What you'll learn
Understand the basics of Pandas, its data structures, and how to install it.
Work with different types of data structures in Pandas.
Use descriptive and inferential statistics methods to analyze data.
Apply element-wise, row or column-wise, and table-wise function application on data.
Reindex, sort, and iterate through data using Pandas.
Use string methods for data cleaning and manipulation.
Customize display options and data types in Pandas.
Perform indexing and selecting operations based on labels, integers, or Boolean values.
Use window functions such as rolling, expanding, and ewm for data analysis.
Group data based on single or multiple columns, apply aggregation functions, and filter or transform data.
Work with categorical data, perform methods such as reorder, remove, add, and rename categories, and visualize categorical data using Pandas.
Visualize data using different types of plots such as line, bar, histogram, scatter, box, area, and heatmap.
Read and write data in different formats such as CSV, Excel, and JSON using Pandas.
Work with sparse data and understand its features.
Requirements
You should have basic knowledge of Python programming with beginner experince
You did not have to buy extra software or course
Description
Introduction to The Pandas Bootcamp | Data Analysis with Pandas Python3The "Introduction to The Pandas Bootcamp | Data Analysis with Pandas Python3" course is designed for anyone who wants to learn how to use Pandas, the popular data manipulation library for Python. This course covers a wide range of topics, from the basics of Pandas installation and data structures to more advanced topics such as window functions and visualization. Whether you are a beginner or an experienced programmer, this course will provide you with a comprehensive understanding of how to use Pandas to analyze and manipulate data efficiently. Through practical programming examples, you will learn how to perform data cleaning and manipulation, aggregation, and grouping, as well as how to work with different data formats such as CSV, Excel, and JSON. By the end of the course, you will have gained the knowledge and skills necessary to work with large datasets and perform complex data analysis tasks using Pandas.********** Instructors Experiences and Education: **********Faisal Zamir is an experienced programmer and an expert in the field of computer science. He holds a Master's degree in Computer Science and has over 7 years of experience working in schools, colleges, and university. Faisal is a highly skilled instructor who is passionate about teaching and mentoring students in the field of computer science.As a programmer, Faisal has worked on various projects and has experience in multiple programming languages, including PHP, Java, and Python. He has also worked on projects involving web development, software engineering, and database management. This broad range of experience has allowed Faisal to develop a deep understanding of the fundamentals of programming and the ability to teach complex concepts in an easy-to-understand manner.As an instructor, Faisal has a proven track record of success. He has taught students of all levels, from beginners to advanced, and has a passion for helping students achieve their goals. Faisal has a unique teaching style that combines theory with practical examples, which allows students to apply what they have learned in real-world scenarios.Overall, Faisal Zamir is a skilled programmer and a talented instructor who is dedicated to helping students achieve their goals in the field of computer science. With his extensive experience and proven track record of success, students can trust that they are learning from an expert in the field.What you will learn from Course Data Analysis with Pandas Python3Understand the basics of Pandas, its data structures, and how to install it.Work with different types of data structures in Pandas.Use descriptive and inferential statistics methods to analyze data.Apply element-wise, row or column-wise, and table-wise function application on data.Reindex, sort, and iterate through data using Pandas.Use string methods for data cleaning and manipulation.Customize display options and data types in Pandas.Perform indexing and selecting operations based on labels, integers, or Boolean values.Use window functions such as rolling, expanding, and ewm for data analysis.Group data based on single or multiple columns, apply aggregation functions, and filter or transform data.Work with categorical data, perform methods such as reorder, remove, add, and rename categories, and visualize categorical data using Pandas.Visualize data using different types of plots such as line, bar, histogram, scatter, box, area, and heatmap.Read and write data in different formats such as CSV, Excel, and JSON using Pandas.Work with sparse data and understand its features.Outlines for Data Analysis with Pandas Python3Chapter 01IntroductionWhat is PandasWhy need of PandasWhat we can do with PandasPandas InstallationPandas Basic ProgramChapter 02Data StructuresTypes of Data Structure Chapter 03SeriesDataFramePanelChapter 04Descriptive StatisticsDescriptive Statistics Methods & Programming ExamplesInferential statistics functionsChapter 05Function ApplicationElement-wiseRow or Column-wiseTable-wiseChapter 06ReindexingReindexing Method with Programming ExamplesIteration Iteration Method with Programming ExamplesSortingSorting Method with Programming ExamplesChapter 07String Methodslower()upper()title()capitalize()swapcase()strip()lstrip()rstrip()split()rsplit()join()replace()contains()startswith()endswith()find()rfind()count()len()Chapter 08Customization OptionsCustomizing display optionsCustomizing data typesCustomizing data cleaning and manipulationIndexing & SelectingLabel-based or integer-based indexing (.loc[] and .iloc[] )Boolean indexingBased on a string (.query())Chapter 09Window Functionsrolling()rolling().apply()rolling().agg()rolling().corr()rolling().cov()rolling().max()rolling().mean()rolling().median()rolling().min()rolling().quantile()rolling().std()rolling().sum()rolling().var()expanding()ewm()Chapter 10Group ByGrouping by a single columnGrouping by multiple columnsAggregating dataApplying multiple aggregation functionsApplying custom functionsFiltering dataTransforming dataGrouping by timeIterating over groupsChapter 11Categorical DataBenefitsPurpose Methods used in Categorial Dataastype()value_counts()unique()reorder_categories()set_categories()remove_categories()add_categories()rename_categories()remove_unused_categories()orderedmin(), max()Chapter 12VisualizationLine plotBar plotHistogramScatter plotBox plotArea plotHeatmapDensity plotChapter 13I/O ToolsReading CSV Writing CSVReading ExcelWriting CSVReading JSONWriting CSVChapter 14Sparse DataFeatures Programming Example30-day money-back guarantee for The Pandas Bootcamp | Data Analysis with Pandas Python3We are confident that The Pandas Bootcamp | Data Analysis with Pandas Python3 course will provide you with the skills and knowledge needed for successful data analysis using Pandas. That's why we offer a 30-day money-back guarantee, giving you peace of mind as you embark on this learning journey. With our expert instructors and a comprehensive curriculum, you'll gain a solid understanding of data structures, descriptive statistics, function applications, customization options, and more. Our course is designed for anyone looking to enhance their data analysis skills, including students, data analysts, business professionals, and aspiring data scientists. Join us today and take the first step towards becoming a proficient Pandas user!Thank you Faisal Zamir
Overview
Section 1: Chapter 01
Lecture 1 01 Pandas Chapter 01 Outlines
Lecture 2 02 What is Pandas
Lecture 3 03 Where we can use Pandas
Lecture 4 04 What we can do with Pandas
Lecture 5 06 Pandas Basic Program
Section 2: Chapter 02
Lecture 6 01 Pandas Chapter 02 Outlines
Lecture 7 02 Series Data Structure
Lecture 8 03 DataFrame Data Strcuture
Lecture 9 04 Panel Data Structure
Aspiring data analysts who want to learn how to use Pandas for data analysis,Data scientists who want to add Pandas to their skillset,Business analysts who need to analyze data using Pandas,Programmers who want to learn about data manipulation and analysis using Python and Pandas,Anyone interested in learning about Pandas and data analysis with Python


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