#python data type numeric

[codeonedigest]

YouTube Short - Quick Cheat Sheet to Python Data types for Beginners | Learn Python Datatypes in 1 minute

Hi, a short #video on #python #datatype is published on #codeonedigest #youtube channel. Learn the python #datatypes in 1 minute. #pythondatatypes #pythondatatypes #pythondatatypestring #pythondatatypedeclaration #pythondatatypeprogram

What is Data type? Python Data Types are used to define the type of a variable. Datatype defines what type of data we are going to store in a variable. The data stored in memory can be of many types. For example, a person’s age is stored as a numeric value and his address is stored as alphanumeric characters. Python has various built-in data types. 1. Numeric data types store numeric…

[helloworldletscode]

Another type of data, used in python, is numerical data.

Unlike strings, numerical values are not quoted with quotation marks:

price = 30

Tip:

Big numbers can be written in a more readable way:

thousand = 1_000

print(thousand)

Output: 1000

million = 1_000_000

print(million)

Output: 1000000

a_really_long_number = 1_000_000_000

print(a_really_long_number)

Output= 1000000000

This way, it would be less confusing for a person dealing with the code!

Numbers can be used to perform some calculations and operations.

Examples:

Operation: Output:

print(110) 110

print(8 + 2) 10

print(10 - 5) 5

print(5 * 3) 15

print(10 / 5) 2.0

Float division: (10 / 5) would give a float number, meaning a number with digits after the coma (like 2.0)

Integer division: using division sign twice (10//5), would give an integer (like 2), without digits after the coma.

Exponentiation: 2**3 = 2*2*2, 5**3=125

👀 Details matter:

num1 = 10

num2 = "10"

Python recognizes num1 as a number and num2 as a string.

It would perform commands differently because of this:

print(2*num1) Output: 20

print(2*num2) Output: 1010

Another example:

print(2*"3+7") Output: 3+7 3+7

print(3+7) Output: 10

print("3+7") Output: 3+7

[trendingnow3-blog]

Day-2: Mastering Python Data Types and String Manipulation: A Comprehensive Guide for Beginners

Day-2: Python Boot Camp 2023

1. Introduction to Python Data Types Data types are an essential concept in programming languages, including Python. They define the type of data a variable can hold, which influences the operations that can be performed on it. Python is a dynamically-typed language, meaning variables can change data types during execution. Understanding data types is crucial as it helps in efficient memory…

[onemanscienceband]

shit like this is why I deliberately try to stick to base language types and hate it when the offered solution to a problem is "just install this other package".

if python's numeric computation is shit because the fundamental data types aren't optimized for those tasks, IMO the solution should have been "use a different language" not "write a constantly in-flux additional package that becomes a de facto standard library". or at least, if you're going to do the latter, you should treat it like you ARE building a language standard library and not introduce all these fucking footguns and compatibility issues that mean you can't just re-use code written less than a year ago

[mvishnukumar]

How much Python should one learn before beginning machine learning?

Before diving into machine learning, a solid understanding of Python is essential. :

Basic Python Knowledge:

Syntax and Data Types: 

Understand Python syntax, basic data types (strings, integers, floats), and operations.

Control Structures: 

Learn how to use conditionals (if statements), loops (for and while), and list comprehensions.

Data Handling Libraries:

Pandas: 

Familiarize yourself with Pandas for data manipulation and analysis. Learn how to handle DataFrames, series, and perform data cleaning and transformations.

NumPy: 

Understand NumPy for numerical operations, working with arrays, and performing mathematical computations.

Data Visualization:

Matplotlib and Seaborn: 

Learn basic plotting with Matplotlib and Seaborn for visualizing data and understanding trends and distributions.

Basic Programming Concepts:

Functions: 

Know how to define and use functions to create reusable code.

File Handling: 

Learn how to read from and write to files, which is important for handling datasets.

Basic Statistics:

Descriptive Statistics: 

Understand mean, median, mode, standard deviation, and other basic statistical concepts.

Probability: 

Basic knowledge of probability is useful for understanding concepts like distributions and statistical tests.

Libraries for Machine Learning:

Scikit-learn: 

Get familiar with Scikit-learn for basic machine learning tasks like classification, regression, and clustering. Understand how to use it for training models, evaluating performance, and making predictions.

Hands-on Practice:

Projects: 

Work on small projects or Kaggle competitions to apply your Python skills in practical scenarios. This helps in understanding how to preprocess data, train models, and interpret results.

In summary, a good grasp of Python basics, data handling, and basic statistics will prepare you well for starting with machine learning. Hands-on practice with machine learning libraries and projects will further solidify your skills.

To learn more drop the message…!

[juliebowie]

Learning About Different Types of Functions in R Programming

Summary: Learn about the different types of functions in R programming, including built-in, user-defined, anonymous, recursive, S3, S4 methods, and higher-order functions. Understand their roles and best practices for efficient coding.

Introduction

Functions in R programming are fundamental building blocks that streamline code and enhance efficiency. They allow you to encapsulate code into reusable chunks, making your scripts more organised and manageable. 

Understanding the various types of functions in R programming is crucial for leveraging their full potential, whether you're using built-in, user-defined, or advanced methods like recursive or higher-order functions. 

This article aims to provide a comprehensive overview of these different types, their uses, and best practices for implementing them effectively. By the end, you'll have a solid grasp of how to utilise these functions to optimise your R programming projects.

