parameters: (cls,
                   url,
                   delimiter=',',
                   header=True,
                   error_bad_lines=False,
                   comment_char='',
                   escape_char='\\',
                   double_quote=True,
                   quote_char='\"',
                   skip_initial_space=True,
                   column_type_hints=None,
                   na_values=["NA"],
                   line_terminator='\n',
                   usecols=[],
                   nrows=None,
                   skiprows=0,
                   verbose=True,
                   nrows_to_infer=100,
                   **kwargs)
return_statement: return cls._read_csv_impl(url,
                                    delimiter=delimiter,
                                    header=header,
                                    error_bad_lines=error_bad_lines,
                                    comment_char=comment_char,
                                    escape_char=escape_char,
                                    double_quote=double_quote,
                                    quote_char=quote_char,
                                    skip_initial_space=skip_initial_space,
                                    column_type_hints=column_type_hints,
                                    na_values=na_values,
                                    line_terminator=line_terminator,
                                    usecols=usecols,
                                    nrows=nrows,
                                    skiprows=skiprows,
                                    verbose=verbose,
                                    store_errors=False,
                                    nrows_to_infer=nrows_to_infer,
                                    **kwargs)[0]
docstring: Constructs an SFrame from a CSV file or a path to multiple CSVs.
  
          Parameters
          ----------
          url : string
              Location of the CSV file or directory to load. If URL is a directory
              or a "glob" pattern, all matching files will be loaded.
  
          delimiter : string, optional
              This describes the delimiter used for parsing csv files.
  
          header : bool, optional
              If true, uses the first row as the column names. Otherwise use the
              default column names : 'X1, X2, ...'.
  
          error_bad_lines : bool
              If true, will fail upon encountering a bad line. If false, will
              continue parsing skipping lines which fail to parse correctly.
              A sample of the first 10 encountered bad lines will be printed.
  
          comment_char : string, optional
              The character which denotes that the remainder of the line is a
              comment.
  
          escape_char : string, optional
              Character which begins a C escape sequence
  
          double_quote : bool, optional
              If True, two consecutive quotes in a string are parsed to a single
              quote.
  
          quote_char : string, optional
              Character sequence that indicates a quote.
  
          skip_initial_space : bool, optional
              Ignore extra spaces at the start of a field
  
          column_type_hints : None, type, list[type], dict[string, type], optional
              This provides type hints for each column. By default, this method
              attempts to detect the type of each column automatically.
  
              Supported types are int, float, str, list, dict, and array.array.
  
              * If a single type is provided, the type will be
                applied to all columns. For instance, column_type_hints=float
                will force all columns to be parsed as float.
              * If a list of types is provided, the types applies
                to each column in order, e.g.[int, float, str]
                will parse the first column as int, second as float and third as
                string.
              * If a dictionary of column name to type is provided,
                each type value in the dictionary is applied to the key it
                belongs to.
                For instance {'user':int} will hint that the column called "user"
                should be parsed as an integer, and the rest will be type inferred.
  
          na_values : str | list of str, optional
              A string or list of strings to be interpreted as missing values.
  
          line_terminator : str, optional
              A string to be interpreted as the line terminator. Defaults to "\n"
              which will also correctly match Mac, Linux and Windows line endings
              ("\\r", "\\n" and "\\r\\n" respectively)
  
          usecols : list of str, optional
              A subset of column names to output. If unspecified (default),
              all columns will be read. This can provide performance gains if the
              number of columns are large. If the input file has no headers,
              usecols=['X1','X3'] will read columns 1 and 3.
  
          nrows : int, optional
              If set, only this many rows will be read from the file.
  
          skiprows : int, optional
              If set, this number of rows at the start of the file are skipped.
  
          verbose : bool, optional
              If True, print the progress.
  
          Returns
          -------
          out : SFrame
  
          See Also
          --------
          read_csv_with_errors, SFrame
  
          Examples
          --------
  
          Read a regular csv file, with all default options, automatically
          determine types:
  
          >>> url = 'https://static.turi.com/datasets/rating_data_example.csv'
          >>> sf = graphlab.SFrame.read_csv(url)
          >>> sf
          Columns:
            user_id int
            movie_id  int
            rating  int
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Read only the first 100 lines of the csv file:
  
          >>> sf = graphlab.SFrame.read_csv(url, nrows=100)
          >>> sf
          Columns:
            user_id int
            movie_id  int
            rating  int
          Rows: 100
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [100 rows x 3 columns]
  
          Read all columns as str type
  
          >>> sf = graphlab.SFrame.read_csv(url, column_type_hints=str)
          >>> sf
          Columns:
            user_id  str
            movie_id  str
            rating  str
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Specify types for a subset of columns and leave the rest to be str.
  
