Hướng dẫn python csv sniffer has_header example - Ví dụ về python csv sniper has_header
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Sau đây là 30 ví dụ mã của csv.sniffer (). Bạn có thể bỏ phiếu cho những người bạn thích hoặc bỏ phiếu cho những người bạn không thích và đi đến dự án gốc hoặc tệp nguồn bằng cách theo các liên kết trên mỗi ví dụ. Bạn cũng có thể muốn kiểm tra tất cả các chức năng/lớp có sẵn của mô -đun CSV hoặc thử chức năng tìm kiếm.30 code examples of csv.Sniffer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module csv, or try the search function  Show Ví dụ 1 def _modify_column_headers(tmp_file_location,
custom_dimension_header_mapping):
logger.info('Modifying column headers to be compatible for data upload')
with open(tmp_file_location, 'r') as check_header_file:
has_header = csv.Sniffer().has_header(check_header_file.read(1024))
if has_header:
with open(tmp_file_location, 'r') as read_file:
reader = csv.reader(read_file)
headers = next(reader)
new_headers = []
for header in headers:
if header in custom_dimension_header_mapping:
header = custom_dimension_header_mapping.get(header)
new_header = 'ga:' + header
new_headers.append(new_header)
all_data = read_file.readlines()
final_headers = ','.join(new_headers) + '\n'
all_data.insert(0, final_headers)
with open(tmp_file_location, 'w') as write_file:
write_file.writelines(all_data)
else:
raise NameError('CSV does not contain headers, please add them '
'to use the modify column headers functionality') Ví dụ #2 def csv_to_dmatrix(string_like, dtype=None): # type: (str) -> xgb.DMatrix
"""Convert a CSV object to a DMatrix object.
Args:
string_like (str): CSV string. Assumes the string has been stripped of leading or trailing newline chars.
dtype (dtype, optional): Data type of the resulting array. If None, the dtypes will be determined by the
contents of each column, individually. This argument can only be used to
'upcast' the array. For downcasting, use the .astype(t) method.
Returns:
(xgb.DMatrix): XGBoost DataMatrix
"""
sniff_delimiter = csv.Sniffer().sniff(string_like.split('\n')[0][:512]).delimiter
delimiter = ',' if sniff_delimiter.isalnum() else sniff_delimiter
logging.info("Determined delimiter of CSV input is \'{}\'".format(delimiter))
np_payload = np.array(list(map(lambda x: _clean_csv_string(x, delimiter), string_like.split('\n')))).astype(dtype)
return xgb.DMatrix(np_payload) Ví dụ #3 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table Ví dụ #4 def test_delimiters(self):
sniffer = csv.Sniffer()
dialect = sniffer.sniff(self.sample3)
# given that all three lines in sample3 are equal,
# I think that any character could have been 'guessed' as the
# delimiter, depending on dictionary order
self.assertIn(dialect.delimiter, self.sample3)
dialect = sniffer.sniff(self.sample3, delimiters="?,")
self.assertEqual(dialect.delimiter, "?")
dialect = sniffer.sniff(self.sample3, delimiters="/,")
self.assertEqual(dialect.delimiter, "/")
dialect = sniffer.sniff(self.sample4)
self.assertEqual(dialect.delimiter, ";")
dialect = sniffer.sniff(self.sample5)
self.assertEqual(dialect.delimiter, "\t")
dialect = sniffer.sniff(self.sample6)
self.assertEqual(dialect.delimiter, "|")
dialect = sniffer.sniff(self.sample7)
self.assertEqual(dialect.delimiter, "|")
self.assertEqual(dialect.quotechar, "'")
dialect = sniffer.sniff(self.sample8)
self.assertEqual(dialect.delimiter, '+')
dialect = sniffer.sniff(self.sample9)
self.assertEqual(dialect.delimiter, '+')
self.assertEqual(dialect.quotechar, "'") Ví dụ #5 def __init__(self, fname, labels):
"""
Initialize the corpus from a file.
