pyspark.sql.DataFrameReader

class pyspark.sql.DataFrameReader(spark: SparkSession)

Interface used to load a DataFrame from external storage systems (e.g. file systems, key-value stores, etc). Use SparkSession.read to access this.

New in version 1.4.

Methods

csv(path[, schema, sep, encoding, quote, …])	Loads a CSV file and returns the result as a DataFrame.
format(source)	Specifies the input data source format.
jdbc(url, table[, column, lowerBound, …])	Construct a DataFrame representing the database table named table accessible via JDBC URL url and connection properties.
json(path[, schema, primitivesAsString, …])	Loads JSON files and returns the results as a DataFrame.
load([path, format, schema])	Loads data from a data source and returns it as a DataFrame.
option(key, value)	Adds an input option for the underlying data source.
options(**options)	Adds input options for the underlying data source.
orc(path[, mergeSchema, pathGlobFilter, …])	Loads ORC files, returning the result as a DataFrame.
parquet(*paths, **options)	Loads Parquet files, returning the result as a DataFrame.
schema(schema)	Specifies the input schema.
table(tableName)	Returns the specified table as a DataFrame.
text(paths[, wholetext, lineSep, …])	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.

pyspark.sql.Window pyspark.sql.DataFrameWriter