human
Complete the below
def autocorrelation_plot(series, ax=None, **kwds):

    ''''Autocorrelation plot for time series.
    Parameters:
    series: Time series
    ax: Matplotlib axis object, optional
    kwds : keywords
    Options to pass to matplotlib plotting method
    Returns:
    ax: Matplotlib axis object'
    '''

=====
Give me a the Python code you requested?
assistant
#Here is the Python code you requested.
    import matplotlib.pyplot as plt
    n = len(series)
    data = np.asarray(series)
    if (ax is None):
        ax = plt.gca(xlim=(1, n), ylim=((-1.0), 1.0))
    mean = np.mean(data)
    c0 = (np.sum(((data - mean) ** 2)) / float(n))
    def r(h):
        return ((((data[:(n - h)] - mean) * (data[h:] - mean)).sum() / float(n)) / c0)
    x = (np.arange(n) + 1)
    y = lmap(r, x)
    z95 = 1.959963984540054
    z99 = 2.5758293035489004
    ax.axhline(y=(z99 / np.sqrt(n)), linestyle='--', color='grey')
    ax.axhline(y=(z95 / np.sqrt(n)), color='grey')
    ax.axhline(y=0.0, color='black')
    ax.axhline(y=((- z95) / np.sqrt(n)), color='grey')
    ax.axhline(y=((- z99) / np.sqrt(n)), linestyle='--', color='grey')
    ax.set_xlabel('Lag')
    ax.set_ylabel('Autocorrelation')
    ax.plot(x, y, **kwds)
    if ('label' in kwds):
        ax.legend()
    ax.grid()
    return ax