简单垂直条形图

<code>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()
/<code>

简单水平条形图

<code># 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()/<code>

水平交错条形图

<code># 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()/<code>

垂直堆叠条形图

<code># 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()
/<code>