How to use spark.read.csv() In Python
It is used to load text files into DataFrame. Using this method we will go through the input once to determine the input schema if inferSchema is enabled. To avoid going through the entire data once, disable inferSchema option or specify the schema explicitly using the schema.
Syntax: spark.read.csv(path)
Returns: DataFrame
Example: Read text file using spark.read.csv().
First, import the modules and create a spark session and then read the file with spark.read.csv(), then create columns and split the data from the txt file show into a dataframe.
Python3
from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() df = spark.read.csv("output.txt") df.selectExpr("split(_c0, ' ')\ as Text_Data_In_Rows_Using_CSV").show(4,False) |
Output:
Read Text file into PySpark Dataframe
In this article, we are going to see how to read text files in PySpark Dataframe.
There are three ways to read text files into PySpark DataFrame.
- Using spark.read.text()
- Using spark.read.csv()
- Using spark.read.format().load()
Using these we can read a single text file, multiple files, and all files from a directory into Spark DataFrame and Dataset.
Text file Used:
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