详情请参考博客: Top 50 matplotlib Visualizations
因编译更新问题，本文将稍作更改，以便能够顺利运行。

1 Time Series Plot

时间串行图用于可视化给定指标如何随时间变化。在这里，您可以看到1949年至1969年间航空客运量的变化。查看此免费视频教程，了解如何实现线图以分析时间串行。

新建文件Time Series Plot.py:

# Import Setup
from Setup import pd
from Setup import plt

# Import Data
df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv')

# Draw Plot
plt.figure(figsize=(16,10), dpi= 80)
plt.plot('date', 'value', data=df, color='tab:red')

# Decoration
plt.ylim(50, 750)
xtick_location = df.index.tolist()[::12]
xtick_labels = [x[-4:] for x in df.date.tolist()[::12]]
plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7)
plt.yticks(fontsize=12, alpha=.7)
plt.title("Air Passengers Traffic (1949 - 1969)", fontsize=22)
plt.grid(axis='both', alpha=.3)

# Remove borders
plt.gca().spines["top"].set_alpha(0.0)    
plt.gca().spines["bottom"].set_alpha(0.3)
plt.gca().spines["right"].set_alpha(0.0)    
plt.gca().spines["left"].set_alpha(0.3)   
plt.show()

运行结果为：

2 Time Series with Peaks and Troughs Annotated

3 Autocorrelation Plot

4 Cross Correlation Plot

5 Time Series Decomposition Plot

6 Multiple Time Series

7 Plotting with different scales using secondary Y axis

8 Time Series with Error Bands

9 Stacked Area Chart

10 Area Chart Unstacked

11 Calendar Heat Map

12 Seasonal Plot