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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 get_clean_factor(factor, forward_returns, groupby=None, binning_by_group=False, quantiles=5, bins=None, groupby_labels=None, max_loss=0.35, zero_aware=False):
 """ Formats the factor data, forward return data, and group mappings into a DataFrame that contains aligned MultiIndex indices of timestamp and asset. The returned data will be formatted to be suitable for Alphalens functions. It is safe to skip a call to this function and still make use of Alphalens functionalities as long as the factor data conforms to the format returned from get_clean_factor_and_forward_returns and documented here Parameters factor : pd.Series - MultiIndex A MultiIndex Series indexed by timestamp (level 0) and asset (level 1), containing the values for a single alpha factor. :: date | asset | | AAPL | 0.5 | BA | -1.1 2014-01-01 | CMG | 1.7 | DAL | -0.1 | LULU | 2.7 forward_returns : pd.DataFrame - MultiIndex A MultiIndex DataFrame indexed by timestamp (level 0) and asset (level 1), containing the forward returns for assets. Forward returns column names must follow the format accepted by pd.Timedelta (e.g. '1D', '30m', '3h15m', '1D1h', etc). 'date' index freq property must be set to a trading calendar (pandas DateOffset), see infer_trading_calendar for more details. This information is currently used only in cumulative returns computation :: | | 1D | 5D | 10D date | asset | | | | AAPL | 0.09|-0.01|-0.079 | BA | 0.02| 0.06| 0.020 2014-01-01 | CMG | 0.03| 0.09| 0.036 | DAL |-0.02|-0.06|-0.029 | LULU |-0.03| 0.05|-0.009 groupby : pd.Series - MultiIndex or dict Either A MultiIndex Series indexed by date and asset, containing the period wise group codes for each asset, or a dict of asset to group mappings. If a dict is passed, it is assumed that group mappings are unchanged for the entire time period of the passed factor data. binning_by_group : bool If True, compute quantile buckets separately for each group. This is useful when the factor values range vary considerably across gorups so that it is wise to make the binning group relative. You should probably enable this if the factor is intended to be analyzed for a group neutral portfolio quantiles : int or sequence[float] Number of equal-sized quantile buckets to use in factor bucketing. Alternately sequence of quantiles, allowing non-equal-sized buckets e.g. [0, .10, .5, .90, 1.] or [.05, .5, .95] Only one of 'quantiles' or 'bins' can be not-None bins : int or sequence[float] Number of equal-width (valuewise) bins to use in factor bucketing. Alternately sequence of bin edges allowing for non-uniform bin width e.g. [-4, -2, -0.5, 0, 10] Chooses the buckets to be evenly spaced according to the values themselves. Useful when the factor contains discrete values. Only one of 'quantiles' or 'bins' can be not-None groupby_labels : dict A dictionary keyed by group code with values corresponding to the display name for each group. max_loss : float, optional Maximum percentage (0.00 to 1.00) of factor data dropping allowed, computed comparing the number of items in the input factor index and the number of items in the output DataFrame index. Factor data can be partially dropped due to being flawed itself (e.g. NaNs), not having provided enough price data to compute forward returns for all factor values, or because it is not possible to perform binning. Set max_loss=0 to avoid Exceptions suppression. zero_aware : bool, optional If True, compute quantile buckets separately for positive and negative signal values. This is useful if your signal is centered and zero is the separation between long and short signals, respectively. 'quantiles' is None. Returns ------- merged_data : pd.DataFrame - MultiIndex A MultiIndex Series indexed by date (level 0) and asset (level 1), containing the values for a single alpha factor, forward returns for each period, the factor quantile/bin that factor value belongs to, and (optionally) the group the asset belongs to. - forward returns column names follow the format accepted by pd.Timedelta (e.g. '1D', '30m', '3h15m', '1D1h', etc) - 'date' index freq property (merged_data.index.levels[0].freq) is the same as that of the input forward returns data. This is currently used only in cumulative returns computation :: | | 1D | 5D | 10D |factor|group|factor_quantile date | asset | | | | | | | AAPL | 0.09|-0.01|-0.079| 0.5 | G1 | 3 | BA | 0.02| 0.06| 0.020| -1.1 | G2 | 5 2014-01-01 | CMG | 0.03| 0.09| 0.036| 1.7 | G2 | 1 | DAL |-0.02|-0.06|-0.029| -0.1 | G3 | 5 | LULU |-0.03| 0.05|-0.009| 2.7 | G1 | 2 """


    initial_amount = float(len(factor.index))

    factor_copy = factor.copy()
    factor_copy.index = factor_copy.index.rename(['date', 'asset'])

    merged_data = forward_returns.copy()
    merged_data['factor'] = factor_copy

    if groupby is not None:
        if isinstance(groupby, dict):
            diff = set(factor_copy.index.get_level_values(
                'asset')) - set(groupby.keys())
            if len(diff) > 0:
                raise KeyError(
                    "Assets {} not in group mapping".format(
                        list(diff)))

            ss = pd.Series(groupby)
            groupby = pd.Series(index=factor_copy.index,
                                data=ss[factor_copy.index.get_level_values(
                                    'asset')].values)

        if groupby_labels is not None:
            diff = set(groupby.values) - set(groupby_labels.keys())
            if len(diff) > 0:
                raise KeyError(
                    "groups {} not in passed group names".format(
                        list(diff)))

            sn = pd.Series(groupby_labels)
            groupby = pd.Series(index=groupby.index,
                                data=sn[groupby.values].values)

        merged_data['group'] = groupby.astype('category')

    merged_data = merged_data.dropna()

    fwdret_amount = float(len(merged_data.index))

    no_raise = False if max_loss == 0 else True
    quantile_data = quantize_factor(
        merged_data,
        quantiles,
        bins,
        binning_by_group,
        no_raise,
        zero_aware
    )

    merged_data['factor_quantile'] = quantile_data

    merged_data = merged_data.dropna()

    binning_amount = float(len(merged_data.index))

    tot_loss = (initial_amount - binning_amount) / initial_amount
    fwdret_loss = (initial_amount - fwdret_amount) / initial_amount
    bin_loss = tot_loss - fwdret_loss

    print("Dropped %.1f%% entries from factor data: %.1f%% in forward "
          "returns computation and %.1f%% in binning phase "
          "(set max_loss=0 to see potentially suppressed Exceptions)." %
          (tot_loss * 100, fwdret_loss * 100, bin_loss * 100))

    if tot_loss > max_loss:
        message = ("max_loss (%.1f%%) exceeded %.1f%%, consider increasing it."
                   % (max_loss * 100, tot_loss * 100))
        raise MaxLossExceededError(message)
    else:
        print("max_loss is %.1f%%, not exceeded: OK!" % (max_loss * 100))

    return merged_data