Pandas & NumPy Python Programming Language Libraries A-Z™

Pandas & NumPy Python Programming Language Libraries A-Z™, available at $89.99, has an average rating of 4.2, with 87 lectures, 13 quizzes, based on 42 reviews, and has 219 subscribers.

You will learn about Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Pandas Pyhon aims to be the fundamental high-level building block for doing practical, real world data analysis in Python Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices. NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. NumPy brings the computational power of languages like C and Fortran to Python. Installing Anaconda Distribution for Windows Installing Anaconda Distribution for MacOs Installing Anaconda Distribution for Linux Introduction to NumPy Library The Power of NumPy Creating NumPy Array with The Array() Function Creating NumPy Array with Zeros() Function Creating NumPy Array with Ones() Function Creating NumPy Array with Full() Function Creating NumPy Array with Arange() Function Creating NumPy Array with Eye() Function Creating NumPy Array with Linspace() Function Creating NumPy Array with Random() Function Properties of NumPy Array Reshaping a NumPy Array: Reshape() Function Identifying the Largest Element of a Numpy Array: Max(), Argmax() Functions Detecting Least Element of Numpy Array: Min(), Argmin() Functions Concatenating Numpy Arrays: Concatenate() Function Splitting One-Dimensional Numpy Arrays: The Split() Function Splitting Two-Dimensional Numpy Arrays: Split(), Vsplit, Hsplit() Function Sorting Numpy Arrays: Sort() Function Indexing Numpy Arrays Slicing One-Dimensional Numpy Arrays Slicing Two-Dimensional Numpy Arrays Assigning Value to One-Dimensional Arrays Assigning Value to Two-Dimensional Array Fancy Indexing of One-Dimensional Arrrays Fancy Indexing of Two-Dimensional Arrrays Combining Fancy Index with Normal Indexing Combining Fancy Index with Normal Slicing Fancy Indexing of One-Dimensional Arrrays Fancy Indexing of Two-Dimensional Arrrays Combining Fancy Index with Normal Indexing Combining Fancy Index with Normal Slicing Introduction to Pandas Library Creating a Pandas Series with a List Creating a Pandas Series with a Dictionary Creating Pandas Series with NumPy Array Object Types in Series Examining the Primary Features of the Pandas Series Most Applied Methods on Pandas Series Indexing and Slicing Pandas Series Creating Pandas DataFrame with List Creating Pandas DataFrame with NumPy Array Creating Pandas DataFrame with Dictionary Examining the Properties of Pandas DataFrames Element Selection Operations in Pandas DataFrames Top Level Element Selection in Pandas DataFrames: Structure of loc and iloc Element Selection with Conditional Operations in Pandas Data Frames Adding Columns to Pandas Data Frames Removing Rows and Columns from Pandas Data frames Null Values in Pandas Dataframes Dropping Null Values: Dropna() Function Filling Null Values: Fillna() Function Setting Index in Pandas DataFrames Multi-Index and Index Hierarchy in Pandas DataFrames Element Selection in Multi-Indexed DataFrames Selecting Elements Using the xs() Function in Multi-Indexed DataFrames Concatenating Pandas Dataframes: Concat Function Merge Pandas Dataframes: Merge() Function Joining Pandas Dataframes: Join() Function Loading a Dataset from the Seaborn Library Aggregation Functions in Pandas DataFrames Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes Advanced Aggregation Functions: Aggregate() Function Advanced Aggregation Functions: Filter() Function Advanced Aggregation Functions: Transform() Function Advanced Aggregation Functions: Apply() Function Pivot Tables in Pandas Library Data Entry with Csv and Txt Files Data Entry with Excel Files Outputting as an CSV Extension Outputting as an Excel File Basic Knowledge of Python Programming Language Basic Knowledge of Numpy Library Basic Knowledge of Mathematics Watch the course videos completely and in order. Internet Connection Any device where you can watch the lesson, such as a mobile phone, computer or tablet. Determination and patience for learning Pandas Python Programming Language Library. This course is ideal for individuals who are Anyone who wants to learn Pands and Numpy or Anyone who want to use effectively linear algebra, or Software developer whom want to learn the Neural Network’s math, or Data scientist whom want to use effectively Numpy array or Anyone interested in data sciences or Anyone who plans a career in data scientist, or Anyone eager to learn python with no coding background or Anyone who is particularly interested in big data, machine learning or Those who want to learn the Pandas Library, which is necessary for data science or Those who want to improve themselves in the field of Python Programming Language and Data science It is particularly useful for Anyone who wants to learn Pands and Numpy or Anyone who want to use effectively linear algebra, or Software developer whom want to learn the Neural Network’s math, or Data scientist whom want to use effectively Numpy array or Anyone interested in data sciences or Anyone who plans a career in data scientist, or Anyone eager to learn python with no coding background or Anyone who is particularly interested in big data, machine learning or Those who want to learn the Pandas Library, which is necessary for data science or Those who want to improve themselves in the field of Python Programming Language and Data science.

