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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 plot_data_filter(data, data_f, b, a, cutoff, fs):
    '''Plot frequency response and filter overlay for butter filtered data

    Args
    ----
    data: ndarray
        Signal array
    data_f: float
        Signal sampling rate
    b: array_like
        Numerator of a linear filter
    a: array_like
        Denominator of a linear filter
    cutoff: float
        Cutoff frequency for the filter
    fs: float
        Sampling rate of the signal

    Notes
    -----
    http://stackoverflow.com/a/25192640/943773
    '''
    import matplotlib.pyplot as plt
    import numpy
    import scipy.signal

    n = len(data)
    T = n/fs
    t = numpy.linspace(0, T, n, endpoint=False)

    # Calculate frequency response
    w, h = scipy.signal.freqz(b, a, worN=8000)

    # Plot the frequency response.
    fig, (ax1, ax2) = plt.subplots(2,1)

    ax1.title.set_text('Lowpass Filter Frequency Response')
    ax1.plot(0.5*fs * w/numpy.pi, numpy.abs(h), 'b')
    ax1.plot(cutoff, 0.5*numpy.sqrt(2), 'ko')
    ax1.axvline(cutoff, color='k')
    ax1.set_xlim(0, 0.5*fs)
    ax1.set_xlabel('Frequency [Hz]')
    ax2.legend()

    # Demonstrate the use of the filter.
    ax2.plot(t, data, linewidth=_linewidth, label='data')
    ax2.plot(t, data_f, linewidth=_linewidth, label='filtered data')
    ax2.set_xlabel('Time [sec]')
    ax2.legend()

    plt.show()

    return None