How to Plot a Time Series in Pandas (With Example)

You can use the following syntax to plot a time series in pandas:

df.plot(x='date', y='sales')

This particular example creates a time series plot using the column called date for the x-axis and the column called sales for the y-axis.

The following example shows how to use this syntax in practice.

Example: How to Plot a Time Series in Pandas

Suppose we have the following pandas DataFrame that shows the total sales made by some store on various days:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'date': ['10-1-2023', '10-2-2023', '10-3-2023', '10-4-2023',
                            '10-5-2023', '10-6-2023', '10-7-2023', '10-8-2023'],
                   'sales': [99, 104, 110, 140, 130, 122, 120, 125]})

#convert date column to datetime format
df['date'] = pd.to_datetime(df['date'])

#view DataFrame
print(df)

        date  sales
0 2023-10-01     99
1 2023-10-02    104
2 2023-10-03    110
3 2023-10-04    140
4 2023-10-05    130
5 2023-10-06    122
6 2023-10-07    120
7 2023-10-08    125

We can use the following syntax to create a time series plot to visualize the total sales by day:

#create time series plot
df.plot(x='date', y='sales')

time series plot in pandas

The x-axis shows the date and the y-axis shows the total sales.

We can also use the following arguments in the plot() function to customize the appearance of the line in the plot:

linewidth: The width of the line
color: The color of the line
linestyle: The style of the line
legend: Whether or not to display the legend in the plot

We can also use the title(), xlabel() and ylabel() functions from matplotlib to add a title and axis-labels to the plot.

The following code shows how to do so:

import matplotlib.pyplot as plt

#create time series plot with custom line
df.plot(x='date', y='sales'),
        linewidth=3, color='purple', linestyle='dashed', legend=False)

#add title and axis labels to plot
plt.title('Sales by Date')
plt.xlabel('Date')
plt.ylabel('Sales')