Create Scatter Plot with Linear Regression Line of Best Fit in Python

DataPlotPlus -

To add title and axis labels in Matplotlib and Python we need to use plt.title() and plt.xlabel()

Steps

  • Import libraries
  • Prepare Data
    • Select columns X and Y
    • Map values to categories
  • Select chart type
  • Set Style
    • chart height
    • aspect
    • colors
    • points decoration
  • Add title
  • Show plot

More information can be found: Creating multiple subplots using

Data

We are loading the seaborn dataset for flights. Additionally we are mapping the month to season in order to get categorical data.

year month passengers season
0 1949 Jan 112 1
1 1949 Feb 118 1
2 1949 Mar 132 1
3 1949 Apr 129 2
4 1949 May 121 2

Example

To plot scatter plot with best fit line we use:

  • sns.lmplot()
  • provide X and T
  • plt.ylabel('Y')

Full example:

import numpy as np; np.random.seed(0)
import matplotlib.pyplot as plt
import seaborn as sns; sns.set_theme()


flights = sns.load_dataset("flights")
flights['season'] = flights['month'].map({"Jan":1, "Feb":1, "Mar":1, "Apr":2, "May":2, "Jun":2, "Jul":2, "Aug":2, "Sep":2, "Oct":1, "Nov":1, "Dec":1, })

sns.set_style("white")
gridobj = sns.lmplot(x="year", y="passengers", hue="season", data=flights,
                     height=9, aspect=1.6, robust=True, palette='tab10',
                     scatter_kws=dict(s=100, linewidths=.9, edgecolors='black'))

plt.title("Scatterplot with line of best fit", fontsize=20)
plt.show()

Output