`labels` = are class labels present in the input file? => skip the first column
"""
logger.info("loading corpus from %s" % fname)
self.fname = fname
self.length = None
self.labels = labels
# load the first few lines, to guess the CSV dialect
head = ''.join(itertools.islice(open(self.fname), 5))
self.headers = csv.Sniffer().has_header(head)
self.dialect = csv.Sniffer().sniff(head)
logger.info("sniffed CSV delimiter=%r, headers=%s" % (self.dialect.delimiter, self.headers)) Ví dụ #6 def __init__(self, fname, labels):
"""
Initialize the corpus from a file.
`labels` = are class labels present in the input file? => skip the first column
"""
logger.info("loading corpus from %s" % fname)
self.fname = fname
self.length = None
self.labels = labels
# load the first few lines, to guess the CSV dialect
head = ''.join(itertools.islice(open(self.fname), 5))
self.headers = csv.Sniffer().has_header(head)
self.dialect = csv.Sniffer().sniff(head)
logger.info("sniffed CSV delimiter=%r, headers=%s" % (self.dialect.delimiter, self.headers)) Ví dụ #7 def __init__(self, fname, labels):
"""
Initialize the corpus from a file.
`labels` = are class labels present in the input file? => skip the first column
"""
logger.info("loading corpus from %s" % fname)
self.fname = fname
self.length = None
self.labels = labels
# load the first few lines, to guess the CSV dialect
head = ''.join(itertools.islice(open(self.fname), 5))
self.headers = csv.Sniffer().has_header(head)
self.dialect = csv.Sniffer().sniff(head)
logger.info("sniffed CSV delimiter=%r, headers=%s" % (self.dialect.delimiter, self.headers)) Ví dụ #8 def test_delimiters(self):
sniffer = csv.Sniffer()
dialect = sniffer.sniff(self.sample3)
# given that all three lines in sample3 are equal,
# I think that any character could have been 'guessed' as the
# delimiter, depending on dictionary order
self.assertIn(dialect.delimiter, self.sample3)
dialect = sniffer.sniff(self.sample3, delimiters="?,")
self.assertEqual(dialect.delimiter, "?")
dialect = sniffer.sniff(self.sample3, delimiters="/,")
self.assertEqual(dialect.delimiter, "/")
dialect = sniffer.sniff(self.sample4)
self.assertEqual(dialect.delimiter, ";")
dialect = sniffer.sniff(self.sample5)
self.assertEqual(dialect.delimiter, "\t")
dialect = sniffer.sniff(self.sample6)
self.assertEqual(dialect.delimiter, "|")
dialect = sniffer.sniff(self.sample7)
self.assertEqual(dialect.delimiter, "|")
self.assertEqual(dialect.quotechar, "'") Ví dụ #9 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table Ví dụ #10 def test_delimiters(self):
sniffer = csv.Sniffer()
dialect = sniffer.sniff(self.sample3)
# given that all three lines in sample3 are equal,
# I think that any character could have been 'guessed' as the
# delimiter, depending on dictionary order
self.assertIn(dialect.delimiter, self.sample3)
dialect = sniffer.sniff(self.sample3, delimiters="?,")
self.assertEqual(dialect.delimiter, "?")
dialect = sniffer.sniff(self.sample3, delimiters="/,")
self.assertEqual(dialect.delimiter, "/")
dialect = sniffer.sniff(self.sample4)
self.assertEqual(dialect.delimiter, ";")
dialect = sniffer.sniff(self.sample5)
self.assertEqual(dialect.delimiter, "\t")
dialect = sniffer.sniff(self.sample6)
self.assertEqual(dialect.delimiter, "|")
dialect = sniffer.sniff(self.sample7)
self.assertEqual(dialect.delimiter, "|")
self.assertEqual(dialect.quotechar, "'")
dialect = sniffer.sniff(self.sample8)
self.assertEqual(dialect.delimiter, '+')
dialect = sniffer.sniff(self.sample9)
self.assertEqual(dialect.delimiter, '+')
self.assertEqual(dialect.quotechar, "'") Ví dụ #11 def csv_to_dmatrix(string_like, dtype=None): # type: (str) -> xgb.DMatrix
"""Convert a CSV object to a DMatrix object.
