The NumPy API is used extensively in Pandas, SciPy, Matplotlib, sci-kit-learn, sci-kit-image and most other data science and scientific Python packages.

1. It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it.

2. It adds powerful data structures to Python that guarantee efficient calculations with arrays and matrices and it supplies an enormous library of high-level mathematical functions that operate on these arrays and matrices.

Installing NumPy

conda install numpy

or

pip install numpy

Importing NumPy

import numpy as np

Difference between a Python list and a NumPy array?

NumPy gives you an enormous range of fast and efficient ways of creating arrays and manipulating numerical data inside them. While a Python list can contain different data types within a single list, all of the elements in a NumPy array should be homogeneous. The mathematical operations that are meant to be performed on arrays would be extremely inefficient if the arrays weren’t homogeneous..

1. An array consumes less memory and is convenient to use.

What is an array?

An array is a central data structure of the NumPy library. An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element.

Other:

The rank of the array is the number of dimensions. The shape of the array is a tuple of integers giving the size of the array along each dimension.

print(a[0])

What are the attributes of an array?

An array is usually a fixed-size container of items of the same type and size. The number of dimensions and items in an array is defined by its shape. The shape of an array is a tuple of non-negative integers that specify the sizes of each dimension

dimensions are called axes

Array attributes reflect information intrinsic to the array itself. If you need to get, or even set, properties of an array without creating a new array, you can often access an array through its attributes.

How to create a Basic array?

1. np.array()

2. np.zeros()

3. np.ones()

4. np.empty()

5. np.arange()

6. np..linspace()

7. dtype

You can also use np.linspace() to create an array with values that are spaced linearly in a specified interval.

dtype=np.int64

Adding, removing, and sorting elements

1. np.sort()

2. np.concatenate()

In addition to sort, which returns a sorted copy of an array, you can use:

• [argsort](<https://numpy.org/doc/stable/reference/generated/numpy.argsort.html#numpy.argsort>), which is an indirect sort along a specified axis,

• [lexsort](<https://numpy.org/doc/stable/reference/generated/numpy.lexsort.html#numpy.lexsort>), which is an indirect stable sort on multiple keys,

• [searchsorted](<https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html#numpy.searchsorted>), which will find elements in a sorted array, and

• [partition](<https://numpy.org/doc/stable/reference/generated/numpy.partition.html#numpy.partition>), which is a partial sort.

How to know the shape and size of an array?

1. ndarray.ndim

2. ndarray.size

3. nadarray.shape

ndarray.ndim will tell you the number of axes, or dimensions, of the array.

ndarray.size will tell you the total number of elements of the array. This is the product of the elements of the array’s shape.

ndarray.shape will display a tuple of integers that indicate the number of elements stored along each dimension of the array. If, for example, you have a 2-D array with 2 rows and 3 columns, the shape of your array is (2, 3).

Can you reshape an array?

1. arr.reshape()

Using arr.reshape() will give a new shape to an array without changing the data. Just remember that when you use the reshape method, the array you want to produce needs to have the same number of elements as the original array. If you start with an array with 12 elements, you’ll need to make sure that your new array also has a total of 12 elements.

a is the array to be reshaped.

newshape is the new shape you want. You can specify an integer or a tuple of integers. If you specify an integer, the result will be an array of that length. The shape should be compatible with the original shape.

order: C means to read/write the elements using C-like index order,F means to read/write the elements using Fortran-like index order, A means to read/write the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise. (This is an optional parameter and doesn’t need to be specified.)

<aside> 💡 Read more about internal organization of NumPy arrays here.

</aside>

How to convert a 1D array into a 2D array (how to add a new axis to an array)

1. np.newaxis

2. np.expand_dims

Using np.newaxis will increase the dimensions of your array by one dimension when used once. This means that a 1D array will become a 2D array, a 2D array will become a 3D array, and so on.

Indexing and Slicing

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

data[1]
2

data[0:2]
array([1, 2])

data[1:]
array([2, 3])

data[-2:]
array([2, 3])

How to create an array from existing data?

1. slicing

2. indexing

3. np.vstack()

4. np.hsplit()

5. .view()

6. copy()

<aside> 💡 I have left it for later reading

</aside>

Basic array operations

1. addition

2. subtraction

3. multiplication

4. division

5. .sum( )

6. b.sum(axis=0

Broadcasting

NumPy understands that the multiplication should happen with each cell. That concept is called broadcasting. Broadcasting is a mechanism that allows NumPy to perform operations on arrays of different shapes. The dimensions of your array must be compatible, for example, when the dimensions of both arrays are equal or when one of them is 1. If the dimensions are not compatible, you will get a ValueError.

