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Introduction to JavaScript

JavaScript is arguably the most widespread language around the world, aimed at making webpages less static and more engaging. It is one of the core parts of front end development as it enables the developers to enrich the websites through the addition of animations, forms and other complex interactions. For English learners with the motivation of coding, JavaScript provides an excellent platform to both practice a language and learn coding.

In this article we will look at what our JavaScript is, why it is useful in web development, what are the main concepts and how to do coding with this language.

What is Java Script?

Java script is a scripting language that is widely used to bring dynamism to the content available on the web. The scripting language was created in about the mid-1990s to improve the interactivity of the web sites. Nowadays, Java Script can be classified as one of the core components of any website since every single modern web page uses it in combination with HTML that defines the content layout and CSS that decorates the page.

JavaScript enhances user experiences by providing dynamism to a webpage. For example, a JavaScript-based webpage has a lot more interactivity compared to a static HTML-only page. Besides form handling, JavaScript can also be used to create animations on the page.

JavaScript is a client-server based programming language that speaks both a web browser and a server. In terms of web browser usage, when JavaScript is employed, the Java program operates fully from inside the browser, modifying the webpage depending on interactions through things such as filling out forms, or simply clicking buttons. Servers are also in the languages grasp as it serves content, maintains databases and even performs other operations.

How to Begin Using JavaScript

To begin using JavaScript, you'll first need a dev environment. The easily available web browser alongside a text along with a text editor eliminates the need for special software. Moreover, to program with Java, you don't need to customize any environment as it follows low customizability requirements.

1. Text Editor: As JavaScript is a code-based language, it requires a text editor. Good examples of these are Visual Studio Code, Sublime Text and Atom among others. These programs offer essential aspects such as code structure diagrams alongside syntax maps among others which are crucial in ensuring efficient development processes.

2. Web Browser: Also, you have to keep in mind that JavaScript is present in web browsers, so you will require one to test your code. Built-in features provided by web browsers such as Google Chrome, Mozilla Firefox, and Microsoft Edge enable users to view and tweak the code within the browser.

A text editor and a web browser are all that you require to be able to commence writing in JavaScript.

Basic Structure of JavaScript

Let us have a look at the primary fundamentals of JavaScript, where I would like to highlight that its primary language structure is, rather than complex, more user-friendly to people who are just starting out. Which can be stated as, the fenced structure of javascript's code consists of variables, functions and operators. We can examine them in details:

[img]1. Variables: A variable can be looked at as much as a box where information is kept in, a header is applied to it so that it can be remembered and retrieved easily when needed in the future. To put raised information into a variable in the set of javascript, one has to put the string 'let', 'const' or 'var' preceding it.

Example:

Variable Example

let name = "John";
const age = 25;

In this case, for instance, name is defined as that which remembers the phrase 'John' while age remembers the number twenty five. The key variance which lies between 'let' and 'const' is that the latter establishes a constant, meaning that once its value has been allocated it cannot be altered.

2. Functions: In JavaScript, a function is a mechanism that allows encapsulation of tasks aiming to execute one or multiple instructions. The strength of a function lies in the fact that it can be used many times.

Example:

Function Example

function greet() {
console.log ("Hello World!")
}
greet();

In this case, the function called greet() outputs on the screen 'hello world'. The function can be executed by calling the function with the same name greet(), or using greet.

3. Operators: In JavaScript, various tasks can be undertaken using operators such as arithmetic calculations, comparison and logical calculations, for example, an addition operator is represented by + and an equal to operator by ===.

Example:

Operator Example

let sum = 5 + 3; // Arithmetic
let isEqual = (5 === 3); // Comparison

In this case, sum will be equal to 9 while isEqual is going to be equal to false.

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12-23

Creating Your First Python Project

I can relate to the mix of emotions one gets while starting to learn a new programming language like python, and every journey right from the start is new and filled with unknown variables. Python seems like an easy language to understand, but it is important to know that simply knowing syntax and the grammar does not enable you to master the language. Building a project is arguably one of the best methods to gain a better grip over python. There will be concepts and ideas that you will realise you can put into practice and so, building a project will help reinforce your confidence.

Therefore in this article, with the help of this simple project let us understand how to construct your first python project step by step in a format where you will be able to easily apply your learnt concepts without diving into the theory all the time. By reading the entire article through to the end, it is highly expected that you will get a concise understanding on how your codes can be structured in an organized manner, how your application can be adequately tested and basic features of your application implemented. The goal would be to make something that works and is helpful even if that is just a small project.

Picking the Idea for a Project

The first step in creating laptop applications using python is picking the suitable idea for your project. For beginners it is recommended to work on a project which is easy but still requires learning of new concepts. While it is easy to settle for something big, I prefer starting off on something small. This will help create the much needed momentum.

A broad idea of what the first project should look like is:

1. It should be something that you would be passionate about.

2. It should include only the very basic concepts of python that you have grasped at that point.

3. Grade-wise it should not be something that a student will take a long time to be able to complete.

Some common suggestions of first projects in python include:

- A to-do list application is an application whereby users can create, remove or update their tasks.

- A simple calculator which can perform basic arithmetical calculations.

- A number guessing game in which a player tries to guess a number that the computer has generated randomly.

- An address book where users can keep name and phone number records.

Relaxing, remember, the aim is to brush up on coding and understanding, so your project doesn't have to be too complicated.

Preparing your Project Structure

As a first step, it's important to have a workspace for your photo project and to do that you don't have to write any code. Having a structured planning of your project will allow for a proper organization and management of your codes as they increase in number.

1. Get Python up and Running: Ensure that your machines have installed various programs best suited for that current system. The majority of devices available in the market already have Python, nonetheless it is advisable to look out for it. And if you are looking to download it, always check the official site for its most recent edition.

2. An IDE should be Determined: Writing Python code is possible in many text editors but if an integrated development environment is employed, it will be easier to compose, examine, and debug scripts. Good examples of such IDEs are PyCharm, VS Code, Sublime Text and many more as they include advanced capabilities like syntax highlighting, code snippets, debugging, etc.

3. Make a Folder for Your Project: Because all your files are going to be housed in this folder, it's crucial to maintain an organized structure especially if the project is expected to scale. Normally, a standard python project would have the following elements arranged at a hierarchy:

- A principal program file like `main.py`

- Other program files serving as modules that would act as different components of the program eg: `tasks.py` or `helpers.py`.

- A folder for saving data files (if applicable).

Remember to take the time to correctly decide how to structure your files at the beginning so that you do not have issues later with expanding your project.

Writing the Code

[img]Now, let's write the code as the environment is set. This part seems to be the best for a novice especially after completing their first Python project. Notably, depending on the nature of your chosen project, you will start with its primary aspects and then move in step wise manner to add more intricate details.

For instance, in case you are creating a to-do list app, you first may want to write an instruction for displaying the list of tasks and then gradually add more instructions for adding a new task, deleting a task and marking a task as done etc.

