SELECT

In Polars SQL, the SELECT statement is used to retrieve data from a table into a DataFrame. The basic syntax of a SELECT statement in Polars SQL is as follows:

SELECT column1, column2, ...
FROM table_name;

Here, column1, column2, etc. are the columns that you want to select from the table. You can also use the wildcard * to select all columns. table_name is the name of the table or that you want to retrieve data from. In the sections below we will cover some of the more common SELECT variants

Python

register · execute

df = pl.DataFrame(
    {
        "city": [
            "New York",
            "Los Angeles",
            "Chicago",
            "Houston",
            "Phoenix",
            "Amsterdam",
        ],
        "country": ["USA", "USA", "USA", "USA", "USA", "Netherlands"],
        "population": [8399000, 3997000, 2705000, 2320000, 1680000, 900000],
    }
)

ctx = pl.SQLContext(population=df, eager=True)

print(ctx.execute("SELECT * FROM population"))

shape: (6, 3)
┌─────────────┬─────────────┬────────────┐
│ city        ┆ country     ┆ population │
│ ---         ┆ ---         ┆ ---        │
│ str         ┆ str         ┆ i64        │
╞═════════════╪═════════════╪════════════╡
│ New York    ┆ USA         ┆ 8399000    │
│ Los Angeles ┆ USA         ┆ 3997000    │
│ Chicago     ┆ USA         ┆ 2705000    │
│ Houston     ┆ USA         ┆ 2320000    │
│ Phoenix     ┆ USA         ┆ 1680000    │
│ Amsterdam   ┆ Netherlands ┆ 900000     │
└─────────────┴─────────────┴────────────┘

GROUP BY

The GROUP BY statement is used to group rows in a table by one or more columns and compute aggregate functions on each group.

Python

execute

result = ctx.execute(
    """
        SELECT country, AVG(population) as avg_population
        FROM population
        GROUP BY country
    """
)
print(result)

shape: (2, 2)
┌─────────────┬────────────────┐
│ country     ┆ avg_population │
│ ---         ┆ ---            │
│ str         ┆ f64            │
╞═════════════╪════════════════╡
│ Netherlands ┆ 900000.0       │
│ USA         ┆ 3.8202e6       │
└─────────────┴────────────────┘

ORDER BY

The ORDER BY statement is used to sort the result set of a query by one or more columns in ascending or descending order.

Python

execute

result = ctx.execute(
    """
        SELECT city, population
        FROM population
        ORDER BY population
    """
)
print(result)

shape: (6, 2)
┌─────────────┬────────────┐
│ city        ┆ population │
│ ---         ┆ ---        │
│ str         ┆ i64        │
╞═════════════╪════════════╡
│ Amsterdam   ┆ 900000     │
│ Phoenix     ┆ 1680000    │
│ Houston     ┆ 2320000    │
│ Chicago     ┆ 2705000    │
│ Los Angeles ┆ 3997000    │
│ New York    ┆ 8399000    │
└─────────────┴────────────┘

JOIN

Python

register_many · execute

income = pl.DataFrame(
    {
        "city": [
            "New York",
            "Los Angeles",
            "Chicago",
            "Houston",
            "Amsterdam",
            "Rotterdam",
            "Utrecht",
        ],
        "country": [
            "USA",
            "USA",
            "USA",
            "USA",
            "Netherlands",
            "Netherlands",
            "Netherlands",
        ],
        "income": [55000, 62000, 48000, 52000, 42000, 38000, 41000],
    }
)
ctx.register_many(income=income)
result = ctx.execute(
    """
        SELECT country, city, income, population
        FROM population
        LEFT JOIN income on population.city = income.city
    """
)
print(result)