More array operations

1. max( )

2. min( )

3. sum( )

4. mean( )

5. product == prod( )

6. standard deviation = = std( )

Creating matrices

Be aware that when NumPy prints N-dimensional arrays, the last axis is looped over the fastest while the first axis is the slowest. For instance:

Generating random numbers

<aside> 💡 Something wrong

</aside>

How to get unique items and counts?

unique_values = np.unique(a)

print(unique_values)
[11 12 13 14 15 16 17 18 19 20]

unique_values, indices_list = np.unique(a, return_index=True)

print(indices_list)
[ 0  2  3  4  5  6  7 12 13 14]

unique_values, occurrence_count = np.unique(a, return_counts=True)

print(occurrence_count)
[3 2 2 2 1 1 1 1 1 1]

Transposing and reshaping a matrix

1. arr.reshape( )

2. arr.transpose( )

3. arr.T

   

How to reverse an array?

1. np.flip( )

You can also reverse the contents of only one column or row. For example, you can reverse the contents of the row at index position 1 (the second row):

arr_2d[1] = np.flip(arr_2d[1])

print(arr_2d)
[[ 1  2  3  4]
 [ 8  7  6  5]
 [ 9 10 11 12]]

You can also reverse the column at index position 1 (the second column):

arr_2d[:,1] = np.flip(arr_2d[:,1])

print(arr_2d)
[[ 1 10  3  4]
 [ 8  7  6  5]
 [ 9  2 11 12]]

Reshaping and flattening multidimensional arrays

There are two popular ways to flatten an array :

1. flatten( )

2. ravel( )

The primary difference between the two is that the new array created using ravel() is actually a reference to the parent array (i.e., a “view”). This means that any changes to the new array will affect the parent array as well. Since ravel does not create a copy, it’s memory efficient.

How to access the docstring for more information

1. help( )

2. ?

3. ??

When it comes to the data science ecosystem, Python and NumPy are built with the user in mind. One of the best examples of this is the built-in access to documentation. Every object contains the reference to a string, which is known as the docstring.

Working with mathematical formulas?

The ease of implementing mathematical formulas that work on arrays is one of the things that make NumPy so widely used in the scientific Python community.

What makes this work so well is that predictions and labels can contain one or a thousand values. They only need to be the same size.

We can visualize like this:

How to save and load NumPy objects?

1. np.save

2. np.savez

3. np.savetxt

4. np.load

5. np.loadtxt

You will, at some point, want to save your arrays to disk and load them back without having to re-run the code. Fortunately, there are several ways to save and load objects with NumPy. The ndarray objects can be saved to and loaded from the disk files with loadtxt and savetxt functions that handle normal text files, load and save functions that handle NumPy binary files with a .npy file extension, and a savez function that handles NumPy files with a .npz file extension.

If you want to store a single ndarray object, store it as a .npy file using np.save. If you want to store more than one ndarray object in a single file, save it as a .npz file using np.savez. You can also save several arrays into a single file in compressed npz format with [savez_compressed](<https://numpy.org/doc/stable/reference/generated/numpy.savez_compressed.html#numpy.savez_compressed>).

Importing and exporting a CSV

It’s simple to read in a CSV that contains existing information. The best and easiest way to do this is to use Pandas.

import pandas as pd

<aside> 💡 a = np.array([2, 1, 5, 7, 4, 6, 8, 14, 10, 9, 18, 20, 22])

</aside>

import matplotlib.pyplot as plt

# If you're using Jupyter Notebook, you may also want to run the following
# line of code to display your code in the notebook:

%matplotlib inline

plt.plot(a)
# If you are running from a command line, you may need to do this:
# >>> plt.show()

Introduction

There are 6 general mechanisms for creating arrays:

1. Conversion from other Python structures (i.e. lists and tuples)

2. Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.)

   1. np.arange( )

   2. np.linspace( )

   3. np.eye(3) #for 2d array creation functions

   4. np.diag( )

   5. np.vander([1,2,3,4], 2)

   6. np.vander((1, 2, 3, 4), 4)

   7. np.zeros( )

   8. np.ones( )

   9. Random generator:

    from numpy.random import default_rng

    default_rng(42).random((2,3))

j. np.indices((3,3))

1. Replicating, joining, or mutating existing arrays

2. Reading arrays from disk, either from standard or custom formats

3. Creating arrays from raw bytes through the use of strings or buffers

4. Use of special library functions (e.g., random)

Indexing on ndarrays

[ndarrays](<https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray>) can be indexed using the standard Python x[obj] syntax, where x is the array and obj the selection.

