课程

Pandas Joins for Spreadsheet Users

中级技能水平

更新时间 2026年4月

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

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PythonData Manipulation4 小时12 视频44 练习3,700 经验值4,447成就声明

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课程描述

Joining two or more datasets is necessary for almost any real-world analysis. You’ve done it before with spreadsheets using VLOOKUP and related functions. Can you build on this experience as you transition to the world of Python? Yes! In this course you will learn the ins and outs of bringing datasets together with pandas, Python’s gold standard for manipulating tabular data. You’ll apply pandas functions to combine data from the National Football League (NFL) framed in a familiar spreadsheet environment. Armed with these skills you will be able to harness the power of pandas and integrate larger, more complex datasets into any analysis.

先决条件

Python for Spreadsheet Users

1

Introduction to joining data

In this chapter, we'll build a foundation for using pandas to join data. You'll learn about the types of joins and how pandas can improve your effectiveness and productivity.

Joining data: a real-world necessity

50 经验值

The need for joining data

50 经验值

Working with split data

100 经验值

Working with complementary data

100 经验值

Concatenation

50 经验值

Concatenating rows

100 经验值

Concatenating rows with duplicated indexes

100 经验值

Concatenating columns

100 经验值

Power and flexibility

50 经验值

Advantages of pandas joins

100 经验值

Simple coding for complex merges

100 经验值

2

VLOOKUP-style joins

In this chapter, you'll learn how to use pandas for joining data in a way similar to using VLOOKUP formulas in a spreadsheet. You'll learn about three types of joins and then focus on the first type, one-to-one joins.

Types of joins

50 经验值

One-to-one joins

100 经验值

One-to-many joins

100 经验值

Many-to-many joins

100 经验值

A closer look at one-to-one joins

50 经验值

Unscrambling the framework.

100 经验值

Replicating VLOOKUP

100 经验值

Merging on two or more keys

100 经验值

Combining common data with inner joins

50 经验值

Object-oriented merges

100 经验值

Basic inner joins

100 经验值

Dealing with different names

100 经验值

Choosing the correct join method

100 经验值

3

One-to-many joins

In this chapter, we'll focus on one-to-many relationships. You'll practice identifying the relationship of key columns and joining data frames by column. You'll also learn how to join two or more data frames based on their indices.

"Out of many, one"

50 经验值

Framework part 2: one-to-many merges

100 经验值

Identifying one-to-many relationships

100 经验值

Joining on key columns

50 经验值

Checking for duplicate keys

100 经验值

Completing a one-to-many merge

100 经验值

Index-based joins

50 经验值

Joining on index

100 经验值

Joining multiple tables

100 经验值

Reviewing the one-to-many join

50 经验值

4

Advanced joins

In the final chapter, you'll learn advanced joining techniques to use when faced with challenging data. You'll be presented with a challenge of your own in the form of a case study that tests your skills.

Joining data in real life

50 经验值

Mixing indexes and columns

100 经验值

Suffixes and indicators

100 经验值

Working with time data

50 经验值

Combining time series

100 经验值

Matching to the nearest time

100 经验值

Recap and case study

50 经验值

Case study challenge - part 1

100 经验值

Case study challenge - part 2

100 经验值

Case study challenge - part 3

100 经验值

Pandas Joins for Spreadsheet Users

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