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SQL Query into Pandas DataFrame - Part 3
After playing some aggregation and grouping in the last part, now we will play harder with table joins.
We will be using the same SQLite database, but now we are going to use some tables. So get all the required csv files here
import pandas as pd
albums_df = pd.read_csv("albums.csv")
artists_df = pd.read_csv("artists.csv")SQL:SELECT
*
FROM albums
JOIN artists ON albums.ArtistId = artists.ArtistIdor
SELECT
*
FROM albums
INNER JOIN artists ON albums.ArtistId = artists.ArtistIdPandas:# For the exact same column name on both table
albums_df.merge(artists_df, on='ArtistId')
# Defining the join column of each tables
albums_df.merge(artists_df, left_on='ArtistId', right_on='ArtistId')
# To make sure we use the INNER one
albums_df.merge(artists_df, left_on='ArtistId', right_on='ArtistId', how='inner')SQL:SELECT
*
FROM albums
LEFT JOIN artists ON albums.ArtistId = artists.ArtistIdPandas:albums_df.merge(artists_df, on='ArtistId', how='left')SQL:SELECT
*
FROM albums
RIGHT JOIN artists ON albums.ArtistId = artists.ArtistIdPandas:albums_df.merge(artists_df, on='ArtistId', how='right')46