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

Kolom apa yang digunakan untuk menggabungkan?

Inner join pertama Anda

Inner join dan jumlah baris yang dikembalikan

Relasi satu-ke-banyak

Opsi

Satu-ke-satu

Satu-ke-banyak

Klasifikasi satu-ke-banyak

Penggabungan one-to-many

Menggabungkan beberapa DataFrame

Total penumpang dalam sebulan

Menggabungkan tiga tabel

Penggabungan one-to-many dengan banyak tabel

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

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

Kemampuan untuk menggabungkan dan bekerja dengan beberapa himpunan data adalah keterampilan penting bagi setiap calon Data Scientist. pandas adalah komponen krusial dalam ekosistem data science Python, dengan Stack Overflow mencatat 5 juta tampilan untuk pertanyaan tentang pandas. Pelajari cara menangani banyak DataFrame dengan menggabungkan, mengatur, menggabung (join), dan membentuk ulang menggunakan pandas. Anda akan bekerja dengan himpunan data dari World Bank dan City of Chicago. Anda akan menyelesaikan kursus ini dengan seperangkat keterampilan yang solid untuk penggabungan data di pandas.

Video menyertakan transkrip langsung yang dapat Anda tampilkan dengan mengeklik "Show transcript" di kiri bawah video.

Glosarium kursus dapat ditemukan di sebelah kanan pada bagian sumber daya.

Untuk memperoleh kredit CPE, Anda perlu menyelesaikan kursus dan mencapai skor 70% pada penilaian yang memenuhi syarat. Anda dapat menuju ke penilaian dengan mengeklik pemberitahuan kredit CPE di sebelah kanan.

Data Manipulation with pandas

Pelajari cara mengelola beberapa DataFrame dengan menggabungkan, mengatur, dan menyusun ulang menggunakan Pandas.

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