Python Numpy - GeeksforGeeks

15 Oct, 2018
Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.
Array in Numpy is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In Numpy, number of dimensions of the array is called rank of the array.A tuple of integers giving the size of the array along each dimension is known as shape of the array. An array class in Numpy is called as ndarray. Elements in Numpy arrays are accessed by using square brackets and can be initialized by using nested Python Lists.
Creating a Numpy ArrayArrays in Numpy can be created by multiple ways, with various number of Ranks, defining the size of the Array. Arrays can also be created with the use of various data types such as lists, tuples, etc. The type of the resultant array is deduced from the type of the elements in the sequences.Note: Type of array can be explicitly defined while creating the array.
Output:
Array with Rank 1: 
 [1 2 3]
Array with Rank 2: 
 [[1 2 3]
 [4 5 6]]

Array created using passed tuple:
 [1 3 2]

 Accessing the array IndexIn a numpy array, indexing or accessing the array index can be done in multiple ways. To print a range of an array, slicing is done. Slicing of an array is defining a range in a new array which is used to print a range of elements from the original array. Since, sliced array holds a range of elements of the original array, modifying content with the help of sliced array modifies the original array content.
Output:
Initial Array: 
[[-1.   2.   0.   4. ]
 [ 4.  -0.5  6.   0. ]
 [ 2.6  0.   7.   8. ]
 [ 3.  -7.   4.   2. ]]
Array with first 2 rows and alternate columns(0 and 2):
 [[-1.  0.]
 [ 4.  6.]]

Elements at indices (1, 3), (1, 2), (0, 1), (3, 0):
 [ 0. 54.  2.  3.]


 Basic Array OperationsIn numpy, arrays allow a wide range of operations which can be performed on a particular array or a combination of Arrays. These operation include some basic Mathematical operation as well as Unary and Binary operations.
Output:
Adding 1 to every element:
 [[2 3]
 [4 5]]

Subtracting 2 from each element:
 [[ 2  1]
 [ 0 -1]]

Sum of all array elements:  10

Array sum:
 [[5 5]
 [5 5]]

 More on Numpy Arrays
Basic Array Operations in Numpy
Advanced Array Operations in Numpy
Basic Slicing and Advanced Indexing in NumPy Python
Every Numpy array is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. Every ndarray has an associated data type (dtype) object. This data type object (dtype) provides information about the layout of the array. The values of an ndarray are stored in a buffer which can be thought of as a contiguous block of memory bytes which can be interpreted by the dtype object. Numpy provides a large set of numeric datatypes that can be used to construct arrays. At the time of Array creation, Numpy tries to guess a datatype, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype.
Constructing a Datatype ObjectIn Numpy, datatypes of Arrays need not to be defined unless a specific datatype is required. Numpy tries to guess the datatype for Arrays which are not predefined in the constructor function.
Output:
Integer Datatype: 
int64

Float Datatype: 
float64

Forcing a Datatype: 
int64

 Math Operations on DataType arrayIn Numpy arrays, basic mathematical operations are performed element-wise on the array. These operations are applied both as operator overloads and as functions. Many useful functions are provided in Numpy for performing computations on Arrays such as sum: for addition of Array elements, T: for Transpose of elements, etc.
Output:
Addition of Two Arrays: 
[[ 7. 13.]
 [ 4. 14.]]

Addition of Array elements: 
19.0

Square root of Array1 elements: 
[[2.         2.64575131]
 [1.41421356 2.44948974]]

Transpose of Array: 
[[4. 2.]
 [7. 6.]]

 More on Numpy Data Type
Data type Object (dtype) in NumPy
Programs on Numpy
Python | Check whether a list is empty or not
Python | Get unique values from a list
Python | Multiply all numbers in the list (3 different ways)
Transpose a matrix in Single line in Python
Multiplication of two Matrices in Single line using Numpy in Python
Python program to print checkerboard pattern of nxn using numpy
Graph Plotting in Python | Set 1, Set 2, Set 3
Useful Numpy Articles
Matrix manipulation in Python
Basic Slicing and Advanced Indexing in NumPy Python
Differences between Flatten() and Ravel()
rand vs normal in Numpy.random in Python
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