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