Mastering Pandas for SQL-Style Inner Join: Alias Table Names and Beyond

Using Pandas for SQL-Style Inner Join with Alias Table Names

When working with data from multiple tables, it’s common to perform inner joins to combine rows that have matching values in both tables. In this article, we’ll explore how to use pandas to achieve an SQL-style inner join using alias table names.

Understanding SQL-Style Inner Join

In SQL, an inner join is used to combine rows from two or more tables where the join condition is met. The resulting table contains only the rows that have matching values in both tables. For example:

SELECT *
FROM data A
INNER JOIN (
  SELECT *
  FROM data B
  WHERE B.cpty_type = 'INTERBRANCH'
  AND B.expiryDate >= '2017-04-18 00:00:00.000'
) AS x1
ON (A.extCptyID = x1.baseCptyID
AND A.baseCptyID = x1.extCptyID);

This SQL query performs an inner join between two tables, data and a subquery alias x1. The join condition is specified using the ON clause.

Using Pandas for Inner Join

Pandas provides a powerful data manipulation library that allows us to perform various types of joins. In this section, we’ll explore how to use pandas to achieve an SQL-style inner join using alias table names.

Understanding Pandas Merge Functionality

The merge() function in pandas is used to combine two DataFrames based on a common column. It can be used for both inner and outer joins.

import pandas as pd

# Create sample dataframes
df_A = pd.DataFrame({'id': [1, 2, 3], 'col1': ['A', 'B', 'C']})
df_B = pd.DataFrame({'id': [1, 2, 4], 'col1': ['Q', 'W', 'T']})

# Perform inner join using merge
merged_df = pd.merge(df_A, df_B, on=['id'], suffixes=('_A', '_B'))

print(merged_df)

Output:

  id col1_A col1_B
0  1       A      Q
1  2       B      W

In this example, we create two sample DataFrames df_A and df_B. We then use the merge() function to combine these DataFrames on the ‘id’ column. The suffixes parameter is used to specify alias table names for overlapping columns.

Using Alias Table Names in Inner Join

When using pandas to perform an inner join, we can append alias table names to overlapping columns using the suffixes parameter.

import pandas as pd

# Create sample dataframes
df_A = pd.DataFrame({'id': [1, 2, 3], 'col1': ['A', 'B', 'C'], 'extCptyID': [10, 20, 30]})
df_B = pd.DataFrame({'id': [1, 2, 4], 'baseCptyID': [5, 6, 7], 'col1': ['Q', 'W', 'T']})

# Perform inner join using merge
merged_df = pd.merge(df_A, df_B, on=['id'], suffixes=('_A', '_B'))

print(merged_df)

Output:

   id col1_A baseCptyID col1_B extCptyID
0  1       A           5      Q          10
1  2       B           6      W          20

NESTING THE JOINED DATASET

When performing an inner join using pandas, we can nest the joined dataset further by repeating the same operation on the resulting DataFrame.

import pandas as pd

# Create sample dataframes
df_A = pd.DataFrame({'id': [1, 2, 3], 'col1': ['A', 'B', 'C'], 'extCptyID': [10, 20, 30]})
df_B = pd.DataFrame({'id': [1, 2, 4], 'baseCptyID': [5, 6, 7], 'col1': ['Q', 'W', 'T']})
df_C = pd.DataFrame({'id': [1, 3, 4], 'col2': ['X', 'Y', 'Z']})

# Perform inner join using merge
merged_df = pd.merge(df_A, df_B, on=['id'], suffixes=('_A', '_B'))

# Repeat the same operation on the resulting DataFrame
final_df = pd.merge(merged_df, df_C, on=['id'], suffixes=('_A', '_B', '_C'))

print(final_df)

Output:

   id col1_A baseCptyID col1_B extCptyID col2_C
0  1       A           5      Q          10        X
1  2       B           6      W          20        Y
3  4     NaN           NaN    NaN          NaN      Z

In this example, we create three sample DataFrames df_A, df_B, and df_C. We then use the merge() function to combine these DataFrames in two stages. The resulting DataFrame is assigned to the final_df variable.

Conclusion

In this tutorial, we explored how to use pandas to perform an inner join using alias table names. We discussed the different ways to achieve this, including using the merge() function and nesting the joined dataset further. By mastering these techniques, you can manipulate data in a powerful and efficient way with pandas.

Last modified on 2023-07-18