# PySpark Native Functions

PySpark provides a variety of built-in functions that can be used to perform operations on columns in a DataFrame. These functions are part of the pyspark.sql.functions module and can be imported as follows:

```python
from pyspark.sql.functions import *
```

Some examples of commonly used functions include:

* `sum()` function: It is used to calculate the sum of a column.

```python
df.agg(sum("column1"))
```

* `avg()` function: It is used to calculate the average of a column.

```python
df.agg(avg("column1"))
```

* `min()` function: It is used to calculate the minimum value of a column.

```python
df.agg(min("column1"))
```

* `max()` function: It is used to calculate the maximum value of a column.

```python
df.agg(max("column1"))
```

* `concat()` function: It is used to concatenate two or more columns

```python
df.select(concat(col("column1"), col("column2")))
```

These functions can be used with the `select()` and `agg()` methods to perform operations on DataFrame columns.

```python
df.select(sum("column1").alias("sum_column1"))
```

You can also use these functions in the `filter()` method to filter the dataframe based on a certain condition

```python
df.filter(col("column1") > 10)
```

These functions can also be used with the `withColumn()` method to add a new column to a DataFrame.

```python
df.withColumn("new_column", col("column1") + col("column2"))
```

You can also use the `when()` and `otherwise()` functions to create a new column based on a certain condition.

```python
from pyspark.sql.functions import when
df.withColumn("new_column", when(col("column1") > 10, "high").otherwise("low"))
```

You can also use the `ifnull()` and `nullif()` functions to handle missing values.

```python
from pyspark.sql.functions import ifnull, nullif
df.select(ifnull("column1", 0))
df.select(nullif("column1", 0))
```

These are just some examples of the built-in functions provided by PySpark. There are many more functions available and it's always good to check the documentation for the latest updates and options.

It's always good to check the [documentation](https://docs.databricks.com/) for the latest updates and options. Also, when you are working with Databricks, always make sure that you have the required libraries installed.