Download the wikipeople sframe then open a new jupyter notebook ,import a turicreate and read the sframe data
Answers
Explanation:
A tabular, column-mutable dataframe object that can scale to big data. The data in SFrame is stored column-wise on the GraphLab Server side, and is stored on persistent storage (e.g. disk) to avoid being constrained by memory size. Each column in an SFrame is a size-immutable SArray, but SFrames are mutable in that columns can be added and subtracted with ease. An SFrame essentially acts as an ordered dict of SArrays.
Currently, we support constructing an SFrame from the following data formats:
csv file (comma separated value)
sframe directory archive (A directory where an sframe was saved previously)
general text file (with csv parsing options, See read_csv())
a Python dictionary
pandas.DataFrame
JSON
Apache Avro
PySpark RDD
and from the following sources:
your local file system
the GraphLab Server’s file system
HDFS
Amazon S3
HTTP(S).
Only basic examples of construction are covered here. For more information and examples, please see the User Guide, API Translator, How-Tos, and data science Gallery.
Parameters:
data : array | pandas.DataFrame | string | dict, optional
The actual interpretation of this field is dependent on the format parameter. If data is an array or Pandas DataFrame, the contents are stored in the SFrame. If data is a string, it is interpreted as a file. Files can be read from local file system or urls (local://, hdfs://, s3://, http://).
format : string, optional
Format of the data. The default, “auto” will automatically infer the input data format. The inference rules are simple: If the data is an array or a dataframe, it is associated with ‘array’ and ‘dataframe’ respectively. If the data is a string, it is interpreted as a file, and the file extension is used to infer the file format. The explicit options are:
“auto”
“array”
“dict”
“sarray”
“dataframe”
“csv”
“tsv”
“sframe