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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def meter_data_from_csv( filepath_or_buffer, tz=None, start_col="start", value_col="value", gzipped=False, freq=None, **kwargs ):
 """ Load meter data from a CSV file. Default format:: start,value 2017-01-01T00:00:00+00:00,0.31 2017-01-02T00:00:00+00:00,0.4 2017-01-03T00:00:00+00:00,0.58 Parameters filepath_or_buffer : :any:`str` or file-handle File path or object. tz : :any:`str`, optional E.g., ``'UTC'`` or ``'US/Pacific'`` start_col : :any:`str`, optional, default ``'start'`` Date period start column. value_col : :any:`str`, optional, default ``'value'`` Value column, can be in any unit. gzipped : :any:`bool`, optional Whether file is gzipped. freq : :any:`str`, optional If given, apply frequency to data using :any:`pandas.DataFrame.resample`. **kwargs Extra keyword arguments to pass to :any:`pandas.read_csv`, such as ``sep='|'``. """


    read_csv_kwargs = {
        "usecols": [start_col, value_col],
        "dtype": {value_col: np.float64},
        "parse_dates": [start_col],
        "index_col": start_col,
    }

    if gzipped:
        read_csv_kwargs.update({"compression": "gzip"})

    # allow passing extra kwargs
    read_csv_kwargs.update(kwargs)

    df = pd.read_csv(filepath_or_buffer, **read_csv_kwargs).tz_localize("UTC")
    if tz is not None:
        df = df.tz_convert(tz)

    if freq == "hourly":
        df = df.resample("H").sum()
    elif freq == "daily":
        df = df.resample("D").sum()

    return df