from math import ceil
from scipy.stats import norm
from scipy import stats

def plot_normal(df, sub_col):
    """ Plot QQ-plot alongside histogram."""
    length = ceil(len(sub_col)/2)

    fig, axes = plt.subplots(length, 4, figsize=(20,17))
    axes = axes.ravel()

    index = 0
    for col in sub_col:
        (mu, sigma) = norm.fit(df[col])
        (a,b) = stats.probplot(df[col], sparams=(mu, sigma)) # QQ plot
        axes[index].plot(*a, color='#014d64', linewidth=5)
        (x_min, x_max) = axes[index].get_xlim()
        x_dots = np.arange(x_min, x_max)
        y_dots = b[0]*x_dots + b[1]
        axes[index].plot(x_dots, y_dots, color='#7c260b')
        axes[index].set_title('QQ Plot', fontsize='large')
        axes[index].set_xlabel('Theoretical Quantiles')
        axes[index].set_ylabel('Ordered Values')

        index += 1
        sns.distplot(df[col], ax=axes[index], kde=True, color='#c7e1e8',
                     kde_kws={'color':'#014d64','label':'KDE'},
                     fit=norm, fit_kws={'color':'#7c260b','label':'NORMAL'}) # distribution plot
        axes[index].legend(loc='best')
        axes[index].set_title('Histogram', fontsize='large')
        index += 1