Slicing

Basic slicing extends Python’s basic concept of slicing to N dimensions. Basic slicing occurs when obj is a [slice](<https://docs.python.org/3/library/functions.html#slice>) object (constructed by start:stop:step notation inside of brackets), an integer, or a tuple of slice objects and integers. [Ellipsis](<https://docs.python.org/3/library/constants.html#Ellipsis>)andand) [newaxis](<https://numpy.org/doc/stable/reference/constants.html#numpy.newaxis>) objects can be interspersed with these as well.

All arrays generated by basic slicing are always views of the original array.

The basic slice syntax is i:j:k where i is the starting index, j is the stopping index, and k is the step ().

Negative i and j are interpreted as n + i and n + j where n is the number of elements in the corresponding dimension. Negative k makes stepping go towards smaller indices.

"You are creating content of any form that is accessible to other people. It also includes making content public for particular communities. "

--ANAIS URLICHS (Youtube: Kunal kushwaha)

Learning in public is the practice of openly sharing your learning journey, progress, and insights with others. It is a relatively new concept that has become increasingly popular in recent years, thanks to the rise of social media and online platforms that make it easy to share information and connect with like-minded people.

Learning in public is a powerful way to accelerate learning, build a brand, and create valuable connections with others. In this blog post, I will explore the benefits of learning in public:

Benefits of Learning in Public

1. When we learn in public, we are forced to synthesize our learning and articulate it in a way that others can understand. This process of synthesis and articulation helps us to deepen our understanding of the subject matter and accelerate our learning.

2. Learning in public creates a sense of accountability, as we are now responsible for sharing our progress and insights with others. This accountability can help you stay motivated and on track, as we are now publicly committed to our learning journey.

3. When we learn in public, we open ourselves up to feedback and support from others. This feedback can help us to identify areas for improvement and provide valuable insights that we may not have otherwise considered.

4. Learning in public is a great way to build our brand and establish ourselves as a thought leader in an industry. By sharing our insights and knowledge with others, we can position ourselves as an expert and build a following of like-minded individuals.

Getting Started with Learning in Public

1. There are many platforms we can use to learn in public, including social media, blogging, podcasting, and video. We should choose the platform that we are most comfortable with and that best suits our learning style.

2. We should set clear goals for our learning journey and share them with our audience. This will help us stay accountable and give our audience a clear understanding of what they can expect from our content.

3. Share the learning process with our audience, including successes, failures, and lessons learned. This will help our audience connect with us on a deeper level and provide valuable insights that they can apply to their learning journeys.

4. Engage with your audience by responding to comments, asking for feedback, and creating opportunities for discussion. This will help you build a community of like-minded individuals who are interested in your content and willing to support your learning journey.

What is Jupyter?

Jupyter Notebook is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It's commonly used in data science, scientific computing, machine learning, and other technical fields to perform exploratory data analysis, write and test code, and present results.

I know many people may be confused due to its name why didn't the creator name it Jupiter instead of Jupyter? Why the 'y' was so important to them? Let's see;

Why the name is kept so?

As we already know that Jupyter what is Jupyter? Let's talk about why the name is kept so:

In beginning, Jupyter can be only used for three languages. They are; Julia, Python and R . All these three are programming languages that are widely in data science and scientific computing. By combining the names of these programming names they came up with the idea name which goes:

JUlia + PYThon + R = JU.PYT.e.R = Jupyter

So this was the story behind the name of Jupyter Notebook. Now let's look at some other programming, libraries Why their name are kept so?

We all know that In Mathematics there are 0,1,2,3,4,5,6,7,8,9 and numbers keep repeating in certain sequences and order like 100, 1000, 98,87 and so on. But have you ever wondered why computers only understand 0s and 1s out of so many numbers? more specifically computer uses a binary system so the main question is Why does a binary system uses 0 and 1?

Let me take you to the basics and most fundamental parts of computers. The most fundamental thing needed for computers to run is electricity. Whenever the electricity flows through the wire, the behaviour shown by the electricity is ON or else OFF. In simple words, the flow of electricity is ON else it is OFF. So, we have only two activities shown by electricity. These two activities by convention are denoted as 0(zero) and 1(one) for OFF and ON respectively. It is a simple and efficient way to represent information using the two basic states of an electronic circuit, which is the foundation of all digital technology today.

