
Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Convert Inputs to Arrays with At Least Two Dimensions in NumPy
To convert inputs to arrays with at least two dimensions, use the ma.atleast_2d() method in Python Numpy. The parameters are One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved.
The method returns an array, or list of arrays, each with a.ndim >= 2. Copies are avoided where possible, and views with two or more dimensions are returned.
Steps
At first, import the required library −
import numpy as np import numpy.ma as ma
Create an array with int elements using the numpy.array() method −
arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]]) print("Array...
", arr) print("
Array type...
", arr.dtype)
Get the dimensions of the Array −
print("
Array Dimensions...
",arr.ndim)
Create a masked array and mask some of them as invalid −
maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]]) print("
Our Masked Array
", maskArr) print("
Our Masked Array type...
", maskArr.dtype)
Get the dimensions of the Masked Array −
print("
Our Masked Array Dimensions...
",maskArr.ndim)
Get the shape of the Masked Array −
print("
Our Masked Array Shape...
",maskArr.shape)
Get the number of elements of the Masked Array −
print("
Elements in the Masked Array...
",maskArr.size)
To convert inputs to arrays with at least two dimensions, use the ma.atleast_2d() method in Python Numpy −
print("
Result...
",np.atleast_2d(1, maskArr))
Example
# Python ma.MaskedArray - Convert inputs to arrays with at least two dimensions import numpy as np import numpy.ma as ma # Create an array with int elements using the numpy.array() method arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]]) print("Array...
", arr) print("
Array type...
", arr.dtype) # Get the dimensions of the Array print("
Array Dimensions...
",arr.ndim) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]]) print("
Our Masked Array
", maskArr) print("
Our Masked Array type...
", maskArr.dtype) # Get the dimensions of the Masked Array print("
Our Masked Array Dimensions...
",maskArr.ndim) # Get the shape of the Masked Array print("
Our Masked Array Shape...
",maskArr.shape) # Get the number of elements of the Masked Array print("
Elements in the Masked Array...
",maskArr.size) # To convert inputs to arrays with at least two dimensions, use the ma.atleast_2d() method in Python Numpy print("
Result...
",np.atleast_2d(1, maskArr))
Output
Array... [[65 68 81] [93 33 39] [73 88 51] [62 45 67]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- 81] [-- 33 39] [73 -- 51] [62 -- 67]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 3) Elements in the Masked Array... 12 Result... [array([[1]]), masked_array( data=[[--, --, 81], [--, 33, 39], [73, --, 51], [62, --, 67]], mask=[[ True, True, False], [ True, False, False], [False, True, False], [False, True, False]], fill_value=999999)]