Describe a NumPy Array in Python

NumPy is a Python library used for numerical computing. It offers robust multidimensional arrays as a Python object along with a variety of mathematical functions. In this article, we will go through all the essential NumPy functions used in the descriptive analysis of an array. Let's start by initializing a sample array for our analysis.

The following code initializes a NumPy array:

import numpy as np


# sample array
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]

In order to describe our NumPy array, we need to find two types of statistics:

• Measures of central tendency.
• Measures of dispersion.

Measures of central tendency

The following methods are used to find measures of central tendency in NumPy:

• mean()- takes a NumPy array as an argument and returns the arithmetic mean of the data.

np.mean(arr)

• 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.

Example:

import numpy as np


arr = np.array([4, 5, 8, 5, 6, 4,
                9, 2, 4, 3, 6])    

# measures of central tendency
mean = np.mean(arr)
median = np.median(arr)

print("Array =", arr)
print("Mean =", mean)
print("Median =", median)

Output: 

Array = [4 5 8 5 6 4 9 2 4 3 6]
Mean = 5.09090909091
Median = 5.0

Measures of dispersion

The following methods are used to find measures of dispersion in NumPy: 

• amin()- it takes a NumPy array as an argument and returns the minimum.

np.amin(arr)

• amax()- it takes a NumPy array as an argument and returns maximum.

np.amax(arr)

• ptp()- it takes a NumPy array as an argument and returns the range of the data.

np.ptp(arr)

• var()- it takes a NumPy array as an argument and returns the variance of the data.

np.var(arr)

• 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. 

import numpy as np


arr = np.array([4, 5, 8, 5, 6, 4, 
                9, 2, 4, 3, 6])

# measures of dispersion
min = np.amin(arr)
max = np.amax(arr)
range = np.ptp(arr)
variance = np.var(arr)
sd = np.std(arr)

print("Array =", arr)
print("Measures of Dispersion")
print("Minimum =", min)
print("Maximum =", max)
print("Range =", range)
print("Variance =", variance)
print("Standard Deviation =", sd)

Output:

Array = [4 5 8 5 6 4 9 2 4 3 6]
Measures of Dispersion
Minimum = 2
Maximum = 9
Range = 7
Variance = 3.90082644628
Standard Deviation = 1.9750509984

Example: Now we can combine the above-mentioned examples to get a complete descriptive analysis of our array.

import numpy as np  


arr = np.array([4, 5, 8, 5, 6, 4,
                9, 2, 4, 3, 6])    

# measures of central tendency
mean = np.mean(arr)
median = np.median(arr)

# measures of dispersion
min = np.amin(arr)
max = np.amax(arr)
range = np.ptp(arr)
variance = np.var(arr)
sd = np.std(arr)

print("Descriptive analysis")
print("Array =", arr)
print("Measures of Central Tendency")
print("Mean =", mean)
print("Median =", median)
print("Measures of Dispersion")
print("Minimum =", min)
print("Maximum =", max)
print("Range =", range)
print("Variance =", variance)
print("Standard Deviation =", sd)

Output:

Descriptive analysis
Array = [4 5 8 5 6 4 9 2 4 3 6]
Measurements of Central Tendency
Mean = 5.09090909091
Median = 5.0
Minimum = 2
Maximum = 9
Range = 7
Variance = 3.90082644628
Standard Deviation = 1.9750509984