What is Numpy?
NumPy is the fundamental library for scientific computing, data manipulation and machine learning in Python.
It is used for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
Numpy stands for Numerical Python. Pronounced as Num-pie
Its developed by Travis Oliphant in 2006. Written in Python and C-Language.
Storage of Array
Elements of array are stored in contiguous memory location i.e. stores all elements of array in continuous memory either it is one dimensional array or multi dimensional array.
It stores firstly, the first row, then second row, then third row and so forth...
Row Major Form
It stores firstly, the first column, then second column, then third column and so forth…
Column Major Form
Installation of NumPy
Importing Python
Windows:-
pip install numpy
Linux:-
$ sudo apt install python-numpy
Installation of numpy can be done in various ways. If you are using Anaconda, then it installed automatically. Otherwise you have to install it separately by using pip.
Open shell in linux or command prompt in windows.
Before going to use NumPy, you need to import python. import command can be used for importing python package. First run the python interpreter and then type
c:\>python # to start python interpreter
>>> import numpy
>>> import numpy as np
NumPy Array Terminology
NumPy array is also called N-dimensional NumPy data array i.e. ndarray.
Axes – Dimensions of NumPy array is referred as axes. Axes are always numbered 0 onwards for ndarrays.
Rank – It means number of axes in an ndarray.
Shape – It means size of array i.e. number of elements along each axis.
Data type (dType) – tells about the type of data. Default data type of ndarray is float.
Itemsize – refers to the size of each element of an ndarray in bytes.
NumPy Array Data Types
What is Array?
Types of Array
In general, Array refers to a named group of homogeneous elements.
A NumPy array is simply a grid that contains values of the same/homogeneous type.
There are two types of array-
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One Dimensional Array
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having single row/column
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known as Vectors
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set of elements having same data type.
2. Multi Dimensional Array
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having multiple rows and columns.
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known as Matrix
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set of elements having same data type.
NumPy Array vs List
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The NumPy array elements are also indexed in memory in the same manner as list’s elements.
Forward Indexing :
0 , 1, 2, 3, 4 …
Backwarkd Indexing :
… -4, -3, -2, -1
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NumPy array size & List size can be changed.
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NumPy array contain homogenous types, while List can contain mixed.
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Equivalent NumPy array cosumed less space.
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NumPy array support vectorized operation, while List does not.
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Numpy array is faster than Python List.
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Numpy array consume less memory.
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Numpy array is very convenient while performing element-wise operation.