What is a Function in R?

In R programming, a function is a reusable block of code designed to perform a specific task. Functions help organise and modularise code, making it more efficient and easier to manage. 

By encapsulating a sequence of operations into a function, you can avoid redundancy, improve readability, and facilitate code maintenance. Functions take inputs, process them, and return outputs, allowing for complex operations to be performed with a simple call.

Basic Structure of a Function in R

The basic structure of a function in R includes several key components:

Function Name: A unique identifier for the function.

Parameters: Variables listed in the function definition that act as placeholders for the values (arguments) the function will receive.

Body: The block of code that executes when the function is called. It contains the operations and logic to process the inputs.

Return Statement: Specifies the output value of the function. If omitted, R returns the result of the last evaluated expression by default.

Here's the general syntax for defining a function in R:

Syntax and Example of a Simple Function

Consider a simple function that calculates the square of a number. This function takes one argument, processes it, and returns the squared value.

In this example:

square_number is the function name.

x is the parameter, representing the input value.

The body of the function calculates x^2 and stores it in the variable result.

The return(result) statement provides the output of the function.

You can call this function with an argument, like so:

This function is a simple yet effective example of how you can leverage functions in R to perform specific tasks efficiently.

Must Read: R Programming vs. Python: A Comparison for Data Science.

Types of Functions in R

In R programming, functions are essential building blocks that allow users to perform operations efficiently and effectively. Understanding the various types of functions available in R helps in leveraging the full power of the language. 

This section explores different types of functions in R, including built-in functions, user-defined functions, anonymous functions, recursive functions, S3 and S4 methods, and higher-order functions.

Built-in Functions

R provides a rich set of built-in functions that cater to a wide range of tasks. These functions are pre-defined and come with R, eliminating the need for users to write code for common operations. 

Examples include mathematical functions like mean(), median(), and sum(), which perform statistical calculations. For instance, mean(x) calculates the average of numeric values in vector x, while sum(x) returns the total sum of the elements in x.

These functions are highly optimised and offer a quick way to perform standard operations. Users can rely on built-in functions for tasks such as data manipulation, statistical analysis, and basic operations without having to reinvent the wheel. The extensive library of built-in functions streamlines coding and enhances productivity.

User-Defined Functions

User-defined functions are custom functions created by users to address specific needs that built-in functions may not cover. Creating user-defined functions allows for flexibility and reusability in code. To define a function, use the function() keyword. The syntax for creating a user-defined function is as follows:

In this example, my_function takes two arguments, arg1 and arg2, adds them, and returns the result. User-defined functions are particularly useful for encapsulating repetitive tasks or complex operations that require custom logic. They help in making code modular, easier to maintain, and more readable.

Anonymous Functions

Anonymous functions, also known as lambda functions, are functions without a name. They are often used for short, throwaway tasks where defining a full function might be unnecessary. In R, anonymous functions are created using the function() keyword without assigning them to a variable. Here is an example:

In this example, sapply() applies the anonymous function function(x) x^2 to each element in the vector 1:5. The result is a vector containing the squares of the numbers from 1 to 5. 

Anonymous functions are useful for concise operations and can be utilised in functions like apply(), lapply(), and sapply() where temporary, one-off computations are needed.

Recursive Functions

Recursive functions are functions that call themselves in order to solve a problem. They are particularly useful for tasks that can be divided into smaller, similar sub-tasks. For example, calculating the factorial of a number can be accomplished using recursion. The following code demonstrates a recursive function for computing factorial:

Here, the factorial() function calls itself with n - 1 until it reaches the base case where n equals 1. Recursive functions can simplify complex problems but may also lead to performance issues if not implemented carefully. They require a clear base case to prevent infinite recursion and potential stack overflow errors.

S3 and S4 Methods

R supports object-oriented programming through the S3 and S4 systems, each offering different approaches to object-oriented design.

S3 Methods: S3 is a more informal and flexible system. Functions in S3 are used to define methods for different classes of objects. For instance:

In this example, print.my_class is a method that prints a custom message for objects of class my_class. S3 methods provide a simple way to extend functionality for different object types.

S4 Methods: S4 is a more formal and rigorous system with strict class definitions and method dispatch. It allows for detailed control over method behaviors. For example:

Here, setClass() defines a class with a numeric slot, and setMethod() defines a method for displaying objects of this class. S4 methods offer enhanced functionality and robustness, making them suitable for complex applications requiring precise object-oriented programming.

Higher-Order Functions

Higher-order functions are functions that take other functions as arguments or return functions as results. These functions enable functional programming techniques and can lead to concise and expressive code. Examples include apply(), lapply(), and sapply().

apply(): Used to apply a function to the rows or columns of a matrix.

lapply(): Applies a function to each element of a list and returns a list.

sapply(): Similar to lapply(), but returns a simplified result.

Higher-order functions enhance code readability and efficiency by abstracting repetitive tasks and leveraging functional programming paradigms.

Best Practices for Writing Functions in R

Writing efficient and readable functions in R is crucial for maintaining clean and effective code. By following best practices, you can ensure that your functions are not only functional but also easy to understand and maintain. Here are some key tips and common pitfalls to avoid.

Tips for Writing Efficient and Readable Functions

Keep Functions Focused: Design functions to perform a single task or operation. This makes your code more modular and easier to test. For example, instead of creating a function that processes data and generates a report, split it into separate functions for processing and reporting.