Args:
string_like (str): CSV string. Assumes the string has been stripped of leading or trailing newline chars.
dtype (dtype, optional): Data type of the resulting array. If None, the dtypes will be determined by the
contents of each column, individually. This argument can only be used to
'upcast' the array. For downcasting, use the .astype(t) method.
Returns:
(xgb.DMatrix): XGBoost DataMatrix
"""
sniff_delimiter = csv.Sniffer().sniff(string_like.split('\n')[0][:512]).delimiter
delimiter = ',' if sniff_delimiter.isalnum() else sniff_delimiter
logging.info("Determined delimiter of CSV input is \'{}\'".format(delimiter))
np_payload = np.array(list(map(lambda x: _clean_csv_string(x, delimiter), string_like.split('\n')))).astype(dtype)
return xgb.DMatrix(np_payload) 0Ví dụ #12 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table Ví dụ #13 def csv_to_dmatrix(string_like, dtype=None): # type: (str) -> xgb.DMatrix
"""Convert a CSV object to a DMatrix object.
Args:
string_like (str): CSV string. Assumes the string has been stripped of leading or trailing newline chars.
dtype (dtype, optional): Data type of the resulting array. If None, the dtypes will be determined by the
contents of each column, individually. This argument can only be used to
'upcast' the array. For downcasting, use the .astype(t) method.
Returns:
(xgb.DMatrix): XGBoost DataMatrix
"""
sniff_delimiter = csv.Sniffer().sniff(string_like.split('\n')[0][:512]).delimiter
delimiter = ',' if sniff_delimiter.isalnum() else sniff_delimiter
logging.info("Determined delimiter of CSV input is \'{}\'".format(delimiter))
np_payload = np.array(list(map(lambda x: _clean_csv_string(x, delimiter), string_like.split('\n')))).astype(dtype)
return xgb.DMatrix(np_payload) 2Ví dụ #14 def csv_to_dmatrix(string_like, dtype=None): # type: (str) -> xgb.DMatrix
"""Convert a CSV object to a DMatrix object.
Args:
string_like (str): CSV string. Assumes the string has been stripped of leading or trailing newline chars.
dtype (dtype, optional): Data type of the resulting array. If None, the dtypes will be determined by the
contents of each column, individually. This argument can only be used to
'upcast' the array. For downcasting, use the .astype(t) method.
Returns:
(xgb.DMatrix): XGBoost DataMatrix
"""
sniff_delimiter = csv.Sniffer().sniff(string_like.split('\n')[0][:512]).delimiter
delimiter = ',' if sniff_delimiter.isalnum() else sniff_delimiter
logging.info("Determined delimiter of CSV input is \'{}\'".format(delimiter))
np_payload = np.array(list(map(lambda x: _clean_csv_string(x, delimiter), string_like.split('\n')))).astype(dtype)
return xgb.DMatrix(np_payload) 3Ví dụ #15 def csv_to_dmatrix(string_like, dtype=None): # type: (str) -> xgb.DMatrix
"""Convert a CSV object to a DMatrix object.
Args:
string_like (str): CSV string. Assumes the string has been stripped of leading or trailing newline chars.
dtype (dtype, optional): Data type of the resulting array. If None, the dtypes will be determined by the
contents of each column, individually. This argument can only be used to
'upcast' the array. For downcasting, use the .astype(t) method.
