Pandas Python Programming Language Library From Scratch A-Z

Pandas mainly used for Python Data Analysis. Learn Pandas for Data Science, Machine Learning, Deep Learning using Python

What you'll 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
Installing Anaconda Distribution for Windows
Installing Anaconda Distribution for MacOs
Installing Anaconda Distribution for Linux
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


Description
Welcome to the "Pandas Python Programming Language Library From Scratch A-Z™" Course

Pandas mainly used for Python Data Analysis. Learn Pandas for Data Science, Machine Learning, Deep Learning using Python



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 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. data analysis, pandas, python data analysis, python, data visualization, pandas python, python pandas, python for data analysis, python data

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.

With this training, where we will try to understand the logic of the PANDAS Library, which is 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

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

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 Panda 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 Python Programming Language Library From Scratch A-Z™" Course

Pandas mainly used for Python Data Analysis. Learn Pandas for Data Science, Machine Learning, Deep Learning using Python

See you at the Course!

Who this course is for:
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
Those who aim for a career in data science

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