Ctrl+K

Merging & Joining

Merging & Joining#

The SQL queries and joins lab covered how we can use joins in a relational database system to create new data structures. pandas has somewhat similar functionality that allows you to merge and combine data from multiple tables.

As with the reshaping functions, Pandas includes functionality geared toward combining or connecting different datasets.

A digest of these operations, courtesty of Pandas documentation:

  • pandas.concat: Merge multiple Series or DataFrame objects along a shared index or column

  • DataFrame.join: Merge multiple DataFrame objects along the columns

  • DataFrame.combine_first: Update missing values with non-missing values in the same location

  • pandas.merge: Combine two Series or DataFrame objects with SQL-style joining

    • pandas.merge_ordered: Combine two Series or DataFrame objects along an ordered axis

    • pandas.merge_asof: Combine two Series or DataFrame objects by near instead of exact matching keys

  • Series.compare and DataFrame.compare: Show differences in values between two Series or DataFrame objects

We’ll cover a couple of these workflows in greater depth.

This Section’s Contents#

Contents