绘 图
参考：https://blog.csdn.net/anneqiqi/article/details/64125186
折线图
import matplotlib.pyplot as plt squares = [1, 4, 9, 16, 25] plt.plot(squares) plt.show()
修改标签文字、线条粗细
import matplotlib.pyplot as plt squares = [1, 4, 9, 16, 25] plt.plot(squares, linewidth = 5) # 设置标签 plt.title("Square Number", fontsize = 24) plt.xlabel("Value", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置刻度大小 plt.tick_params(axis = 'both', labelsize = 14) plt.show()
校正图形
import matplotlib.pyplot as plt input_values = [1, 2, 3, 4, 5] squares = [1, 4, 9, 16, 25] plt.plot(input_values, squares, linewidth = 5) # 第一个参数为x轴的值即输入值 """ plot()函数默认第一个数据点为(0, 0) """ # 设置标签 plt.title("Square Number", fontsize = 24) plt.xlabel("Value", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置刻度大小 plt.tick_params(axis = 'both', labelsize = 14) plt.show()
使用 scatter() 函数绘制散点图、设置其样式
import matplotlib.pyplot as plt plt.scatter(2, 4) # 设置一个坐标 plt.show()
import matplotlib.pyplot as plt plt.scatter(2, 4, s = 200) # 设置一个坐标 plt.title("Square Numbers", fontsize = 24) plt.xlabel("Valve", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置刻度标记的大小 plt.tick_params(axis = 'both', which = 'major', labelsize = 14) plt.show()
使用 scatter() 绘制一系列的点
import matplotlib.pyplot as plt x_values = [1, 2, 3, 4, 5] y_values = [1, 4, 9, 16, 25] plt.scatter(x_values, y_values, s = 100) # s参数表示点的直径 plt.title("Square Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 刻度设置 plt.tick_params(axis = 'both', which = 'major', labelsize = 14) plt.show()
自动计算数据
"""自动计算数据""" import matplotlib.pyplot as plt x_values = list(range(1, 1001)) y_values = [x**2 for x in x_values] plt.scatter(x_values, y_values, s = 4) plt.title("Square Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置每个坐标轴的取值范围 plt.axis([0, 1100, 0, 1100000]) plt.show()
删除数据点的轮廓
"""自动计算数据""" import matplotlib.pyplot as plt x_values = list(range(1, 1001)) y_values = [x**2 for x in x_values] plt.scatter(x_values, y_values, edgecolor = 'none', s = 4) # edgecolor 参数蓝色点黑色轮廓，去掉黑色轮廓后显示为蓝色实心点 plt.title("Square Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置每个坐标轴的取值范围 plt.axis([0, 1100, 0, 1100000]) plt.show()
自定义颜色
"""自动计算数据""" import matplotlib.pyplot as plt x_values = list(range(1, 1001)) y_values = [x**2 for x in x_values] plt.scatter(x_values, y_values, c = 'red', edgecolor = 'none', s = 4) # edgecolor 参数蓝色点黑色轮廓，去掉黑色轮廓后显示为蓝色实心点 # c 参数表示颜色，可以用元组 RGB 值表示颜色 plt.title("Square Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置每个坐标轴的取值范围 plt.axis([0, 1100, 0, 1100000]) plt.show()
使用颜色映射
"""自动计算数据""" import matplotlib.pyplot as plt x_values = list(range(1, 1001)) y_values = [x**2 for x in x_values] plt.scatter(x_values, y_values, c = y_values,cmap = plt.cm.Blues, edgecolor = 'none', s = 4) # c 参数的值越大，blues 颜色的值越深，渐变 plt.title("Square Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置每个坐标轴的取值范围 plt.axis([0, 1100, 0, 1100000]) plt.show()
自动保存图表
"""自动计算数据""" import matplotlib.pyplot as plt x_values = list(range(1, 1001)) y_values = [x**3 for x in x_values] plt.scatter(x_values, y_values, c = y_values,cmap = plt.cm.Blues, edgecolor = 'none', s = 4) # c 参数的值越大，blues 颜色的值越深，渐变 plt.title("Square Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 14) plt.ylabel("Square of Value", fontsize = 14) # 设置每个坐标轴的取值范围 plt.axis([0, 1100, 0, 1100000]) plt.show() plt.savefig('xieyi.png', bbox_inches = 'tight') # 自动保存图表，第二个参数裁剪图表周围多余空白区域
随机漫步
from random import choice import matplotlib.pyplot as plt class RandomWalk(): # 一个生成随机漫步数据的类 def __init__(self, num_points = 500): self.num_points = num_points self.x_values = [0] self.y_values = [0] def fill_walk(self): while len(self.x_values) < self.num_points: x_direction = choice([1, -1]) x_distance = choice([0, 1, 2, 3, 4]) x_step = x_direction * x_distance y_direction = choice([1, 1]) y_distance = choice([0, 1, 2, 3, 4]) y_step = y_direction * y_distance if x_step == 0 and y_step ==0: continue next_x = self.x_values[-1] + x_step next_y = self.y_values[-1] + y_step self.x_values.append(next_x) self.y_values.append(next_y) rw = RandomWalk() rw.fill_walk() plt.scatter(rw.x_values, rw.y_values, s = 10) plt.title("Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 20) plt.ylabel("Numbers of Values", fontsize = 20) plt.show()
模拟多次随机漫步
