将趋势线添加到日期时间 matplotlib 折线图

Question

I have a pandas dataframe df:

times = pd.date_range(start="2018-09-09",end="2020-02-02")
values = np.random.rand(512)

# Make df
df = pd.DataFrame({'Time' : times, 
                   'Value': values})

And I can plot this easily using plt.plot:

But now I want to add a trendline. I tried using some answers:

How can I draw scatter trend line on matplot? Python-Pandas
Which doesn't work:

TypeError: unsupported operand type(s) for +: 'datetime.datetime' and 'float'

Then I found the following question and answer:

TypeError: ufunc subtract cannot use operands with types dtype('<M8[ns]') and dtype('float64')

But these don't work as well. There my understanding of the issue stops, and I can't find anything else.

My code so far:

# Get values for the trend line analysis
x = df['Time'].dt.to_pydatetime()

# Calculate a fit line
trend = np.polyfit(x, df['Value'], 1)
fit = np.poly1d(trend)

# General plot again
figure(figsize=(12, 8))
plt.plot(x, df['Value'])
plt.xlabel('Date')
plt.ylabel('Value')

# Now trendline
plt.plot(x, fit(x), "r--")

# And show
plt.show()

Answer 1

One approach is to convert the dates using matplotlib's date2num() function and its counterpart the num2date function:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.dates as dates

np.random.seed(123)
times = pd.date_range(start="2018-09-09",end="2020-02-02")
values = np.random.rand(512)
df = pd.DataFrame({'Time' : times, 
                   'Value': values})


# Get values for the trend line analysis
x_dates = df['Time']
x_num = dates.date2num(x_dates)

# Calculate a fit line
trend = np.polyfit(x_num, df['Value'], 1)
fit = np.poly1d(trend)

# General plot again
#figure(figsize=(12, 8))
plt.plot(x_dates, df['Value'])
plt.xlabel('Date')
plt.ylabel('Value')

# Not really necessary to convert the values back into dates
#but added as a demonstration in case one wants to plot non-linear curves
x_fit = np.linspace(x_num.min(), x_num.max())
plt.plot(dates.num2date(x_fit), fit(x_fit), "r--")

# And show
plt.show()

Sample output: