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Load CSV file with Spark


Question

I'm new to Spark and I'm trying to read CSV data from a file with Spark. Here's what I am doing :

sc.textFile('file.csv')
    .map(lambda line: (line.split(',')[0], line.split(',')[1]))
    .collect()

I would expect this call to give me a list of the two first columns of my file but I'm getting this error :

File "<ipython-input-60-73ea98550983>", line 1, in <lambda>
IndexError: list index out of range

although my CSV file as more than one column.

2015/02/28
1
111
2/28/2015 2:41:00 PM

Accepted Answer

Are you sure that all the lines have at least 2 columns? Can you try something like, just to check?:

sc.textFile("file.csv") \
    .map(lambda line: line.split(",")) \
    .filter(lambda line: len(line)>1) \
    .map(lambda line: (line[0],line[1])) \
    .collect()

Alternatively, you could print the culprit (if any):

sc.textFile("file.csv") \
    .map(lambda line: line.split(",")) \
    .filter(lambda line: len(line)<=1) \
    .collect()
2016/12/30
63
12/30/2016 6:25:57 PM


from pyspark.sql import SparkSession

spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .config("spark.some.config.option", "some-value") \
    .getOrCreate()

df = spark.read.csv("/home/stp/test1.csv",header=True,sep="|")

print(df.collect())
2020/09/03

And yet another option which consist in reading the CSV file using Pandas and then importing the Pandas DataFrame into Spark.

For example:

from pyspark import SparkContext
from pyspark.sql import SQLContext
import pandas as pd

sc = SparkContext('local','example')  # if using locally
sql_sc = SQLContext(sc)

pandas_df = pd.read_csv('file.csv')  # assuming the file contains a header
# pandas_df = pd.read_csv('file.csv', names = ['column 1','column 2']) # if no header
s_df = sql_sc.createDataFrame(pandas_df)
2015/11/14

Simply splitting by comma will also split commas that are within fields (e.g. a,b,"1,2,3",c), so it's not recommended. zero323's answer is good if you want to use the DataFrames API, but if you want to stick to base Spark, you can parse csvs in base Python with the csv module:

# works for both python 2 and 3
import csv
rdd = sc.textFile("file.csv")
rdd = rdd.mapPartitions(lambda x: csv.reader(x))

EDIT: As @muon mentioned in the comments, this will treat the header like any other row so you'll need to extract it manually. For example, header = rdd.first(); rdd = rdd.filter(lambda x: x != header) (make sure not to modify header before the filter evaluates). But at this point, you're probably better off using a built-in csv parser.

2017/05/23

This is in PYSPARK

path="Your file path with file name"

df=spark.read.format("csv").option("header","true").option("inferSchema","true").load(path)

Then you can check

df.show(5)
df.count()
2020/01/21

If you want to load csv as a dataframe then you can do the following:

from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)

df = sqlContext.read.format('com.databricks.spark.csv') \
    .options(header='true', inferschema='true') \
    .load('sampleFile.csv') # this is your csv file

It worked fine for me.

2017/11/09