names, values) fig.suptitle('Categorical Plotting')

個人喜好用1畫一個圖，4畫多個圖。?

class?matplotlib.axes.Axes(fig,?rect,?facecolor=None,?frameon=True,?sharex=None,?sharey=None,?label='',?xscale=None,?yscale=None, **kwargs):

The?Axes?contains most of the figure elements:?Axis,?Tick,?Line2D,?Text,?Polygon, etc., and sets the coordinate system.

總結

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