Use Descriptive Names: Choose function names that clearly indicate their purpose. For instance, use calculate_mean() rather than calc() to convey the function’s role more explicitly.

Avoid Hardcoding Values: Use parameters instead of hardcoded values within functions. This makes your functions more flexible and reusable. For example, instead of using a fixed threshold value within a function, pass it as a parameter.

Common Mistakes to Avoid

Overcomplicating Functions: Avoid writing overly complex functions. If a function becomes too long or convoluted, break it down into smaller, more manageable pieces. Complex functions can be harder to debug and understand.

Neglecting Error Handling: Failing to include error handling can lead to unexpected issues during function execution. Implement checks to handle invalid inputs or edge cases gracefully.

Ignoring Code Consistency: Consistency in coding style helps maintain readability. Follow a consistent format for indentation, naming conventions, and comment style.

Best Practices for Function Documentation

Document Function Purpose: Clearly describe what each function does, its parameters, and its return values. Use comments and documentation strings to provide context and usage examples.

Specify Parameter Types: Indicate the expected data types for each parameter. This helps users understand how to call the function correctly and prevents type-related errors.

Update Documentation Regularly: Keep function documentation up-to-date with any changes made to the function’s logic or parameters. Accurate documentation enhances the usability of your code.

By adhering to these practices, you’ll improve the quality and usability of your R functions, making your codebase more reliable and easier to maintain.

Read Blogs: 

Pattern Programming in Python: A Beginner’s Guide.

Understanding the Functional Programming Paradigm.

Frequently Asked Questions

What are the main types of functions in R programming? 

In R programming, the main types of functions include built-in functions, user-defined functions, anonymous functions, recursive functions, S3 methods, S4 methods, and higher-order functions. Each serves a specific purpose, from performing basic tasks to handling complex operations.

How do user-defined functions differ from built-in functions in R? 

User-defined functions are custom functions created by users to address specific needs, whereas built-in functions come pre-defined with R and handle common tasks. User-defined functions offer flexibility, while built-in functions provide efficiency and convenience for standard operations.

What is a recursive function in R programming?

A recursive function in R calls itself to solve a problem by breaking it down into smaller, similar sub-tasks. It's useful for problems like calculating factorials but requires careful implementation to avoid infinite recursion and performance issues.

Conclusion

Understanding the types of functions in R programming is crucial for optimising your code. From built-in functions that simplify tasks to user-defined functions that offer customisation, each type plays a unique role. 

Mastering recursive, anonymous, and higher-order functions further enhances your programming capabilities. Implementing best practices ensures efficient and maintainable code, leveraging R’s full potential for data analysis and complex problem-solving.

[computerlanguages]

Computer Language

Computer languages, also known as programming languages, are formal languages used to communicate instructions to a computer. These instructions are written in a syntax that computers can understand and execute. There are numerous programming languages, each with its own syntax, semantics, and purpose. Here are some of the main types of programming languages:

1.Low-Level Languages:

Machine Language: This is the lowest level of programming language, consisting of binary code (0s and 1s) that directly corresponds to instructions executed by the computer's hardware. It is specific to the computer's architecture.

Assembly Language: Assembly language uses mnemonic codes to represent machine instructions. It is a human-readable form of machine language and closely tied to the computer's hardware architecture

2.High-Level Languages:

Procedural Languages: Procedural languages, such as C, Pascal, and BASIC, focus on defining sequences of steps or procedures to perform tasks. They use constructs like loops, conditionals, and subroutines.

Object-Oriented Languages: Object-oriented languages, like Java, C++, and Python, organize code around objects, which are instances of classes containing data and methods. They emphasize concepts like encapsulation, inheritance, and polymorphism.

Functional Languages: Functional languages, such as Haskell, Lisp, and Erlang, treat computation as the evaluation of mathematical functions. They emphasize immutable data and higher-order functions.

Scripting Languages: Scripting languages, like JavaScript, PHP, and Ruby, are designed for automating tasks, building web applications, and gluing together different software components. They typically have dynamic typing and are interpreted rather than compiled.

Domain-Specific Languages (DSLs): DSLs are specialized languages tailored to a specific domain or problem space. Examples include SQL for database querying, HTML/CSS for web development, and MATLAB for numerical computation.

3.Other Types:

Markup Languages: Markup languages, such as HTML, XML, and Markdown, are used to annotate text with formatting instructions. They are not programming languages in the traditional sense but are essential for structuring and presenting data.

Query Languages: Query languages, like SQL (Structured Query Language), are used to interact with databases by retrieving, manipulating, and managing data.

Constraint Programming Languages: Constraint programming languages, such as Prolog, focus on specifying constraints and relationships among variables to solve combinatorial optimization problems.

[pythonway]

Python: What is it all about?

Python is a high-level, interpreted programming language.

A programming language is a formal language that is used to create instructions that can be executed by a computer. Programming languages are used to develop a wide range of software applications, from simple scripts to complex operating systems.

It is widely used for a variety of applications.

What is Python?

Python is a dynamic, object-oriented programming language.

A dynamic programming language is a programming language in which the type of a variable is not known until script is run. This is in contrast to static typing, in which the type of a variable is set explicitly .

It is an interpreted language, meaning that it is executed line by line by an interpreter, rather than being compiled into machine code like some other languages. This makes it easy to develop and test Python programs quickly and efficiently.

