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以下代码示例展示了如何读取Excel文件并绘制折线图
import numpy as npimport matplotlib.pyplot as pltimport xlrd# 打开Excel文件workbook = xlrd.open_workbook('/home/myName/python_ws/excel_matplot_ws/read_plot/plot_data.xlsx')# 获取所有工作表名称worksheets = workbook.sheet_names()# 获取指定工作表mySheet = workbook.sheet_by_name(u'Sheet1')# 读取数据myData1 = []myData2 = []myData3 = []myData4 = []myData5 = []# 读取多行数据for c in range(1, 8): myData1.append(mySheet.cell(1, c).value)for c in range(1, 8): myData2.append(mySheet.cell(2, c).value)for c in range(1, 8): myData3.append(mySheet.cell(3, c).value)for c in range(1, 8): myData4.append(mySheet.cell(4, c).value)for c in range(1, 8): myData5.append(mySheet.cell(5, c).value)# 绘制折线图x = range(0, 7)plt.title("测试结果")plt.xlabel("数值类型")plt.ylabel("数值大小：ms")# 绘制多条折线l1, = plt.plot(x, myData1, linestyle="-", linewidth=1.5)l2, = plt.plot(x, myData2, color="blue", linewidth=1.5, linestyle="-")l3, = plt.plot(x, myData3, color="red", linewidth=2.5, linestyle="--")l4, = plt.plot(x, myData4, color="cyan", linestyle="-")l5, = plt.plot(x, myData5, color="green", linestyle=":")# 添加图例plt.legend(handles=[l1, l2, l3, l4, l5], labels=['PoseOptimization', 'OptimizeSim3', 'LocalBundleAdjustment', 'OptimizeEssentialGraph', 'GlobalBundleAdjustment'], loc='best')plt.show() 以下代码示例展示了如何绘制柱状图
import numpy as npimport matplotlib.pyplot as plt# 数据x1 = [1, 3, 7, 9]x2 = [2, 4, 6, 8, 10]y1 = [15, 3, 3, 3, 20]y2 = [6, 12, 22, 15, 12]# 绘制柱状图plt.bar(x1, y1, label='bar1')plt.bar(x2, y2, label='bar2')plt.xlabel('x轴')plt.ylabel('y轴')plt.title('柱形图')plt.legend()plt.show() 以下代码示例展示了如何在柱状图中添加数值注释
import numpy as npimport matplotlib.pyplot as pltdef auto_label(rects): for rect in rects: height = rect.get_height() plt.text(rect.get_x() + rect.get_width()/2, height, f'{height}', ha='center', va='bottom')def auto_text(rects): for rect in rects: plt.text(rect.get_x(), rect.get_height(), f'{rect.get_height()}', ha='left', va='bottom')# 数据men_means = [20, 34, 30, 35, 27]women_means = [25, 32, 34, 20, 25]index = np.arange(len(['L1', 'L2', 'L3', 'L4', 'L5']))# 绘制柱状图width = 0.2plt.figure(figsize=(10, 6))ax = plt.subplots()rect1 = ax.bar(index - width/2, men_means, color='lightcoral', width=width, label='Men')rect2 = ax.bar(index + width/2, women_means, color='springgreen', width=width, label='Women')ax.set_title('按性别统计分数')ax.set_xticks(ticks=index)ax.set_xticklabels(['L1', 'L2', 'L3', 'L4', 'L5'])ax.set_ylabel('分数')ax.set_ylim(0, 50)auto_label(rect1)auto_label(rect2)auto_text(rect1)auto_text(rect2)ax.legend(loc='upper right', frameon=False)plt.tight_layout()plt.show() 以下代码示例展示了如何绘制直方图
import numpy as npimport matplotlib.pyplot as plt# 数据population_ages = [22,55,62,45,21,22,34,42,42,4,99,102,110,120,121,122,130,111,115,112,80,75,65,54,44,43,42,48]bins = [0,10,20,30,40,50,60,70,80,90,100,110,120,130]# 绘制直方图plt.hist(population_ages, bins=bins, label='直方图')plt.xlabel('x轴')plt.ylabel('y轴')plt.title('直方图')plt.legend()plt.show() 以上代码示例展示了如何使用Python绘制不同类型的数据可视化图表，包括折线图、柱状图和直方图等。
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