Load up the CSV file as a pandas dataframe
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def to_dataframe(self, **kwargs):
        """"""
        return pandas.io.parsers.read_csv(self.path, sep=self.d, **kwargs)
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def load(self):
        
        df = pd.read_excel(self.input_file, skiprows=11)
        df = df.dropna(subset=['Annual'])
        return df
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def read_csv(self, dtype=False, parse_dates=True, *args, **kwargs):
        

        import pandas

        t = self.resolved_url.get_resource().get_target()

        kwargs = self._update_pandas_kwargs(dtype, parse_dates, kwargs)

        return pandas.read_csv(t.fspath, *args, **kwargs)
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def load_csv(self):
        
        if path.exists(self.csv_filepath):
            self.results = self.results.append(
                pandas.read_csv(self.csv_filepath))
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def _to_df(self, query, conn, index_col=None, coerce_float=True, params=None,
               parse_dates=None, columns=None):
        
        return pd.io.sql.read_sql(str(query), conn, index_col=index_col,
                                  coerce_float=coerce_float, params=params,
                                  parse_dates=parse_dates, columns=columns)
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def load_contents(self):
        
        with open(self.name + ".csv") as f:
            list_of_rows = f.readlines()

        list_of_rows = map(
            lambda x: x.strip(),
            map(
                lambda x: x.replace("\"", ""),
                list_of_rows
            )
        )

        for row in list_of_rows:
            self.put_row(make_row(self.columns, row.split(',')))
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def read_pandas(fname, *args, **kwargs):
    """"""
    if not os.path.exists(fname):
        raise ValueError('no data file `{}` found!'.format(fname))
    if fname.endswith('csv'):
        df = pd.read_csv(fname, *args, **kwargs)
    else:
        xl = pd.ExcelFile(fname)
        if len(xl.sheet_names) > 1 and 'sheet_name' not in kwargs:
            kwargs['sheet_name'] = 'data'
        df = pd.read_excel(fname, *args, **kwargs)
    return df
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def load_df_from_file(file_path, sep=",", header=0):
  """"""
  with tf.gfile.Open(file_path) as infile:
    df = pd.read_csv(infile, sep=sep, header=header)
  return df
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def load_frame(url, skiprows):
    
    return pd.read_csv(
        url,
        skiprows=skiprows,
        skipinitialspace=True,
        na_values=["Bank holiday", "Not available"],
        parse_dates=["Date"],
        index_col="Date",
    ).dropna(how='all') \
     .tz_localize('UTC') \
     .rename(columns=COLUMN_NAMES)
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def dataframe(self):
        """"""

        if self._dataframe is None:
            try:
                import pandas as pd
            except ImportError:
                raise RuntimeError('To enable dataframe support, '
                                   'run \'pip install datadotworld[pandas]\'')

            self._dataframe = pd.DataFrame.from_records(self._iter_rows(),
                                                        coerce_float=True)

        return self._dataframe
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def savecsv(filename, datadict, mode="w"):
    """"""
    if mode == "a" :
        header = False
    else:
        header = True
    with open(filename, mode) as f:
        _pd.DataFrame(datadict).to_csv(f, index=False, header=header)
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def dataframe(self, dtype=False, parse_dates=True,  *args, **kwargs):
        """"""

        import pandas as pd

        rg = self.row_generator

        t = self.resolved_url.get_resource().get_target()

        if t.target_format == 'csv':
            return self.read_csv(dtype, parse_dates, *args, **kwargs)

        # Maybe generator has it's own Dataframe method()
        try:

            return rg.dataframe( *args, **kwargs)
        except AttributeError:
            pass

        # Just normal data, so use the iterator in this object.
        headers = next(islice(self, 0, 1))
        data = islice(self, 1, None)

        df = pd.DataFrame(list(data), columns=headers,  *args, **kwargs)

        self.errors = df.metatab_errors = rg.errors if hasattr(rg, 'errors') and rg.errors else {}

        return df
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def load_csv(filename, **kw):
    '''
    
    '''
    import pandas
    if any(filename.endswith(s) for s in ('.gz', '.bz2', '.lzma')):
        with gzip.open(filename, 'rt') as fl: data = pandas.read_csv(fl, **kw)
    else:
        with open(filename, 'rt') as fl: data = pandas.read_csv(fl, **kw)
    return data
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def load_data(self, path, *args, **kwargs):
        """"""
        self.df = self._load(path,*args, **kwargs)
        self.series = self.df.iloc[:,0]
        print ("Success! file length: " +str(self.df.shape[0]))
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def load(self, df, centerings):
        """"""
        return super().load(
            df,
            {key: value
             for key, value in centerings.items()
             if key in self.filter_}
        )
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def to_dataframe(self, columns=None):
    """"""
    if not self.is_value_set:
      raise ValueError('Value must be set before converting to a DataFrame.')
    if not pandas:
      raise RuntimeError('Install pandas to convert to pandas.DataFrame')
    return pandas.DataFrame.from_records(self.value, columns=columns)
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def importcsv(self):
        '''  '''
        csv_path = os.path.join(os.path.dirname(__file__), self.stock_no_files)
        with open(csv_path) as csv_file:
            csv_data = csv.reader(csv_file)
            result = {}
            for i in csv_data:
                try:
                    result[i[0]] = str(i[1]).decode('utf-8')
                except ValueError:
                    if i[0] == 'UPDATE':
                        self.last_update = str(i[1]).decode('utf-8')
                    else:
                        pass
        return result