docs/source/user-guide/common-operations/functions.md

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# Functions

DataFusion provides a large number of built-in functions for performing complex queries without requiring user-defined functions.
In here we will cover some of the more popular use cases. If you want to view all the functions go to the {py:mod}`Functions <datafusion.functions>` API Reference.

```{note}
For Apache Spark-compatible versions of these functions (with Spark
NULL-propagation, 1-indexed substrings, HALF_UP rounding, etc.), see
[Spark-Compatible Functions](spark-functions.md).
```

We'll use the pokemon dataset in the following examples.

```{code-cell} ipython3
from datafusion import SessionContext

ctx = SessionContext()
ctx.register_csv("pokemon", "pokemon.csv")
df = ctx.table("pokemon")
```

## Mathematical

DataFusion offers mathematical functions such as {py:func}`~datafusion.functions.pow` or {py:func}`~datafusion.functions.log`

```{code-cell} ipython3
from datafusion import col, literal
from datafusion import functions as f

df.select(
    f.pow(col('"Attack"'), literal(2)) - f.pow(col('"Defense"'), literal(2))
).limit(10)

```

## Conditional

There 3 conditional functions in DataFusion {py:func}`~datafusion.functions.coalesce`, {py:func}`~datafusion.functions.nullif` and {py:func}`~datafusion.functions.case`.

```{code-cell} ipython3
df.select(
    f.coalesce(col('"Type 1"'), col('"Type 2"')).alias("dominant_type")
).limit(10)
```

## Temporal

For selecting the current time use {py:func}`~datafusion.functions.now`

```{code-cell} ipython3
df.select(f.now())
```

Convert to timestamps using {py:func}`~datafusion.functions.to_timestamp`

```{code-cell} ipython3
df.select(f.to_timestamp(col('"Total"')).alias("timestamp"))
```

Extracting parts of a date using {py:func}`~datafusion.functions.date_part` (alias {py:func}`~datafusion.functions.extract`)

```{code-cell} ipython3
df.select(
   f.date_part("month", f.to_timestamp(col('"Total"'))).alias("month"),
   f.extract("day", f.to_timestamp(col('"Total"'))).alias("day")
)
```

## String

In the field of data science, working with textual data is a common task. To make string manipulation easier,
DataFusion offers a range of helpful options.

```{code-cell} ipython3
df.select(
    f.char_length(col('"Name"')).alias("len"),
    f.lower(col('"Name"')).alias("lower"),
    f.left(col('"Name"'), literal(4)).alias("code")
)
```

This also includes the functions for regular expressions like {py:func}`~datafusion.functions.regexp_replace` and {py:func}`~datafusion.functions.regexp_match`

```{code-cell} ipython3
df.select(
    f.regexp_match(col('"Name"'), literal("Char")).alias("dragons"),
    f.regexp_replace(col('"Name"'), literal("saur"), literal("fleur")).alias("flowers")
)
```

## Casting

Casting expressions to different data types using {py:func}`~datafusion.functions.arrow_cast`

```{code-cell} ipython3
df.select(
    f.arrow_cast(col('"Total"'), "Float64").alias("total_as_float"),
    f.arrow_cast(col('"Total"'), "Int32").alias("total_as_int")
)
```

## Other

The function {py:func}`~datafusion.functions.in_list` allows to check a column for the presence of multiple values:

```{code-cell} ipython3
types = [literal("Grass"), literal("Fire"), literal("Water")]
(
    df.select(f.in_list(col('"Type 1"'), types, negated=False).alias("basic_types"))
      .limit(20)
      .to_pandas()
)

```

# Handling Missing Values

DataFusion provides methods to handle missing values in DataFrames:

## fill_null

The `fill_null()` method replaces NULL values in specified columns with a provided value:

```python
# Fill all NULL values with 0 where possible
df = df.fill_null(0)

# Fill NULL values only in specific string columns
df = df.fill_null("missing", subset=["name", "category"])
```

The fill value will be cast to match each column's type. If casting fails for a column, that column remains unchanged.

## fill_nan

The `fill_nan()` method replaces NaN values in floating-point columns. NaN is
distinct from NULL, which `fill_null()` handles:

```python
# Replace NaN with 0.0 in every floating-point column
df = df.fill_nan(0.0)

# Replace NaN only in specific columns
df = df.fill_nan(0.0, subset=["price"])
```

(fill_column_names)=

## Column names with uppercase letters or dots

`fill_null()` and `fill_nan()` fail on a DataFrame that has any column whose
name contains an uppercase letter or a `.`, even when that column is not in
`subset`
([apache/datafusion#25829](https://github.com/apache/datafusion/issues/25829)):

```python
df = ctx.from_pydict({"Price": [float("nan"), 2.0], "qty": [float("nan"), 1.0]})
df.fill_nan(0.0, subset=["qty"])
# Schema error: No field named price. Did you mean '..."Price"'?
```

Until that is fixed, rename such columns first. Quote the old name so it is
not normalized:

```python
df.with_column_renamed('"Price"', "price").fill_nan(0.0, subset=["qty"])
```