In this article, we will learn how to groupby multiple values and plotting the results in one go. Here, we take “exercise.csv” file of a dataset from seaborn library then formed different groupby data and visualize the result.
For this procedure, the steps required are given below :
- Import libraries for data and its visualization.
- Create and import the data with multiple columns.
- Form a groupby object by grouping multiple values.
- Visualize the grouped data.
Below is the implementation with some examples :
Example 1 :
In this example, we take the “exercise.csv” file of a dataset from the seaborn library then formed groupby data by grouping two columns “pulse” and “diet” together on the basis of a column “time” and at last visualize the result.
Python3
# importing packagesimport seaborn# load dataset and viewdata = seaborn.load_dataset('exercise')print(data)# multiple groupby (pulse and diet both)df = data.groupby(['pulse', 'diet']).count()['time']print(df)# plot the resultdf.plot()plt.xticks(rotation=45)plt.show() |
Output :
Unnamed: 0 id diet pulse time kind
0 0 1 low fat 85 1 min rest
1 1 1 low fat 85 15 min rest
2 2 1 low fat 88 30 min rest
3 3 2 low fat 90 1 min rest
4 4 2 low fat 92 15 min rest
.. ... .. ... ... ... ...
85 85 29 no fat 135 15 min running
86 86 29 no fat 130 30 min running
87 87 30 no fat 99 1 min running
88 88 30 no fat 111 15 min running
89 89 30 no fat 150 30 min running
[90 rows x 6 columns]
pulse diet
80 no fat NaN
low fat 1.0
82 no fat NaN
low fat 1.0
83 no fat 2.0
...
140 low fat NaN
143 no fat 1.0
low fat NaN
150 no fat 1.0
low fat NaN
Name: time, Length: 78, dtype: float64
Example 2: This example is the modification of the above example for better visualization.
Python3
# importing packagesimport seaborn# load datasetdata = seaborn.load_dataset('exercise')# multiple groupby (pulse and diet both)df = data.groupby(['pulse', 'diet']).count()['time']# plot the resultdf.unstack().plot()plt.xticks(rotation=45)plt.show() |
Output :
Example 3:
In this example, we take “exercise.csv” file of a dataset from seaborn library then formed groupby data by grouping three columns “pulse”, “diet” , and “time” together on the basis of a column “kind” and at last visualize the result.
Python3
# importing packagesimport seaborn# load dataset and viewdata = seaborn.load_dataset('exercise')print(data)# multiple groupby (pulse, diet and time)df = data.groupby(['pulse', 'diet', 'time']).count()['kind']print(df)# plot the resultdf.plot()plt.xticks(rotation=30)plt.show() |
Output :
Unnamed: 0 id diet pulse time kind
0 0 1 low fat 85 1 min rest
1 1 1 low fat 85 15 min rest
2 2 1 low fat 88 30 min rest
3 3 2 low fat 90 1 min rest
4 4 2 low fat 92 15 min rest
.. ... .. ... ... ... ...
85 85 29 no fat 135 15 min running
86 86 29 no fat 130 30 min running
87 87 30 no fat 99 1 min running
88 88 30 no fat 111 15 min running
89 89 30 no fat 150 30 min running
[90 rows x 6 columns]
pulse diet time
80 no fat 1 min NaN
15 min NaN
30 min NaN
low fat 1 min 1.0
15 min NaN
...
150 no fat 15 min NaN
30 min 1.0
low fat 1 min NaN
15 min NaN
30 min NaN
Name: kind, Length: 234, dtype: float64
Example 4: This example is the modification of the above example for better visualization.
Python3
# importing packagesimport seaborn# load datasetdata = seaborn.load_dataset('exercise')# multiple groupby (pulse, diet, and time)df = data.groupby(['pulse', 'diet', 'time']).count()['kind']# plot the resultdf.unsatck().plot()plt.xticks(rotation=30)plt.show() |
Output :
