Pandas & NumPy Python Programming Language Libraries A-Z™

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 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, 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.

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

Reed Courses Certificate of Completion

Digital certificate - Included

• Installations 06:31

  • 1: Installing Anaconda Distribution For MAC 03:42
  • 2: Installing Anaconda Distribution For Windows 02:49

• NumPy Library Introduction 23:28

  • 3: INTRODUCTION TO NUMPY LIBRARY 06:24
  • 4: THE POWER OF NUMPY 16:04
  • 5: quiz 01:00

• Creating NumPy Array in Python 41:43

  • 6: CREATE A NUMPY ARRAY WITH THE ARRAY() FUNCTION 08:16
  • 7: CREATE A NUMPY ARRAY WITH THE ZEROS() FUNCTION 05:05
  • 8: CREATE A NUMPY ARRAY WITH THE ONES() FUNCTION 03:06
  • 9: CREATE A NUMPY ARRAY WITH THE FULL() FUNCTION 02:49
  • 10: CREATE A NUMPY ARRAY WITH THE ARANGE() FUNCTION 02:55
  • 11: CREATE A NUMPY ARRAY WITH THE EYE() FUNCTION 03:08
  • 12: CREATE A NUMPY ARRAY WITH THE LINSPACE() FUNCTION 01:31
  • 13: CREATE A NUMPY ARRAY WITH THE RANDOM() FUNCTION 08:29
  • 14: Properties of NumPy Array 05:24
  • 15: quiz 01:00

• Functions in the NumPy Library 41:32

  • 16: RESHAPING A NUMPY ARRAY RESHAPE() FUNCTION 05:57
  • 17: Identifying the Largest Element of a Numpy Array Max(), Argmax() Functions 03:45
  • 18: Detecting Least Element of Numpy Array Min(), Argmin() Functions 02:35
  • 19: Concatenating Numpy Arrays Concatenate() Function 09:40
  • 20: Splitting One-Dimensional Numpy Arrays The Split() Function 05:46
  • 21: Splitting Two-Dimensional Numpy Arrays Split(), Vsplit, Hsplit() Function 09:33
  • 22: Sorting Numpy Arrays Sort() Function 04:16

• Indexing, Slicing, and Assigning NumPy Arrays 1:04:58

  • 23: Indexing Numpy Arrays 07:39
  • 24: Slicing One-Dimensional Numpy Arrays 06:08
  • 25: Slicing Two-Dimensional Numpy Arrays 09:30
  • 26: Assigning Value to One-Dimensional Arrays 05:02
  • 27: Assigning Value to two-Dimensional Arrays 09:57
  • 28: Fancy Indexing of One-Dimensional Arrrays 06:09
  • 29: Fancy Indexing of Two-Dimensional Arrrays 12:32
  • 30: Combining Fancy Index with Normal Indexing 03:25
  • 31: Combining Fancy Index with Normal Slicing 04:36

• Operations in Numpy Library 34:54

  • 32: Operations with Comparison Operators 06:09
  • 33: Arithmetic Operations in Numpy 15:10
  • 34: Statistical Operations in Numpy 06:35
  • 35: Solving second-degree equations with NumPy 07:00

• Pandas Library Introduction 07:38

  • 36: Introduction to Pandas Library 06:38
  • 37: quiz 01:00

• Series Structures in the Pandas Library 49:39

  • 38: Creating a Pandas Series with a List 10:21
  • 39: Creating a Pandas Series with a Dictionary 04:53
  • 40: Creating Pandas Series with NumPy Array 03:10
  • 41: Object Types in Series 05:14
  • 42: Examining the Primary Features of the Pandas Series 04:55
  • 43: Most Applied Methods on Pandas Series 12:53
  • 44: Indexing and Slicing Pandas Series 07:13
  • 45: quiz 01:00

• DataFrame Structures in Pandas Library 20:09

  • 46: Creating Pandas DataFrame with List 05:33
  • 47: Creating Pandas DataFrame with NumPy Array 03:03
  • 48: Creating Pandas DataFrame with Dictionary 04:01
  • 49: Examining the Properties of Pandas DataFrames 06:32
  • 50: quiz 01:00

• Element Selection Operations in DataFrame Structures 46:58

  • 51: Element Selection Operations in Pandas DataFrames Lesson 1 07:41
  • 52: Element Selection Operations in Pandas Data Frames Lesson 2 06:04
  • 53: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 08:42
  • 54: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 07:33
  • 55: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 05:35
  • 56: Element Selection with Conditional Operations in Pandas Data Frames 11:23

• Structural Operations on Pandas DataFrame 54:51

  • 57: Adding Columns to Pandas Data Frames 08:16
  • 58: Removing Rows and Columns from Pandas Data frames 04:00
  • 59: Null Values in Pandas Dataframes 14:42
  • 60: Dropping Null Values Dropna() Function 07:14
  • 61: Filling Null Values0 Fillna() Function 11:36
  • 62: Setting Index in Pandas DataFrames 07:03
  • 63: quiz 02:00

• Multi-Indexed DataFrame Structures 21:32

  • 64: Multi-Index and Index Hierarchy in Pandas DataFrames 09:17
  • 65: Element Selection in Multi-Indexed DataFrames 05:12
  • 66: Selecting Elements Using the xs() Function in Multi-Indexed DataFrames 07:03

• Structural Concatenation Operations in Pandas DataFrame 59:01

  • 67: Concatenating Pandas Dataframes Concat Function 12:40
  • 68: Merge Pandas Dataframes Merge() Function Lesson 1 10:45
  • 69: Merge Pandas Dataframes Merge() Function Lesson 2 05:37
  • 70: Merge Pandas Dataframes Merge() Function Lesson 3 09:44
  • 71: Merge Pandas Dataframes Merge() Function Lesson 4 07:34
  • 72: Joining Pandas Dataframes Join() Function 11:41
  • 73: quiz 01:00

• Functions That Can Be Applied on a DataFrame 1:41:41

  • 74: Loading a Dataset from the Seaborn Library 06:41
  • 75: Examining the Data Set 07:29
  • 76: Aggregation Functions in Pandas DataFrames 21:45
  • 77: Examining the Dataset 10:38
  • 78: Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes 18:14
  • 79: Advanced Aggregation Functions Aggregate() Function 07:40
  • 80: Advanced Aggregation Functions Filter() Function 06:30
  • 81: Advanced Aggregation Functions The Transform() Function 11:38
  • 82: Advanced Aggregation Functions The Apply() Function 10:06
  • 83: quiz 01:00

• Pivot Tables in Pandas Library 19:49

  • 84: Examining the Dataset 08:14
  • 85: Pivot Tables in Pandas Library 10:35
  • 86: quiz 01:00

• File Operations in Pandas Library 36:03

  • 87: Accessing and Making Files Available 05:11
  • 88: Data Entry with Csv and Txt Files 13:35
  • 89: Data Entry with Excel Files 04:25
  • 90: Output of File with CSV Extension 07:09
  • 91: Outputting as an Excel File 03:43
  • 92: quiz 02:00

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 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, 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.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.

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

•

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!

• 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

• Basic Knowledge of Python Programming Language

• No prior knowledge of Numpy and Pandas is required

• Free software and tools used during the course

• Basic computer knowledge

• Desire to learn Python, Pandas and Numpy libraries

• Nothing else! It’s just you, your computer and your ambition to get started today

• Desire to learn Numpy & Pandas for Data science, Machine Learning, Deep Learning using Python

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