While writing your code, you can:

- Divide the work such that the project becomes a collection of smaller projects.

- Begin with basic parts and build upon them with time.

- Incorporate testing frequently so that each aspect is validated.

It is very common to make errors in this stage of the project so do not get too upset. Debugging and finding solutions are also a part of the process of learning.

Setting Up Tests For Your Application

After writing the basic code for your application, it is your responsibility to test it correctly. Testing helps eliminate bugs and assures that all parts of the application work properly. For smaller ones, you can do manual testing of your project by running a program and using the system to see if it produces the desired results.

For example, when you implement a number guessing game, you would surely want to check:

- Is a random number selection done by the game?

- Does it ask the user for input correctly?

- Is the feedback accurate (for example, not too high/not too low)?

For bigger applications you might want to consider using automatic testing. In Python there are libraries for unit testing, such as unittest and pytest, which allow you to define tests for the functionalities of your application to confirm that the application behaves correctly in various conditions.

Even if your assignment is small in size, it is better that you develop a habit of testing your code, as this is vital for a successful career in programming.

Phases of Modification and Enhancement of the Project

When you have a first operable version of the project do not end there. It is hardly possible that the first version of any project is the best one, there is always a potential for improvement. Testing your project may have exposed the following things that can be done:

- Refine the user interface (UI)

- Change the code to enhance execution

- Make the system more feature-rich.

For instance, if you constructed a basic calculator, you may incorporate more functionalities like multiplying or dividing, or you can implement a graphical user interface with Tkinter libraries.

Working on new functionalities is also core to most projects in addition to impressing others or companies with your ability to come up with sophisticated ideas since you also advance your knowledge of Python. Coming up with multiple drafts of your project helps you appreciate the finer points of programming and further enhance your skills.

Writing The Project

You may consider this as optional, especially for small projects and so out high on the importance notch if you wish for such. Scribbling precise comments and explanations suitably in your codes avails a better understanding of it and also helps others or your future self to use it.

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12-23

Advanced Python Features: Generators and Decorators

Python is a very powerful programming language that enables developing simple, comprehensible, and clean code. After advancing a little bit in Python, you will notice a number of advanced techniques that make the language even more powerful. So, every new concept you learn is somehow significant, but there is a notable subprocess – two such processes or concepts are called generators and decorators, which,although at the beginning seem a bit tough, ultimately help one to write more optimized and reusable code components.

One of the main purposes of decorators and generators is to extend the Python functions, with generators being a bit different in that they don't take function inputs instead, their purpose is to enable advocating the management of resources. In this article, we will consider building blocks of generators and decorators in Python, their usage, and why they are needed.

What are Generators?

Generators are considered as a subclass of iterable (for example list, tuple), but instead of having all the values pre allocated, they have them generated on the fly. With this simple mechanism, generators are specifically helpful for the cases when working with very large collections that need plenty of resources "under the hood" to operate smoothly. Dispensers also have one specific point standing out which is, they facilitate scrolling through an information dataset, reviewing one element to the subsequent one while avoiding temporarily duplicating the complete set of elements.

In Python, we can use the keyword 'yield' to create a generator. Unlike the regular function that executes and returns a value, a generator can be said to run to pause and continue at some later stage. This means whenever the function is called to run, it will run only till the next yield statement which enables the functions to be efficient for large data sets. The drawback with this approach is that the values are not too predictive.

How Generators Work

While defining a generator function, you use yield instead of return for the statements. Also, the yield statement gives back a value to the calling function but it also saves the current state of the function. This means that in any subsequent calls, the function execution can resume from where it was stopped.

Let us look at an example to illustrate defining a simple generator function that yields the squares of numbers.

Generator Example

def square_numbers(nums):
    for num in nums:
        yield num * num

In this case, the function creates a list of numbers, yields their square to the generator, and then every time the generator is invoked, it returns the next number square until all squares have been returned. Each time the function was called it would generate the next square so the function was never run to completion.

You can go ahead and use a generator in exactly the same way as any other iterable:

Using Generator

nums = [1, 2, 3, 4, 5]
squares = square_numbers(nums)
for square in squares:
    print(square)

Now every time the 'square_numbers()' method is called, it will yield a single square, until all squares have been yielded, the loop will run. This way, there is no need to keep an entire list of squares in memory in other words, it is memory efficient.

Why Use Generators?

The generator's strength mainly stems from its ability to work on a large dataset or computation easily. Since values within them are computed and emitted at runtime, less memory is required in comparison to that of a list or some other data structure. Also, with generators it's possible to implement use cases that generate an infinite sequence, something that is otherwise impossible to do since it would require too much memory to store.

Some of the characteristics of the generators are as follows:

- Memory Efficiency: Only the current value is kept in memory and not the whole collection.

- Lazy Evaluation: The next value is computed only when required, which is excellent for long-running tasks or tasks that require lots of resources.

- Infinite Sequences: Ranges simply could not be represented due to how vast they are but with generators reading in files, or numbers can continuously be generated.

What are Decorators?

Another sophisticated aspect of python is Decorators which enables you to change or extend the function or method behaviors without modifying its implementation directly. To put it in other words, decorators can be defined as functions that receive other functions as arguments and output a new one that will usually be an extension of the behavior of the first one.

In Python, decorators are frequent not only for the purpose of logging but also for access control, memoization, validation, and so on. This avoids the need for code duplication and makes it possible and easy to enhance the previous functionality with new features.

How Decorators Work

A decorator is a function of the first order, that is, one that takes as an input argument a function and yields as output a modified version of the function. The output function in most cases would broaden the scope of the first function being decorated. The decorators are invoked through the use of @ symbol which is placed before the required function definition.

[img]Here's a simple example of a decorator that prints a message before and after a function call:

Decorator Example

def decorator_function(func):
    def wrapper():
        print("Before the function is called.")
        func()
        print("After the function is called.")
    return wrapper

@decorator_function
def say_hello():
    print("Hello!")

say_hello()

In this example:

- decorator_function which is a function that decorates the say_hello function.

- The wrapper function is the one which performs additional operations before and after the original function say_hello is executed.

- The @decorator_function is a shorthand notation to mean say_hello = decorator_function(say_hello).

Now, every time the say_hello function is called, the decorator adds extra print statements before and after the 'say hello' message.

Multiple Decorators

In the world of Python programming, it is possible to use more than one decorator in a single function. In the situation where a function has more than one decoration, the function will be processed in an order beginning with the uppermost and ending with the lowermost, where the latter between the two is the one attached inside the other.