Returns:
(xgb.DMatrix): XGBoost DataMatrix
"""
sniff_delimiter = csv.Sniffer().sniff(string_like.split('\n')[0][:512]).delimiter
delimiter = ',' if sniff_delimiter.isalnum() else sniff_delimiter
logging.info("Determined delimiter of CSV input is \'{}\'".format(delimiter))
np_payload = np.array(list(map(lambda x: _clean_csv_string(x, delimiter), string_like.split('\n')))).astype(dtype)
return xgb.DMatrix(np_payload) 4Ví dụ #16 def csv_to_dmatrix(string_like, dtype=None): # type: (str) -> xgb.DMatrix
"""Convert a CSV object to a DMatrix object.
Args:
string_like (str): CSV string. Assumes the string has been stripped of leading or trailing newline chars.
dtype (dtype, optional): Data type of the resulting array. If None, the dtypes will be determined by the
contents of each column, individually. This argument can only be used to
'upcast' the array. For downcasting, use the .astype(t) method.
Returns:
(xgb.DMatrix): XGBoost DataMatrix
"""
sniff_delimiter = csv.Sniffer().sniff(string_like.split('\n')[0][:512]).delimiter
delimiter = ',' if sniff_delimiter.isalnum() else sniff_delimiter
logging.info("Determined delimiter of CSV input is \'{}\'".format(delimiter))
np_payload = np.array(list(map(lambda x: _clean_csv_string(x, delimiter), string_like.split('\n')))).astype(dtype)
return xgb.DMatrix(np_payload) 5Ví dụ #17 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table Ví dụ #18 def test_delimiters(self):
sniffer = csv.Sniffer()
dialect = sniffer.sniff(self.sample3)
# given that all three lines in sample3 are equal,
# I think that any character could have been 'guessed' as the
# delimiter, depending on dictionary order
self.assertIn(dialect.delimiter, self.sample3)
dialect = sniffer.sniff(self.sample3, delimiters="?,")
self.assertEqual(dialect.delimiter, "?")
dialect = sniffer.sniff(self.sample3, delimiters="/,")
self.assertEqual(dialect.delimiter, "/")
dialect = sniffer.sniff(self.sample4)
self.assertEqual(dialect.delimiter, ";")
dialect = sniffer.sniff(self.sample5)
self.assertEqual(dialect.delimiter, "\t")
dialect = sniffer.sniff(self.sample6)
self.assertEqual(dialect.delimiter, "|")
dialect = sniffer.sniff(self.sample7)
self.assertEqual(dialect.delimiter, "|")
self.assertEqual(dialect.quotechar, "'")
dialect = sniffer.sniff(self.sample8)
self.assertEqual(dialect.delimiter, '+')
dialect = sniffer.sniff(self.sample9)
self.assertEqual(dialect.delimiter, '+')
self.assertEqual(dialect.quotechar, "'") Ví dụ #19 def csv_to_dmatrix(string_like, dtype=None): # type: (str) -> xgb.DMatrix
"""Convert a CSV object to a DMatrix object.
Args:
string_like (str): CSV string. Assumes the string has been stripped of leading or trailing newline chars.
dtype (dtype, optional): Data type of the resulting array. If None, the dtypes will be determined by the
contents of each column, individually. This argument can only be used to
'upcast' the array. For downcasting, use the .astype(t) method.
Returns:
(xgb.DMatrix): XGBoost DataMatrix
"""
sniff_delimiter = csv.Sniffer().sniff(string_like.split('\n')[0][:512]).delimiter
delimiter = ',' if sniff_delimiter.isalnum() else sniff_delimiter
logging.info("Determined delimiter of CSV input is \'{}\'".format(delimiter))
np_payload = np.array(list(map(lambda x: _clean_csv_string(x, delimiter), string_like.split('\n')))).astype(dtype)
return xgb.DMatrix(np_payload) 8Ví dụ #20 def test_delimiters(self):
sniffer = csv.Sniffer()
dialect = sniffer.sniff(self.sample3)
# given that all three lines in sample3 are equal,
# I think that any character could have been 'guessed' as the
# delimiter, depending on dictionary order
self.assertIn(dialect.delimiter, self.sample3)
dialect = sniffer.sniff(self.sample3, delimiters="?,")
self.assertEqual(dialect.delimiter, "?")
