pyspark.sql.DataFrameReader.text

DataFrameReader.text(paths, wholetext=False, lineSep=None, pathGlobFilter=None, recursiveFileLookup=None)[source]

Loads text files and returns a DataFrame whose schema starts with a string column named “value”, and followed by partitioned columns if there are any. The text files must be encoded as UTF-8.

By default, each line in the text file is a new row in the resulting DataFrame.

Parameters

  • paths – string, or list of strings, for input path(s).

  • wholetext – if true, read each file from input path(s) as a single row.

  • lineSep – defines the line separator that should be used for parsing. If None is set, it covers all \r, \r\n and \n.

  • pathGlobFilter – an optional glob pattern to only include files with paths matching the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter. It does not change the behavior of `partition discovery`_.

  • recursiveFileLookup – recursively scan a directory for files. Using this option disables `partition discovery`_.

>>> df = spark.read.text('python/test_support/sql/text-test.txt')
>>> df.collect()
[Row(value='hello'), Row(value='this')]
>>> df = spark.read.text('python/test_support/sql/text-test.txt', wholetext=True)
>>> df.collect()
[Row(value='hello\nthis')]

New in version 1.6.

pyspark.sql.DataFrameReader.table pyspark.sql.DataFrameWriter.bucketBy