10 examples of 'python read csv' 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 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 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 read_csv(array, filename):
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    with open(filename, 'rb') as input_file:
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        foo = csv.reader(input_file)
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        for row in foo:
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            array.append(row)
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    return array

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def read_csv(fh):
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    """Read the CSV input"""
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    exercises = []
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    for row in csv.DictReader(fh, delimiter=','):
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        name = row['exercise']
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        low, high = row['reps'].split('-')
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        exercises.append((name, int(low), int(high)))
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    return exercises

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def load_csv(data):
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    sio = StringIO(data)
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    return list(unicodecsv.DictReader(sio, encoding="utf-8"))

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def load_csv_data(csv_file):
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    import csv
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    categories = []
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    comments = []
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    with open(csv_file, 'rb') as raw_dataset:
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        dataset_reader = csv.reader(raw_dataset, delimiter=',')
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        next(dataset_reader, None) # Filter Header
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        for row in dataset_reader:
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            categories.append(int(row[0]))
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            comments.append(preprocess_comment(row[2]))
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    return categories, comments

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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

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def read_csv_file(file_name):
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    """
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    Return patient ID and sinogram array produced by reading a RayStation sinogram
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    CSV file with the provided file name.
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    Files are produced by ExportTomoSinogram.py, Brandon Merz,
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    RaySearch customer forum, 1/18/2018.
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        Format:
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            First row contains demographics. Subsequent rows correspond to couch positions.
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            Leaf-open time range from zero to one.
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                "Patient name: ANONYMOUS^PATIENT, ID: 00000",,,,,,,,,
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                ,0,0,0,0,0,0,0,0,0,0,0,0,0.39123373,0.366435635 ...
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    """
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    with open(file_name, 'r') as csvfile:
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        pat_name, pat_num = csvfile.readline().split('ID:')
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        pat_name = pat_name.replace('Patient name:', '')
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        pat_name_last, pat_name_first = pat_name.split('^')
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        pat_name_last = ''.join([c for c in pat_name_last if c in LETTERS])
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        pat_name_first = ''.join([c for c in pat_name_first if c in LETTERS])
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        pat_num = ''.join([c for c in pat_num if c in DIGITS])
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        document_id = pat_num + ' - ' + pat_name_last + ', ' + pat_name_first
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        reader = csv.reader(csvfile, delimiter=',')
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        array = np.asarray([line[1:] for line in reader]).astype(float)
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    return document_id, array