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
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]0
Ví 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]2
Ví 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]3
Ví 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]4
Ví 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]5
Ví 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]8
Ví 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 table0
Ví 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 table1
Ví 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 table2
Ví 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 table3
Ví 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 table3
Ví 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 table8
Ví 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 table9