In 2005, the NumPy package was created by Travis Oliphant with infusing the characteristics of the ancestor module Numeric into another module Numarray. Today, we are going to discuss everything regarding NumPy Array in Python Programming. BTech Geeks Python NumPy Array Tutorial Pdf aids beginners and professionals to learn the basics to advance core fundamentals of NumPy such as Creating, Searching, Selecting Elements, modifying, converting, and more.
- What is NumPy in Python?
- List of Concepts Covered in Python Numpy Array Tutorial
- What is Python NumPy Array?
- Why Use NumPy?
- Where is NumPy used?
- Benefits of Choosing NumPy Array in Python
- Which Language is NumPy Used in Programming?
- How to create a NumPy array?
- Python NumPy Array Examples
- NumPy Array Python Interview Questions for Freshers
NumPy is an open-source numeric python library that deals quite comfortably with multi-dimensional arrays and matrix multiplication. Also, the library of numpy performs mathematical and statistical operations in Python and aids in the programming of mathematical, scientific, engineering, and data science.
Moreover, easy integration with C/C++ and Fortran. You can also work in the creation of the N-dimensional array, linear algebra, random number, Fourier transform, etc. Look for more information from the below available NumPy related tutorials.
Creating Numpy Arrays
- Create NumPy Arrays from list, tuple, or list of lists
- Create NumPy Arrays from a range of evenly spaced numbers using np.arrange().
- Create NumPy Array of zeros (0’s) using np.zeros()
- Create 1D / 2D NumPy Array filled with ones (1’s) using np.ones()
- Create NumPy Array of different shapes & initialize with identical values using numpy.full()
- Create NumPy Array with same sized samples over an interval in Python using numpy.linspace()
- Create a NumPy Array of bool value.
Adding Elements in Numpy Array
- Append/ Add an element to Numpy Array in Python (3 Ways)
- How to append elements at the end of a Numpy Array in Python using numpy.append()
- Create an empty 2D Numpy Array / matrix and append rows or columns in python
Searching in Numpy Arrays
- Find the index of value in Numpy Array
- Find max value & it’s index in Numpy Array | numpy.amax()
- Find unique values in a numpy array with frequency & indices
- numpy.where() : Tutorial & Examples | Python
- numpy.amin() | Find minimum value in Numpy Array and it’s index
Get Metadata of Numpy Array
Selecting elements from Numpy Array
- Select element or sub array by index from numpy array
- Select rows / columns by index from a 2D numpy array
- Select elements by conditions from Numpy Array
Modifying a Numpy Array
- How to append elements to a Numpy Array
- Delete elements, rows or columns from a Numpy Array by index positions using numpy.delete() in Python
- How to sort a Numpy Array in Python ?
- Sorting 2D Numpy Array by column or row in Python
- How to Reverse a 1D & 2D numpy array using np.flip() and  operator in Python
- Append rows or columns to a 2D Numpy Array
- numpy.reshape() Tutorial with examples
- numpy.flatten() – Tutorial with examples
- Numpy: flatten() vs ravel()
Converting NumPy Array to Other Data Structures
- Convert Matrix / 2D Array to 1D Array
- Convert a 1D array to a 2D array or Matrix
- Convert NumPy array to list in python
- Convert 2D NumPy array to list of lists in python
Numpy Array and File I/O
Verify Contents of Numpy Array
Counting Elements in Numpy Array
- Count occurrences of a value in NumPy array in Python
- Count number of True elements in a NumPy Array in Python
- numpy.count_nonzero() – Tutorial with examples
Advance Topics about Numpy Array
- What is a Structured Numpy Array and how to create and sort it in Python?
- numpy.zeros() & numpy.ones() | Create a numpy array of zeros or ones
It is the core library for scientific computing, and it stands for Numerical Python. One of the powerful N-dimensional array objects is in the form of rows and columns is called the Python NumPy Array. From the lists of nested python, we can start NumPy arrays and also access its elements.
In a Numpy array, the axis is the elements and separated by the same amount of bytes in the memory. To perform NumPy Array Operations, we need to install Numpy first. Also, it has functions to work in the domain of linear algebra, matrices, and Fourier transform.
Unlike others, NumPy is fast, more memory efficient, convenient to work with matrix multiplication and reshaping. On the other hand, TensorFlow and Scikit study to utilize NumPy array for calculating the matrix multiplication in the back end.
NumPy Arrays in Python offer tools to integrate C, C++, etc. So, it is helpful in linear algebra, random number capability, and many more. Also, you can use this numpy array as an effective multi-dimensional container for common data.
However, you can observe many convincing arguments for learning and grasping this new standard. Here, we have mentioned the top four benefits of NumPy that can get into your code:
- More speed: It applies C language written algorithms that perform in nanoseconds instead of seconds.
- Fewer loops: You can reduce loops and avoid getting tangled up in iteration rules by NumPy.
- Clearer code: Without loops, the program will seem like the equations you’re seeking to determine in NumPy.
- Better quality: NumPy is quick, friendly, and bug-free as there are thousands of contributors working behind it.
NumPy is a library and is composed somewhat in Python, however, the majority of parts that need fast computation are written in C or C++ language.
We have many ways to create numpy arrays in python but now we are going to create an array using arrange() and shape() Numpy Array methods. Let’s, check the below example and learn how to create NumPy Arrays:
import numpy as np A = np.arange(4) print('A =', A) B = np.arange(12).reshape(2, 6) print('B =', B)
A = [0 1 2 3]
B = [[ 0 1 2 3 4 5]
[ 6 7 8 9 10 11]]
Single-dimensional Numpy Array:
import numpy as np a=np.array([41,62,53]) print(a)
Output: [41 62 53]
Output: [[ 1 2 3]
[4 5 6]]
The list of top 10 interview questions of python numpy array are given here for helping job seekers to prepare well and crack the interviews effectively.
- What is the Numpy array?
- Why should we use Numpy rather than Matlab, Idl, octave or Yorick?
- How can you identify the datatype of a given NumPy array?
- How can you Install NumPy on Windows?
- What are the various features of NumPy?
- List the steps to create a 1D array and 2D array
- What is the difference between matrix and array?
- How do you check for an empty (zero Element) array?
- Create a two 2-D array. Plot it using matplotlib
- What are the uses of NumPy?