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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 set_targets(x, delta=10):
 """ Sets target market trend for a date Args: x: Pandas DataFrame of market features delta: Positive number defining a price buffer between what is classified as a bullish/bearish market for the training set. delta is equivalent to the total size of the neutral price zone. delta / 2 is equivalent to either the positive or negative threshold of the neutral price zone. Returns: Pandas Series of numpy int8 market trend targets """

    data = [] # Keep track of targets
    for row, _ in x.iterrows():
        if row == x.shape[0] - 1: # Can't predict yet, done.
            break

        # Get closing prices
        curr_close = x.close[row]
        next_close = x.close[row + 1]
        high_close = next_close + (delta / 2) # Pos. neutral zone threshold
        low_close = next_close - (delta / 2)  # Neg. neutral zone threshold

        # Get target
        if curr_close < low_close:
            target = TARGET_CODES['bearish']
        elif curr_close > high_close:
            target = TARGET_CODES['bullish']
        else:
            target = TARGET_CODES['neutral']
        data.append(target)

    return pd.Series(data=data, dtype=np.int32, name='target')