Python绘图实例1

实例 1：

import datetime
import matplotlib.pyplot as plt
from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange
import numpy as np
    
date1 = datetime.datetime(2020, 4, 2)
date2 = datetime.datetime(2020, 4, 6)
delta = datetime.timedelta(hours = 6)
dates = drange(date1, date2, delta)
    
y = np.arange(len(dates))
    
fig, ax = plt.subplots()
ax.plot_date(dates, y ** 2, 'g')
    
ax.set_xlim(dates[0], dates[-1])
    
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_minor_locator(HourLocator(range(0, 25, 6)))
ax.xaxis.set_major_formatter(DateFormatter('% Y-% m-% d'))
    
ax.fmt_xdata = DateFormatter('% Y-% m-% d % H:% M:% S')
fig.autofmt_xdate()
   
fig.suptitle('matplotlib.figure.Figure.autofmt_xdate() 
function Examplenn', fontweight ="bold")
  
plt.show()

对于横坐标是日期的，可以使用fig.autofmt_xdate(),达到横坐标倾斜的效果，与plt.xticks(rotation=45)效果一致。
输出图：

代码来源：https://www.geeksforgeeks.org/matplotlib-figure-figure-autofmt_xdate-in-python/

实例2：

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
    
years = mdates.YearLocator()   
months = mdates.MonthLocator()  
years_fmt = mdates.DateFormatter('% Y')
    
with cbook.get_sample_data('goog.npz') as datafile:
    data = np.load(datafile)['price_data'].view(np.recarray)
        
fig, ax = plt.subplots()
ax.plot('date', 'adj_close', 
         data = data[:300],
         color ="green")
    
ax.xaxis.set_major_locator(years) 
ax.format_ydata = lambda x: '$% 1.2f' % x
ax.grid(True)
   
fig.autofmt_xdate()
   
fig.suptitle('matplotlib.figure.Figure.autofmt_xdate() 
function Examplenn', fontweight ="bold")
  
plt.show()

实例3：
绘制散点图+折线图：sns.relplot(x="timepoint", y="signal", hue="region", , dashes=False, markers=True, kind="line", data=fmri)

bash seaborn.lineplot 官网参数：https://seaborn.pydata.org/generated/seaborn.lineplot.html

实例4：
代码来源：https://zhuanlan.zhihu.com/p/494651966
代码：

sns.relplot(x='time', y='signal', data=df)
plt.plot(time, data_mean, color='deeppink', label='mean')
plt.plot(time, sin_waves, 'b-', label='ideal')
plt.fill_between(time, data_min, data_max, color='violet', alpha=0.2)
plt.legend()
plt.show()

plt.plot(time, data_mean, color='deeppink', label='mean')
plt.plot(time, sin_waves, 'b-', label='ideal')
plt.fill_between(time, data_mean - data_std, data_mean + data_std, color='violet', alpha=0.2)
plt.legend()
plt.show()

关于绘图颜色搭配，可以参考这个网站：https://mycolor.space/?hex=%23472FE7&sub=1

参考资料：
用seaborn/matplot绘制误差带阴影图：https://colainlibrary.com/archives/7cd1c910.html

Manage markers for lineplots with Python seaborn：https://vicastel.net/post-its/python_seaborn_lineplot_markers