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Joining Data with pandas

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

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Descrição do Curso

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.

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1
Data Merging Basics
Livre
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.
Reproduzir Capítulo Agora
Inner join
50 xp
What column to merge on?
50 xp
Your first inner join
100 xp
Inner joins and number of rows returned
100 xp
One-to-many relationships
50 xp
One-to-many classification
100 xp
One-to-many merge
100 xp
Merging multiple DataFrames
50 xp
Total riders in a month
100 xp
Three table merge
100 xp
One-to-many merge with multiple tables
100 xp
2
Merging Tables With Different Join Types
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.
Reproduzir Capítulo Agora
Left join
50 xp
Counting missing rows with left join
100 xp
Enriching a dataset
100 xp
How many rows with a left join?
50 xp
Other joins
50 xp
Right join to find unique movies
100 xp
Popular genres with right join
100 xp
Using outer join to select actors
100 xp
Merging a table to itself
50 xp
Self join
100 xp
How does pandas handle self joins?
50 xp
Merging on indexes
50 xp
Index merge for movie ratings
100 xp
Do sequels earn more?
100 xp
3
Advanced Merging and Concatenating
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.
Reproduzir Capítulo Agora
Filtering joins
50 xp
Steps of a semi join
100 xp
Performing an anti join
100 xp
Performing a semi join
100 xp
Concatenate DataFrames together vertically
50 xp
Concatenation basics
100 xp
Concatenating with keys
100 xp
Verifying integrity
50 xp
Validating a merge
50 xp
Concatenate and merge to find common songs
100 xp
4
Merging Ordered and Time-Series Data
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.
Reproduzir Capítulo Agora
Using merge_ordered()
50 xp
Correlation between GDP and S&P500
100 xp
Phillips curve using merge_ordered()
100 xp
merge_ordered() caution, multiple columns
100 xp
Using merge_asof()
50 xp
Using merge_asof() to study stocks
100 xp
Using merge_asof() to create dataset
100 xp
merge_asof() and merge_ordered() differences
100 xp
Selecting data with .query()
50 xp
Explore financials with .query()
50 xp
Subsetting rows with .query()
100 xp
Reshaping data with .melt()
50 xp
Select the right .melt() arguments
50 xp
Using .melt() to reshape government data
100 xp
Using .melt() for stocks vs bond performance
100 xp
Course wrap-up
50 xp

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Nas seguintes faixas

Certificação disponível

Analista de dados com Python

Ir para a trilha

Certificação disponível

Cientista de dados associado em Python

Ir para a trilha

Data Manipulation com Python

Ir para a trilha

Aaren Stubberfield

Senior Data Scientist @ Microsoft

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