Python:在一个图形中绘制多个散点图
Python:在一个图形中绘制多个散点图
提问于 2018-05-26 16:38:47
我有一个具有多个类别的数据集，我想要在单个图形中绘制，以查看某些内容是如何变化的。我在数据集中有一个给定类别的列表，我希望在同一张图中看到所有这些类别
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图，而不是所有的图。或者，我可以在一个图形中使用多个散点图，但我很难尝试将其绘制出来。
目前我正在处理10个类别，但我正在努力使其具有动态性。
回答 1
Stack Overflow用户
回答已采纳
发布于 2018-06-05 05:48:02
如果您想要观察随着时间的推移的发展，使用带有标记的线形图可能更好地可视化每个类别中的变化：
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的尺度，而且你对数据进行了排序，在我看来，你甚至可以更好地在分类图中看到分布：
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：
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/50540904
复制相关文章
相似问题
腾讯云开发者