from random import choice import matplotlib.pyplot as plt class RandomWalk(): # 一个生成随机漫步数据的类 def __init__(self, num_points = 500): self.num_points = num_points self.x_values = [0] self.y_values = [0] def fill_walk(self): while len(self.x_values) < self.num_points: x_direction = choice([1, -1]) x_distance = choice([0, 1, 2, 3, 4]) x_step = x_direction * x_distance y_direction = choice([1, 1]) y_distance = choice([0, 1, 2, 3, 4]) y_step = y_direction * y_distance if x_step == 0 and y_step ==0: continue next_x = self.x_values[-1] + x_step next_y = self.y_values[-1] + y_step self.x_values.append(next_x) self.y_values.append(next_y) while True: rw = RandomWalk() rw.fill_walk() plt.scatter(rw.x_values, rw.y_values, s = 10) plt.title("Numbers", fontsize = 24) plt.xlabel("Values", fontsize = 20) plt.ylabel("Numbers of Values", fontsize = 20) plt.show() keep_runing = input("Make another walk?(y/n):") if keep_runing == 'n': break
设置随机漫步图的样式
from random import choice import matplotlib.pyplot as plt class RandomWalk(): # 一个生成随机漫步数据的类 def __init__(self, num_points = 500): self.num_points = num_points self.x_values = [0] self.y_values = [0] def fill_walk(self): while len(self.x_values) < self.num_points: x_direction = choice([1, -1]) x_distance = choice([0, 1, 2, 3, 4]) x_step = x_direction * x_distance y_direction = choice([1, 1]) y_distance = choice([0, 1, 2, 3, 4]) y_step = y_direction * y_distance if x_step == 0 and y_step ==0: continue next_x = self.x_values[-1] + x_step next_y = self.y_values[-1] + y_step self.x_values.append(next_x) self.y_values.append(next_y) while True: rw = RandomWalk() rw.fill_walk() # 给点着色 point_number = list(range(rw.num_points)) plt.scatter(rw.x_values, rw.y_values, c = point_number, cmap = plt.cm.Blues, edgecolor = 'none', s = 20) plt.show() keep_runing = input("Make another walk?(y/n):") if keep_runing == 'n': break
重新绘制起点和重终点
隐藏坐标轴
增加点数
调整屏幕以合适的尺寸
from random import choice import matplotlib.pyplot as plt class RandomWalk(): # 一个生成随机漫步数据的类 def __init__(self, num_points = 500): self.num_points = num_points self.x_values = [0] self.y_values = [0] def fill_walk(self): while len(self.x_values) < self.num_points: x_direction = choice([1, -1]) x_distance = choice([0, 1, 2, 3, 4]) x_step = x_direction * x_distance y_direction = choice([1, 1]) y_distance = choice([0, 1, 2, 3, 4]) y_step = y_direction * y_distance if x_step == 0 and y_step ==0: continue next_x = self.x_values[-1] + x_step next_y = self.y_values[-1] + y_step self.x_values.append(next_x) self.y_values.append(next_y) while True: rw = RandomWalk(50000) # 增加点数 rw.fill_walk() # 设置绘图窗口的尺寸 plt.figure(figsize = (10, 6)) # 给点着色，绘制点并将图形显示出来 point_number = list(range(rw.num_points)) plt.scatter(rw.x_values, rw.y_values, c = point_number, cmap = plt.cm.Blues, edgecolor = 'none', s = 1) # 突出起点和终点 plt.scatter(0, 0, c = 'green', edgecolors = 'none', s = 100) plt.scatter(rw.x_values[-1], rw.y_values[-1], c = 'red', edgecolors = 'none', s = 100) # 隐藏坐标轴 plt.axes().get_xaxis().set_visible(False) plt.axes().get_yaxis().set_visible(False) plt.show() keep_runing = input("Make another walk?(y/n):") if keep_runing == 'n': break
使用 Pygal 包：生成矢量图像，不同屏幕自动调整大小
from random import randint import pygal class Die(): def __init__(self, num_sides = 6): self.num_sides = num_sides def roll(self): return randint(1, self.num_sides) die = Die() results = [] for roll_num in range(100): result = die.roll() results.append(result) print(results) # 分析结果 frequencies = [] for value in range(1, die.num_sides+1): frequency = results.count(value) frequencies.append(frequency) print(frequencies) # 可视化数据 hist = pygal.Bar() hist.title = "Results of rolling one D6 1000 times" hist.x_values = ['1','2','3', '4', '5', '6'] hist.x_title = "Result" hist.y_title = "Frequencies" hist.add('D6', frequencies) hist.render_to_file('xieyi.svg') # 渲染为一个svg文件,svg用浏览器打开
from random import randint import pygal class Die(): def __init__(self, num_sides = 6): self.num_sides = num_sides def roll(self): return randint(1, self.num_sides) die_1 = Die() die_2 = Die() results = [] for roll_num in range(1000): result = die_1.roll() + die_2.roll() results.append(result) print(results) # 分析结果 frequencies = [] max_result = die_1.num_sides + die_2.num_sides for value in range(2, max_result+1): frequency = results.count(value) frequencies.append(frequency) print(frequencies) # 可视化数据 hist = pygal.Bar() hist.title = "Results of rolling one D6 1000 times" hist.x_labels = ['2','3', '4', '5', '6', '7', '8', '9','10', '11', '12'] hist.x_title = "Result" hist.y_title = "Frequencies" hist.add('D6 + D6', frequencies) hist.render_to_file('xieyi01.svg') # 渲染为一个svg文件,svg用浏览器打开