Describe a NumPy Array in Python - GeeksforGeeks

02 Sep, 2021
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:
Python3
import numpy as np  # sample arrayarr = 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.
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:
Python3
import numpy as np  arr = np.array([4, 5, 8, 5, 6, 4,                9, 2, 4, 3, 6])    # measures of central tendencymean = 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
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. 
Python3
import numpy as np  arr = np.array([4, 5, 8, 5, 6, 4,                9, 2, 4, 3, 6]) # measures of dispersionmin = 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.
Python3
import numpy as np   arr = np.array([4, 5, 8, 5, 6, 4,                9, 2, 4, 3, 6])    # measures of central tendencymean = np.mean(arr)median = np.median(arr) # measures of dispersionmin = 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
sagar0719kumar
Python numpy-Statistics Functions
Python-numpy
Python
Writing code in comment?
Please use ide.geeksforgeeks.org,
generate link and share the link here.
How to Install PIP on Windows ?
How to drop one or multiple columns in Pandas Dataframe
How To Convert Python Dictionary To JSON?
Check if element exists in list in Python
Defaultdict in Python
Python | os.path.join() method
Python | Get unique values from a list
Selecting rows in pandas DataFrame based on conditions
Create a directory in Python
Python | Pandas dataframe.groupby()