Python is also a general-purpose language, meaning that it can be used for a wide range of tasks. It comes with a comprehensive standard library that provides modules for common tasks such as file handling, networking, and data manipulation. This makes it easy to get started with Python and to develop complex applications without having to write a lot of code from scratch.

Origin of Python

Python was created by Guido van Rossum in the late 1980s as a successor to the ABC programming language. ABC was a simple, interpreted language that was designed for teaching programming concepts. However, van Rossum felt that ABC was too limited, and he wanted to create a more powerful and versatile language.

Python was influenced by a number of other programming languages, including C, Modula-3, and Lisp. Van Rossum wanted to create a language that was simple and easy to learn, but also powerful enough to be used for a variety of applications. He also wanted to create a language that was portable across different platforms.

Python was first released in 1991, and it quickly gained popularity as a teaching language and for scripting tasks. In the late 1990s and early 2000s, Python began to be used for more complex applications, such as web development and data science. Today, Python is one of the most popular programming languages in the world, and it is used for a wide variety of applications.

The name "Python" is a reference to the British comedy group Monty Python. Van Rossum was a fan of the group, and he thought that the name "Python" was appropriate for his new language because it was "short, unique, and slightly mysterious."

How is Python Used?

Python is used in a wide variety of applications, including:

Web development: Python is a popular choice for web development, thanks to its simplicity and the availability of powerful frameworks such as Django, Flask, FastAPI.

Data science: Python is widely used for data science and machine learning, thanks to its extensive data analysis and visualization libraries such as NumPy, Pandas, dask, Matplotlib, plotly, seaborn, alatair.

Machine learning: Python is a popular choice for machine learning, thanks to its support for a wide range of machine learning algorithms and libraries such as scikit-learn, TensorFlow, Keras, transformers, PyTorch.

Scripting: Python is often used for automating repetitive tasks or creating custom tools.

Advantages

Python offers a number of advantages over other programming languages, including:

Simplicity: Python is a relatively simple language to learn and use, making it a good choice for beginners.

Readability: Python code is known for its readability, making it easy to understand and maintain.

Extensibility: Python is highly extensible, thanks to its large community of developers and the availability of numerous libraries and frameworks.

Portability: Python is a cross-platform language, meaning that it can be run on a variety of operating systems without modification.

Drawbacks

Despite all of it's advantages, python also has several drawbacks.

Speed: Python is an interpreted language, which means that it is slower than compiled languages such as C++ and Java. This can be a disadvantage for applications that require high performance.

Memory usage: Python programs can use a lot of memory, especially when working with large datasets. This can be a disadvantage for applications that need to run on devices with limited memory.

Lack of type checking: Python is a dynamically typed language, which means that it does not check the types of variables at compile time. This can lead to errors that are difficult to find and debug.

Global interpreter lock (GIL): The GIL is a lock that prevents multiple threads from executing Python code simultaneously. This can be a disadvantage for applications that need to use multiple cores or processors.

[proeduorganization]

Data Types in Python

Introduction Hi All. In this post, I will tell you about the data types supported in python. Python provides several built-in data types that are commonly used. Here’s an overview of some of the main data types: Numeric Types: Python provides three types of numeric types: Integer (int): Integers are whole numbers without a decimal point. They can be positive, negative, or zero. Example: 5,…

[online-learning-e-tuitions]

How you can use python for data wrangling and analysis

Python is a powerful and versatile programming language that can be used for various purposes, such as web development, data science, machine learning, automation, and more. One of the most popular applications of Python is data analysis, which involves processing, cleaning, manipulating, and visualizing data to gain insights and make decisions.

In this article, we will introduce some of the basic concepts and techniques of data analysis using Python, focusing on the data wrangling and analysis process. Data wrangling is the process of transforming raw data into a more suitable format for analysis, while data analysis is the process of applying statistical methods and tools to explore, summarize, and interpret data.

To perform data wrangling and analysis with Python, we will use two of the most widely used libraries: Pandas and NumPy. Pandas is a library that provides high-performance data structures and operations for manipulating tabular data, such as Series and DataFrame. NumPy is a library that provides fast and efficient numerical computations on multidimensional arrays, such as ndarray.

We will also use some other libraries that are useful for data analysis, such as Matplotlib and Seaborn for data visualization, SciPy for scientific computing, and Scikit-learn for machine learning.

To follow along with this article, you will need to have Python 3.6 or higher installed on your computer, as well as the libraries mentioned above. You can install them using pip or conda commands. You will also need a code editor or an interactive environment, such as Jupyter Notebook or Google Colab.

Let’s get started with some examples of data wrangling and analysis with Python.

Example 1: Analyzing COVID-19 Data

In this example, we will use Python to analyze the COVID-19 data from the World Health Organization (WHO). The data contains the daily situation reports of confirmed cases and deaths by country from January 21, 2020 to October 23, 2023. You can download the data from here.

