Add trend line to pandas

Question

I have time-series data, as followed:

                  emplvl
date                    
2003-01-01  10955.000000
2003-04-01  11090.333333
2003-07-01  11157.0         


        

Answer 1

Here's a quick example on how to do this using pandas.ols:

import matplotlib.pyplot as plt
import pandas as pd

x = pd.Series(np.arange(50))
y = pd.Series(10 + (2 * x + np.random.randint(-5, + 5, 50)))
regression = pd.ols(y=y, x=x)
regression.summary

-------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ <x> + <intercept>

Number of Observations:         50
Number of Degrees of Freedom:   2

R-squared:         0.9913
Adj R-squared:     0.9911

Rmse:              2.7625

F-stat (1, 48):  5465.1446, p-value:     0.0000

Degrees of Freedom: model 1, resid 48

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x     2.0013     0.0271      73.93     0.0000     1.9483     2.0544
     intercept     9.5271     0.7698      12.38     0.0000     8.0183    11.0358
---------------------------------End of Summary---------------------------------

trend = regression.predict(beta=regression.beta, x=x[20:]) # slicing to only use last 30 points
data = pd.DataFrame(index=x, data={'y': y, 'trend': trend})
data.plot() # add kwargs for title and other layout/design aspects
plt.show() # or plt.gcf().savefig(path)

Answer 2

In general you should create your matplotlib figure and axes object ahead of time, and explicitly plot the dataframe on that:

from matplotlib import pyplot
import pandas
import statsmodels.api as sm

df = pandas.read_csv(...)

fig, ax = pyplot.subplots()
df.plot(x='xcol', y='ycol', ax=ax)

Then you still have that axes object around to use directly to plot your line:

model = sm.formula.ols(formula='ycol ~ xcol', data=df)
res = model.fit()
df.assign(fit=res.fittedvalues).plot(x='xcol', y='fit', ax=ax)