def binarize_histogram(dataframe, variable):
    tmp = dataframe
    tmp['binarize'] = np.where(tmp[variable] > np.mean(tmp[variable]), 1, 0)
    
    hist_1 = tmp[tmp['binarize'] == 1][variable].dropna()
    hist_0 = tmp[tmp['binarize'] == 0][variable].dropna()
    
    plt.subplot(1, 2, 1)
    plt.hist(hist_0, alpha=.6, color='lightgrey')
    plt.axvline(np.mean(hist_0))
    plt.title("{0} <= {1}".format(variable, round(np.mean(hist_0), 3)))
    plt.subplot(1, 2, 2)
    plt.hist(hist_1, alpha=.6, color='lightgrey')
    plt.axvline(np.mean(hist_1))
    plt.title("{0} >= {1}".format(variable, round(np.mean(hist_0), 3)))

binarize_histogram(df, 'adfert')

def grouped_boxplot(dataframe, variable, group_by):
    tmp = dataframe
    stratify_by = tmp[group_by].unique()
    
    if len(stratify_by) / 2 > 3:
        fig, ax = plt.subplots(2, len(stratify_by),sharey=True)
    else:
        fig, ax = plt.subplots(1, len(stratify_by),sharey=True)
        
    for i, n in enumerate(stratify_by):
        ax[i].boxplot(tmp[tmp[group_by] == n][variable])
        ax[i].set_title(n)
        
grouped_boxplot(df, 'adfert', 'region')

def grouped_scatterplot(dataframe, x, y, group_by):
    tmp = dataframe
    stratify_by = tmp[group_by].unique()
    
    if len(stratify_by) / 2 > 3:
        fig, ax = plt.subplots(2, len(stratify_by),sharey=True)
    else:
        fig, ax = plt.subplots(1, len(stratify_by),sharey=True)
        
    for i, n in enumerate(stratify_by):
        tmp_group_plt = tmp[tmp[group_by] == n]
        ax[i].plot(tmp_group_plt[x], tmp_group_plt[y], 'o')
        ax[i].set_title(n)

grouped_scatterplot(df, 'school', 'adfert', 'region')