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Pandas & NumPy Python Programming Language Libraries A-Z™

Name: Pandas & NumPy Python Programming Language Libraries A-Z™
Rating: 4.6 (51 reviews)

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

Created byOak Academy, OAK Academy Team, Ali̇ CAVDAR

Last updated 6/2026

English

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
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.

Course content

17 sections • 87 lectures • 10h 44m total length

Installing Anaconda Distribution for Windows10:35
In this lesson we will learn how to install anaconda distributor on windows operating system.
Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization.
Notebook Project Files Link regarding NumPy Python Programming Language Library0:02
Nearly every scientist working in Python draws on the power of NumPy.
Installing Anaconda Distribution for MacOs6:17
In this lesson we will learn how to install anaconda distributor on MacOs operating system.
Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.
6 Article Advice And Links about Numpy, Numpy Pyhon0:26
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.
Installing Anaconda Distribution for Linux14:43
In this lesson we will learn how to install anaconda distributor on Linux operating system.
Machine learning 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 bootcamp is one of the most important skills you can learn.

Introduction to NumPy Library6:24
In this lesson, we will get to know the Numpy Library.
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.
The Power of NumPy16:04
In this lesson, we will examine the features that distinguish Numpy from other libraries.
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. Arrays are very frequently used in data science, where speed and resources are very important.
Quiz

Creating NumPy Array with The Array() Function8:16
In this lesson we will learn to create NumPy Array using array() function.
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
Creating NumPy Array with Zeros() Function5:05
In this lesson we will learn to create NumPy Array using zeros() function.
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
Creating NumPy Array with Ones() Function3:06
In this lesson we will learn to create NumPy Array using ones() function.
NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
Creating NumPy Array with Full() Function2:49
In this lesson we will learn to create NumPy Array using full() function.
The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
Creating NumPy Array with Arange() Function2:55
In this lesson we will learn to create NumPy Array using arange() function.
NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.
Creating NumPy Array with Eye() Function3:08
In this lesson we will learn to create NumPy Array using eye() function.
Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.
Creating NumPy Array with Linspace() Function1:31
In this lesson we will learn to create NumPy Array using linspace() function.
Nearly every scientist working in Python draws on the power of NumPy.
Creating NumPy Array with Random() Function8:29
In this lesson we will learn to create NumPy Array using random() function.
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.
Properties of NumPy Array5:24
In this lesson, we will examine how we can access the properties of the Numpy Array.
Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.
Quiz

Reshaping a NumPy Array: Reshape() Function5:57
In this lesson we will learn the reshape() Function that allows us to reshape Arrays.
What is data science?
We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science python uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Python data science seeks to find patterns in data and use those patterns to predict future data.
Identifying the Largest Element of a Numpy Array3:45
In this lesson we will learn to find the largest element in NumPy Arrays.
Data science using python includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a python for data science, it progresses by creating new algorithms to analyze data and validate current methods.
Detecting Least Element of Numpy Array: Min(), Ar2:35
In this lesson we will learn to find the smallest element in NumPy Arrays.
What is python?
Machine learning 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 bootcamp is one of the most important skills you can learn.
Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization.
Concatenating Numpy Arrays: Concatenate() Functio9:40
In this lesson we will learn the function of combining NumPy Arrays
What is NumPy?
NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
Splitting One-Dimensional Numpy Arrays: The Split5:45
In this lesson we will learn the function of splitting One-Dimensional NumPy Arrays
What is NumPy is used for?
NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.
Splitting Two-Dimensional Numpy Arrays: Split(),9:33
In this lesson we will learn the function of splitting Two-Dimensional NumPy Arrays
What is the difference between NumPy and Python?
NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. The elements in a NumPy array are all required to be of the same data type, and thus will be the same size in memory.
Sorting Numpy Arrays: Sort() Function4:16
In this lesson we will learn the Sort Function that we will use to sort NumPy Arrays.
What is NumPy arrays in Python?
A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.
Quiz

Indexing Numpy Arrays7:39
In this lesson we will learn how to Index NumPy Arrays.
Why NumPy is used in Machine Learning?
NumPy is a very popular python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific
Slicing One-Dimensional Numpy Arrays6:08
In this lesson, we'll learn how to Slice One-Dimensional NumPy Arrays.
What is NumPy array example?
It is basically a table of elements which are all of the same type and indexed by a tuple of positive integers. The dimensions are called axis in NumPy. The NumPy's array class is known as ndarray or alias array. The numpy. array is not the same as the standard Python library class array.
Slicing Two-Dimensional Numpy Arrays9:30
In this lesson, we'll learn how to Slice Two-Dimensional NumPy Arrays.

