Python csv.reader方法代碼示例

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def get_ip_geo_localization(self, ip):

        self._logger.debug("Getting {0} geo localization ".format(ip))
        if ip.strip() != "" and ip is not None:

            result = linecache.getline(self._ip_localization_file, bisect.bisect(self._ip_localization_ranges, Util.ip_to_int(ip)))
            result.strip('\n')

            reader = csv.reader([result])
            row = reader.next()

            geo_loc = ";".join(row[4:6]) + " " + ";".join(row[8:9])            
            domain = row[9:10][0]

            result = {"geo_loc": geo_loc, "domain": domain}

        return result 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def fetch_agents_from_file(self, filenm, agent_type):
        """
        Read in a list of bartering agents from a csv file
        """

        max_detect = self.props.get("max_detect",
                                    ebm.GLOBAL_KNOWLEDGE)
        with open(filenm) as f:
            reader = csv.reader(f)
            for row in reader:
                agent = agent_type(row[0], max_detect=max_detect)
                self.add_agent(agent)
                for i in range(1, len(row) - 2, STEP):
                    good = row[i]
                    self.market.add_good(good)
                    agent.endow(good,
                                int(row[i + 1]),
                                eval("lambda qty: "
                                     + row[i + 2]))
        logging.info("Goods = " + str(self.market)) 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def load_predictions(env, nclasses):
    path = os.path.join(env.stats_dir(), "predictions.csv")

    if not os.path.exists(path):
        raise FileExistsError(path)

    with open(path, newline='') as csvfile:
        y_score = []
        y_test = []
        csv_reader = csv.reader(csvfile, dialect="excel")
        for row in csv_reader:
            assert len(row) == nclasses * 2
            y_score.append(list(map(float, row[:nclasses])))
            y_test.append(list(map(float, row[nclasses:])))
        
        y_score = np.array(y_score)
        y_test = np.array(y_test)

        return y_test, y_score 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def __init__(self, filename):
    """Initializes instance of DatasetMetadata."""
    self._true_labels = {}
    self._target_classes = {}
    with open(filename) as f:
      reader = csv.reader(f)
      header_row = next(reader)
      try:
        row_idx_image_id = header_row.index('ImageId')
        row_idx_true_label = header_row.index('TrueLabel')
        row_idx_target_class = header_row.index('TargetClass')
      except ValueError:
        raise IOError('Invalid format of dataset metadata.')
      for row in reader:
        if len(row) < len(header_row):
          # skip partial or empty lines
          continue
        try:
          image_id = row[row_idx_image_id]
          self._true_labels[image_id] = int(row[row_idx_true_label])
          self._target_classes[image_id] = int(row[row_idx_target_class])
        except (IndexError, ValueError):
          raise IOError('Invalid format of dataset metadata') 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def load_images(input_dir, metadata_file_path, batch_shape):
    """Retrieve numpy arrays of images and labels, read from a directory."""
    num_images = batch_shape[0]
    with open(metadata_file_path) as input_file:
        reader = csv.reader(input_file)
        header_row = next(reader)
        rows = list(reader)

    row_idx_image_id = header_row.index('ImageId')
    row_idx_true_label = header_row.index('TrueLabel')
    images = np.zeros(batch_shape)
    labels = np.zeros(num_images, dtype=np.int32)
    for idx in xrange(num_images):
        row = rows[idx]
        filepath = os.path.join(input_dir, row[row_idx_image_id] + '.png')

        with tf.gfile.Open(filepath, 'rb') as f:
            image = np.array(
                Image.open(f).convert('RGB')).astype(np.float) / 255.0
        images[idx, :, :, :] = image
        labels[idx] = int(row[row_idx_true_label])
    return images, labels 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def accumulate_result(validate_lst, prob):
    sum_result = {}
    cnt_result = {}
    size = prob.shape[0]
    fi = csv.reader(open(validate_lst))
    for i in range(size):
        line = fi.__next__() # Python2: line = fi.next()
        idx = int(line[0])
        if idx not in cnt_result:
            cnt_result[idx] = 0.
            sum_result[idx] = np.zeros((1, prob.shape[1]))
        cnt_result[idx] += 1
        sum_result[idx] += prob[i, :]
    for i in cnt_result.keys():
        sum_result[i][:] /= cnt_result[i]
    return sum_result


# In[9]: 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def parse_topology_file(self):
        topology_list = []
        if os.path.isfile(self.file_path):
            with open(self.file_path) as topology_file:
                topology_file_csv = csv.reader(topology_file)
                for row in topology_file_csv:
                    map_size = len(bytearray(json.dumps(topology_list)))
                    if map_size >= BYTES_PER_FLUSH:
                        self._send_data(topology_list)
                        topology_list = []
                    if topology_file_csv.line_num == 1:
                        continue
                    key = ""
                    for index in xrange(len(row)):
                        if index == 0:
                            key = row[index]
                            continue
                        value1 = str(key) + "@@@@" + str(row[index])
                        value2 = str(row[index]) + "@@@@" + str(key)
                        if value1 not in topology_list:
                            topology_list.append(value1)
                        if value2 not in topology_list:
                            topology_list.append(value2)
                self._send_data(topology_list) 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def _collect_data(directory):
  """Traverses directory collecting input and target files.

