# Overview
Given a PySpark DataFrame, we can select the columns based on a regex using the function **`colRegex`** in PySpark.

## Syntax

```python
DataFrame.colRegex(colName: str)
```

## Parameter

- **`colName`**: This represents a string or a column name specified as a regex.


## Example

import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('edpresso').getOrCreate()

data = [("James","Smith","USA","CA"),
    ("Michael","Rose","USA","NY"),
    ("Robert","Williams","USA","CA"),
    ("Maria","Jones","USA","FL")
  ]

columns = ["firstname","lastname","country","state"]
df = spark.createDataFrame(data = data, schema = columns)
df.select(df.colRegex("`.+name$`")).show()

python39

> **Note:** If you see any warnings in the output, please ignore them.
## Explanation

- Lines 1–2: We import `pyspark` and `SparkSession`.

- Line 4: We create `SparkSession` with the application name `edpresso`.

- Lines 6–10: We define the dummy data for the DataFrame.

- Line 12: We define the columns for the dummy data.

- Line 13: We create a spark 
DataFrame with the dummy data in lines 6–10 and the columns in line 13.

- Line 14: We select the subset of the columns by using a regex. The regex matches the column names ending with the keyword `name`. The `colRegex()` method retrieves all columns ending with a `name`.

pyspark.tar.gz

How to select columns based on regex in PySpark

Use the PySpark colRegex function to select DataFrame columns matching a regex, such as ending with "name".