问Python:在一个图形中绘制多个散点图

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sample = [
['For business', 0.7616104043587437],
['For home and cottages', 0.6890139579274699],
['Consumer electronics', 0.039868871866136635],
['Personal things', 0.7487893699793786],
['Services', 0.747226678171249],
['Services', 0.23463661173977313],
['Animals', 0.6504301798258314],
['For home and cottages', 0.49567857024037665],
['For home and cottages', 0.9852681814098107],
['Transportation', 0.8134867587477912],
['Animals', 0.49988690699674654],
['Consumer electronics', 0.15086800344617235],
['For business', 0.9485494576819328],
['Hobbies and Leisure', 0.25766871111905243],
['For home and cottages', 0.31704508627659533],
['Animals', 0.6192114570078333],
['Personal things', 0.5755788287287359],
['Hobbies and Leisure', 0.10106922056341394],
['Animals', 0.16834618003738577],
['Consumer electronics', 0.7570803588496894]
]
train = pd.DataFrame(data=sample,  columns=['parent_category_name','deal_probability'])
parent_categories = train['parent_category_name'].unique()
parent_categories_size = len(parent_categories)
fig, ax = plt.subplots(figsize=(12,10))
colors = iter(cm.rainbow(np.linspace(0, 1, parent_categories_size)))

for parent_category_n in range(parent_categories_size):
    parent_1 = train[train['parent_category_name'] == parent_categories[parent_category_name]]
    ax.scatter(
        range(parent_1.shape[0]), 
        np.sort(parent_1.deal_probability.values),
        color = next(colors)
    )
plt.ylabel('likelihood that an ad actually sold something', fontsize=12)
plt.title('Distribution of likelihood that an ad actually sold something')