Learn how you can merge disparate data using inner joins. By combining information from multiple sources you’ll uncover compelling insights that may have previously been hidden. You’ll also learn how the relationship between those sources, such as one-to-one or one-to-many, can affect your result. 

Inner join

What column to merge on?

Your first inner join

Inner joins and number of rows returned

One-to-many relationships

One-to-many classification

One-to-many merge

Merging multiple DataFrames

Total riders in a month

Three table merge

One-to-many merge with multiple tables

Data Merging Basics

Take your knowledge of joins to the next level. In this chapter, you’ll work with TMDb movie data as you learn about left, right, and outer joins. You’ll also discover how to merge a table to itself and merge on a DataFrame index. 

Left join

Counting missing rows with left join

Enriching a dataset

How many rows with a left join?

Other joins

Right join to find unique movies

Popular genres with right join

Using outer join to select actors

Merging a table to itself

Self join

How does pandas handle self joins?

Merging on indexes

Index merge for movie ratings

Do sequels earn more?

Merging Tables With Different Join Types

In this chapter, you’ll leverage powerful filtering techniques, including semi-joins and anti-joins. You’ll also learn how to glue DataFrames by vertically combining and using the pandas.concat function to create new datasets. Finally, because data is rarely clean, you’ll also learn how to validate your newly combined data structures.

Filtering joins

Steps of a semi join

Performing an anti join

Performing a semi join

Concatenate DataFrames together vertically

Concatenation basics

Concatenating with keys

Verifying integrity

Validating a merge

Concatenate and merge to find common songs

 Advanced Merging and Concatenating

In this final chapter, you’ll step up a gear and learn to apply pandas' specialized methods for merging time-series and ordered data together with real-world financial and economic data from the city of Chicago. You’ll also learn how to query resulting tables using a SQL-style format, and unpivot data using the melt method.

Using merge_ordered()

Correlation between GDP and S&P500

Phillips curve using merge_ordered()

merge_ordered() caution, multiple columns

Using merge_asof()

Using merge_asof() to study stocks

Using merge_asof() to create dataset

Options

merge_asof()

Both

merge_ordered()

merge_asof() and merge_ordered() differences

Selecting data with .query()

Explore financials with .query()

Subsetting rows with .query()

Reshaping data with .melt()

Select the right .melt() arguments

Using .melt() to reshape government data

Using .melt() for stocks vs bond performance

Course wrap-up

Merging Ordered and Time-Series Data

Chicago Wards

Chicago Business Licenses

Chicago Census

Chicago Demographics by Zip Code

Chicago Business Owners

Chicago Land Use

Chicago Taxi Vehicles

Chicago Taxi Owners

CTA Ridership

CTA Calendar

CTA Stations

Movies

Movie Actors

Movie Ratings

Movie Casts

Movie Crews

Movie Genres

Movie Sequels

Movie Financial Data

Movie Tag Lines

S&P 500

World Bank GDP

World Bank Population

Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.

Data Manipulation with pandas

Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using Pandas. You'll gain a solid skillset for data-joining.

Joining Data with pandas

Learn to combine data from multiple tables by joining data together using pandas.

Associate Data Scientist  in Python

Data Analyst  in Python

Data Manipulation  in Python

Python Data Fundamentals

It can be used to do fuzzy matching of dates between tables.

After matching two tables, if there are missing values at the top of the table from the right table, this function can fill them in.

This function can set the suffix for overlapping column names.

This function can be used when working with ordered or time-series data.

It allows for a right join during the merge.

If it cannot match the rows of the tables exactly, it can use forward fill to interpolate the missing data.

merge_asof() and merge_ordered() differences

Joining Data with pandas

Hands-on interactive exercise