Python学习笔记—数据可视化之美（二）

import numpy as npimport matplotlib.pyplot as pltimport xlrd
plt.rcParams['font.sans-serif'] = ['SimHei']  # 如果要显示中文字体，则在此处设为：SimHeiplt.rcParams['axes.unicode_minus'] = False  # 显示负号
t = xlrd.open_workbook('美化数据.xls')sheet = t.sheet_by_index(0)x1_data=[] #需要将数据储存在空列表中才可调用绘图y1_data=[]x2_data=[]y2_data=[]x3_data=[]y3_data=[]x4_data=[]y4_data=[]for row in range(sheet.nrows):  content1 = sheet.cell_value(row,0)  x1_data.append(content1)  content2 = sheet.cell_value(row,1)  y1_data.append(content2)  content3 = sheet.cell_value(row,2)  x2_data.append(content3)  content4 = sheet.cell_value(row,3)  y2_data.append(content4)  content5 = sheet.cell_value(row, 4)  x3_data.append(content5)  content6 = sheet.cell_value(row, 5)  y3_data.append(content6)  content7 = sheet.cell_value(row, 6)  x4_data.append(content7)  content8 = sheet.cell_value(row, 7)  y4_data.append(content8)
plt.figure(figsize=(10, 5))plt.grid(linestyle="--")  ax = plt.gca()ax.spines['top'].set_visible(False)  # 去掉上边框ax.spines['right'].set_visible(False)  # 去掉右边框
plt.plot(x1_data, y1_data, color="black", label="数据1", linewidth=1.5)plt.plot(x2_data, y2_data, "k--", label="数据2", linewidth=1.5)plt.plot(x3_data, y3_data, color="red", label="数据3", linewidth=1.5)plt.plot(x4_data, y4_data, "r--", label="数据4", linewidth=1.5)
plt.title("正向骨架曲线", fontsize=12, fontweight='bold')  # 默认字体大小为12plt.xlabel("位移", fontsize=13, fontweight='bold')plt.ylabel("荷载", fontsize=13, fontweight='bold')plt.xlim(0,16)  # 设置x轴的范围plt.ylim(0,80)plt.xticks(np.linspace(0,16,9,endpoint=True),fontsize = 10)plt.yticks(np.linspace(0,80,9,endpoint=True),fontsize = 10)# plt.legend()          #显示各曲线的图例plt.legend(loc=2, numpoints=1)leg = plt.gca().get_legend()ltext = leg.get_texts()plt.setp(ltext, fontsize=12, fontweight='bold')  # 设置图例字体的大小和粗细
plt.show()

plt.plot(x1_data, y1_data, color="#0D59E6", label="数据1", linewidth=1.5)plt.plot(x2_data, y2_data, color="#14827B",linestyle='--', label="数据2", linewidth=1.5)plt.plot(x3_data, y3_data, color="#E81A8F", label="数据3", linewidth=1.5)plt.plot(x4_data, y4_data, color="#76A1F2",linestyle='--', label="数据4", linewidth=1.5)

plt.plot(x1_data, y1_data, color="#845EC2", label="数据1", linewidth=1.5)plt.plot(x2_data, y2_data, color="#BE93FD",linestyle='--', label="数据2", linewidth=1.5)plt.plot(x3_data, y3_data, color="#00C9A7",label="数据3", linewidth=1.5)plt.plot(x4_data, y4_data, color="#76A1F2",linestyle='--', label="数据4", linewidth=1.5)