NumPy & Pandas: Analyze & Transform Data

本课程是 Data Analysis with NumPy and Pandas 专项课程的一部分

位教师：EDUCBA

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3个模块

深入了解一个主题并学习基础知识。

9 小时完成

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自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Perform numerical operations and memory optimization with NumPy.
Create, join, pivot, and clean Pandas DataFrames effectively.
Apply aggregation, filtering, and workflows on real datasets.

您将获得的技能

要了解的详细信息

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作业

13 项作业

授课语言：英语（English）

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本课程是 Data Analysis with NumPy and Pandas 专项课程专项课程的一部分

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该课程共有3个模块

By completing this course, learners will be able to analyze datasets using NumPy and Pandas, perform efficient numerical operations, reshape and clean data, handle missing values, and apply end-to-end data analysis workflows on real-world datasets. The course begins with the foundations of NumPy, focusing on array structures, memory optimization, and statistical operations. It then transitions into Pandas, guiding learners through creating DataFrames, performing joins, pivots, and unpivots, as well as exploring, sorting, and cleaning data. Finally, learners will advance to practical applications, mastering aggregation, filtering, and conditional operations before applying these skills to real-world projects like the Wine dataset.

What makes this course unique is its step-by-step progression from core numerical computing concepts to applied data analysis projects, ensuring that learners not only understand the theory but also gain hands-on practice. Whether you are a beginner aiming to strengthen your foundations or a professional seeking to improve your data analysis efficiency, this course will equip you with the essential skills to transform raw data into actionable insights using NumPy and Pandas.

This module introduces learners to the fundamentals of NumPy, including its advantages over Python lists, array structures, and efficient operations. Learners will explore slicing, reshaping, statistical calculations, and concatenation to build a solid foundation in numerical computing.

涵盖的内容

11个视频4个作业

11个视频总计88分钟

Introduction to Numpy 8分钟
Importing Numpy Package and Basic Commands 9分钟
Comparision Between List 9分钟
Numpy on Basis of Memory and Time 5分钟
Why we are using Numpy and why not List 12分钟
Numpy Operations and Subsetting 11分钟
2D Numpy Arrays 7分钟
Subsetting Operations 8分钟
Descriptive Statistics in Numpy Arrays 7分钟
Array Updating 4分钟
Concatenate Functions 7分钟

4个作业总计60分钟

NumPy Fundamentals 10分钟
Why NumPy is Powerful 10分钟
Statistical and Structural Operations 10分钟
Graded-Mastering NumPy for Data Foundations 30分钟

This module guides learners through Pandas, covering how to create DataFrames, perform joins, reshape data, and explore datasets. Learners will also practice cleaning, renaming, and dropping variables, equipping them with skills for effective data preparation.

涵盖的内容

17个视频5个作业

17个视频总计136分钟

Introduction to Pandas 9分钟
Creating Dataframe from Series and Dictionary 9分钟
Making Dataframe from Dictionary 6分钟
Concatenate Dataframe 8分钟
Joins and Pivot 8分钟
Unipivot Dataframe 8分钟
Dataframe Operations 9分钟
Slicing 8分钟
Dicing 7分钟
Sorting Dataframes 6分钟
Summary Statistics 7分钟
Dealing with Duplicate Values 7分钟
Importing Dataset 12分钟
Head Tail and Unique Function 8分钟
Accessing Column 7分钟
Rename Variables 7分钟
Dropping Variables 8分钟

5个作业总计70分钟

Creating and Combining DataFrames 10分钟
Joins, Pivots, and Transformations 10分钟
Exploring and Sorting Data 10分钟
Cleaning and Structuring Data 10分钟
Graded-Working with Pandas for Data Wrangling 30分钟

This module focuses on advanced Pandas features such as grouping, filtering, and handling missing values. Learners will also explore real-world data analysis workflows, including importing datasets, applying conditions, and working with practical case studies like the Wine dataset.