  Args:
   directory: base path to extracted audio and transcripts.
  Returns:
   list of (media_base, media_filepath, label) tuples
  """
  # Returns:
  data_files = []
  transcripts = [
      filename for filename in os.listdir(directory)
      if filename.endswith(".csv")
  ]
  for transcript in transcripts:
    transcript_path = os.path.join(directory, transcript)
    with open(transcript_path, "r") as transcript_file:
      transcript_reader = csv.reader(transcript_file)
      _ = transcript_reader.next()  # Skip headers.
      for transcript_line in transcript_reader:
        media_name, label = transcript_line[0:2]
        filename = os.path.join(directory, media_name)
        data_files.append((media_name, filename, label))
  return data_files 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def test_write_csv(self):
        filename = os.path.join(sys.path[0], "test_csv_writing.csv")
        overview = {
            "this": [1, 2],
            "is": [3, 4],
            "a": [5, 6],
            "test": [7, 8]}

        Export.Export._write_csv(filename, overview)

        with open(filename, "r") as test_csv:
            reader = csv.reader(test_csv)
            test_dict = dict((header, []) for header in next(reader))
            for row in reader:
                for row_index, key in enumerate(test_dict.keys()):
                    test_dict[key].append(int(row[row_index]))

        assert test_dict == overview
        os.remove(filename) 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def csv_to_json(mapping_name, csv_file):
    reader = csv.reader(csv_file)
    reader.next()  # skip header row
    mappings[mapping_name] = {}
    for row in reader:
        # print row
        if row[1] != '' and row[2] != '':
            mappings[mapping_name][row[0]] = {
                'key1': row[1],
                'val1': row[2]
            }
            if len(row) > 4:
                if row[3] != '' and row[4] != '':
                    mappings[mapping_name][row[0]]['key2'] = row[3];
                    mappings[mapping_name][row[0]]['val2'] = row[4]; 
            if len(row) > 6:
                if row[5] != '' and row[6] != '':
                    mappings[mapping_name][row[0]]['key3'] = row[5];
                    mappings[mapping_name][row[0]]['val3'] = row[6]; 
    # print mappings 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def csv_to_dict_list(s):

    if s is None:
        return None

    result = []
    cols = None
    try:
        reader = csv.reader(io.StringIO(s))
        cols = next(reader)
        row = next(reader)

        while True:
            result.append({cols[i]: row[i] for i in list(range(0, len(cols)))})
            row = next(reader)

    except StopIteration:
        if cols is None:
            return None
        else:
            return result


# noinspection PyMethodMayBeStatic 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def __init__(self, training_data_in=[], data_file=None):
    ''' 
        Initiliaze the Predictor with some training data
        The training data should be a list of [mcs, input_fraction, time]
    '''
    self.training_data = []
    self.training_data.extend(training_data_in)
    if data_file:
      with open(data_file, 'rb') as csvfile:
        reader = csv.reader(csvfile, delimiter=' ')
        for row in reader:
          if row[0][0] != '#':
            parts = row[0].split(',')
            mc = int(parts[0])
            scale = float(parts[1])
            time = float(parts[2])
            self.training_data.append([mc, scale, time]) 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def _basic_init(self):
        raw = rget(self._url)
        cr = csv.reader(raw.text.splitlines(), delimiter=",")
        my_list = list(cr)
        factor = float(my_list[-1][3])
        dd = {
            "date": [
                dt.datetime.strptime(my_list[i + 1][0], "%Y-%m-%d")
                for i in range(len(my_list) - 1)
            ],
            "netvalue": [
                float(my_list[i + 1][3]) / factor for i in range(len(my_list) - 1)
            ],
            "totvalue": [float(my_list[i + 1][3]) for i in range(len(my_list) - 1)],
            "comment": [0 for _ in range(len(my_list) - 1)],
        }
        index = pd.DataFrame(data=dd)
        index = index.iloc[::-1]
        index = index.reset_index(drop=True)
        self.price = index[index["date"].isin(opendate)]
        self.price = self.price[self.price["date"] <= yesterdaydash()]
        self.name = my_list[-1][2] 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def import_gazetteer(f, limit):
    t = csv.reader(f, delimiter="\t")
    i = 0
    for row in t:
        ft = Feature()
        if Feature.objects.filter(url=row[0]).count() > 0:
            print "duplicate row " + row[0]
        else:
            ft.url = row[0]
            ft.preferred_name = row[1]

            try:
                fcode = FeatureType.objects.get(code=row[2])
            except:
                fcode = None

            ft.feature_type = fcode
            ft.admin1 = row[4]
            ft.admin2 = row[5]
            ft.geometry = Point(float(row[7]), float(row[6]))
            ft.save()
            print "saved " + ft.preferred_name
            i += 1
            if i > limit:
                break 

# 需要導入模塊: import csv [as 別名]
# 或者: from csv import reader [as 別名]
def parse_geoplanet_places_csv(csv_file):
    csv_reader = csv.reader(open(csv_file, 'rb'), dialect='excel-tab', quoting=csv.QUOTE_NONE)
    for row in csv_reader:
            out_line = ['P', row[0], row[1], row[6], row[7], row[8], row[10], row[18]+" 00:00:00+00", "POINT("+row[5]+" "+row[4]+")" ] 
            print "\t".join(out_line)
            
    return csv_file


 #* WOE_ID                      0- primary "place" key
  #* ISO                         1- ISO 3166-1 country/territory code
  #* State                       2- WOEID of admin state
  #* County                      3- WOEID of admin county
  #* Local_Admin                 4- WOEID of local admin
  #* Country                     5- WOEID of country
  #* Continent                   6- WOEID of continent