Load CSV File in PySpark

CSV is a commonly used data format. Spark provides rich APIs to load files from HDFS as data frame.  This page provides examples about how to load CSV from HDFS using Spark. If you want to read a local CSV file in Python, refer to this page Python: Load / Read Multiline CSV File instead.

Sample data file

The CSV file content looks like the following:

ID,Text1,Text2
1,Record 1,Hello World!
2,Record 2,Hello Hadoop!
3,Record 3,"Hello 
Kontext!"
4,Record 4,Hello!

hadoop fs -copyFromLocal data.csv /data.csv

Load CSV file

We can use 'read' API of SparkSession object to read CSV with the following options:

• header = True: this means there is a header line in the data file.
• sep=, : comma is the delimiter/separator. Since our file is using comma, we don't need to specify this as by default is is comma.
• multiLine = True: this setting allows us to read multi-line records. 

Sample code

from pyspark.sql import SparkSession

appName = "Python Example - PySpark Read CSV"
master = 'local'

# Create Spark session
spark = SparkSession.builder \
    .master(master) \
    .appName(appName) \
    .getOrCreate()

# Convert list to data frame
df = spark.read.format('csv') \
                .option('header',True) \
                .option('multiLine', True) \
                .load('/data.csv')
df.show()
print(f'Record count is: {df.count()}')

Output

The output looks like the following:

+---+--------+---------------+
| ID|   Text1|          Text2|
+---+--------+---------------+
|  1|Record 1|   Hello World!|
|  2|Record 2|  Hello Hadoop!|
|  3|Record 3|Hello
Kontext!|
|  4|Record 4|         Hello!|
+---+--------+---------------+

Record count is: 4