          >>> sf = graphlab.SFrame.read_csv(url,
          ...                               column_type_hints={
          ...                               'user_id':int, 'rating':float
          ...                               })
          >>> sf
          Columns:
            user_id str
            movie_id  str
            rating  float
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |  3.0   |
          |  25907  |   1663   |  3.0   |
          |  25923  |   1663   |  3.0   |
          |  25924  |   1663   |  3.0   |
          |  25928  |   1663   |  2.0   |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Not treat first line as header:
  
          >>> sf = graphlab.SFrame.read_csv(url, header=False)
          >>> sf
          Columns:
            X1  str
            X2  str
            X3  str
          Rows: 10001
          +---------+----------+--------+
          |    X1   |    X2    |   X3   |
          +---------+----------+--------+
          | user_id | movie_id | rating |
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10001 rows x 3 columns]
  
          Treat '3' as missing value:
  
          >>> sf = graphlab.SFrame.read_csv(url, na_values=['3'], column_type_hints=str)
          >>> sf
          Columns:
            user_id str
            movie_id  str
            rating  str
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |  None  |
          |  25907  |   1663   |  None  |
          |  25923  |   1663   |  None  |
          |  25924  |   1663   |  None  |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Throw error on parse failure:
  
          >>> bad_url = 'https://static.turi.com/datasets/bad_csv_example.csv'
          >>> sf = graphlab.SFrame.read_csv(bad_url, error_bad_lines=True)
          RuntimeError: Runtime Exception. Unable to parse line "x,y,z,a,b,c"
          Set error_bad_lines=False to skip bad lines
docstring_summary: Constructs an SFrame from a CSV file or a path to multiple CSVs.
docstring_tokens: Constructs an SFrame from a CSV file or a path to multiple CSVs .
function: def read_csv(cls,
                   url,
                   delimiter=',',
                   header=True,
                   error_bad_lines=False,
                   comment_char='',
                   escape_char='\\',
                   double_quote=True,
                   quote_char='\"',
                   skip_initial_space=True,
                   column_type_hints=None,
                   na_values=["NA"],
                   line_terminator='\n',
                   usecols=[],
                   nrows=None,
                   skiprows=0,
                   verbose=True,
                   nrows_to_infer=100,
                   **kwargs):
          """
          Constructs an SFrame from a CSV file or a path to multiple CSVs.
  
          Parameters
          ----------
          url : string
              Location of the CSV file or directory to load. If URL is a directory
              or a "glob" pattern, all matching files will be loaded.
  
          delimiter : string, optional
              This describes the delimiter used for parsing csv files.
  
          header : bool, optional
              If true, uses the first row as the column names. Otherwise use the
              default column names : 'X1, X2, ...'.
  
          error_bad_lines : bool
              If true, will fail upon encountering a bad line. If false, will
              continue parsing skipping lines which fail to parse correctly.
              A sample of the first 10 encountered bad lines will be printed.
  
          comment_char : string, optional
              The character which denotes that the remainder of the line is a
              comment.
  
          escape_char : string, optional
              Character which begins a C escape sequence
  
          double_quote : bool, optional
              If True, two consecutive quotes in a string are parsed to a single
              quote.
  
          quote_char : string, optional
              Character sequence that indicates a quote.
  
          skip_initial_space : bool, optional
              Ignore extra spaces at the start of a field
  
          column_type_hints : None, type, list[type], dict[string, type], optional
              This provides type hints for each column. By default, this method
              attempts to detect the type of each column automatically.
  
              Supported types are int, float, str, list, dict, and array.array.
  
              * If a single type is provided, the type will be
                applied to all columns. For instance, column_type_hints=float
                will force all columns to be parsed as float.
              * If a list of types is provided, the types applies
                to each column in order, e.g.[int, float, str]
                will parse the first column as int, second as float and third as
                string.
              * If a dictionary of column name to type is provided,
                each type value in the dictionary is applied to the key it
                belongs to.
                For instance {'user':int} will hint that the column called "user"
                should be parsed as an integer, and the rest will be type inferred.
  
          na_values : str | list of str, optional
              A string or list of strings to be interpreted as missing values.
  
          line_terminator : str, optional
              A string to be interpreted as the line terminator. Defaults to "\n"
              which will also correctly match Mac, Linux and Windows line endings
              ("\\r", "\\n" and "\\r\\n" respectively)
  
          usecols : list of str, optional
              A subset of column names to output. If unspecified (default),
              all columns will be read. This can provide performance gains if the
              number of columns are large. If the input file has no headers,
              usecols=['X1','X3'] will read columns 1 and 3.
  
          nrows : int, optional
              If set, only this many rows will be read from the file.
  
          skiprows : int, optional
              If set, this number of rows at the start of the file are skipped.
  
          verbose : bool, optional
              If True, print the progress.
  
          Returns
          -------
          out : SFrame
  
          See Also
          --------
          read_csv_with_errors, SFrame
  
          Examples
          --------
  
          Read a regular csv file, with all default options, automatically
          determine types:
  
          >>> url = 'https://static.turi.com/datasets/rating_data_example.csv'
          >>> sf = graphlab.SFrame.read_csv(url)
          >>> sf
          Columns:
            user_id int
            movie_id  int
            rating  int
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Read only the first 100 lines of the csv file:
  
          >>> sf = graphlab.SFrame.read_csv(url, nrows=100)
          >>> sf
          Columns:
            user_id int
            movie_id  int
            rating  int
          Rows: 100
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [100 rows x 3 columns]
  
          Read all columns as str type
  
          >>> sf = graphlab.SFrame.read_csv(url, column_type_hints=str)
          >>> sf
          Columns:
            user_id  str
            movie_id  str
            rating  str
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Specify types for a subset of columns and leave the rest to be str.
  
          >>> sf = graphlab.SFrame.read_csv(url,
          ...                               column_type_hints={
          ...                               'user_id':int, 'rating':float
          ...                               })
          >>> sf
          Columns:
            user_id str
            movie_id  str
            rating  float
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |  3.0   |
          |  25907  |   1663   |  3.0   |
          |  25923  |   1663   |  3.0   |
          |  25924  |   1663   |  3.0   |
          |  25928  |   1663   |  2.0   |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Not treat first line as header:
  
          >>> sf = graphlab.SFrame.read_csv(url, header=False)
          >>> sf
          Columns:
            X1  str
            X2  str
            X3  str
          Rows: 10001
          +---------+----------+--------+
          |    X1   |    X2    |   X3   |
          +---------+----------+--------+
          | user_id | movie_id | rating |
          |  25904  |   1663   |   3    |
          |  25907  |   1663   |   3    |
          |  25923  |   1663   |   3    |
          |  25924  |   1663   |   3    |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10001 rows x 3 columns]
  
          Treat '3' as missing value:
  
          >>> sf = graphlab.SFrame.read_csv(url, na_values=['3'], column_type_hints=str)
          >>> sf
          Columns:
            user_id str
            movie_id  str
            rating  str
          Rows: 10000
          +---------+----------+--------+
          | user_id | movie_id | rating |
          +---------+----------+--------+
          |  25904  |   1663   |  None  |
          |  25907  |   1663   |  None  |
          |  25923  |   1663   |  None  |
          |  25924  |   1663   |  None  |
          |  25928  |   1663   |   2    |
          |   ...   |   ...    |  ...   |
          +---------+----------+--------+
          [10000 rows x 3 columns]
  
          Throw error on parse failure:
  
          >>> bad_url = 'https://static.turi.com/datasets/bad_csv_example.csv'
          >>> sf = graphlab.SFrame.read_csv(bad_url, error_bad_lines=True)
          RuntimeError: Runtime Exception. Unable to parse line "x,y,z,a,b,c"
          Set error_bad_lines=False to skip bad lines
          """
  
          return cls._read_csv_impl(url,
                                    delimiter=delimiter,
                                    header=header,
                                    error_bad_lines=error_bad_lines,
                                    comment_char=comment_char,
                                    escape_char=escape_char,
                                    double_quote=double_quote,
                                    quote_char=quote_char,
                                    skip_initial_space=skip_initial_space,
                                    column_type_hints=column_type_hints,
                                    na_values=na_values,
                                    line_terminator=line_terminator,
                                    usecols=usecols,
                                    nrows=nrows,
                                    skiprows=skiprows,
                                    verbose=verbose,
                                    store_errors=False,
                                    nrows_to_infer=nrows_to_infer,
                                    **kwargs)[0]
function_tokens: def read_csv ( cls , url , delimiter = ',' , header = True , error_bad_lines = False , comment_char = '' , escape_char = '\\' , double_quote = True , quote_char = '\"' , skip_initial_space = True , column_type_hints = None , na_values = [ "NA" ] , line_terminator = '\n' , usecols = [ ] , nrows = None , skiprows = 0 , verbose = True , nrows_to_infer = 100 , * * kwargs ) : return cls . _read_csv_impl ( url , delimiter = delimiter , header = header , error_bad_lines = error_bad_lines , comment_char = comment_char , escape_char = escape_char , double_quote = double_quote , quote_char = quote_char , skip_initial_space = skip_initial_space , column_type_hints = column_type_hints , na_values = na_values , line_terminator = line_terminator , usecols = usecols , nrows = nrows , skiprows = skiprows , verbose = verbose , store_errors = False , nrows_to_infer = nrows_to_infer , * * kwargs ) [ 0 ]