shape: (6, 4)
┌─────────────┬─────────────┬────────┬────────────┐
│ country     ┆ city        ┆ income ┆ population │
│ ---         ┆ ---         ┆ ---    ┆ ---        │
│ str         ┆ str         ┆ i64    ┆ i64        │
╞═════════════╪═════════════╪════════╪════════════╡
│ USA         ┆ New York    ┆ 55000  ┆ 8399000    │
│ USA         ┆ Los Angeles ┆ 62000  ┆ 3997000    │
│ USA         ┆ Chicago     ┆ 48000  ┆ 2705000    │
│ USA         ┆ Houston     ┆ 52000  ┆ 2320000    │
│ USA         ┆ Phoenix     ┆ null   ┆ 1680000    │
│ Netherlands ┆ Amsterdam   ┆ 42000  ┆ 900000     │
└─────────────┴─────────────┴────────┴────────────┘

Functions

Polars provides a wide range of SQL functions, including:

• Mathematical functions: ABS, EXP, LOG, ASIN, ACOS, ATAN, etc.
• String functions: LOWER, UPPER, LTRIM, RTRIM, STARTS_WITH,ENDS_WITH.
• Aggregation functions: SUM, AVG, MIN, MAX, COUNT, STDDEV, FIRST etc.
• Array functions: EXPLODE, UNNEST,ARRAY_SUM,ARRAY_REVERSE, etc.

For a full list of supported functions go the API documentation. The example below demonstrates how to use a function in a query

Python

execute

result = ctx.execute(
    """
        SELECT city, population
        FROM population
        WHERE STARTS_WITH(country,'U')
    """
)
print(result)

shape: (5, 2)
┌─────────────┬────────────┐
│ city        ┆ population │
│ ---         ┆ ---        │
│ str         ┆ i64        │
╞═════════════╪════════════╡
│ New York    ┆ 8399000    │
│ Los Angeles ┆ 3997000    │
│ Chicago     ┆ 2705000    │
│ Houston     ┆ 2320000    │
│ Phoenix     ┆ 1680000    │
└─────────────┴────────────┘

Table Functions

In the examples earlier we first generated a DataFrame which we registered in the SQLContext. Polars also support directly reading from CSV, Parquet, JSON and IPC in your SQL query using table functions read_xxx.

Python

execute

result = ctx.execute(
    """
        SELECT *
        FROM read_csv('docs/assets/data/iris.csv')
    """
)
print(result)

shape: (150, 5)
┌──────────────┬─────────────┬──────────────┬─────────────┬───────────┐
│ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width ┆ species   │
│ ---          ┆ ---         ┆ ---          ┆ ---         ┆ ---       │
│ f64          ┆ f64         ┆ f64          ┆ f64         ┆ str       │
╞══════════════╪═════════════╪══════════════╪═════════════╪═══════════╡
│ 5.1          ┆ 3.5         ┆ 1.4          ┆ 0.2         ┆ Setosa    │
│ 4.9          ┆ 3.0         ┆ 1.4          ┆ 0.2         ┆ Setosa    │
│ 4.7          ┆ 3.2         ┆ 1.3          ┆ 0.2         ┆ Setosa    │
│ 4.6          ┆ 3.1         ┆ 1.5          ┆ 0.2         ┆ Setosa    │
│ 5.0          ┆ 3.6         ┆ 1.4          ┆ 0.2         ┆ Setosa    │
│ …            ┆ …           ┆ …            ┆ …           ┆ …         │
│ 6.7          ┆ 3.0         ┆ 5.2          ┆ 2.3         ┆ Virginica │
│ 6.3          ┆ 2.5         ┆ 5.0          ┆ 1.9         ┆ Virginica │
│ 6.5          ┆ 3.0         ┆ 5.2          ┆ 2.0         ┆ Virginica │
│ 6.2          ┆ 3.4         ┆ 5.4          ┆ 2.3         ┆ Virginica │
│ 5.9          ┆ 3.0         ┆ 5.1          ┆ 1.8         ┆ Virginica │
└──────────────┴─────────────┴──────────────┴─────────────┴───────────┘