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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 adult(display=False):
 """ Return the Adult census data in a nice package. """

    dtypes = [
        ("Age", "float32"), ("Workclass", "category"), ("fnlwgt", "float32"),
        ("Education", "category"), ("Education-Num", "float32"), ("Marital Status", "category"),
        ("Occupation", "category"), ("Relationship", "category"), ("Race", "category"),
        ("Sex", "category"), ("Capital Gain", "float32"), ("Capital Loss", "float32"),
        ("Hours per week", "float32"), ("Country", "category"), ("Target", "category")
    ]
    raw_data = pd.read_csv(
        cache(github_data_url + "adult.data"),
        names=[d[0] for d in dtypes],
        na_values="?",
        dtype=dict(dtypes)
    )
    data = raw_data.drop(["Education"], axis=1)  # redundant with Education-Num
    filt_dtypes = list(filter(lambda x: not (x[0] in ["Target", "Education"]), dtypes))
    data["Target"] = data["Target"] == " >50K"
    rcode = {
        "Not-in-family": 0,
        "Unmarried": 1,
        "Other-relative": 2,
        "Own-child": 3,
        "Husband": 4,
        "Wife": 5
    }
    for k, dtype in filt_dtypes:
        if dtype == "category":
            if k == "Relationship":
                data[k] = np.array([rcode[v.strip()] for v in data[k]])
            else:
                data[k] = data[k].cat.codes

    if display:
        return raw_data.drop(["Education", "Target", "fnlwgt"], axis=1), data["Target"].values
    else:
        return data.drop(["Target", "fnlwgt"], axis=1), data["Target"].values