Enroll now: Pandas & NumPy Python Programming Language Libraries A-Z™

Summary

Title: Pandas & NumPy Python Programming Language Libraries A-Z™

Price: $89.99

Average Rating: 4.2

Number of Lectures: 87

Number of Quizzes: 13

Number of Published Lectures: 87

Number of Published Quizzes: 13

Number of Curriculum Items: 100

Number of Published Curriculum Objects: 100

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks.
Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames.
Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Pandas Pyhon aims to be the fundamental high-level building block for doing practical, real world data analysis in Python
Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices.
NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.
NumPy brings the computational power of languages like C and Fortran to Python.
Installing Anaconda Distribution for Windows
Installing Anaconda Distribution for MacOs
Installing Anaconda Distribution for Linux
Introduction to NumPy Library
The Power of NumPy
Creating NumPy Array with The Array() Function
Creating NumPy Array with Zeros() Function
Creating NumPy Array with Ones() Function
Creating NumPy Array with Full() Function
Creating NumPy Array with Arange() Function
Creating NumPy Array with Eye() Function
Creating NumPy Array with Linspace() Function
Creating NumPy Array with Random() Function
Properties of NumPy Array
Reshaping a NumPy Array: Reshape() Function
Identifying the Largest Element of a Numpy Array: Max(), Argmax() Functions
Detecting Least Element of Numpy Array: Min(), Argmin() Functions
Concatenating Numpy Arrays: Concatenate() Function
Splitting One-Dimensional Numpy Arrays: The Split() Function
Splitting Two-Dimensional Numpy Arrays: Split(), Vsplit, Hsplit() Function
Sorting Numpy Arrays: Sort() Function
Indexing Numpy Arrays
Slicing One-Dimensional Numpy Arrays
Slicing Two-Dimensional Numpy Arrays
Assigning Value to One-Dimensional Arrays
Assigning Value to Two-Dimensional Array
Fancy Indexing of One-Dimensional Arrrays
Fancy Indexing of Two-Dimensional Arrrays
Combining Fancy Index with Normal Indexing
Combining Fancy Index with Normal Slicing
Fancy Indexing of One-Dimensional Arrrays
Fancy Indexing of Two-Dimensional Arrrays
Combining Fancy Index with Normal Indexing
Combining Fancy Index with Normal Slicing
Introduction to Pandas Library
Creating a Pandas Series with a List
Creating a Pandas Series with a Dictionary
Creating Pandas Series with NumPy Array
Object Types in Series
Examining the Primary Features of the Pandas Series
Most Applied Methods on Pandas Series
Indexing and Slicing Pandas Series
Creating Pandas DataFrame with List
Creating Pandas DataFrame with NumPy Array
Creating Pandas DataFrame with Dictionary
Examining the Properties of Pandas DataFrames
Element Selection Operations in Pandas DataFrames
Top Level Element Selection in Pandas DataFrames: Structure of loc and iloc
Element Selection with Conditional Operations in Pandas Data Frames
Adding Columns to Pandas Data Frames
Removing Rows and Columns from Pandas Data frames
Null Values in Pandas Dataframes
Dropping Null Values: Dropna() Function
Filling Null Values: Fillna() Function
Setting Index in Pandas DataFrames
Multi-Index and Index Hierarchy in Pandas DataFrames
Element Selection in Multi-Indexed DataFrames
Selecting Elements Using the xs() Function in Multi-Indexed DataFrames
Concatenating Pandas Dataframes: Concat Function
Merge Pandas Dataframes: Merge() Function
Joining Pandas Dataframes: Join() Function
Loading a Dataset from the Seaborn Library
Aggregation Functions in Pandas DataFrames
Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes
Advanced Aggregation Functions: Aggregate() Function
Advanced Aggregation Functions: Filter() Function
Advanced Aggregation Functions: Transform() Function
Advanced Aggregation Functions: Apply() Function
Pivot Tables in Pandas Library
Data Entry with Csv and Txt Files
Data Entry with Excel Files
Outputting as an CSV Extension
Outputting as an Excel File
Basic Knowledge of Python Programming Language
Basic Knowledge of Numpy Library
Basic Knowledge of Mathematics
Watch the course videos completely and in order.
Internet Connection
Any device where you can watch the lesson, such as a mobile phone, computer or tablet.
Determination and patience for learning Pandas Python Programming Language Library.

Who Should Attend

Anyone who wants to learn Pands and Numpy
Anyone who want to use effectively linear algebra,
Software developer whom want to learn the Neural Network’s math,
Data scientist whom want to use effectively Numpy array
Anyone interested in data sciences
Anyone who plans a career in data scientist,
Anyone eager to learn python with no coding background
Anyone who is particularly interested in big data, machine learning
Those who want to learn the Pandas Library, which is necessary for data science
Those who want to improve themselves in the field of Python Programming Language and Data science

Target Audiences

Anyone who wants to learn Pands and Numpy
Anyone who want to use effectively linear algebra,
Software developer whom want to learn the Neural Network’s math,
Data scientist whom want to use effectively Numpy array
Anyone interested in data sciences
Anyone who plans a career in data scientist,
Anyone eager to learn python with no coding background
Anyone who is particularly interested in big data, machine learning
Those who want to learn the Pandas Library, which is necessary for data science
Those who want to improve themselves in the field of Python Programming Language and Data science

Hello there,

Welcome to the ” Pandas & NumPy Python Programming Language Libraries A-Z™ “ Course

NumPy & Python Pandas for Python Data Analysis, Data Science, Machine Learning, Deep Learning using Python from scratch

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. Pandas is built on top of another package named Numpy, which provides support for multi-dimensional arrays.

Pandas is mainly used for data analysisand associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. data analysis, pandas, numpy, numpy stack, numpy python, python data analysis, python, Python numpy, data visualization, pandas python, python pandas, python for data analysis, python data, data visualization.

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Pandas Pyhon aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.

Python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.

Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Moreover, Numpy forms the foundation of the Machine Learning stack.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray , it provides a lot of supporting functions that make working with ndarray very easy.

NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

With this training, where we will try to understand the logic of the PANDAS and NumPy Libraries, which are required for data science, which is seen as one of the most popular professions of the 21st century, we will work on many real-life applications.

The course content is created with real-life scenarios and aims to move those who start from scratch forward within the scope of the PANDAS Library.

PANDAS Library is one of the most used libraries in data science.

Yes, do you know that data science needs will create 11.5 million job opportunities by 2026?

Well, the average salary for data science careers is $100,000. Did you know that? Data Science Careers Shape the Future.

It isn’t easy to imagine our life without data science and Machine learning. Word prediction systems, Email filtering, and virtual personal assistants like Amazon’s Alexa and iPhone’s Siri are technologies that work based on machine learning algorithms and mathematical models.

Data science and Machine learning-only word prediction system or smartphone does not benefit from the voice recognition feature. Machine learning and data science are constantly applied to new industries and problems. Millions of businesses and government departments rely on big data to be successful and better serve their customers. So, data science careers are in high demand.

If you want to learn one of the most employer-requested skills?

Do you want to use the pandas’ library in machine learning and deep learning by using the Python programming language?

If you’re going to improve yourself on the road to data science and want to take the first step.

In any case, you are in the right place!

“Pandas Python Programming Language Library From Scratch A-Z™” course for you.

In the course, you will grasp the topics with real-life examples. With this course, you will learn the Pandas library step by step.

You will open the door to the world of Data Science, and you will be able to go deeper for the future.

This Pandas course is for everyone!

No problem if you have no previous experience! This course is expertly designed to teach (as a refresher) everyone from beginners to professionals.

During the course, you will learn the following topics:

Installing Anaconda Distribution for Windows
Installing Anaconda Distribution for MacOs
Installing Anaconda Distribution for Linux
Introduction to Pandas Library
Series Structures in the Pandas Library
Most Applied Methods on Pandas Series
DataFrame Structures in Pandas Library
Element Selection Operations in DataFrame Structures
Structural Operations on Pandas DataFrame
Multi-Indexed DataFrame Structures
Structural Concatenation Operations in Pandas DataFrame
Functions That Can Be Applied on a DataFrame
Pivot Tables in Pandas Library
File Operations in Pandas Library
Creating NumPy Arrays in Python
Functions in the NumPy Library
Indexing, Slicing, and Assigning NumPy Arrays
Operations in Numpy Library

With my up-to-date Course, you will have the chance to keep yourself up to date and equip yourself with Pandas skills. I am also happy to say that I will always be available to support your learning and answer your questions.

What is a Pandas in Python?

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays.

What is Pandas used for?

Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames. Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.

What is difference between NumPy and pandas?

NumPy library provides objects for multi-dimensional arrays, whereas Pandas is capable of offering an in-memory 2d table object called DataFrame. NumPy consumes less memory as compared to Pandas. Indexing of the Series objects is quite slow as compared to NumPy arrays.

Why do we need pandas in Python?

Pandas is built on top of two core Python libraries—matplotlib for data visualization and NumPy for mathematical operations. Pandas acts as a wrapper over these libraries, allowing you to access many of matplotlib’s and NumPy’s methods with less code.

Is pandas easy to learn?

Pandas is one of the first Python packages you should learn because it’s easy to use, open source, and will allow you to work with large quantities of data. It allows fast and efficient data manipulation, data aggregation and pivoting, flexible time series functionality, and more.

Why do you want to take this Course?

Our answer is simple: The quality of teaching.

Whether you work in machine learning or finance, Whether you’re pursuing a career in web development or data science, Python and data science are among the essential skills you can learn.

Python’s simple syntax is particularly suitable for desktop, web, and business applications.

The Python instructors at OAK Academy are experts in everything from software development to data analysis and are known for their practical, intimate instruction for students of all levels.

Our trainers offer training quality as described above in every field, such as the Python programming language.

London-based OAK Academy is an online training company. OAK Academy provides IT, Software, Design, and development training in English, Portuguese, Spanish, Turkish, and many languages on the Udemy platform, with over 1000 hours of video training courses.

OAK Academy not only increases the number of training series by publishing new courses but also updates its students about all the innovations of the previously published courses.

When you sign up, you will feel the expertise of OAK Academy’s experienced developers. Our instructors answer questions sent by students to our instructors within 48 hours at the latest.

Quality of Video and Audio Production

All our videos are created/produced in high-quality video and audio to provide you with the best learning experience.

In this course, you will have the following:

• Lifetime Access to the Course

• Quick and Answer in the Q&A Easy Support

• Udemy Certificate of Completion Available for Download

• We offer full support by answering any questions.

• “For Data Science Using Python Programming Language: Pandas Library | AZ™” course.<br>Come now! See you at the Course!

• We offer full support by answering any questions.

Now dive into my ” Pandas & NumPy Python Programming Language Libraries A-Z™ “ Course

NumPy & Python Pandas for Python Data Analysis, Data Science, Machine Learning, Deep Learning using Python from scratch

See you at the Course!

Course Curriculum

Chapter 1: Installations

Lecture 1: Installing Anaconda Distribution for Windows

Lecture 2: Notebook Project Files Link regarding NumPy Python Programming Language Library

Lecture 3: Installing Anaconda Distribution for MacOs

Lecture 4: 6 Article Advice And Links about Numpy, Numpy Pyhon

Lecture 5: Installing Anaconda Distribution for Linux

Chapter 2: NumPy Library Introduction

Lecture 1: Introduction to NumPy Library

Lecture 2: The Power of NumPy

Chapter 3: Creating NumPy Array in Python

Lecture 1: Creating NumPy Array with The Array() Function

Lecture 2: Creating NumPy Array with Zeros() Function

Lecture 3: Creating NumPy Array with Ones() Function

Lecture 4: Creating NumPy Array with Full() Function

Lecture 5: Creating NumPy Array with Arange() Function

Lecture 6: Creating NumPy Array with Eye() Function

Lecture 7: Creating NumPy Array with Linspace() Function

Lecture 8: Creating NumPy Array with Random() Function

Lecture 9: Properties of NumPy Array

Chapter 4: Functions in the NumPy Library

Lecture 1: Reshaping a NumPy Array: Reshape() Function

Lecture 2: Identifying the Largest Element of a Numpy Array

Lecture 3: Detecting Least Element of Numpy Array: Min(), Ar

Lecture 4: Concatenating Numpy Arrays: Concatenate() Functio

Lecture 5: Splitting One-Dimensional Numpy Arrays: The Split

Lecture 6: Splitting Two-Dimensional Numpy Arrays: Split(),

Lecture 7: Sorting Numpy Arrays: Sort() Function

Chapter 5: Indexing, Slicing, and Assigning NumPy Arrays

Lecture 1: Indexing Numpy Arrays

Lecture 2: Slicing One-Dimensional Numpy Arrays

Lecture 3: Slicing Two-Dimensional Numpy Arrays

Lecture 4: Assigning Value to One-Dimensional Arrays

Lecture 5: Assigning Value to Two-Dimensional Array

Lecture 6: Fancy Indexing of One-Dimensional Arrrays

Lecture 7: Fancy Indexing of Two-Dimensional Arrrays

Lecture 8: Combining Fancy Index with Normal Indexing

Lecture 9: Combining Fancy Index with Normal Slicing

Chapter 6: Operations in Numpy Library

Lecture 1: Operations with Comparison Operators

Lecture 2: Arithmetic Operations in Numpy

Lecture 3: Statistical Operations in Numpy

Lecture 4: Solving Second-Degree Equations with NumPy

Chapter 7: Pandas Library Introduction

Lecture 1: Introduction to Pandas Library

Lecture 2: Pandas Project Files Link

Chapter 8: Series Structures in the Pandas Library

Lecture 1: Creating a Pandas Series with a List

Lecture 2: Creating a Pandas Series with a Dictionary

Lecture 3: Creating Pandas Series with NumPy Array

Lecture 4: Object Types in Series

Lecture 5: Examining the Primary Features of the Pandas Seri

Lecture 6: Most Applied Methods on Pandas Series

Lecture 7: Indexing and Slicing Pandas Series

Chapter 9: DataFrame Structures in Pandas Library

Lecture 1: Creating Pandas DataFrame with List

Lecture 2: Creating Pandas DataFrame with NumPy Array

Lecture 3: Creating Pandas DataFrame with Dictionary

Lecture 4: Examining the Properties of Pandas DataFrames

Chapter 10: Element Selection Operations in DataFrame Structures

Lecture 1: Element Selection Operations in Pandas DataFrames: Lesson 1

Lecture 2: Element Selection Operations in Pandas DataFrames: Lesson 2

Lecture 3: Top Level Element Selection in Pandas DataFrames:Lesson 1

Lecture 4: Top Level Element Selection in Pandas DataFrames:Lesson 2

Lecture 5: Top Level Element Selection in Pandas DataFrames:Lesson 3

Lecture 6: Element Selection with Conditional Operations in

Chapter 11: Structural Operations on Pandas DataFrame

Lecture 1: Adding Columns to Pandas Data Frames

Lecture 2: Removing Rows and Columns from Pandas Data frames

Lecture 3: Null Values in Pandas Dataframes

Lecture 4: Dropping Null Values: Dropna() Function

Lecture 5: Filling Null Values: Fillna() Function

Lecture 6: Setting Index in Pandas DataFrames

Chapter 12: Multi-Indexed DataFrame Structures

Lecture 1: Multi-Index and Index Hierarchy in Pandas DataFrames

Lecture 2: Element Selection in Multi-Indexed DataFrames

Lecture 3: Selecting Elements Using the xs() Function in Multi-Indexed DataFrames

Chapter 13: Structural Concatenation Operations in Pandas DataFrame

Lecture 1: Concatenating Pandas Dataframes: Concat Function

Lecture 2: Merge Pandas Dataframes: Merge() Function: Lesson 1

Lecture 3: Merge Pandas Dataframes: Merge() Function: Lesson 2

Lecture 4: Merge Pandas Dataframes: Merge() Function: Lesson 3

Lecture 5: Merge Pandas Dataframes: Merge() Function: Lesson 4

Lecture 6: Joining Pandas Dataframes: Join() Function

Chapter 14: Functions That Can Be Applied on a DataFrame

Lecture 1: Loading a Dataset from the Seaborn Library

Lecture 2: Examining the Data Set 1

Lecture 3: Aggregation Functions in Pandas DataFrames

Lecture 4: Examining the Data Set 2

Lecture 5: Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes

Lecture 6: Advanced Aggregation Functions: Aggregate() Function

Instructors

Oak Academy
Web & Mobile Development, IOS, Android, Ethical Hacking, IT
OAK Academy Team
instructor
Ali̇ CAVDAR
DATA SCIENTIST AND IT INSTRUCTOR

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5 stars: 31 votes

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