A demonstration of how you can use parse multiple decorators in a code:

Multiple Decorators Example

def decorator_function_a(func):
    def wrapped_function():
        print("Decorator A")
        func()
    return wrapped_function

def decorator_function_b(func):
    def wrapped_function():
        print("Decorator B")
        func()
    return wrapped_function

@decorator_function_a
@decorator_function_b
def hello():
    print("hi there")

hello()

In this case:

1. The first decoration applied to the function say_hello is the say_hello function.

2. The second decoration applied to decorator_two is decorator_one.

3. The result after invoking the function say_hello can be seen below:

Decorator A
Decorator B
hi there

What is the point of using Decorators?

The use of decorators has various benefits, especially with regard to the level of abstraction of the code and the readability of code. They help to add or even change properties straightforwardly and systematically. Examples of the benefits are:

- Separation of Concerns: Uses of decorators allow for the separation of the functional aspects for example one may create a decorator which is dedicated to logging, another that handles permissions and many more.

- Code Reusability: In that case, one would only need to create a few decorators and be able to apply them to several functions, thus getting rid of code repetition.

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12-23

Automating Tasks with Python Scripts

Today's world is motivated by technological advancement, making automation techniques the best option to increase efficiency while minimizing the amount of human labor involved in carrying out redundant tasks. Python scripts offer one of the simplest yet most effective methods of automating procedures. Being a multipurpose immersed language with numerous libraries, Python is, in particular, tackling automation projects. Using Python scripts, one can automate anything from routine management tasks to sophisticated data analytics.

This post is about automating tasks using simple scripts. I will cover what automation is, how we can do it with Python and what are the tasks that can be done using Python.

What is meant by Task Automation?

Task automation is a technology that is used to execute tasks without human intervention. It eliminates the need to manually perform recurring tasks and implements an automated process for completing the steps using software. In other words, with programming languages such as Python, task automation means the creation of scripts that instruct a computer to perform a certain task or an entire series of tasks more effectively and faster than it could be done by hand.

Numerous work activities can be automated. These include:

- Data Processing: The automation of extracting data and working with data as well as analyzing data.

- File Management: The action of moving, renaming, or otherwise altering files and their organization is done automatically.

- Web Scraping: The use of the computer to gather data from websites.

- Email Sending: Sending email reminders, notifications, or any marketing related emails automatically.

Python as a Means for Automating Tasks

Automating tasks is made easier with Python for many reasons. To begin with, it is a relatively straightforward language to master, even for a novice. The language has a quite consistent and easy to comprehend syntax, hence scripts can be quickly written and understood. There are numerous libraries created for the language, many of which are focused on executing tasks which require automation.

For example, there are some libraries like os, shutil, smtplib, and schedule that are meant to send emails, schedule tasks, interact with the Operating System, and handle files. Combining these libraries with the ones natively available in Python gives rise to very effective automation scripts.

Composing a Simple Python Script

Starting with approaching task execution automation, Python users have to start from grasping the core nature of a Python file, also known as a script. Scripts are files which contain a sequence of Python commands. Here is how a Python file is structured:

1. Import Libraries: The very start of Python scripts is denoted by importing required libraries or modules, e.g., when intending to perform file interaction, you can call for this library 'os'.

2. Define Functions: Functions help in packaging the set of instructions into a recomposing building block. Doing this while automating certain activities gives you the ability to perform that operation at any time.

3. Main Script Logic: This part implements the specific automation logic. In most cases, it consists of input, calculations and file read/write actions.

4. Running the Script: As soon as the script is complete it may be executed from the command prompt or the development environment. After this, the actions defined are performed without the need of any manual instructions.

Automating File Handling and File Management

Even files are handled by Python scripts. Most people have to do several file-related jobs such as renaming files, moving them into various folders and deleting some files, over and over again. Python may be useful in streamlining such tasks.

For example, file renaming, moving, copying and deletion can be done with the help of Libraries of python the os and shutil. Here is an example:

File Handling Example

import os
import shutil

# Renaming a file
os.rename('old_file.txt', 'new_file.txt')

# Moving a file to another directory
shutil.move('new_file.txt', '/path/to/destination/')

In this example:

- os.rename() is used to rename a file, for instance change its name from 'whatever' to 'whatsoever'.

- shutil.move() moves the file to a new website address that has been provided.

When Python is given instruction to do all these simple tasks, so many hours of time can be saved that could have been utilized in elaborate file handling techniques which include sorting, deleting and moving files from one directory to another: particularly if it's a bulk of files.

Automating Email Sending

Another common task that can be simplified and accomplished using Python includes sending emails. There are so many situations where email sending is a need. For example news letters, notification emails, and even reminder emails that are usually needed by businesses and people. This can be done using the 'smtplib' library of python.

This script:

- Sends an email via SMTP server, using smtplib for the connection.

- Composes an email consisting of subject and message through email.mime.text.MIMEText.

[img]Let's take a look at the code first for an example of how to send an email using Python:

Email Sending Example

import smtplib
from email.mime.text import MIMEText

# First, prepare your email
subject = "Meeting tomorrow"
body = "This is an email used to notify the recipient about a meeting scheduled for 10:00 AM tomorrow."
msg = MimeText(body)
msg['Subject'] = subject
msg['From'] = 'your input email'
msg['To'] = 'person to send an email'

# Then, configure your SMTP server settings and send the letter.
server = smtplib.SMTP('smtp.example.com', 587)
server.starttls()
server.login('your email', your_password)
server.sendmail('your email', 'person to send an email', messagedata)
server.quit()

# Send the email to the recipient.

This is a good practice for doing such common tasks as sending an email and allows you to automate tasks such as sending them on a daily basis, thus eliminating the effort it takes to send them out manually.

Automating Web Scraping

Putting it simply, web scraping is searching for specific data contained in different websites, this is another application where Python shines. By using some modules, such as BeautifulSoup and requests, you could scrape data from any site and save it in a well-defined structure such as CSV or JSON.

You can create a Python script that would extract product prices from an e-shop and compare them over the years. First of all such development would depend on the structure of the targeted websites, but in general steps, one would do the following:

- Use HTTP requests to navigate to the desired page.

- Use parsers to scrape off the pertinent information needed.

- Organize information through writing into a file or any relevant database.

Due to its effective nature, web scraping can be utilized for various purposes ranging from data mining to marketing and even competitor research.

Executing Tasks Automatically Using Scheduling

In some cases there can be tasks that need to be performed automatically like, for example, scheduled backups, database updates, or generating reports. Python code for this purpose would be run every so often using the schedule library.

For example, you may have wanted to set up a script that would check for new emails every hour or would create file backups everyday. This can be applied through the use of the schedule library as shown in the following basic example:

Scheduling Example

import schedule
import time

def task():
    print("Running task...")

# Schedule the task to run every 5 seconds
schedule.every(5).seconds.do(task)

while True:
    schedule.run_pending()
    time.sleep(1)

This means that the task() function will be invoked 5-second intervals. The schedule.run_pending() function simply checks if a task is pending and its time has come, then it runs that task.

This inbuilt includes argument scheduling, allows to set future calls of specified python scripts for certain time or days thus when the time comes there would be no need of manually running the script because it is already coded to run automatically.

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12-23

Python for Data Visualization: Getting Started with Matplot

Data is often represented in different fields in a variety of forms, hence knowing how to represent their data is one of the key skills for any individual who has to deal with data. They will be able to identify patterns, identify trends and also identify relationships within the data. Matplotlib: is one of the most common libraries used in Python when it comes to visualising data. So, Matplotlib is robust, adaptable, and user-friendly, which makes it an ideal package for novices to the field of data science and analytics.
Here we will take a more detailed look into areas that you can work on using Matplotlib specifically in data visualization when it comes to the basic concepts and the plots that can be constructed.

What's Matplotlib?

Matplotlib is an open-source plotting library which is built on top of the Python programming language. It was purposefully created with the idea of developing comprehensible static, animated and interactive visualizations in the language of Python. It is capable of producing a wide variety of graphs such as line plots, bar graphs, histograms and scatter graphs which makes it one of the most important tools among data analysts and data scientists.
The reference object for Matplotlib is the multiple plot Try warning with plenty of examples. The main task of the try is to allow a person to create rich selling and integrative plots that represent a complex set of data in a simple and easy to understand way. The library is built on top of NumPy libraries, so it is also easy to use for numerical and scientific computations.

Installing and Configuring Matplotlib

First and foremost, let's begin by installing Matplotlib. If you utilize the Anaconda Python environment, this software package may already be on your system. In other circumstances, you may obtain it via the pip command line. The installation command goes as:

Installation

pip install matplotlib

When Matplotlib has finished downloading, you may call it in your python code with this statement:

Importing Matplotlib

import matplotlib.pyplot as plt

Considering Name-related issues, the abbreviation of the term needed has become popularized so that the term 'plt' is used so often. In this manner, all the functions and classes associated with Matplotlib that you require for your plots and visualizations are easily accessible.

Drawing Your First Graph

In the beginning, let's make a line plot which is the type of graph that depicts the changing of a variable against time or any given data. To be able to create a line plot, you will need x,y coordinates as the input, in which the letter x represents the independent variable while y represents the dependent variable.
Take a look at this simple figure:

Simple Line Plot

import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y = [1, 4, 9, 16, 25]
plt.plot(x, y)
plt.show()

From this code we can see that:
- The Plot is created by the command plt.plot(x,y) Where x is along the horizontal axis and y vertical axis.
- The command plt.show() displays the plot created.
This code will create a simple graph in the form of a line which will show the increasing trend of y in relation to x.

Customizing Your Plot

You will find that Matplotlib has almost no limits in terms of customizing the looks of your plots. You can change the aspect of the graph in terms of colors and styles and you would still be allowed to put in a title and the x and y labels along with the legend. Some modifications would include the following:

Customized Line Plot

import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y = [1, 4, 9, 16, 25]
plt.plot(x, y,color='green', linestyle='--', marker='o', markersize=8)
plt.title('Sample Line Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.grid(True)
plt.show()

In comparison:
- The line color was changed to green with color='green'
- We used dashed lines with linestyle='--'
- Each datapoint on the graph will have a round marker with marker='o'
- Each symbol on the new graph is scaled up with markersize=8
- To insert the title into the visualization use the command plt.title('Sample Line Plot').
- The axis labels are also included as one puts x in plt.xlabel('X Axis') and y in plt.ylabel('Y Axis').
- Additionally, to improve the plot, you can use the command plt.grid(True) to add a grid layout.
These customizations enable you to enhance the clarity and aesthetics of the plot and the information displayed in the graph.

Types of Plots in Matplotlib

Matplotlib provides a wide range of plot types intended for data visualization from different perspectives. Most widely used ones are:
- Line Plot: Most suitable to use when showing any trends over time or when visualizing a time series.
- Bar Chart: Also used mostly for the comparison between items in different categories.
- Histogram: This type of graph shows the number of data points that fall within a range of values, called bins.
- Scatter Plot: A graph that plots two sets of data points against each other to present the relations between them.
- Pie Chart: Helps to visualize proportions between parts of data in a circular format.
Similar suites of customizations would also be applicable for other plots such as pie graphs or scatter plots, thus allowing you to adjust it to other characteristics of your data and audience.
[img]

Subplots

If the intention is to include several plots within a single figure, then the use of subplots may be warranted. It can be noted that designing subplots involves embedding various plots in a grid format in a single figure. And to do that in Matplotlib, one has to make use of the plt.subplot() method.
For Example:

Subplot Example

import matplotlib.pyplot as plt

# Create the first plot
plt.subplot(1, 2, 1)   (rows, columns, index)

plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.title('First Plot')

# Create the second plot
plt.subplot(1, 2, 2)

plt.plot([1, 2, 3, 4], [1, 2, 3, 4])
plt.title('Second Plot')

plt.tight_layout()  

plt.show()

In this case:
- The proposed grid consists of 1 row and 2 columns with the first plot located at the first position of the grid when plt.subplot(1, 2, 1) is used.
- The second plot is positioned at the second slot when the command plt.subplot(1, 2, 2) is given.
- The option plt.tight_layout() places the two subplots in their respective positions and automatically adjusts the spacing between them so that they do not overlap with each other.

Saving Your Plots

Given that the desired figure has been created and styled, it is possible to save it as an image file. To facilitate this, the method called savefig() is provided in matplotlib. It can be indicated what the filename will be and accordingly its extension such as PNG, JPEG or on the other hand PDF.
An approach for making use of this feature is as follows or try it yourself using the example below:

Saving Plot

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

plt.plot(x, y)
plt.savefig('line_plot.png')   Save the plot as PNG image
plt.show()

Subsequently, the diagram will automatically be available in your working folder as line_plot.png. Other options that may be specified include resolution (DPI) as well as the option of saving the image with a transparent backdrop.

Conclusion

In conclusion, Matplotlib is an important part of Python for visualization of data since in a short format you can write the code for a range of outlines and charts. Therefore, by working with the basic concepts of Matplotlib you are able to bear the greatest understanding of the data hence aiding in the analysis. And as you keep using Python for the purpose of data visualization, you will notice that with Matplotlib you will be able to afford to showcase your data in aesthetically pleasing and meaningful ways.

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养花风水

12-23

Introduction to Machine Learning with Python

It sounds daunting, however it is quite the opposite one tends to think. The combination of python and machine learning seems to go hand in hand, doesn't it? Mechatronics on the other hand can be viewed from a different lens, that of pure programming which specializes in practical solutions unlike the rest of programming languages that aims to code without any limitations. Python's overwhelming number of advantages forms the foundation of machine learning as we start off our foray into the sea of algorithms and structures, frameworks, everything towards evolving your web applications.

