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

Contare le righe mancanti con una left join

Arricchire un insieme di dati

Quante righe con un left join?

Altri tipi di join

Right join per trovare i film unici

Generi più popolari con right join

Usare un outer join per selezionare gli attori

Unire una tabella con sé stessa

Auto-join (self join)

Come gestisce pandas i self join?

Merge sugli indici

Join sugli indici per le valutazioni dei film

I sequel incassano di più?

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

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

CTA Stations

Movie Actors

Movie Casts

Movie Crews

Movie Tag Lines

S&P 500

World Bank GDP

World Bank Population

Chicago Taxi Owners

Chicago Taxi Vehicles

Chicago Wards

Chicago Census

Chicago Business Licenses

Chicago Demographics by Zip Code

Chicago Business Owners

Chicago Land Use

CTA Ridership

CTA Calendar

Movies

Movie Ratings

Movie Genres

Movie Sequels

Movie Financial Data

Saper combinare e lavorare con più insiemi di dati è una competenza essenziale per ogni aspirante Data Scientist. pandas è un pilastro dell’ecosistema data science in Python, e su Stack Overflow le domande su pandas contano 5 milioni di visualizzazioni. Impara a gestire più DataFrame combinandoli, organizzandoli, unendoli e rimodellandoli con pandas. Lavorerai con insiemi di dati della World Bank e della City of Chicago. Concluderai il corso con un solido set di competenze per unire i dati in pandas.

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Data Manipulation with pandas

Impara a gestire più DataFrame combinandoli, organizzandoli, unendoli e rimodellandoli con Pandas.

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