What are the benefits of NumPy in Python?
NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types. This allows the code to be optimized even further.
Assigning Value to One-Dimensional Arrays5:02
In this lesson, we'll learn how to assign values to One-Dimensional NumPy Arrays.
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.
Assigning Value to Two-Dimensional Array9:57
In this lesson, we'll learn how to assign values to Two-Dimensional NumPy Arrays.
NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.
Fancy Indexing of One-Dimensional Arrrays6:09
In this lesson, we will introduce Fancy Indexing. And we will learn how to do Fancy indexing in One-Dimensional NumPy Arrays.
The array object in NumPy is called ndarray , it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.
Fancy Indexing of Two-Dimensional Arrrays12:32
In this lesson, we will learn how to perform Fancy indexing on Two-Dimensional NumPy Arrays.
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
Combining Fancy Index with Normal Indexing3:25
In this lesson, we will learn to use Fancy indexing and Normal Indexing together in a coordinated way.
NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
Combining Fancy Index with Normal Slicing4:36
In this lesson, we will learn to use Fancy indexing and Normal Slicing together in a coordinated way.
NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

Operations with Comparison Operators6:09
In this lesson, we will operate on NumPy Arrays using Comparison Operators.
The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
Arithmetic Operations in Numpy15:10
In this lesson, we will operate on NumPy Arrays using Arithmetic Operators.
NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.
Statistical Operations in Numpy6:35
In this lesson, we will operate NumPy Arrays to generate statistical outputs.
Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.
Solving Second-Degree Equations with NumPy7:00
In this lesson we will solve quadratic equations using the NumPy Library.
Nearly every scientist working in Python draws on the power of NumPy.

Introduction to Pandas Library6:38
In this lesson, we will get to know the Pandas Library.
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.
Pandas Project Files Link
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.
Quiz

Creating a Pandas Series with a List10:21
In this lesson we will learn how to create a Pandas Series using a list.
Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames.
Creating a Pandas Series with a Dictionary4:53
In this lesson we will learn how to create a Pandas Series using a dictionary.
Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.
Creating Pandas Series with NumPy Array3:10
In this lesson we will learn how to create a Pandas Series using a Numpy array.
Object Types in Series5:14
In this lesson, we will examine the types of objects that the Pandas Series can contain to demonstrate its flexibility.
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.
Examining the Primary Features of the Pandas Seri4:55
In this lesson, we will examine the properties of Pandas Series using various functions and methods.
Pandas Pyhon aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
Most Applied Methods on Pandas Series12:53
In this course, we will examine the most common methods that can be applied to the Pandas Series.
Additionally, Pandas has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.
Indexing and Slicing Pandas Series7:13
In this lesson, we will apply element selection operations in Pandas Series within the scope of indexing and slicing methods.
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.
quiz

Creating Pandas DataFrame with List5:33
In this lesson we will learn how to create a Pandas DataFrame using a list.
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.
Creating Pandas DataFrame with NumPy Array3:03
In this lesson we will learn how to create a Pandas DataFrame using a Numpy array.
PANDAS Library is one of the most used libraries in data science.
Creating Pandas DataFrame with Dictionary4:01
In this lesson we will learn how to create a Pandas DataFrame using a dictionary.
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.
Examining the Properties of Pandas DataFrames6:32
In this lesson, we will examine the properties of Pandas DataFrame using various functions and methods.
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.
quiz

Element Selection Operations in Pandas DataFrames: Lesson 17:41
In this lesson, we will perform element selection operations from Pandas DataFrame with Python capabilities.
Element Selection Operations in Pandas DataFrames: Lesson 26:04
In this lesson, we will continue to select elements from Pandas DataFrame with python capabilities.
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.
Top Level Element Selection in Pandas DataFrames:Lesson 18:42
In this lesson, we will perform element selection from Pandas DataFrame using loc and iloc constructs.
What is Panda used for?
Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames.
Top Level Element Selection in Pandas DataFrames:Lesson 27:33
In this lesson, we will continue the process of selecting elements from the Pandas DataFrame using the loc and iloc constructs.
Pandas allows importing data from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel.
Top Level Element Selection in Pandas DataFrames:Lesson 35:35
In this lesson, we will continue the process of selecting elements from the Pandas DataFrame using the loc and iloc constructs.
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.
Element Selection with Conditional Operations in11:23
In this lesson, we will perform element selection by making Conditional operations from Pandas DataFrame.
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.
quiz

Requirements

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

Description

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 2030?

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!

Who this course is for:

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

Pandas & NumPy Python Programming Language Libraries A-Z™

What you'll learn

Explore related topics

Course content

Installations5 lectures • 32min

NumPy Library Introduction2 lectures • 22min

Creating NumPy Array in Python9 lectures • 41min

Functions in the NumPy Library7 lectures • 42min

Indexing, Slicing, and Assigning NumPy Arrays9 lectures • 1hr 5min

Operations in Numpy Library4 lectures • 35min

Pandas Library Introduction2 lectures • 7min

Series Structures in the Pandas Library7 lectures • 49min

DataFrame Structures in Pandas Library4 lectures • 19min

Element Selection Operations in DataFrame Structures6 lectures • 47min

Requirements

Description

Who this course is for: