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Udemy - Pandas and NumPy Python Programming Language Libraries A-Z

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Udemy - Pandas and NumPy Python Programming Language Libraries A-Z (Size: 2.7 GB)
  1. Accessing and Making Files Available.mp4 32.2 MB
  1. Adding Columns to Pandas Data Frames.mp4 31 MB
  1. Concatenating Pandas Dataframes Concat Function.mp4 58.1 MB
  1. Creating NumPy Array with The Array() Function.mp4 27.3 MB
  1. Creating Pandas DataFrame with List.mp4 21.1 MB
  1. Creating a Pandas Series with a List.mp4 36.2 MB
  1. Element Selection Operations in Pandas DataFrames Lesson 1.mp4 27.5 MB
  1. Examining the Data Set 3.mp4 35.6 MB
  1. Indexing Numpy Arrays.mp4 24.7 MB
  1. Installing Anaconda Distribution for Windows.mp4 122.6 MB
  1. Introduction to NumPy Library.mp4 43.4 MB
  1. Introduction to Pandas Library.mp4 32.3 MB
  1. Loading a Dataset from the Seaborn Library.mp4 35 MB
  1. Multi-Index and Index Hierarchy in Pandas DataFrames.mp4 39.5 MB
  1. Operations with Comparison Operators.mp4 19.9 MB
  1. Pandas & NumPy Python Programming Language Libraries A-Z™.html 307.2 B
  1. Reshaping a NumPy Array Reshape() Function.mp4 24.5 MB
  10. Quiz.html 204.8 B
  10. quiz.html 204.8 B
  2. Arithmetic Operations in Numpy.mp4 67 MB
  2. Creating NumPy Array with Zeros() Function.mp4 22.7 MB
  2. Creating Pandas DataFrame with NumPy Array.mp4 11.2 MB
  2. Creating a Pandas Series with a Dictionary.mp4 16.8 MB
  2. Data Entry with Csv and Txt Files.mp4 59.4 MB
  2. Element Selection Operations in Pandas DataFrames Lesson 2.mp4 29.4 MB
  2. Element Selection in Multi-Indexed DataFrames.mp4 22.3 MB
  2. Examining the Data Set 1.mp4 39.2 MB
  2. Identifying the Largest Element of a Numpy Array.mp4 14.3 MB
  2. Merge Pandas Dataframes Merge() Function Lesson 1.mp4 51.3 MB
  2. Notebook Project Files Link regarding NumPy Python Programming Language Library.html 204.8 B
  2. Pandas Project Files Link.html 204.8 B
  2. Pivot Tables in Pandas Library.mp4 50 MB
  2. Removing Rows and Columns from Pandas Data frames.mp4 14.4 MB
  2. Slicing One-Dimensional Numpy Arrays.mp4 20.8 MB
  2. The Power of NumPy.mp4 55.7 MB
  3. Aggregation Functions in Pandas DataFrames.mp4 83.7 MB
  3. Creating NumPy Array with Ones() Function.mp4 14.9 MB
  3. Creating Pandas DataFrame with Dictionary.mp4 14.7 MB
  3. Creating Pandas Series with NumPy Array.mp4 11 MB
  3. Data Entry with Excel Files.mp4 19.8 MB
  3. Detecting Least Element of Numpy Array Min(), Ar.mp4 9.5 MB
  3. Installing Anaconda Distribution for MacOs.mp4 57.9 MB
  3. Merge Pandas Dataframes Merge() Function Lesson 2.mp4 27.4 MB
  3. Null Values in Pandas Dataframes.mp4 62.3 MB
  3. Quiz.html 204.8 B
  3. Selecting Elements Using the xs() Function in Multi-Indexed DataFrames.mp4 28.2 MB
  3. Slicing Two-Dimensional Numpy Arrays.mp4 31.4 MB
  3. Statistical Operations in Numpy.mp4 30.2 MB
  3. Top Level Element Selection in Pandas DataFramesLesson 1.mp4 35.5 MB
  3. quiz.html 204.8 B
  4. 6 Article Advice And Links about Numpy, Numpy Pyhon.html 4.2 KB
  4. Assigning Value to One-Dimensional Arrays.mp4 16.9 MB
  4. Concatenating Numpy Arrays Concatenate() Functio.mp4 35.8 MB
  4. Creating NumPy Array with Full() Function.mp4 10.5 MB
  4. Dropping Null Values Dropna() Function.mp4 31.7 MB
  4. Examining the Data Set 2.mp4 42.6 MB
  4. Examining the Properties of Pandas DataFrames.mp4 23.9 MB
  4. Merge Pandas Dataframes Merge() Function Lesson 3.mp4 53.8 MB
  4. Object Types in Series.mp4 18 MB
  4. Outputting as an CSV Extension.mp4 32.8 MB
  4. Solving Second-Degree Equations with NumPy.mp4 22.4 MB
  4. Top Level Element Selection in Pandas DataFramesLesson 2.mp4 29 MB
  4. quiz.html 204.8 B
  5. Assigning Value to Two-Dimensional Array.mp4 32.5 MB
  5. Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes.mp4 80.9 MB
  5. Creating NumPy Array with Arange() Function.mp4 11.5 MB
  5. Examining the Primary Features of the Pandas Seri.mp4 17.4 MB
  5. Filling Null Values Fillna() Function.mp4 47.9 MB
  5. Installing Anaconda Distribution for Linux.mp4 119.8 MB
  5. Merge Pandas Dataframes Merge() Function Lesson 4.mp4 37.4 MB
  5. Outputting as an Excel File.mp4 18.1 MB
  5. Splitting One-Dimensional Numpy Arrays The Split.mp4 19.4 MB
  5. Top Level Element Selection in Pandas DataFramesLesson 3.mp4 20.5 MB
  5. quiz.html 204.8 B
  6. Advanced Aggregation Functions Aggregate() Function.mp4 27 MB
  6. Creating NumPy Array with Eye() Function.mp4 11.8 MB
  6. Element Selection with Conditional Operations in.mp4 42.5 MB
  6. Fancy Indexing of One-Dimensional Arrrays.mp4 19 MB
  6. Joining Pandas Dataframes Join() Function.mp4 51.9 MB
  6. Most Applied Methods on Pandas Series.mp4 44.1 MB
  6. Setting Index in Pandas DataFrames.mp4 36.4 MB
  6. Splitting Two-Dimensional Numpy Arrays Split(),.mp4 33.3 MB
  6. quiz.html 204.8 B
  7. Advanced Aggregation Functions Filter() Function.mp4 23 MB
  7. Creating NumPy Array with Linspace() Function.mp4 6.9 MB
  7. Fancy Indexing of Two-Dimensional Arrrays.mp4 42.6 MB
  7. Indexing and Slicing Pandas Series.mp4 26.9 MB
  7. Sorting Numpy Arrays Sort() Function.mp4 15.7 MB
  7. quiz.html 204.8 B
  8. Advanced Aggregation Functions Transform() Function.mp4 43.7 MB
  8. Combining Fancy Index with Normal Indexing.mp4 11.9 MB
  8. Creating NumPy Array with Random() Function.mp4 39.9 MB
  8. Quiz.html 204.8 B
  8. quiz.html 204.8 B
  9. Advanced Aggregation Functions Apply() Function.mp4 38.3 MB
  9. Combining Fancy Index with Normal Slicing.mp4 15.4 MB
  9. Properties of NumPy Array.mp4 20.6 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 102 total files

Description


Pandas & NumPy Python Programming Language Libraries A-Z™
https://DevCourseWeb.com

Published 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 87 lectures (10h 56m) | Size: 2.8 GB

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

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.

Description
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