For those of you wondering what python has to do with this machine learning with web applications, let me clear the air by explaining the concepts. The ML revolution has pushed us into a vortex of Modernity where everything involves data transformation and interpreting data. Everything we do nowadays has been integrated into an application or a web platform whether that's sorting out your pictures into folders or searching for information. Simply put, ML is the backbone of data functioning which makes sense to all of us given how important it is to manage data.

The simplest of definitions one can provide for ML is that it is a form of AI that allows computers to work autonomously without any programmed instructions which opens a whole world of possibilities that makes programming a different ballpark entirely. All one needs is an initial set of instructions that helps them get started or the ML web applications to be more precise. Mechatronics has evolved into a supercomputer that constantly trains itself and learns from the patterns provided to it. The core structure of mechatronics sets it apart from traditional programming which has a core structure of writing programmed instructions that set a defined target to achieve.

One of the best features with ML is that there is no limit to the number of categories to divide applications into, to mention a few, supervised learning, unsupervised learning and then there are a few different types of learning like reinforcement learning where the machine learns from mistakes.

- Supervised learning refers to cases when a model is trained with the help of pre-labeled data. In that its input data is accompanied by the desired output. This makes the self-learning classifier useful in tasks like classification and regression.

- Unsupervised learning means using pieces of data that have no label. The idea here is to analyze and seek for patterns or groupings that exist in the data e.g. clustering and dimensionality reduction.

- Reinforcement learning teaches models how to make a chain of decisions by rewarding them for correct ones and penalizing for the bad ones. This type of learning is regularly seen in robotics and games.

Machine Learning and Python: A Tasty Match

There are many reasons as to why Python is loved so much within the Machine Learning community. It's an intuitive language to learn and utilize which is essential for the beginners in the field of data science. Python makes it possible for a developer to focus more on problem solving since its syntax is uncomplicated and straight to the point.

Thanks to its intuitive design, Python is also equipped with a lot of powerful libraries and frameworks, which make it suitable for work in Machine Learning. For example, libraries like NumPy, Pandas, Matplotlib, and Scikit-learn are widely used for data manipulation and visualization as well as building Modeling machine learning models. Python also fits in with other technologies, which allows its use in a diverse range of projects.

Python for Machine Learning – Libraries

1. NumPy – as the name suggests, numpy python is fundamental to scientific computing with Python. The package contains support for various big data processing which comprises matrix arrays and many functions.

2. Pandas – This is a library which provides dataset for high level data structures and its corresponding methods for processing such types of data. Some of the common data preparation tools for machine learning include data cleaning, transformation and data analysis, which this library helps to accomplish.

[img]3. Matplotlib – This is a plotting library for the python programming language and its extension and enables the creation of static, animated, and interactive visualizations in Python. As with most things in life, a picture speaks a thousand words so visualizations are often important for data and model performance understanding.

4. Scikit-learn – Scikit-learn is among the very popular libraries for implementing machine learning models. It has many easy to use algorithms for supervised and unsupervised learning so it is quite beginner friendly. It is equipped with ready components for model training, evaluation and deployment.

5. Tensorflow and Keras - On the deep learning end of machine learning, Tensorflow and Keras provide solid frameworks for developing complex models such as neural networks.

Getting Started with Machine Learning in Python

The first step is to set up the environment to start working with machine learning in Python. In this case, you will install Python and several other crucial libraries. Using tools such as Anaconda is a good idea because they are pre-loaded with several data science libraries, and can save you time.

After installing Python, the next step would be to acquire and prepare data. This data is vital to create machine learning models as it trains them, so it is essential that it is accurate and well structured. Pandas can be useful in editing this data by removing inaccurate data or manipulating it.

Consolidating your data allows you to move to build a model in the next step. In supervised learning, this means choosing an appropriate algorithm, more specifically, linear regression for predicting numerical values or logistic regression for binary valued outputs. Implementing these algorithms is pretty simple using Scikit-learn.

The model is thus trained with the given data set and performance is assessed. There are several measures that can be used to evaluate the accuracy of the model depending on the problem. These include precision, recall and F1 score for classification problems, or Mean Squared Error (MSE) for regression problems.

Key Concepts in Machine Learning

1. Data Preprocessing: Data often comes in an untidy and partial form. So in order to make a machine learning model, the first thing that needs to be done is data preprocessing. It consists of the processes of cleaning the data, filling in the missing data, and just making the data to be in the correct format for model building.

2. Feature Selection: In any dataset, not all features (columns or attributes) are relevant for the task at hand. Feature selection is the task of identifying and selecting a subset of the most useful variables (features, predictors) to be used in model construction.

3. Training and Testing: In order to create a good model, the data is usually split into separate sets; one for training purposes and one for testing. The model obtains the training set and training is performed, after which the model is tested with the test set to ensure that it does not overfit.

4. Over and Under fitting: It occurs when the model remembers too much information from the training data, especially through traps, and is then unable to make predictions in real world applications, this phenomenon is labelled as overfitting. In contrast, if a model is too primitive and cannot fathom enough data trends, it is said to be underfitting.

5. Performance of a model: When the model is ready after training, it has to be validated or tested on an out of sample dataset (the testing set). Following this evaluation, it will be determined how effective the model operates or if it is compatible for use.

6. Hyperparameters tuning: Most machine learning models will have hyperparameters, which are values used with tuning. Examples of such can be the learning rate or how deeply a decision tree goes.

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12-23

Today, the most vital task is considered to be web development, and Python, with all its advantages, is a great language for constructing sites. Out of the many frameworks available for Python, Flask is quite famous due to its simplicity and flexibility. The framework is quite simple and, with little effort from the developer, allows one to create great site applications. This article will provide further detail on the main concepts related to web applications with the Flask framework including the creation of a simple application in Flask, the use of templates, and routing.

The Flask Application

Flask is a web framework for the Python programming language, which has the main goal to be user-friendly and easy to extend. In a distinct variety of other frameworks, Flask does not consist of many tools or libraries integrated within the framework. Instead, it contains many basic features which enable users to develop web apps and then users can build on top of them according to their promotion goals. Thus, Flask is great for small scale applications as well as for different developers that wish to make use of a lot of components for their applications. Flask supports the WSGI (Web Server Gateway Interface) specification, which allows Python-based web applications to interact with web servers. It also supports the Jinja2 template engine which is useful in rendering dynamic HTML content.

Getting Started with Flask

You can use Flask by first installing it. You can install Flask in the simplest way through Python's package manager `pip`. In case you don't have Flask on your machine you can install it by running one of the commands below.


pip install flask

After the Flask framework is installed, you can commence web application development. Create a Python file such as app.py where you will write your code. So the first thing you'd do is import the Flask class, and then create an instance of the class:


from flask import Flask

app = Flask(__name__)

The core of the application is the `Flask` class, and the parameter `__name__` allows Flask to locate the application. Once the application instance is created, you are ready to implement routes and views.

