Import a file
=============

Unlike the `upload <uploading-data.html>`__ function, which is a push from the client to the server, the import function is a parallelized reader and pulls information from the server from a location specified by the client. The path is a server-side path. This is a fast, scalable, highly optimized way to read data. H2O-3 pulls the data from a data store and initiates the data transfer as a read operation.

`See more on supported file formats <http://docs.h2o.ai/h2o/latest-stable/h2o-docs/getting-data-into-h2o.html#supported-file-formats>`__ to ensure that you are using a supported file type.

.. note:: 
	
	When parsing a data file containing timestamps that do not include a timezone, the timestamps will be interpreted as UTC (GMT). You can override the parsing timezone using the following:

	- **Python**: ``h2o.cluster().timezone = "America/Los Angeles"``
	- **R**: ``h2o.setTimezone("America/Los Angeles")``

.. tabs::
   .. code-tab:: python

		# Import a file from S3:
		import h2o
		h2o.init()
		airlines = "http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip"
		airlines_df = h2o.import_file(path=airlines)

		# Import a file from HDFS, you must include the node name:
		import h2o
		h2o.init()
		airlines = "hdfs://node-1:/user/smalldata/airlines/allyears2k_headers.zip"
		airlines_df = h2o.import_file(path=airlines)

   .. code-tab:: r R

		# To import airlines file from H2O’s package:
		library(h2o)
		h2o.init()
		iris_path <- system.file("extdata", "iris.csv", package="h2o")
		iris <- h2o.importFile(path = iris_path)

		# To import from S3:
		library(h2o)
		h2o.init()
		airlines_path <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k.zip"
		airlines <- h2o.importFile(path = airlines_path)

		# To import from HDFS, you must include the node name:
		library(h2o)
		h2o.init()
		airlines_path <- "hdfs://node-1:/user/smalldata/airlines/allyears2k_headers.zip" 
		airlines <- h2o.importFile(path = airlines_path)
	  


