pyspark.sql.DataFrameReader.csv#

DataFrameReader.csv(path, schema=None, sep=None, encoding=None, quote=None, escape=None, comment=None, header=None, inferSchema=None, ignoreLeadingWhiteSpace=None, ignoreTrailingWhiteSpace=None, nullValue=None, nanValue=None, positiveInf=None, negativeInf=None, dateFormat=None, timestampFormat=None, maxColumns=None, maxCharsPerColumn=None, maxMalformedLogPerPartition=None, mode=None, columnNameOfCorruptRecord=None, multiLine=None, charToEscapeQuoteEscaping=None, samplingRatio=None, enforceSchema=None, emptyValue=None, locale=None, lineSep=None, pathGlobFilter=None, recursiveFileLookup=None, modifiedBefore=None, modifiedAfter=None, unescapedQuoteHandling=None)[source]#

Loads a CSV file and returns the result as a DataFrame.

This function 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 schema.

New in version 2.0.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

pathstr or list: string, or list of strings, for input path(s), or RDD of Strings storing CSV rows.
schemapyspark.sql.types.StructType or str, optional: an optional pyspark.sql.types.StructType for the input schema or a DDL-formatted string (For example col0 INT, col1 DOUBLE).

Other Parameters

Extra options: For the extra options, refer to Data Source Option for the version you use.

Examples

Write a DataFrame into a CSV file and read it back.

>>> import tempfile
>>> with tempfile.TemporaryDirectory(prefix="csv") as d:
...     # Write a DataFrame into a CSV file
...     df = spark.createDataFrame([{"age": 100, "name": "Hyukjin Kwon"}])
...     df.write.mode("overwrite").format("csv").save(d)
...
...     # Read the CSV file as a DataFrame with 'nullValue' option set to 'Hyukjin Kwon'.
...     spark.read.csv(d, schema=df.schema, nullValue="Hyukjin Kwon").show()
+---+----+
|age|name|
+---+----+
|100|NULL|
+---+----+