NumPy: A Comprehensive Guide to Python's Array Library, Study notes of Computer Science

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Numpy

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Numpy library is written in c language.

There are three major advantages :

• Fast Process
• Use less memory to store data
• Convenient

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(numpy)

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First run below commands in cmd window.

• pip list
• pip install numpy (https://pypi.org/project/numpy/)

How to install Numpy

Import NumPy

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Data Types in NumPy

NumPy has some extra data types, and refer to data types with one character, like i for integers, u for unsigned

integers etc.

Below is a list of all data types in NumPy and the characters used to represent them.

• i - integer
• b - boolean
• u - unsigned integer
• f - float
• c - complex float
• m - timedelta
• M - datetime
• O - object
• S - string
• U - unicode string
• V - fixed chunk of memory for other type ( void )

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Checking the Data Type of an Array

The NumPy array object has a property called dtype that returns the data type of the array:

Example :

import numpy as np

arr = np.array([1, 2, 3, 4])

print(arr.dtype)

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array() Function

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Creating 1D Arrya using array() Function

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index

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NumPy is used to work with arrays. The array object in NumPy is called ndarray.

We can create a NumPy ndarray object by using the array() function.

Example :

Import numpy as np

arr1 = np.array([1,2,3,4,5])

Print(arr1)

Print(type(arr1)

Ourtput :

[1 2 3 4 5]

<class ‘numpy.ndarray’>

Create a NumPy ndarray Object

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Check Number of Dimensions?

NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have.

Example :

import numpy as np

a = np.array(42) b = np.array([1, 2, 3, 4, 5]) c = np.array([[1, 2, 3], [4, 5, 6]]) d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])

print(a.ndim) print(b.ndim) print(c.ndim) print(d.ndim)

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Higher Dimensional Arrays

An array can have any number of dimensions.

When the array is created, you can define the number of dimensions by using the ndmin argument.

Example :

import numpy as np

arr = np.array([1, 2, 3, 4], ndmin=5)

print(arr) print('number of dimensions :', arr.ndim)

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