You can use the following methods to perform a groupby and plot with a pandas DataFrame:

Method 1: Group By & Plot Multiple Lines in One Plot

#define index column
df.set_index('day', inplace=True)

#group data by product and display sales as line chart
df.groupby('product')['sales'].plot(legend=True)

Method 2: Group By & Plot Lines in Individual Subplots

pd.pivot_table(df.reset_index(),
               index='day', columns='product', values='sales'
              ).plot(subplots=True)

The following example shows how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'day': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
                   'product': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'],
                   'sales': [4, 7, 8, 12, 15, 8, 11, 14, 19, 20]})

#view DataFrame
df

	day	product	sales
0	1	A	4
1	2	A	7
2	3	A	8
3	4	A	12
4	5	A	15
5	1	B	8
6	2	B	11
7	3	B	14
8	4	B	19
9	5	B	20

Method 1: Group By & Plot Multiple Lines in One Plot

The following code shows how to group the DataFrame by the ‘product’ variable and plot the ‘sales’ of each product in one chart:

#define index column
df.set_index('day', inplace=True)

#group data by product and display sales as line chart
df.groupby('product')['sales'].plot(legend=True)

The x-axis displays the day, the y-axis displays the sales, and each individual line displays the sales of the individual products.

Method 2: Group By & Plot Lines in Individual Subplots

The following code shows how to group the DataFrame by the ‘product’ variable and plot the ‘sales’ of each product in individual subplots:

pd.pivot_table(df.reset_index(),
               index='day', columns='product', values='sales'
              ).plot(subplots=True)

The first plot shows the sales of product A and the second plot shows the sales of product B.

For example, we could specify the subplots to be in a grid with one row and two columns:

pd.pivot_table(df.reset_index(),
               index='day', columns='product', values='sales'
              ).plot(subplots=True, layout=(1,2))

Additional Resources

The following tutorials explain how to create other common visualizations in pandas: