10 examples of 'how to read csv file in python without pandas' in Python

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def readcsv(filename, header=True):
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    return pd.read_csv(filename, header=None) if not header else pd.read_csv(filename)

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def _csv_to_pandas_df(filepath,
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                      separator=DEFAULT_SEPARATOR,
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                      quote_char=DEFAULT_QUOTE_CHARACTER,
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                      escape_char=DEFAULT_ESCAPSE_CHAR,
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                      contain_headers=True,
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                      lines_to_skip=0,
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                      date_columns=None,
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                      rowIdAndVersionInIndex=True):
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    test_import_pandas()
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    import pandas as pd
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    # DATEs are stored in csv as unix timestamp in milliseconds
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    def datetime_millisecond_parser(milliseconds): return pd.to_datetime(milliseconds, unit='ms', utc=True)
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    if not date_columns:
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        date_columns = []
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    line_terminator = str(os.linesep)
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    df = pd.read_csv(filepath,
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                     sep=separator,
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                     lineterminator=line_terminator if len(line_terminator) == 1 else None,
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                     quotechar=quote_char,
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                     escapechar=escape_char,
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                     header=0 if contain_headers else None,
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                     skiprows=lines_to_skip,
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                     parse_dates=date_columns,
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                     date_parser=datetime_millisecond_parser)
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    if rowIdAndVersionInIndex and "ROW_ID" in df.columns and "ROW_VERSION" in df.columns:
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        # combine row-ids (in index) and row-versions (in column 0) to
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        # make new row labels consisting of the row id and version
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        # separated by a dash.
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        zip_args = [df["ROW_ID"], df["ROW_VERSION"]]
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        if "ROW_ETAG" in df.columns:
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            zip_args.append(df['ROW_ETAG'])
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        df.index = row_labels_from_id_and_version(zip(*zip_args))
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        del df["ROW_ID"]
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        del df["ROW_VERSION"]
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        if "ROW_ETAG" in df.columns:
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            del df['ROW_ETAG']
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    return df

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def test_read_csv():
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    io = FileIO()
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    filename = os.path.join(os.path.dirname(
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                            os.path.abspath(__file__)),
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                            'stock_N225.csv')
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    df = io.read_from_csv("N225", filename)
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    result = round(df.ix['2015-03-20', 'Adj Close'], 2)
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    expected = 19560.22
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    eq_(expected, result)

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def _pandas_read_csv(filepath, **kwargs):
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    """
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    Wrapper function around the Pandas read_csv function.
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    :param filepath: The file to read.
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    :type filepath: str, StringIO
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    :param kwargs: Extra key word arguments to be applied.
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    :return: A pandas DataFrame.
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    :rtype: pandas.DataFrame
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    """
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    try:
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        return pd.read_csv(filepath, **kwargs)
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    except FileNotFoundError:
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        raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), filepath)
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    except Exception as error:
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        raise error

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def _dataframe_from_csv(reader, delimiter, with_header, skipspace):
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    """Returns csv data as a pandas Dataframe object"""
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    sep = delimiter
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    header = 0
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    if not with_header:
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        header = None
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    return pd.read_csv(
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        reader,
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        header=header,
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        sep=sep,
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        skipinitialspace=skipspace,
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        encoding='utf-8-sig'
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    )

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def get_data_from_file(csv_content, files):
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    # Get description or fix from the file reference parsed in JsonToCsv class
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    data = ''
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    number_from_file = re.search('\d+', csv_content)
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    if not number_from_file:
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        return data
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    else:
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        file_number = number_from_file.group()
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    if file_number in files['filenames']:
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        filename = files['filenames'][file_number]
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    else:
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        return data
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    with open(path.join(files['path'], filename)) as file_object:
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        data = file_object.read()
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    return data

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def pandas_read_csv(self, usecols=None, **kwargs):
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    """ Use pandas.read_csv with the right keyword arguments
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    In particular we know what dtypes should be, which columns are dates,
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    etc...
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    """
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    dtypes, dates = dshape_to_pandas(self.schema)
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    if usecols:
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        if builtins.all(isinstance(c, int) for c in usecols):
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            usecols = get(usecols, self.columns)
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        dates = [name for name in dates if name in usecols]
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    header = kwargs.pop('header', self.header)
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    header = 0 if self.header else None
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    result = pd.read_csv(self.path,
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                         names=kwargs.pop('names', self.columns),
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                         usecols=usecols,
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                         compression={'gz': 'gzip',
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                                      'bz2': 'bz2'}.get(ext(self.path)),
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                         dtype=kwargs.pop('dtype', dtypes),
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                         parse_dates=kwargs.pop('parse_dates', dates),
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                         encoding=kwargs.pop('encoding', self.encoding),
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                         header=header,
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                         **merge(kwargs, clean_dialect(self.dialect)))
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    return result

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def unicode_csv_reader(unicode_csv_data, dialect=csv.excel, **kwargs):
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    """ csv.py doesn't do Unicode; encode temporarily as UTF-8."""
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    csv_reader = csv.reader(utf_8_encoder(unicode_csv_data),
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                            dialect=dialect, **kwargs)
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    for row in csv_reader:
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        # decode UTF-8 back to Unicode, cell by cell:
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        yield [unicode(cell, 'utf-8') for cell in row]

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def readFile():
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	#make the format of the csv file. Our format is a vector with 13 features and a label which show the condition of the
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	#sample hc/pc : helathy case, parkinson case
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  	names = ['Feature1', 'Feature2', 'Feature3', 'Feature4','Feature5','Feature6','Feature7','Feature8','Feature9',
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	'Feature10','Feature11','Feature12','Feature13','Label']
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	#path to read the samples, samples consist from healthy subjects and subject suffering from Parkinson's desease.
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	path = ''
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	#read file in csv format
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	data = pd.read_csv(path,names=names )
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	#return an array of the shape (2103, 14), lines are the samples and columns are the features as we mentioned before
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	return data

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def readFile():
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	#make the format of the csv file. Our format is a vector with 13 features and a label which show the condition of the
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	#sample hc/pc : helathy case, parkinson case
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  	names = ['Feature1', 'Feature2', 'Feature3', 'Feature4','Feature5','Feature6','Feature7','Feature8','Feature9',
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	'Feature10','Feature11','Feature12','Feature13','Label']
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	#path to read the samples, samples consist from healthy subjects and subject suffering from Parkinson's desease.
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	path = 'mfcc_multiclass.txt'
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	#read file in csv format
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	data = pd.read_csv(path,names=names )
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	#return an array of the shape (2103, 14), lines are the samples and columns are the features as we mentioned before
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	return data