First, we need to import the libraries that we will use:import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

Next, we need to load the data into a Pandas DataFrame:df = pd.read_csv('WHO-COVID-19-global-data.csv')

We can use the head() method to see the first five rows of the DataFrame:df.head()

Date_reportedCountry_codeCountryWHO_regionNew_casesCumulative_casesNew_deathsCumulative_deaths2020–01–21AFAfghanistanEMRO00002020–01–22AFAfghanistanEMRO00002020–01–23AFAfghanistanEMRO00002020–01–24AFAfghanistanEMRO00002020–01–25AFAfghanistanEMRO0000

We can use the info() method to see some basic information about the DataFrame, such as the number of rows and columns, the data types of each column, and the memory usage:df.info()

Output:

RangeIndex: 163800 entries, 0 to 163799 Data columns (total 8 columns): # Column Non-Null Count Dtype — — — — — — — — — — — — — — — 0 Date_reported 163800 non-null object 1 Country_code 162900 non-null object 2 Country 163800 non-null object 3 WHO_region 163800 non-null object 4 New_cases 163800 non-null int64 5 Cumulative_cases 163800 non-null int64 6 New_deaths 163800 non-null int64 7 Cumulative_deaths 163800 non-null int64 dtypes: int64(4), object(4) memory usage: 10.0+ MB “><class 'pandas.core.frame.DataFrame'> RangeIndex: 163800 entries, 0 to 163799 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date_reported 163800 non-null object 1 Country_code 162900 non-null object 2 Country 163800 non-null object 3 WHO_region 163800 non-null object 4 New_cases 163800 non-null int64 5 Cumulative_cases 163800 non-null int64 6 New_deaths 163800 non-null int64 7 Cumulative_deaths 163800 non-null int64 dtypes: int64(4), object(4) memory usage: 10.0+ MB

We can see that there are some missing values in the Country_code column. We can use the isnull() method to check which rows have missing values:df[df.Country_code.isnull()]

Output:

Date_reportedCountry_codeCountryWHO_regionNew_casesCumulative_casesNew_deathsCumulative_deaths2020–01–21NaNInternational conveyance (Diamond Princess)WPRO00002020–01–22NaNInternational conveyance (Diamond Princess)WPRO0000……………………2023–10–22NaNInternational conveyance (Diamond Princess)WPRO07120132023–10–23NaNInternational conveyance (Diamond Princess)WPRO0712013

We can see that the missing values are from the rows that correspond to the International conveyance (Diamond Princess), which is a cruise ship that had a COVID-19 outbreak in early 2020. Since this is not a country, we can either drop these rows or assign them a unique code, such as ‘IC’. For simplicity, we will drop these rows using the dropna() method:df = df.dropna()

We can also check the data types of each column using the dtypes attribute:df.dtypes

Output:Date_reported object Country_code object Country object WHO_region object New_cases int64 Cumulative_cases int64 New_deaths int64 Cumulative_deaths int64 dtype: object

We can see that the Date_reported column is of type object, which means it is stored as a string. However, we want to work with dates as a datetime type, which allows us to perform date-related operations and calculations. We can use the to_datetime() function to convert the column to a datetime type:df.Date_reported = pd.to_datetime(df.Date_reported)

We can also use the describe() method to get some summary statistics of the numerical columns, such as the mean, standard deviation, minimum, maximum, and quartiles:df.describe()

Output:

New_casesCumulative_casesNew_deathsCumulative_deathscount162900.000000162900.000000162900.000000162900.000000mean1138.300062116955.14016023.4867892647.346237std6631.825489665728.383017137.25601215435.833525min-32952.000000–32952.000000–1918.000000–1918.00000025%-1.000000–1.000000–1.000000–1.00000050%-1.000000–1.000000–1.000000–1.00000075%-1.000000–1.000000–1.000000–1.000000max -1 -1 -1 -1

We can see that there are some negative values in the New_cases, Cumulative_cases, New_deaths, and Cumulative_deaths columns, which are likely due to data errors or corrections. We can use the replace() method to replace these values with zero:df = df.replace(-1,0)

Now that we have cleaned and prepared the data, we can start to analyze it and answer some questions, such as:

Which countries have the highest number of cumulative cases and deaths?

How has the pandemic evolved over time in different regions and countries?

What is the current situation of the pandemic in India?

To answer these questions, we will use some of the methods and attributes of Pandas DataFrame, such as:

groupby() : This method allows us to group the data by one or more columns and apply aggregation functions, such as sum, mean, count, etc., to each group.

sort_values() : This method allows us to sort the data by one or more

loc[] : This attribute allows us to select a subset of the data by labels or conditions.

plot() : This method allows us to create various types of plots from the data, such as line, bar, pie, scatter, etc.

If you want to learn Python from scratch must checkout e-Tuitions to learn Python online, They can teach you Python and other coding language also they have some of the best teachers for their students and most important thing you can also Book Free Demo for any class just goo and get your free demo.

[d0nutzgg]

Using Cython to Optimize Python Code

Python is a popular and versatile programming language, but it can be slow compared to other languages like C or C++. This is where Cython comes in. Cython is a tool that allows you to write Python-like code, but compiles it to C, resulting in faster performance.

Cython is particularly useful for scientific and numerical computing, where performance is critical. It can be used to optimize existing Python code, or write new code in Cython for improved performance. In this article, we’ll take a look at how to use Cython and why it’s valuable for Python programmers to learn.

Getting started with Cython is easy. You can install it using pip, the Python package manager, by running the following command in your terminal:

Once you have Cython installed, you can start using it to optimize your Python code. The basic idea is to write your code in Cython, then compile it to C, which can then be imported and used in your Python code.

