
The **`spark.read.csv()` method** is used to read a single CSV or a directory of CSV files to a spark DataFrame. Various different options can be specified via the `spark.read.option()` method.

## Syntax

```
spark.read.option("option_name", "option_value").csv(file_path)
```

## Parameter

- `file_path`: This is the CSV file to be read.

## Return value

This method returns a spark DataFrame.

## Code example
Let's look at the code below: 

"emp name","company","department","country"
"James","Educative","Engg","USA"
"Michael","Google","HR","Asia"
"Robert","Meta","Marketing","Russia"
"Maria","Netflix","Finance","Ukraine"

import pyspark
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("answers").getOrCreate()

path = "data.csv"

df = spark.read.option("header",'True').option('delimiter', ',').csv(path)
df.printSchema()

python39

##
### Code explanation

- **Lines 1–2:** We import `pyspark` and `SparkSession`.
- **Line 4:** We create `SparkSession` with the application name `answers`.
- **Line 6:** We define the path to the CSV file.
- **Line 8:** We convert the CSV file to a DataFrame using the `csv()` method. Multiple options are chained together using the `option()` method.
- **Line 9:** We print the DataFrame schema.

pyspark.tar.gz

How to import a CSV file in pyspark

Use spark.read.csv(file_path) with options to import CSV files into a spark DataFrame.