Routing in Flask

Routing is arguably one of the most crucial aspects of web development as it is the process of linking URLs with specific functions of your application. Flask makes creating routes very simple, the routes can be created using the routing mechanism which utilizes the `@app.route` decorator to link the function which is to be called when a user visits the link with the link itself. For how a route can be set in Flask, consider the following:


@app.route('/')
def home():
    return "Hello, World!"

In the example above, the `@app.route('/')` decorator maps the home url '(') which is also the base url of the application to the function home. Therefore, whenever a user goes to the base address of your application, the home function will be invoked and it shall output on the browser `Hello, World!`. Routes can also be made additionally by writing a new function which has a new url:


@app.route('/about')
def about():
    return "This is the About page."

Running the Application

With your routes set, you will now need to run the application using the run method on the app object to start the Flask application. Calling that method will run a dev web server on your PC and it will allow you to test the web app on your web browser.


if __name__ == '__main__':
    app.run(debug=True)

Because the `debug=True` flag is on, I can gain insights into what went wrong while also being able to make changes and automatically reload the application. The code specified will execute on port 5000, to check whether the application has worked I can visit `http://127.0.0.1:5000/` which is the output location of the routes seeded earlier.

Flask allows the use of Templates

Thus serving web pages that vary in content ('of different data or variables from users' input)' is the norm in a 'web development' context. Rather more useful is the feature that allows for the generation of multiple webpages that are instead solely based on a few templates. Jinja2 is the templating engine for flask, meaning it provides the capability to write python code within an HTML file. Of note, an important aspect in regard to templates would be the prerequisite of creating a `templates` folder that will contain files pertaining to the above context. As an instance make an `index.html` file under the templates folder as follows:


<!DOCTYPE html>
<html>
<head>
    <title>My Flask App</title>
</head>
<body>
    <h1>Welcome to my Flask app, {{ name }}!</h1>
</body>
</html>

Bartering string with '{{ name }}' is a feature in the HTML file above that can be replaced by the python application with a value that has been programmed in. Next, update your code by modifying the function as follows:


from flask import render_template

@app.route('/')
def home():
    return render_template('index.html', name="John Doe")

[img]Keep in mind that `name` can be any name that is suitable in your context as `render_template` replaces it with the actual name provided in the template. Therefore, upon calling the above function flask will load an index.html file passing the value name as "John Doe" to this particular template.

Processing Forms and User Commends

In addition, when a request is made and it is accompanied by form data, the flasks request object will have the form data. The following example will show you how to incorporate that feature. For example, you might have a form that requests a user's name, in which case you could implement it with an HTML form as follows:


<form action="/submit" method="POST">
    <label for="name">Name:</label>
    <input type="text" id="name" name="name">
    <input type="submit" value="Submit">
</form>

The action which is a tag attribute `/submit` defines the endpoint to which the form data will be directed on submitting the form and how it will be sent across, in this case it will be directed to the submit route and the data will be sent as a POST request. In your code written in Python, at the time of form submission, you can perform the following actions:


from flask import request
@app.route('/submit', methods = ['POST'])
def submit():
    name = request.form['name']
    return f"Hello, {name}!"

So here with the help of `request.form['name']` the data that is filled out in that form is called. This means that the user's provided name is included in the response.

Conclusion

Using Flask makes it easy and comfortable for you to create web applications using Python. It provides developers with efficient means for defining routes, rendering templates and obtaining input from the users. The simple structure of Flask makes it suitable for different types of applications but especially small and medium projects where developers need the flexibility to implement only the required components. Hence, you are armed with the basic knowledge of Flask and would now very easily be able to create applications using Python and the web.

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12-23

Whenever one talks about data harvesting, the term web scraping pops up as that has become popular in recent years. With the great resources available online, web scraping has become the go to tool for most people. Owing to its ease and considerable amount of libraries, Python has also grown to be one of the most preferred languages for scraping websites. In this context, one of the most efficient and widely employed libraries is BeautifulSoup. This article is intended to grasp the concept of web scraping focusing on the Python language and the BeautifulSoup library with the aim of extracting and processing data from web pages.

How Do You Define Web Scraping

To put it simply, web scraping is the extraction of data from websites. Content on many sites is presented in HTML, which is a format created for people to read. But HTML can also be created for machines and algorithms to read, which facilitates web scraping. In simple words, web scraping is the process of compiling the data found on various pages and saving in the desired format for analysis, research or any other use in the future. Even if web scraping appears to be remarkably beneficial, it should be used responsibly. Certain websites explicitly refuse scraping in their Terms of Use, so always be sure to comply with the posted rules of the site you are scraping data from.

Introduction to BeautifulSoup

BeautifulSoup is a library available in Python that does the parsing and navigation of HTML documents in a faster and easier way. It provides easy and usable methods for moving around in the webpage's HTML tree, making it easier to extract certain pieces of information. You may also use BeautifulSoup to manipulate scripts of Java that dynamically creates HTML content. One of the reasons a lot of people are able to use Broudie Soup is its ease of use and user interface. A handful of command text is sufficient for you to start scraping. It also resolves most common problems such as broken or non-standard HTML pages. There are many HTML parsers, and BeautifulSoup is said to be functional with these, but most of the time, it is fine with the inbuilt one.

Setting Up The Necessary Libraries

As noted earlier, before starting to deal with web scraping through BeautifulSoup, one has to get the appropriate libraries. The two big libraries you will need first are `requests` and `beautifulsoup4`. `requests` library makes it possible for you to obtain the contents of the web and `beautifulsoup4` serves the purpose of parsing and traversing through the HTML. To set up these libraries, you can use the command below in your terminal or command prompt:


pip install requests beautifulsoup4

As soon as you install that, it's time to start coding in python to crawl through sites.

Getting Contents of The Web

The first step of any web scraping activity is getting the page of the site you wish to scrape. And to do this, you employ the use of the `requests` library. `requests.get()` issues an HTTP GET request to the designated universal resource locator (url) and brings back the page's html. Use the code below to see how to make requests and get a webpage:


import requests

url = "https://example.com"

response = requests.get(url)

html_content = response.text

In this example, `response.text' has the HTML of the page. Now that you have this HTML, you can pass it to Beautifulsoup which will begin parsing and extracting data.

BeautifulSoup HTML Parsing

When you HTML content of a page, it is not useful in such a state as it needs to be parsed in order to be worked with. To assist you in this regard, BeautifulSoup is provided. The `BeautifulSoup` class takes in the html content as its argument and then constructs a parse tree which helps you in searching and traversing the html. Wondering how to create a soup? Here's how:


from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, 'html.parser')

In this code, … tells BeautifulSoup which parser to employ. Other parsers can be obtained, yet the standard one suffices for the majority of tasks

How to Move in the HTML

An HTML file is made in the form of a tree having a set of elements arranged in a hierarchical form. BeautifulSoup also has some functions that allow you to navigate and search through this tree. One can search for specific tags, get content enclosed within those tags or get the attributes of those tags.

Searching for an Exact Tag

Whenever you require an extraction of a particular HTML element tag, you simply apply the `find()` function. For example, in order to get the content of the first header on the page, now using h1 we can use:


h1_tag = soup.find('h1')
print(h1_tag.text)

So here in `soup.find('h1')` the first item in the list generated by the tag '< h1 >' is searched and then by the use of '.text', the textual content within the tag is retrieved.

Bulk retrieving of tags

If you further want to enhance the search, like looking to search for all the anchor tags < a > on the page, you can simply apply the find_all method. This method simply returns the entire match result of the applied tag as an array.


a_tags = soup.find_all('a')
for a in a_tags:
     print(a.get('href'))

For instance, in this example, `soup.find_all('a')` clicks on all the anchor items in the list whilst a.get('href') then picks the href address of individual links in the list.

Extracting Information inside the Tags

[img]Many HTML tags come with additional information within them that might be helpful in accomplishing certain tasks. An example being: anchor tags which will come with an `href' tag, which would specify the link's website. And to achieve this, the `get()` method will do the trick For instance, if you wish to retrieve the src from an image tag code, which possesses the URL of an image, then it can be done in the following way.


img_tag = soup.find('img')
img_url = img_tag.get('src')
print(img_url)

This will yield the URL of the image, that of the src of the first image tag found on the page.

Traversing the HTML Document

Traversing the HTML Document is one of the defining characteristics of BeautifulSoup. As we all know every tag in the Html document is associated with a lot of attributes and methods that enable one to traverse through the tree structure. For example, Accessing the parent, child or sibling elements of a given tag. Suppose you want to know the parent element of a given tag, then you can execute the following code.


child_tag = soup.find('p')
parent_tag = child_tag.parent
print(parent_tag)

This will indeed print the first parent tag of the first p tag available on the webpage.

Looking up Content that is Dynamically Loaded

There are many websites in this world that are using JavaScript and therefore the site content has to be loaded up. Beautiful soup unfortunately is not able to help out here and render the content as it only parses the content of the static HTML documents. Yes, but you can use the Python libraries for example Selenium or Playwright to load Java scripts and fetch the HTML content that the script will be offering. Once you have the final HTML in your hand, you can forward it to BeautifulSoup for fresh parsing.

Conclusion

Let's face it, Web scraping is probably one of the most powerful ways to collect data from the web, not to mention that Python has an assistance called the BeautifulSoup library which gives functionality to easily navigate or parse through cluttered HTML content. BeautifulSoup in combination with the requests library empowers you to visit web pages, find them useful and edit content for other purposes. As you will delve into the topic of web-scraping, you will come across more sophisticated ways like filling out forms and sending them, sending, and receiving cookies, and sending requests using AJAX.

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12-23

In today's technologically advanced world, it has become a common necessity for many professionals to be able to extract and utilize information from different providers and platforms. For coders, especially those starting with Python, learning how to interface with APIs (Application Programming Interfaces) is a must. APIs are used to enable the interaction between computer software applications for the purpose of exchanging data and functionality. As a popular and multipurpose programming language, Python has great libraries and tools that make it more convenient to work with APIs. In this article, we will cover the A-Z of working with APIs in Python, including how to make calls to the API, receive responses to the API calls, and incorporate the features into your programming.

Understanding APIs

How about looking at APIs in a whole new light? or should I say taking a fresh start looking at something new. Before exploring what Python can do, let's take a look at how an API functions. APIs make it feasible for two different software systems to communicate. It's like when you're in a restaurant ordering food. The menu lists your options (the API), the waiter places your order (the request), and when it's ready, he/she delivers it to you (the response). So, if you look at it, requesting something using an API is no different. One sends the request and in return, the API provides the information in the format requested, mostly in JSON or XML. Modern day programming is filled with APIs from obtaining weather information, working on social networks, sending emails and even operating cloud services. APIs can also be used with the programming language Python to work with other data and develop custom applications.

Configuring Your Python Environment for API Use

When working with APIs in Python, the first step is to set up the required libraries. To make HTTP requests, the first thing to have is the `requests` library which makes it easier to send HTTP requests and manage responses. To do this, you must install the 'requests' library if it is not already installed on your system. You can do this through the following command:


pip install requests

When this is done, you can use the `requests` package in your Python program to send HTTP requests to any API server. An example of how to include the library and make a basic GET request is provided below:


import requests

response = requests.get("https://api-example.com/data")

Making API Calls

To make use of an API, you need to make an HTTP call to it, most of the requests made are of the following types: - GET: Used for getting information from a server. - POST: Generally used for sending files or information when filling forms to a server. - PUT: Used to make modifications on the already existing information of a server. - DELETE: A request specifically used for the removal of information from the server. Let's begin with the GET request, which is frequently utilized for getting data from APIs. A straightforward GET request is made by utilizing the `requests.get()` method as we have seen in the previous example. This makes a request to the server and gets back a response. As an example, querying an API that provides data about books may make use of the following code.


import requests
response = requests.get("https://api.example.com/books")

Here, the `response` object encompasses the information provided by the server including its status code, headers and the body part of the request. [img]

Receiving Responses

With the request already dispatched, there is the need to tackle the response. In Python, the 'requests' library boasts several techniques for manipulation of response data. For instance, the status code can be verified to establish if the request succeeded. A code of 200 is a clear indication that the request was properly executed. Consider this example,


import requests

response = requests.get("https://api.example.com/books")

if response.status_code == 200:
    print("Request was successful")
    print(response.json())   Python dictionary representation of the response 
else:
    print(f"Error: {response.status_code}")

Calling the `json()` method can convert the information contained in the API that was sent in the form of JSON into a dictionary in Python facilitating its manipulation.

Extracting API Data Alright, Now What?

When retrieving API data, chances are that you will have to perform a good deal of extraction and modification of data for the sake of the application. Most APIs, if not all, send JSON files which consist of key, and value pairs. In Python, this can be classified as a dictionary type structure which is quite common and simple to use. For the sake of this example, let us suppose the API issues a following request for book data in a JSON format.


{
  "books": [
    {"title": "The Great Gatsby", "author": "F. Scott Fitzgerald"},
    {"title": "1984", "author": "George Orwell"}
  ]
}

In this case, each of the books would be able to be retrieved as follows:


import requests

response = requests.get("https://api.example.com/books")

if response.status_code == 200:
    data = response.json()
    books = data["books"]
    for book in books:
        print(f"Title: {book['title']}, Author: {book['author']}")

Lets Discuss API Call Errors

When it comes to APIs, there are likely situations and times when errors and exceptions may arise, and it is advisable that one prepares for such circumstances. There are numerous reasons as to why one may encounter an error, for example the URL endpoint which is being hit is inactive, you are sending a corrupted request, or the endpoint, in this case a server, decides to send an error back. In the case of these events occurring, in Python you are able to catch them by simply wrapping the piece of code in "try" and "except" blocks. For instance:


import requests

try:
    response = requests.get("https://api.example.com/books")
    response.raise_for_status()   Handles errors, if there are any
    data = response.json()
except requests.exceptions.RequestException as e:
    print(f"Error making request: {e}")
except ValueError:
    print("Error processing JSON response")

With the help of exception class, you can avoid application crashes in case of an error.

Conclusion

There is a wide range of opportunities for developers with the use of APIs in Python. You may be retrieving information from a location on the web or other services, the ability to make requests, work through responses and return the data in a required format is a useful point. Using the `requests` library makes the work with APIs far easier and allows developers to create more sophisticated and interesting applications. With a basic understanding of the concepts and principles provided in this article, you can start using APIs in your own Python applications and develop your programming skills further.

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12-23

When it comes to working with data while programming, the Pandas library in python is one of the most popular and sought after libraries. It aims at making good structures that can be relied on to be flexible and fast for interacting with structured data. If you are a data analyst, data scientist or engineer for any company cleaning, transforming or analyzing data with the help of pandas becomes quite crucial. Pandas is an extension of another popular library that is, NumPy, however it has a more advanced approach when it comes to manipulating the data whether it is in the form of tables, time series or even CSV and Excel files. Two of the most significant data structures in Pandas are Series and DataFrame. Thanks to these structures you will be able to carry out numerous data manipulation tasks quite effortlessly.

The Pandas Library

In order to start using Pandas, first it is important to import the library. This is usually done using the following import command:

import pandas as pd

In most conventions , Pandas is imported using an alias 'pd' which simplifies the calling of the pandas functions in your program.

Pandas Data Structures

In Pandas, the two primary data structures are Series and DataFrame. It is important to note these structures as they are the basis for most of the data manipulation process in Pandas.

Series

A Series can be defined as a one-dimensional array. It can include any type of data including integers, strings, and other objects. A Series resembles a list in python, but in addition to the data, it also contains labels (called indices). This allows for fast and easy access to data or the ability to modify it.

import pandas as pd

data = [1, 2, 3, 4]

series = pd.Series(data)

print(series)

DataFrame

In broad terms a DataFrame is a two dimensional structure containing tabular data. It contains rows and columns, just like a table or excel spreadsheet. Columns can be of various data types. It is very advantageous especially when dealing with data that has more than one variable.

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age' : [24, 27, 22]}
df = pd.DataFrame(data)
print(df)

Methods to Import Data

In the corporate world there are a plethora of file formats and one of them includes CSV, Excel, JSON, and SQL databases. These file formats are in fact easy to import especially with the help of Pandas. This ability to read data from different prominent reasoning of why Pandas is very popular in data analysis Bring that fun to read data into different formats of file even more interesting In the below example as an illustration I am pulling in some data from a CSV file into a defined structure `DataFrame in a Pandas read times as written by Alok Sanwalpour To do this use: Remember to replace 'data' and subsequently 'data' file as it pertains to your case. Note that the type of data is contained in the tag following the quotation marks. So for instance zero data type container is not allowed.

df = pd.read_csv('data.csv')

This means once you load all the required data into the structure DataFrame you will start modifying and transforming it is based on your requirement

Practical Perspective on the Data Manipulation Techniques

When data is loaded in a DataFrame manipulating it as desired by the user is possible by a range of operations cleaning transformations and analyzing of one's data One of the top objectives is pulling together and construction of a range and variety of data matrix Ø Selecting Data One of the early on of the approach targeted to the structural changes on a data frame or matrices is enabling selecting shaping data which is represented by pointed out column lines or reaching specific digits - If you want to specify a column, use the column name, for example,

age_column = df['Age']

- To select a row by its index, use the `loc[]` or `iloc[]` methods, for instance,

row_by_index = df.loc[0]  Selecting first row

- If you only need a certain value by telling which row and column name you want to locate that specific value in using `loc[]`,

value = df.loc[0, 'Name']  It is a value which is in first row and 'Name' column

Selecting Data

You can also select data on certain criteria, for instance, we can select all rows that have the 'Age' column value greater than '25' as shown below,

filtered_data = df[df['Age'] > 25]

Sorting Data

Dataframes can also be sorted by numerous columns, and this can either be in ascending or descending order.

sorted_df = df.sort_values(by='Age', ascending=False)

Dealing with NaN values

The common term which is used to refer to null values is NaN. NaN very often appears in dataset in real life situations. There are certain functionalities that are provided by Pandas to deal with such data. - When you want to look for any data that is not present, apply the `isnull()` method:

df.isnull()

- In case the null values exist in any row and you want to get rid of those rows, use the dropna() method:

df_cleaned = df.dropna()

- Or else, you can replace the null values with 0 using the `fillna()`:

df_filled = df.fillna(0)

Transforming Information of a Frame

In case you would like to filter some data, or sort it, you will still be able to update the data present in the DataFrame. For instance, if you wish you can create new columns, or adjust the ones already present, or even use some function to change some values in several columns. - Creating and populating a new column:

df['Salary'] = [50000, 60000, 55000]

- Creating and populating a new column:

df['Age'] = df['Age'].apply(lambda x: x + 1)

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Aggregating Information

If you wish to do some operation over the data set created, for instance, if you want to do aggregation replacing certain values, you can always make the use of grouping. Grouping a Pandas DataFrame is possible through one or more columns and afterwards some of the aggregation functions that can be defined include sum, mean, count, and others.

grouped_data = df.groupby('Age').sum()

Combining Data

Since files are saved into multiple tables or into different files, those tables are sometimes called as datasets. These datasets are combined by merging in Pandas forms. The merging of DataFrames is done by the `merge()` method in which one or more columns are common.

df_merged = pd.merge(df1, df2, on='ID')

Tables with various dimensions

Pandas tables also contain specific functionality that allows to create dimensional tables also called pivot tables which are useful for making summaries and analysis of data. You can make pivot tables by the method `pivot_table()`.

pivot_df = df.pivot_table(values='Salary', index='Age', aggfunc='mean')

Compendious review of fundamental steps

Now with the help of Pandas, you can perform several other data manipulation activities such as; - Uploading files from a range of types - Data selection or filtering and sorting of data - Removing null values of datasets - Changing the datasets' value, raising new datasets or functions. - Segmenting the data and combining it. - Also joining data tables and dimensional tables. All of these activities are critical in the processing of the data from its raw form for purposes of analysis. Indeed, with the power of deploying complex algorithms on vast datasets with a swiftness that's quite remarkable, it's no wonder that Pandas is an invaluable resource to any data scientist and analyst. Whether you're in the business of wrangling data, reshaping or transforming it in order to analyze or visualize the data, definitely there are ways in which one can handle data with the help of pandas.

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