Here’s a simple example that demonstrates how to use Cython. Let’s say we have a Python function that calculates the sum of squares of numbers in a list:

We can optimize this function by writing it in Cython, then compiling it to C. Here’s the Cython version:

In this example, we’ve added a cdef statement to declare the variables as C data types, which results in faster performance. We can then compile this Cython code to C using the following command in our terminal:

This will generate a .c file that can be imported and used in your Python code.

Cython is a powerful tool that allows you to write Python-like code and optimize it for performance. Whether you’re working on scientific and numerical computing or just looking to improve the performance of your code, Cython is worth learning.

Some great resources for learning Cython include the official documentation, tutorials and example code on the Cython website, and the “Cython: A Guide for Python Programmers” book by Kurt Smith.

Here is the Cython Wiki:

As well as the ReadTheDocs for Cython:

There is also a great tutorial series on using Cython by Pythonist on Youtube

By using Cython, you can take your Python skills to the next level and achieve faster performance for your code. Give it a try and see the results for yourself!

[snapeshiftai]

What is best programming language for Artificial Intelligence projects?

There isn’t a single “best” programming language for artificial intelligence (AI) projects, as the choice of language depends on various factors such as the specific AI task, the libraries and frameworks available, your familiarity with the language, and the requirements of the project.

However, here are some popular programming languages often used in AI development:

Python: Python is one of the most widely used languages in the AI community due to its simplicity, readability, and availability of numerous AI libraries and frameworks. Libraries like TensorFlow, PyTorch, and sci-kit-learn provide powerful tools for machine learning and deep learning tasks. Python’s versatility also allows for rapid prototyping and experimentation.

R: R is a programming language specifically designed for statistical computing and data analysis. It has a rich collection of packages and libraries focused on machine learning, statistical modeling, and data visualization. R is often preferred by statisticians and researchers working in AI and data science domains

. Java: Java is a popular general-purpose programming language that is widely used in enterprise applications. It has strong support for large-scale systems and offers a range of libraries and frameworks for AI development, such as Deeplearning4j and Weka. Java’s performance and scalability make it a good choice for AI projects that require efficient execution.

C++: C++ is a powerful, low-level programming language known for its performance and efficiency. It is commonly used in AI projects that require high computational speed or have strict resource constraints. Frameworks like TensorFlow and OpenCV provide C++ APIs for AI tasks, and libraries like Eigen can be useful for linear algebra and numerical computations.

Julia: Julia is a relatively new language specifically designed for high-performance numerical computing. It combines the ease of use of dynamic languages like Python with the performance of languages like C++. Julia’s strengths lie in scientific computing and machine learning applications, and it aims to provide a productive and efficient environment for AI development.

MATLAB: MATLAB is a proprietary programming language and environment that is widely used in various scientific and engineering disciplines. It offers powerful tools for numerical computing, data analysis, and visualization. MATLAB’s extensive set of toolboxes, including those for machine learning and deep learning, make it a popular choice for AI researchers and practitioners.

Lisp: Lisp is a family of programming languages known for their flexibility and expressive power. Common Lisp and Scheme are popular variants used in AI development. Lisp’s features, such as support for symbolic processing and its ability to manipulate code as data, make it well-suited for tasks like natural language processing, expert systems, and AI research.

Prolog: Prolog is a declarative programming language based on logic programming. It is particularly useful for tasks involving rule-based reasoning and symbolic computation. Prolog is often employed in areas such as expert systems, natural language processing, and knowledge representation.

Scala: Scala is a statically typed programming language that runs on the Java Virtual Machine (JVM). It combines object-oriented and functional programming paradigms and offers a concise syntax. Scala’s interoperability with Java and its strong support for concurrent programming make it a suitable choice for AI projects that require scalability and parallel processing.

Julia: I mentioned Julia earlier, but it’s worth highlighting again. Julia is gaining popularity in the AI community due to its speed, ease of use, and extensive mathematical libraries. Its just-in-time (JIT) compilation capabilities allow for fast execution, and its focus on numerical computing makes it a good fit for scientific computing and machine learning tasks.

It’s worth noting that the choice of programming language is often influenced by the existing ecosystem and community support. Python, with its extensive libraries and frameworks, is generally considered a good starting point for most AI projects due to its flexibility, ease of use, and rich ecosystem. However, depending on the specific requirements and constraints of your project, other languages may also be suitable.

[evidyatechnologies]

Data Science Made Easy: How the Best Online Training Institute in Hyderabad Can Help You Succeed

Are you searching for an apt Data Science course that will provide you with additional skills? Then you have arrived at the correct location. Data science courses are state-of-the-art courses that are in demand currently. Data Science is a branch that combines algorithms, scientific computing, and statistics to extract insights from unstructured data.

The data science course in E Vidya Technologies has several lessons structured in the order, from introducing data science and analytics to python and pandas to ending it with Machine Learning. This course will help you grasp several Data Science, NLP and Machine Learning concepts. Furthermore, with data science training in hyderabad, you will get clarity on the basics and good knowledge about the advanced python and web scraping levels. 

Expert Training

Learning is a skill that takes time, and the process is different for different students. Thus, you should always find courses taught by certified professionals with expertise in this field. Usually, these trainers plan a complete schedule and curriculum for every student. Therefore, students should feel free of the pressure, and the sessions should be interactive.

