A time series plot is useful for visualizing data values that change over time.

This tutorial explains how to create various time series plots using the data visualization package in Python.

Example 1: Plot a Single Time Series

The following code shows how to plot a single time series in seaborn:

import pandas as pd
import matplotlib.pyplotas plt
import seaborn as sns

#create DataFrame
df = pd.DataFrame({'date': ['1/2/2021',
                            '1/3/2021',
                            '1/4/2021',
                            '1/5/2021',
                            '1/6/2021',
                            '1/7/2021',
                            '1/8/2021'],
                   'value': [4, 7, 8, 13, 17, 15, 21]})

sns.lineplot(x='date', y='value', data=df)

Note that we can also customize the colors, line width, line style, labels, and titles of the plot:

#create time series plot with custom aesthetics 
sns.lineplot(x='date', y='value', data=df, linewidth=3, color='purple',
             linestyle='dashed').set(title='Time Series Plot')

#rotate x-axis labels by 15 degrees
plt.xticks(rotation=15)

Example 2: Plot Multiple Time Series

The following code shows how to plot multiple time series in seaborn:

import pandas as pd
import matplotlib.pyplotas plt
import seaborn as sns

#create DataFrame
df = pd.DataFrame({'date': ['1/1/2021',
                            '1/2/2021',
                            '1/3/2021',
                            '1/4/2021',
                            '1/1/2021',
                            '1/2/2021',
                            '1/3/2021',
                            '1/4/2021'],
                   'sales': [4, 7, 8, 13, 17, 15, 21, 28],
                   'company': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B']})

#plot multiple time series
sns.lineplot(x='date', y='sales', hue='company', data=df)

Note that the hue argument is used to provide different colors to each line in the plot.

The following tutorials explain how to perform other common functions in seaborn: