How do I create a DataFrame using PySpark in Databricks and load data from a CSV file into it?
To create a DataFrame using PySpark in Databricks and load data from a CSV file into it, follow these steps:

1. Define variables for the file path and schema.
2. Read the CSV file into a DataFrame using the `spark.read.csv()` function.
3. Specify the schema for the DataFrame using the `schema` parameter of the `spark.read.csv()` function.
4. Optionally, display the DataFrame using the `display()` function.

Here's an example of how to create a DataFrame and load data from a CSV file in PySpark:

```python
# Define variables for the file path and schema
file_path = "/path/to/your/csv/file.csv"
schema = "column1 string, column2 integer, column3 double"

# Read the CSV file into a DataFrame
df = spark.read.csv(file_path, schema=schema, header=True)

# Display the DataFrame
display(df)
```

Replace `/path/to/your/csv/file.csv` with the actual path to your CSV file. The `schema` variable should be a string that defines the schema of the CSV file. In this example, the schema has three columns: `column1` is a string, `column2` is an integer, and `column3` is a double.

Note that the `header=True` parameter indicates that the CSV file has a header row. If your CSV file does not have a header row, omit this parameter or set it to `False`.