SQL Query into Pandas DataFrame - Part 3
Dendi Handian
Posted on September 7, 2021
After playing some aggregation and grouping in the last part, now we will play harder with table joins.
The Playground Database
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
Preparing the DataFrame
import pandas as pd
albums_df = pd.read_csv("albums.csv")
artists_df = pd.read_csv("artists.csv")
Join Queries into Pandas DataFrame
INNER JOIN:
SQL:
SELECT
*
FROM albums
JOIN artists ON albums.ArtistId = artists.ArtistId
or
SELECT
*
FROM albums
INNER JOIN artists ON albums.ArtistId = artists.ArtistId
Pandas:
# 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')
LEFT JOIN
SQL:
SELECT
*
FROM albums
LEFT JOIN artists ON albums.ArtistId = artists.ArtistId
Pandas:
albums_df.merge(artists_df, on='ArtistId', how='left')
RIGHT JOIN
SQL:
SELECT
*
FROM albums
RIGHT JOIN artists ON albums.ArtistId = artists.ArtistId
Pandas:
albums_df.merge(artists_df, on='ArtistId', how='right')
💖 💪 🙅 🚩
Dendi Handian
Posted on September 7, 2021
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