Summarizing numerical data is an important part of data analysis, and there are many ways to do it in Python. Here are a few common techniques:

1.    Mean: The mean is the average of all the values in the dataset. You can calculate the mean using NumPy's mean() function:

import numpy as np
data = [1, 2, 3, 4, 5]
mean = np.mean(data)
print(mean)

 

2.    Median: The median is the middle value in the dataset when the values are sorted in order. You can calculate the median using NumPy's median() function:

import numpy as np
data = [1, 2, 3, 4, 5]
median = np.median(data)
print(median)

 

3.    Mode: The mode is the value that appears most frequently in the dataset. You can calculate the mode using SciPy's mode() function:

from scipy import stats
data = [1, 2, 2, 3, 4, 4, 4, 5]
mode = stats.mode(data)
print(mode)

 

4.    Range: The range is the difference between the largest and smallest values in the dataset. You can calculate the range using NumPy's ptp() function:

import numpy as np
data = [1, 2, 3, 4, 5]
range = np.ptp(data)
print(range)

 

These are just a few examples of how you can summarize numerical data in Python. There are many other techniques you can use, depending on the specific needs of your analysis.