简单垂直条形图
GDP = [12406.8, 13908.57, 9386.87, 9143.64] # 绘图 plt.bar(range(4), GDP, align='center', color='steelblue', alpha=0.8) # 添加轴标签 plt.ylabel('GDP') # 添加标题 plt.title('GDP') # 添加刻度标签 plt.xticks(range(4), ['beijing', 'shanghai', 'tianjing', 'chongjing']) # 设置Y轴的刻度范围 plt.ylim([5000, 15000]) # 为每个条形图添加数值标签 for x, y in enumerate(GDP): plt.text(x, y + 100, '%s' % round(y, 1), ha='center') plt.show() |
简单水平条形图
# 导入绘图模块 import matplotlib.pyplot as plt # 构建数据 price = [39.5, 39.9, 45.4, 38.9, 33.34] # 绘图 plt.barh(range(5), price, align='center', color='steelblue', alpha=0.8) # 添加轴标签 plt.xlabel('price') # 添加标题 plt.title('Books at different prices') # 添加刻度标签 plt.yticks(range(5), ['Amazon', 'Dangdang', 'China Books Network', 'Jingdong', 'Tianmao']) # 设置Y轴的刻度范围 plt.xlim([32, 47]) # 为每个条形图添加数值标签 for x, y in enumerate(price): plt.text(y + 0.1, x, '%s' % y, va='center') plt.savefig('foo.png') # 显示图形 plt.show() python数据分析条形图的各种绘制方式 |
水平交错条形图
# 导入绘图模块 import matplotlib.pyplot as plt import numpy as np Y2016 = [15600, 12700, 11300, 4270, 3620] Y2017 = [17400, 14800, 12000, 5200, 4020] labels = ['Beijing', 'Shanghai', 'Hong Kong', 'Shenzhen', 'Guangzhou'] bar_width = 0.45 # 绘图 plt.bar(np.arange(5), Y2016, label='2016', color='steelblue', alpha=0.8, width=bar_width) plt.bar(np.arange(5) + bar_width, Y2017, label='2017', color='indianred', alpha=0.8, width=bar_width) # 添加轴标签 plt.xlabel('Top5 City') plt.ylabel('Number of households') # 添加标题 plt.title('Billionaires Top5 Cities') # 添加刻度标签 plt.xticks(np.arange(5) + bar_width, labels) # 设置Y轴的刻度范围 plt.ylim([2500, 19000]) # 为每个条形图添加数值标签 for x2016, y2016 in enumerate(Y2016): plt.text(x2016, y2016 + 100, '%s' % y2016, ha='center') for x2017, y2017 in enumerate(Y2017): plt.text(x2017 + bar_width, y2017 + 100, '%s' % y2017, ha='center') # 显示图例 plt.legend() plt.savefig('foo.png') # 显示图形 plt.show() python数据分析条形图的各种绘制方式 |
垂直堆叠条形图
# 导入模块 import matplotlib.pyplot as plt import numpy as np import pandas as pd # 导入数据 traffic_volume = {'Index': ['railway', 'green', 'water transport', 'air transport'], 'Jan': [31058, 255802, 52244, 57], 'Feb': [28121, 179276, 46482, 42], 'Mar': [32185, 285446, 50688, 59], 'Api': [30133, 309576, 54728, 57], 'May': [30304, 319713, 55813, 60], 'Jun': [29934, 320028, 59054, 58], 'Jul': [31002, 319809, 57353, 55], 'Aug': [31590, 331077, 57583, 57]} data = pd.DataFrame(traffic_volume) print(data) # 绘图 plt.bar(np.arange(8), data.loc[0, :][1:], color='red', alpha=0.8, label='railway', align='center') plt.bar(np.arange(8), data.loc[1, :][1:], bottom=data.loc[0, :][1:], color='green', alpha=0.8, label='highway', align='center') plt.bar(np.arange(8), data.loc[2, :][1:], bottom=data.loc[0, :][1:] + data.loc[1, :][1:], color='m', alpha=0.8, label='water transport', align='center') plt.bar(np.arange(8), data.loc[3, :][1:], bottom=data.loc[0, :][1:] + data.loc[1, :][1:] + data.loc[2, :][1:], color='black', alpha=0.8, label='air transport', align='center') # 添加轴标签 plt.xlabel('month') plt.ylabel('Cargo volume (10,000 tons)') # 添加标题 plt.title('Monthly logistics volume in 2017') # 添加刻度标签 plt.xticks(np.arange(8), data.columns[1:]) # 设置Y轴的刻度范围 plt.ylim([0, 500000]) # 为每个条形图添加数值标签 for x_t, y_t in enumerate(data.loc[0, :][1:]): plt.text(x_t, y_t / 2, '%sW' % (round(y_t / 10000, 2)), ha='center', color='white', fontsize=8) for x_g, y_g in enumerate(data.loc[0, :][1:] + data.loc[1, :][1:]): plt.text(x_g, y_g / 2, '%sW' % (round(y_g / 10000, 2)), ha='center', color='white', fontsize=8) for x_s, y_s in enumerate(data.loc[0, :][1:] + data.loc[1, :][1:] + data.loc[2, :][1:]): plt.text(x_s, y_s - 20000, '%sW' % (round(y_s / 10000, 2)), ha='center', color='white', fontsize=8) # 显示图例 plt.legend(loc='upper center', ncol=4) # 显示图形 plt.show() python数据分析条形图的各种绘制方式 |
以上就是各种条形图的绘制方式，你Get到了吗?
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