Describe a NumPy Array in Python
Last Updated :
11 Nov, 2025
NumPy is a Python library for numerical computing. It provides multidimensional arrays and many mathematical functions to efficiently perform operations on them. In this article, we will perform a descriptive analysis of a NumPy array to understand its key statistics.
Initializing a NumPy Array
Initializing a NumPy Array means creating a new array with some starting values using NumPy function np.array().
Python
import numpy as np
arr = np.array([4, 5, 8, 5, 6, 4, 9, 2, 4, 3, 6])
print(arr)
Output[4 5 8 5 6 4 9 2 4 3 6]
To analyze a NumPy array effectively, we focus on two key types of statistics:
- Central Tendency
- Dispersion
Measures of Central Tendency
Measures of central tendency summarize a dataset by identifying a typical or central value, such as the mean or median, that represents the overall trend of the data.
1. mean(): takes a NumPy array as an argument and returns the arithmetic mean of the data.
np.mean(arr)
2. median(): takes a NumPy array as an argument and returns the median of the data.
np.median(arr)
The following example illustrates the usage of the mean() and median() methods.
Python
import numpy as np
arr = np.array([12, 5, 7, 2, 61, 1, 1, 5])
mean = np.mean(arr)
median = np.median(arr)
print("Mean:", mean)
print("Median:", median)
OutputMean: 11.75
Median: 5.0
Measures of Dispersion
Measures of dispersion describe how spread out or varied the values in a dataset are, showing whether the data points are close to the average or widely scattered.
1. amin() : it takes a NumPy array as an argument and returns the minimum.
np.amin(arr)
2. amax() : it takes a NumPy array as an argument and returns maximum.
np.amax(arr)
3. ptp() : it takes a NumPy array as an argument and returns the range of the data.
np.ptp(arr)
4. var() : it takes a NumPy array as an argument and returns the variance of the data.
np.var(arr)
5. std() : it takes a NumPy array as an argument and returns the standard variation of the data.
np.std(arr)
Example: The following code illustrates amin(), amax(), ptp(), var() and std() methods.
Python
import numpy as np
arr = np.array([12, 5, 7, 2, 61, 1, 1, 5])
min_val = np.amin(arr)
max_val = np.amax(arr)
rng = np.ptp(arr)
var = np.var(arr)
std = np.std(arr)
print("Min:", min_val)
print("Max:", max_val)
print("Range:", rng)
print("Variance:", var)
print("Std Dev:", std)
OutputMin: 1
Max: 61
Range: 60
Variance: 358.1875
Std Dev: 18.925842121290138
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