Note to potential editors: please leave both “Python” and “Polars” in the question title, because:

• there are many questions about looking up values in another dataframe in the pandas context;
• not everyone (e.g. search engines, or beginners) knows how to use the [python-polars] tag to drill down to polars specific questions.

Python – Pandas x Polars – Values mapping (Lookup value) discusses as a solution:

import numpy as np
import polars as pl

# Dimension Table
letters_ids = pl.DataFrame({
    'Letters': ['A', 'B', 'C'],
    'Letters_id': [1, 2, 3]
})

# Fact Table
many_letters = pl.DataFrame({
    'Letters': np.random.choice(['A', 'B', 'C'], 10)
})

# Convert the two columns DataFrame into a Python's dictionary.
letters_dict = dict(letters_ids.iter_rows())

# Maps the dictionary
many_letters = many_letters.with_columns(
    pl.col('Letters').map_dict(letters_dict).alias('letters_mapped')
)

But as far as I can see with the latest version of polars for Python, map_dict is not an option:

--------Version info---------
Polars:               1.1.0
Index type:           UInt32
Platform:             Linux-5.15.0-113-generic-x86_64-with-glibc2.35
Python:               3.12.3 (main, Apr 15 2024, 18:25:56) [Clang 17.0.6 ]

----Optional dependencies----
adbc_driver_manager:  <not installed>
cloudpickle:          <not installed>
connectorx:           <not installed>
deltalake:            <not installed>
fastexcel:            <not installed>
fsspec:               <not installed>
gevent:               <not installed>
great_tables:         <not installed>
hvplot:               0.10.0
matplotlib:           3.9.0
nest_asyncio:         1.6.0
numpy:                2.0.0
openpyxl:             <not installed>
pandas:               2.2.2
pyarrow:              16.1.0
pydantic:             <not installed>
pyiceberg:            <not installed>
sqlalchemy:           2.0.31
torch:                <not installed>
xlsx2csv:             <not installed>
xlsxwriter:           <not installed>

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[1], line 21
     17 letters_dict = dict(letters_ids.iter_rows())
     19 # Maps the dictionary
     20 many_letters = many_letters.with_columns(
---> 21     pl.col('Letters').map_dict(letters_dict).alias('letters_mapped')
     22 )

AttributeError: 'Expr' object has no attribute 'map_dict'

The other option I know of is to perform a join. This unnecessarily causes duplication of a lot of data (imagine that left is a not larger than right, and right is thus “broadcast” over left), but is this the price one must pay in order to perform a lookup operation efficiently in terms of speed?