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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 bandstop(data, freqmin, freqmax, df, corners=4, zerophase=False):
 """ Butterworth-Bandstop Filter. Filter data removing data between frequencies ``freqmin`` and ``freqmax`` using ``corners`` corners. The filter uses :func:`scipy.signal.iirfilter` (for design) and :func:`scipy.signal.sosfilt` (for applying the filter). :type data: numpy.ndarray :param data: Data to filter. :param freqmin: Stop band low corner frequency. :param freqmax: Stop band high corner frequency. :param df: Sampling rate in Hz. :param corners: Filter corners / order. :param zerophase: If True, apply filter once forwards and once backwards. This results in twice the number of corners but zero phase shift in the resulting filtered trace. :return: Filtered data. """

    fe = 0.5 * df
    low = freqmin / fe
    high = freqmax / fe
    # raise for some bad scenarios
    if high > 1:
        high = 1.0
        msg = "Selected high corner frequency is above Nyquist. " + \
              "Setting Nyquist as high corner."
        warnings.warn(msg)
    if low > 1:
        msg = "Selected low corner frequency is above Nyquist."
        raise ValueError(msg)
    z, p, k = iirfilter(corners, [low, high],
                        btype='bandstop', ftype='butter', output='zpk')
    sos = zpk2sos(z, p, k)
    if zerophase:
        firstpass = sosfilt(sos, data)
        return sosfilt(sos, firstpass[::-1])[::-1]
    else:
        return sosfilt(sos, data)