dialect = sniffer.sniff(self.sample3, delimiters="/,")
self.assertEqual(dialect.delimiter, "/")
dialect = sniffer.sniff(self.sample4)
self.assertEqual(dialect.delimiter, ";")
dialect = sniffer.sniff(self.sample5)
self.assertEqual(dialect.delimiter, "\t")
dialect = sniffer.sniff(self.sample6)
self.assertEqual(dialect.delimiter, "|")
dialect = sniffer.sniff(self.sample7)
self.assertEqual(dialect.delimiter, "|")
self.assertEqual(dialect.quotechar, "'")
dialect = sniffer.sniff(self.sample8)
self.assertEqual(dialect.delimiter, '+')
dialect = sniffer.sniff(self.sample9)
self.assertEqual(dialect.delimiter, '+')
self.assertEqual(dialect.quotechar, "'") Ví dụ #21 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table 0Ví dụ #22 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table 1Ví dụ #23 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table 2Ví dụ #24 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table 3Ví dụ #25 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table 3Ví dụ #26 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table Ví dụ #27 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table Ví dụ #28 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table Ví dụ #29 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table 8Ví dụ #30 def from_csv(fp, field_names = None, **kwargs):
dialect = csv.Sniffer().sniff(fp.read(1024))
fp.seek(0)
reader = csv.reader(fp, dialect)
table = PrettyTable(**kwargs)
if field_names:
table.field_names = field_names
else:
if py3k:
table.field_names = [x.strip() for x in next(reader)]
else:
table.field_names = [x.strip() for x in reader.next()]
for row in reader:
table.add_row([x.strip() for x in row])
return table 9
Làm cách nào để kiểm tra xem tệp CSV có chứa python tiêu đề không?Nhập CSV với Open ('Ví dụ.sniffer = csv. Sniffer() has_header = sniffer. has_header(csvfile.
Làm cách nào để đọc một tệp CSV theo từng dòng trong Python?Sử dụng độc giả.. Bước 1: Để đọc các hàng trong Python, trước tiên, chúng ta cần tải tệp CSV trong một đối tượng.Vì vậy, để tải tệp CSV vào một đối tượng sử dụng phương thức Open () .. Bước 2: Tạo đối tượng đầu đọc bằng cách chuyển đối tượng tệp được tạo ở trên cho hàm đầu đọc .. Bước 3: Sử dụng cho vòng lặp trên đối tượng đầu đọc để có được mỗi hàng .. Làm thế nào tôi có thể biết nếu một tệp có tiêu đề?Để kiểm tra xem tệp tiêu đề có được bao gồm hay không trong mã C hoặc C ++ hay không, chúng ta cần kiểm tra xem macro được xác định trong tệp tiêu đề có được xác định trong mã máy khách không.Các tập tin tiêu đề tiêu chuẩn như toán học.H có macro độc đáo của riêng họ (như _math_h) mà chúng ta cần kiểm tra.Hãy xem xét ví dụ này về kiểm tra nếu toán học.check if the macro defined in the header file is being defined in the client code. Standard header files like math. h have their own unique macro (like _MATH_H ) which we need to check. Consider this example of checking if math.
Làm cách nào để phân tích tệp CSV trong Python?Các bước để đọc tệp CSV:.. Nhập thư viện CSV.Nhập CSV .. Mở tệp CSV.Các ..... Sử dụng đối tượng CSV.Reader để đọc tệp CSV.csvreader = csv.Reader (tệp). Trích xuất tên trường.Tạo một danh sách trống gọi là tiêu đề..... Trích xuất các hàng/hồ sơ..... Đóng tệp .. |