Training Videos

Students prefer training videos to be at flexible hours. This gives them the opportunity to do other activities during that time. Also, equal intervals between the lectures give them time to recall and practice. Moreover, students can work on their doubts at this time. So there needs to be a 24/7 working team to train and clarify students' queries.

Resume Building

The goal after completing a course is to secure a job. For this, the perfect resume should be built. Thus, the student should undergo technical and non-technical mock interviews. E Vidya Technologies course offers both types of mock interviews, which will help you gain a command of the learned subject, and you will feel confident to apply for a job. The mock technical interviews include questions on the subject. In contrast, non-technical mock interviews focus on your communication skills. Students can improve themselves by receiving feedback on these interviews.

The minimum set of batch

In every course, having a limited number of students benefits both students and teachers. Students can discuss with their colleagues in a better way. Teachers will be able to focus on every student's progress. The limited set of students is an excellent source of competitiveness to each other.

These factors will give you a good hold on data science. Because data science is a vast subject with numerous sub-sections, you will take time to understand the complex structure. It would be best to have a tutor to structure your course at your convenience.

Conclusion

In short, to select the best online data science course, you must look for flexible studying hours, quizzes and tests, better training videos by experts and reasonable job Assistance. One such option is E Vidya Technologies which offers the best software training institute in hyderabad. So, what are you waiting for? Enroll yourself and start a fantastic journey in data science!

[eduardotleite]

ANOVA Analysis

This is the task of Week 1 of the course Data Analysis Tools at the Coursera Plataform. The challenge is to execute an Analysis of Variance using the ANOVA Statistical Test. This type of analysis assesses whether the means of two or more groups are statistically different from each other. Is used whenever you want to compare the means (quantitative variables) of groups (categorical variables). The null hypothesis is that there is no difference in the mean of the quantitative variable across groups (categorical variable), while the alternative is that there is a difference.

DataSet Used – Gap Minder Gapminder identifies systematic misconceptions about important global trends and proportions and uses reliable data to develop easy to understand teaching materials to rid people of their misconceptions. Gapminder is an independent Swedish foundation with no political, religious, or economic affiliations. should visit it: https://www.gapminder.org/.

The dataset used has 16 variables and 213 rows. I choosed to analyze income per person (incomeperperson) and life expectancy (lifeexpectancy).

And how is the Question?

Is the life expectancy different among four categories of income per person (A,B,C,D,E)?

Since the income per person is a quantitative variable, I transformed it into a categorical variable, using parameters sugested by IBGE to classify the social class of according of income. For the parameters, I analyzed the boxplot posted below.

the data in image is in portuguese, because the IBGE is an Brazilian institute.

The Code

I used the Anaconda to code in Python for this task. The code is posted below.

import numpy import pandas as pd import statsmodels.api as sm import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi import matplotlib.pyplot as plt import seaborn as sns import researchpy as rp import pycountry_convert as pc

df = pd.read_csv('gapminder.csv') df = df[['lifeexpectancy', 'incomeperperson']]

df['lifeexpectancy'] = df['lifeexpectancy'].apply(pd.to_numeric, errors='coerce') df['incomeperperson'] = df['incomeperperson'].apply(pd.to_numeric, errors='coerce')

def income_categories(row): if row["incomeperperson"]>15000: return "A" elif row["incomeperperson"]>5000: return "B" elif row["incomeperperson"]>3000: return "C" elif row["incomeperperson"]>1000: return "D" else: return "E"

df=df[(df['lifeexpectancy']>=1) & (df['lifeexpectancy']<=120) & (df['incomeperperson'] > 0) ]

df["Income_category"]=df.apply(income_categories, axis=1)

df = df[["Income_category","incomeperperson","lifeexpectancy"]].dropna()

df["Income_category"]=df.apply(income_categories, axis=1)

print (rp.summary_cont(df['lifeexpectancy']))

fig1, ax1 = plt.subplots() df_new = [df[df['Income_category']=='A']['lifeexpectancy'], df[df['Income_category']=='B']['lifeexpectancy'], df[df['Income_category']=='C']['lifeexpectancy'], df[df['Income_category']=='D']['lifeexpectancy'], df[df['Income_category']=='E']['lifeexpectancy']] ax1.set_title('life expectancy') ax1.boxplot(df_new) plt.show()

results = smf.ols('lifeexpectancy ~ C(Income_category)', data=df).fit() print (results.summary())

print ("Tukey") mc1 = multi.MultiComparison(df['lifeexpectancy'], df['Income_category']) print (mc1) res1 = mc1.tukeyhsd() print (res1.summary())

print ('means for for life expectancy by Income') m1= df.groupby('Income_category').mean() print (m1)

print ('Results') print ('standard deviations for life expectancy by Income') sd1 = df.groupby('Income_category').std() print (sd1)

Results – ANOVA Analysis

Aiming to answer the question of the task, I ran a test ANOVA. As shown below, from the 176 rows, 171 were used for the test, i have used a filter to remove some wrong values, as non numeric, negative, etc, reducing the rows of the original dataset

The ANOVA analysis shows a graph for each category (above) and, as we can see, the life expectancy of A class, have the life expectative of 80.39 years while the E class have the life expectative of 59.15 years.

[changeyoulifee]

Are there job opportunities in Data Science?

Yes, data science is a perfect career with tremendous future advancement opportunities. Already, demand is high, salaries are competitive, and the perks are numerous — which is why Data Scientist has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor.