In an electronic circuit, the flow of electricity can be either on or off, which can be represented by a 1 or a 0, respectively, right. By using only two states, a binary system can represent any type of information using a combination of 0s and 1s, also known as "bits". This makes it easy for computers to store and process large amounts of information in a relatively small space, and it can be done very quickly and efficiently. By the way, Bit is a different topic let's leave it for another day.

Coming back to the topic, the binary system is easy to understand and implement, which makes it a popular choice for digital systems. It also allows for easy error detection and correction, as well as easy data compression in comparison to other numbers(Visualize back in those days finding bug in black&white tv searching for 0 and 1 hehe).

To conclude the answer given above, the binary system uses 0 and 1 because it is a simple and efficient way to represent information using the two basic states of an electronic circuit and it allows for easy error detection, correction and data compression.

Now we are clear about why 0 and 1? but now we will be knowing How 0 and 1 work?

So, now we got the answer of why only 0 and 1. But still, we don’t know what to do with that 0 and 1. Let me tell you the inner processes that happen unnoticed but first, I need to tell you about ASCII (American Standard Code for Information Interchange). Don’t bother about what this complex name is.

Just remember that there is something called “ASCII”

ASCII

ASCII stands for American Standard Code for Information Interchange. It is a code that assigns a unique number (or code) to each character, such as letters, numbers, and symbols, that are used in text-based communications. This makes it easy for computers to store, process and transmit text data.

For example, the letter "A" is assigned code 65, the letter "B" is assigned code 66, and so on. This way, when a computer receives code 65, it knows to display the letter "A" on the screen. ASCII code allows the computer to understand and display text in a way that humans can read.

ASCII is a very old standard, it has been used for decades, and it contains only 128 characters, which includes capital letters, small letters, digits, punctuation marks and some control characters like new lines, tabs etc.

In simple words, ASCII is a way for computers to understand and display text, by assigning a unique number to each character.

ASCII (American Standard Code for Information Interchange) is used because it is a standard way to represent characters, such as letters, numbers, and symbols, in a digital format. It assigns a unique number (code) to each character, making it easy for computers to store and manipulate text data. ASCII is widely supported and is still commonly used today, especially in systems and applications that rely on plain text data.

twist

Let me tell you something, ASCII is an old-school chart, it is now completely replaced by Unicode**.** As you know, ASCII has only 256 characters whereas Unicode has more than a billion characters due to which nowadays smartphones have so many characters, emojis, GIFs, sound-emoji and unused characters in our smartphones. So this is the inner operations that I was saying before talking about ASCII

Unicode

Unicode is a universal character encoding standard that assigns unique numbers, called code points, to every character, symbol, and emoji used in all languages and scripts of the world. It was developed to address the limitations of older character encoding standards, such as ASCII, which could only represent a small number of characters.

Unlike ASCII, which can only represent 128 characters, Unicode can represent over a hundred thousand different characters and symbols. This includes not only the letters and numbers of various scripts (e.g. Latin, Greek, Chinese, Arabic, etc.), but also mathematical symbols, emoji, and even historical scripts.

Unicode is designed to be compatible with ASCII, so many of the characters in the ASCII character are set to have the same code point in Unicode. However, Unicode also includes many additional characters and symbols that are not found in ASCII.

Unicode is widely used in computer systems and applications, including operating systems, web browsers, and programming languages, to ensure that text is displayed correctly and consistently across different languages and platforms.

In simple terms, Unicode is a way for computers to understand and display text in any language or script, by assigning a unique number to each character

Remember our question is still unanswered :)

If I want to write HI message to my crush. Let's see the Unicode table above:

Using a Unicode code table is a little harder than ASCII

You can see:

• H lies at 72

• I lie at 73

  So, now you can visualize that whenever I send my crush a message of HI it will send decimals i.e. 72 and 73 numbers by converting them into 0s and 1s which are 01001000 01001001

Closing notes :)

We've just scratched the surface of what ASCII and Unicode are and how they work. In this blog post, we've covered the basics of these character encoding standards and why they're important for digital communications. But there's still so much more to learn about these topics, such as how a programming language works and how software is made using that language.

I'll be diving deeper into these topics in upcoming blog posts, so stay tuned! In the meantime, if you have any questions or comments about this post, please don't hesitate to reach out. Thanks for reading! :)