For more info, see here:
https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset

The Linnerrud dataset is loaded using the load_linnerud() function:

from sklearn import datasets

# Load the dataset
linnerud = datasets.load_linnerud()

This returns a ‘Bunch’ object. Usually, it’s more user-friendly to use the as_frame parameter which causes the ‘data’ and ‘target’ values to be loaded as data frames inside the Bunch object:

# Load the dataset
linnerud = datasets.load_linnerud(as_frame=True)

print(type(linnerud['data']))
## <class 'pandas.core.frame.DataFrame'>

Either way, the Bunch object has the following keys:

# Show the dataset's keys
print(list(linnerud))
## ['data', 'feature_names', 'target', 'target_names', 'frame', 'DESCR', 'data_filename', 'target_filename', 'data_module']

More info on each of the keys:

Key Description
DESCR Description of the dataset
data_filename Location of the CSV file containing the data being imported
target_filename Location of the CSV file containing the target data being imported
feature_names Names of the 3 exercises (Chins, Situps, Jumps)
data The 20 data points for each of the 3 exercises, formatted as a 20x3 array
target_names Names of the 3 physiological variables (Weight, Waist and Pulse)
target The target data, namely the physiological measurements 
# Description of the dataset
print(linnerud['DESCR'])
## .. _linnerrud_dataset:
## 
## Linnerrud dataset
## -----------------
## 
## **Data Set Characteristics:**
## 
##     :Number of Instances: 20
##     :Number of Attributes: 3
##     :Missing Attribute Values: None
## 
## The Linnerud dataset is a multi-output regression dataset. It consists of three
## exercise (data) and three physiological (target) variables collected from
## twenty middle-aged men in a fitness club:
## 
## - *physiological* - CSV containing 20 observations on 3 physiological variables:
##    Weight, Waist and Pulse.
## - *exercise* - CSV containing 20 observations on 3 exercise variables:
##    Chins, Situps and Jumps.
## 
## .. topic:: References
## 
##   * Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
##     Editions Technic.
# Location of the CSV file containing the data being imported
print(linnerud['data_filename'])
## linnerud_exercise.csv
# Location of the CSV file containing the target data being imported
print(linnerud['target_filename'])
## linnerud_physiological.csv
# Names of the 3 exercises (Chins, Situps, Jumps)
print(linnerud['feature_names'])
## ['Chins', 'Situps', 'Jumps']
# The 20 data points for each of the 3 exercises, formatted as a 20x3 array
print(linnerud['data'])
##     Chins  Situps  Jumps
## 0     5.0   162.0   60.0
## 1     2.0   110.0   60.0
## 2    12.0   101.0  101.0
## 3    12.0   105.0   37.0
## 4    13.0   155.0   58.0
## 5     4.0   101.0   42.0
## 6     8.0   101.0   38.0
## 7     6.0   125.0   40.0
## 8    15.0   200.0   40.0
## 9    17.0   251.0  250.0
## 10   17.0   120.0   38.0
## 11   13.0   210.0  115.0
## 12   14.0   215.0  105.0
## 13    1.0    50.0   50.0
## 14    6.0    70.0   31.0
## 15   12.0   210.0  120.0
## 16    4.0    60.0   25.0
## 17   11.0   230.0   80.0
## 18   15.0   225.0   73.0
## 19    2.0   110.0   43.0
# Names of the 3 physiological variables (Weight, Waist and Pulse)
print(linnerud['target_names'])
## ['Weight', 'Waist', 'Pulse']
# The target data, namely the physiological measurements
print(linnerud['target'])
##     Weight  Waist  Pulse
## 0    191.0   36.0   50.0
## 1    189.0   37.0   52.0
## 2    193.0   38.0   58.0
## 3    162.0   35.0   62.0
## 4    189.0   35.0   46.0
## 5    182.0   36.0   56.0
## 6    211.0   38.0   56.0
## 7    167.0   34.0   60.0
## 8    176.0   31.0   74.0
## 9    154.0   33.0   56.0
## 10   169.0   34.0   50.0
## 11   166.0   33.0   52.0
## 12   154.0   34.0   64.0
## 13   247.0   46.0   50.0
## 14   193.0   36.0   46.0
## 15   202.0   37.0   62.0
## 16   176.0   37.0   54.0
## 17   157.0   32.0   52.0
## 18   156.0   33.0   54.0
## 19   138.0   33.0   68.0

The groups of feature data can be plotted against the target data as follows:

import matplotlib.pyplot as plt

# Plot
fig, axs = plt.subplots(3, 3)
fig.suptitle('Linnerud Dataset')
for i in range(3):
    for j in range(3):
        axs[i, j].scatter(linnerud['data'].iloc[:, i], linnerud['target'].iloc[:, j])
        axs[i, j].set_xlabel(list(linnerud['data'])[i])
        axs[i, j].set_ylabel(list(linnerud['target'])[j])
plt.tight_layout()
plt.show()