There will be many questions in your mind like:-

Is data science jobs in demand?

What job can a data scientist do?

Is data science an IT job?

Which field is best for data science?

Is data science a stressful job?

Is data science easy or hard?

Is data science need coding?

Does data science have a future?

Who can study data science?

And any type other Questions?

Today we give you the answer to that question that always keeps running in your mind. First of all, let’s be clear that in the coming time only Data Science & Artificial intelligence is going to be in high demand in the job. Because today every company needs data to target the client, and to grow its reach or business. Doing online business even in conditions like Lockdown The company’s growth was at the top. That is why in the coming times we can say that the demand for Data Science and Artificial Intelligence is going to be the highest.

Now we give you the answers to whatever questions keep running in your mind, which question is given above.

Is data science jobs in demand? :- Data science jobs are becoming increasingly in demand as big data and technology industries grow. Find out which jobs are the hottest and how to prepare for your career. The data science industry is growing and changing at a rapid pace.

What job can a data scientist do? :- A data scientist might do the following tasks on a day-to-day basis: Find patterns and trends in datasets to uncover insights. Create algorithms and data models to forecast outcomes. Use machine learning techniques to improve the quality of data or product offerings.

Is data science an IT job? :- A Data Scientist job is most definitely an IT-enabled job. Every IT professional is a domain expert responsible for handling a particular technical aspect of their organization.

Which field is best for data science? :- Best Field list

Data Analyst.

Data Engineers.

Database Administrator.

Machine Learning Engineer.

Data Scientist.

Data Architect.

5. Is data science a stressful job? :- Several data professionals have defined data analytics as a stressful career. So, if you are someone planning on taking up data analytics and science as a career, it is high time that you rethink and make an informed decision.

6. Is data science easy or hard?:- Data Science is hard to learn is primarily a misconception that beginners have during their initial days. As they discover the unique domain of data science, they realize that data science is just another field of study that can be learned by working hard.

7. Is data science need coding?:- All jobs in Data Science require some degree of coding and experience with technical tools and technologies. To summarize Data Engineer: Moderate amount of Python, more knowledge of SQL, and optional but preferable knowledge on a Cloud Platform.

8. Does data science have a future?:- Data scientists are likely to face a growing demand for their skills in the field of cybersecurity. As the world becomes increasingly reliant on digital information, the need to protect this information from hackers and other cyber threats will become more important.

9. Who can study data science?:- Anyone, whether a newcomer or a professional, willing to learn Data Science can opt for it. Engineers, Marketing Professionals, Software, and IT professionals can take up part-time or external programs in Data Science. For regular courses in Data Science, basic high school-level subjects are the minimum requirement.

Now you must be satisfied because all your questions have been answered, now you can choose your career in Data Science And Artificial intelligence. If you are satisfied with my answer, then by reposting our post, you can reach many people so that everyone can get their right knowledge. If you are satisfied with my answer, then by reposting our post, you can reach many people so that everyone can get their right knowledge. For more information, you can also visit our website (https://www.digicrome.com) and you can get information by submitting the form.

[tenologin]

Top 5 Python Libraries for Data Science: NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow

Data science is a field that involves using statistical and computational techniques to extract insights and knowledge from data. Python is a popular programming language for data science, and there are a number of libraries that are particularly useful for tasks such as data manipulation, analysis, visualisation, and machine learning.

Top 5 Python Libraries for Data Science:

NumPy

Pandas

Matplotlib 

Scikit-learn 

TensorFlow

NumPy

NumPy is a Python library for manipulating large, multi-dimensional numerical arrays and matrices.It provides a number of functions for performing mathematical operations on these arrays, such as linear algebra, statistical analysis, and more.

 NumPy is a fundamental library for scientific computing with Python and is often used in conjunction with other libraries, such as Pandas and Matplotlib.

Pandas

Pandas is a library for data manipulation and analysis. It provides a number of functions for reading and writing data, as well as tools for organising, reshaping, and cleaning data. Pandas is particularly useful for working with tabular data, such as data stored in a spreadsheet or in a CSV file. 

It provides functions for filtering and sorting data, as well as for handling missing values and duplicates. Pandas is often used in conjunction with NumPy to perform statistical analyses.

Matplotlib

Matplotlib is a library for creating visualisations of data. It provides a number of functions for creating plots and charts of various types, including line plots, scatter plots, bar charts, and histograms.

Matplotlib is particularly useful for exploring and visualising large datasets, as it allows you to quickly and easily create a wide range of plots to help you understand the patterns and trends in your data.

Scikit-learn

 Scikit-learn is a library for machine learning in Python. It provides a number of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for evaluating the performance of these  algorithms.

 Scikit-learn is easy to use and well-documented, making it a popular choice for machine learning tasks in Python.

TensorFlow

 TensorFlow is a library for machine learning and deep learning in Python. It provides a number of functions for creating and training neural networks, and is widely used for a variety of applications, including natural language processing, image recognition, and more. 

TensorFlow is a powerful library that can be used to build complex machine learning models, and it has a large and active community of users and developers.

In conclusion, Python has a number of powerful libraries for data science, including NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow. These libraries are widely used in the field and can be very useful for tasks such as data manipulation, analysis, visualisation, and machine learning. Whether you are a beginner or an experienced data scientist, these libraries can help you to extract insights and knowledge from your data and build powerful models.