Question

我有一个包含多个类别的数据集，我想在一个图中绘制图，看看有什么变化。我有一个数据集中给定类别的列表，我希望看到它们都在同一图中绘制

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')

我不知道为什么我只能看到最后一个情节而不是所有情节。或者，我可以在一个图中使用多个散点图，但我很难尝试绘制此图。

目前我正在处理10个类别，但我试图让它变得动态。

Answer 1

如果您想观察一段时间内的发展情况，可以更好地看一下带有标记的线图，看看每个类别的变化：

import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.cm as cm

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()

fig, ax = plt.subplots(figsize=(10,8))
colors = iter(cm.rainbow(np.linspace(0, 1, len(parent_categories))))

for parent_category in parent_categories:
    ax.plot(range(len(train[train["parent_category_name"] == parent_category])), 
            sorted(train[train["parent_category_name"] == parent_category].deal_probability.values),
            color = next(colors),
            marker = "o",
            label = parent_category)

plt.ylabel('likelihood that an ad actually sold something', fontsize=12)
plt.title('Distribution of likelihood that an ad actually sold something')
plt.legend(loc = "best")
plt.show()

输出：

但由于这是一个任意比例，你对数据进行排序，在我看来，你甚至可以更好地看到分类图中的传播：

train = pd.DataFrame(data=sample,  columns=['parent_category_name','deal_probability'])
parent_categories = train['parent_category_name'].unique()

fig, ax = plt.subplots(figsize=(18,9))
colors = iter(cm.rainbow(np.linspace(0, 1, len(parent_categories))))

for parent_category in parent_categories:
    ax.scatter(
        train[train["parent_category_name"] == parent_category].parent_category_name.values, 
        train[train["parent_category_name"] == parent_category].deal_probability.values,
        color = next(colors),
        label = parent_category
    )

plt.ylabel('likelihood that an ad actually sold something', fontsize=12)
plt.title('Distribution of likelihood that an ad actually sold something')
plt.legend(loc = "best")
plt.show()

输出：

Python：在一个图中绘制多个Scatter图

1 个答案: