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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 chicago(return_X_y=True):
 """Chicago air pollution and death rate data Parameters return_X_y : bool, if True, returns a model-ready tuple of data (X, y) otherwise, returns a Pandas DataFrame Returns ------- model-ready tuple of data (X, y) OR Pandas DataFrame Notes ----- X contains [['time', 'tmpd', 'pm10median', 'o3median']], with no NaNs y contains 'death', the deaths per day, with no NaNs Source: R gamair package `data(chicago)` Notes ----- https://cran.r-project.org/web/packages/gamair/gamair.pdf https://rdrr.io/cran/gamair/man/chicago.html Columns: death : total deaths (per day). pm10median : median particles in 2.5-10 per cubic m pm25median : median particles < 2.5 mg per cubic m (more dangerous). o3median : Ozone in parts per billion so2median : Median Sulpher dioxide measurement time : time in days tmpd : temperature in fahrenheit """

    # recommend PoissonGAM
    chi = pd.read_csv(PATH + '/chicago.csv', index_col=0).astype(float)
    if return_X_y:
        chi = chi[['time', 'tmpd', 'pm10median', 'o3median', 'death']].dropna()

        X = chi[['time', 'tmpd', 'pm10median', 'o3median']].values
        y = chi['death'].values

        return X, y
    else:
        return chi