pyspark.sql.DataFrameReader.text¶

DataFrameReader.text(paths: Union[str, List[str]], wholetext: bool = False, lineSep: Optional[str] = None, pathGlobFilter: Union[bool, str, None] = None, recursiveFileLookup: Union[bool, str, None] = None, modifiedBefore: Union[bool, str, None] = None, modifiedAfter: Union[bool, str, None] = None) → DataFrame[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.

New in version 1.6.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

pathsstr or list: string, or list of strings, for input path(s).

Other Parameters

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

Examples

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

>>> import tempfile
>>> with tempfile.TemporaryDirectory() as d:
...     # Write a DataFrame into a text file
...     df = spark.createDataFrame([("a",), ("b",), ("c",)], schema=["alphabets"])
...     df.write.mode("overwrite").format("text").save(d)
...
...     # Read the text file as a DataFrame.
...     spark.read.schema(df.schema).text(d).sort("alphabets").show()
+---------+
|alphabets|
+---------+
|        a|
|        b|
|        c|
+---------+

pyspark.sql.DataFrameReader.table

pyspark.sql.DataFrameWriter.bucketBy