Search code examples
pythonpandasmatplotlibseaborntrendline

Adding a trend line to a line plot

Apologies if this has already been asked but I can't find the answer anywhere. I want to add an overall trend line to a plt plot. Sample data:

import pandas as pd
data = pd.DataFrame({'year': [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 
                              2019],
                     'value': [2, 5, 8, 4, 1, 6, 10, 14, 8]})

import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [28, 26]
data.plot(x = "year", y = "value", fontsize = 30)
plt.xlabel('Time', fontsize = 30)

enter image description here

How can I add a trend line?

Solution

  • If you are looking for a simple linear regression fit, you can use directly either lmplot or regplot from seaborn. It performs the linear regression and plots the fit (line) with a 95% confidence interval (shades, default value). You can also use NumPy to perform the fit. In case you want to use NumPy, comment below and I will update.

    import seaborn as sns

# Your DataFrame here 

# sns.lmplot(x='year',y='value',data=data,fit_reg=True) 

sns.regplot(x='year',y='value',data=data, fit_reg=True)

    enter image description here

    From the Docs

    The regplot() and lmplot() functions are closely related, but the former is an axes-level function while the latter is a figure-level function that combines regplot() and FacetGrid which allows you to plot conditional relationships amongst your data on different subplots in the grid.