2025 Pandas Bootcamp Data Analysis with Pandas Python3

所在平台: Udemy

课程主页: https://www.udemy.com/course/the-pandas-bootcamp-data-analysis-with-pandas-python3/

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课程名称:2025 Pandas 数据分析训练营(Python3) 课程概述: 《2025 Pandas 数据分析训练营(Python3)》旨在帮助任何希望学习如何使用 Pandas 这一流行的数据处理库的学员。课程内容涵盖从 Pandas 的基础安装和数据结构到更高级的窗口函数和可视化等多方面主题。无论你是初学者还是有经验的程序员,这门课程都将为你提供使用 Pandas 高效分析和处理数据的全面理解。通过实用的编程实例,你将学习如何进行数据清理和处理、聚合和分组,以及如何处理不同的数据格式如 CSV、Excel 和 JSON。课程结束时,你将掌握使用 Pandas 处理大数据集和执行复杂数据分析任务所需的知识和技能。 讲师简介: Faisal Zamir 是一位经验丰富的程序员和计算机科学专家,拥有计算机科学硕士学位,并在学校、学院和大学有超过7年的工作经验。他是一位技能高超、热爱教学与指导学生的讲师。Faisal 在多个编程语言(包括 PHP、Java 和 Python)上有丰富的项目经验,并参与过多个与网页开发、软件工程及数据库管理相关的项目。他的广泛经验使其能够深入理解编程的基础,并将复杂的概念以易懂的方式传授给学生。Faisal 有着良好的教学记录,能够帮助各个水平的学生实现他们的目标,结合理论和实际示例的独特风格,使学生能够将学到的知识应用于实际场景。 课程将教授的内容: 1. 理解 Pandas 的基础知识、数据结构及安装方法。 2. 运用不同类型的 Pandas 数据结构。 3. 使用描述性和推断性统计方法分析数据。 4. 应用逐元素、按行或列、以及表级的函数处理数据。 5. 使用 Pandas 进行重新索引、排序和数据迭代。 6. 利用字符串方法进行数据清理和处理。 7. 定制 Pandas 的显示选项和数据类型。 8. 基于标签、整数或布尔值进行索引和选择操作。 9. 使用滚动、扩展和加权窗口等窗口函数进行数据分析。 10. 基于单列或多列对数据进行分组,应用聚合函数并进行数据过滤或变换。 11. 处理分类数据,执行重新排序、移除、添加和重命名类别,并使用 Pandas 可视化分类数据。 12. 用不同类型的图表(如线图、条形图、直方图、散点图、箱形图、区域图和热图)进行数据可视化。 13. 使用 Pandas 读取和写入不同格式的数据,包括 CSV、Excel 和 JSON。 14. 处理稀疏数据及其特征。 本课程适合希望提升数据分析技能的任何人,包括学生、数据分析师、商业专业人士及有志于成为数据科学家的人员。加入我们,迈出成为熟练 Pandas 用户的第一步!感谢 Faisal Zamir。

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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 Pandas Course for Data Science Introduction - What is Pandas, Why need of Pandas, What we can do with Pandas, Pandas Installation, Pandas Basic ProgramData Structures - Types of Data StructuresSeries - Series Operations, Series Attributes, Series Methods, DataFrame, PanelDataFrame - DataFrame Operations, DataFrame Attributes, DataFrame Methods, PanelDescriptive Statistics - Descriptive Statistics Methods & Programming Examples, Inferential Statistics FunctionsFunction Application - Element-wise, Row or Column-wise, Table-wiseReindexing - Reindexing Method with Programming Examples, Iteration, Iteration Method with Programming Examples, Sorting, Sorting Method with Programming ExamplesString Methods - lower, upper, title, capitalize, swapcase, strip, lstrip, rstrip, split, rsplit, join, replace, contains, startswith, endswith, find, rfind, count, lenCustomization Options - Customizing Display Options, Customizing Data Types, Customizing Data Cleaning and Manipulation, Indexing & Selecting (Label-based or integer-based indexing, Boolean indexing, Based on a string.query)Window Function - Rolling Window, Expanding Window, Exponentially Weighted Window, Weighted WindowGroupby Operations - Splitting Data, Applying Function on Data, Combining Results, Operations on Subset Data, Aggregation, Transformation, FiltrationCategorical Data - Benefits, Purpose, Methods Used in Categorical Data (astype, value_counts, unique, reorder_categories, set_categories, remove categories, add categories, rename categories, remove unused categories)Visualization - Line Plot, Bar Plot, Histogram, Scatter Plot, Box Plot, Area Plot, Heatmap, Density PlotI/O Tools - Reading CSV, Writing CSV, Reading Excel, Writing Excel, Reading JSON, Writing JSONDate Time Functions - to_datetime, Date Range, strftime, TimestampOur 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

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