Numpy has created a vast ecosystem spanning numerous fields of science.
Numpy has created a vast ecosystem spanning numerous fields of science.
Welcome to the fascinating world of NumPy! If you’re new to data science or just starting to dip your toes into coding, you’ve landed in the perfect place.
NumPy, short for Numerical Python, is a fundamental package for scientific computing in Python. It’s the backbone that supports a wide array of data science operations, from basic mathematical operations to complex machine learning algorithms. In this guide, we’ll break down the basics of NumPy in a simple, engaging manner, peppered with code examples to ensure you grasp the concepts thoroughly.
NumPy is an open-source Python library that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. It’s designed for scientific computation, enabling complex mathematical operations to be expressed in a few lines of code. At its core, the library’s main object is the powerful ndarray
(n-dimensional array), which is a grid of values, all of the same type, indexed by a tuple of non-negative integers.
Before NumPy, Python was capable of handling lists. However, lists are slow to process and not suitable for mathematical operations, especially when dealing with large datasets. NumPy arrays are stored at one continuous place in memory, unlike lists, making it efficient for:
Using this command will ensure the installation of the latest version of NumPy that is compatible with your system.
pip install numpy
import numpy
import numpy as np
import numpy
num_arr=numpy.array([42,541,234,4,45,454,5])
print(num_arr)
print(type(num_arr))
[ 42 541 234 4 45 454 5]
<class 'numpy.ndarray'>
Creating a NumPy array is straightforward. Let’s explore a few ways to do this.
# Create a 1D NumPy array
my_array = np.array([1, 2, 3, 4, 5])
print(my_array)
# Create a 2D NumPy array (also known as a matrix)
my_2d_array = np.array([[1, 2, 3], [4, 5, 6]])
print(my_2d_array)
NumPy also provides functions to create arrays filled with zeros, ones, or a range of numbers – handy for initializing large arrays.
import numpy
num_arr=numpy.array([42,541,234,4,45,454,5])
print(num_arr)
print(type(num_arr))
[ 42 541 234 4 45 454 5]
<class 'numpy.ndarray'>
# Array of zeros
zeros = np.zeros((3, 4))
print(zeros)
# Array of ones
ones = np.ones((2, 2))
print(ones)
# Array with a sequence of numbers
sequence = np.arange(10, 20)
print(sequence)
# Evenly spaced numbers over a specified interval
linspace = np.linspace(0, 1, 5)
print(linspace)
One of the strengths of NumPy is its ability to perform operations on arrays with ease.
You can easily perform arithmetic operations on arrays element-wise.
a = np.array([1, 2, 3, 4])
b = np.array([10, 20, 30, 40])
# Addition
print(a + b)
# Subtraction
print(b - a)
# Multiplication
print(a * b)
# Division
print(b / a)
NumPy offers a vast library of mathematical functions to perform operations such as trigonometric calculations, logarithms, and more.
import numpy
num_arr=numpy.array([42,541,234,4,45,454,5])
print(num_arr)
print(type(num_arr))
[ 42 541 234 4 45 454 5]
<class 'numpy.ndarray'>
# Square roots
print(np.sqrt(a))
# Exponential
print(np.exp(a))
# Sine function
print(np.sin(a))
Performing statistical calculations is straightforward with NumPy’s aggregation functions.
# Sum
print(np.sum(a))
# Maximum value
print(np.max(a))
# Mean
print(np.mean(a))
Functions | Explanation |
---|---|
copy() | Copy functions create an identical, independent array from the original. |
view() | The view reflects the original array, and any change to the view or the array is similarly affected. |
shape() | Returns the shape of the array with respect to its dimensions. |
reshape() | Changes the dimensions or changes the number of elements in the array. |
concatnate() | Concatenate joins two or more arrays by using its 0th axis default and the axis is changeable. |
stack() | Stack joins two 1D arrays into one using axis parameters. |
hstack() | H Stack helps to stack arrays with the rows. |
vstack() | V stack helps to stack arrays with the columns. |
dstack() | D stack helps to stack arrays with height. |
array_split() | split an array into a specific number of arrays. |
hsplit() | split 2D arrays among their rows into a specific number of arrays. |
where() | searches an element in the array and returns the indexes of matches. |
searchsorted() | SearchSorted() is used for inserting elements according to order in the array and Returns an index for the element to be inserted. |
sort() | Returns a sorted array into a specified order. |
numpy.broadcast | numpy. broadcast |
Numpy array | Python List |
---|---|
A numpy array is limited to a single datatype in single array. | A numpy array is limited to a single datatype in a single array. |
The Numpy array occupies less memory. | Python list calculations are slower than Numpy array |
Numpy arrays are at the heart of all Numpy systems. | Python lists occupy more memory while containing similar items. |
Numpy array calculations are faster because of the above fact. | Python has multiple sequenced data storage techniques that have different functionalities. |
you can use ndarray to create multidimensional arrays. etc matrices , data tables. | Python has nested data set capabilities. |
Numpy has created a vast ecosystem spanning numerous fields of science.
It empowers the users with the computational powers of the C and FORTRAN languages. All these functionalities have been brought over to Python. Since the codes are easily readable, they can be used to make complex calculations. Numpy provides the ability to exploit highly functional machines’ powers for calculations. So many libraries exploit these capabilities to provide their functions. Such as Tensorflow, xtensor, Dask, CuPy, JAX, and Xarray.
In the field of data science, Numpy is capable of assisting at all steps of analytics.
This article only introduces the basic, fundamental features of numpy. In the following articles, you will be introduced to a plethora of numpy features.
This can be a super guide for you to start and excel in your data science career.
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