Python学习第三十七天，plotly库的使用（二）

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统计图表

很多统计学图表也预先定义在了Plotly中，主要包括下图所示的箱型图、直方图、热力图、等高线图等。

热力图：

import plotly.express as px
import plotly.figure_factory as ff
stock = px.data.stocks().corr()
corr = stock.apply(lambda x:round(x,2))
index = corr.index.tolist()
columns = corr.columns.tolist()
data1 = []
for i in index:
    data2 = []
    for j in columns:
        data2.append(corr.loc[i,j])
    data1.append(data2)

z = data1
# 两个轴，可以任意指定
x = index
y = columns
# 显示的文本内容
z_text = data1

fig = ff.create_annotated_heatmap(
    z,
    x=x,
    y=y,
    annotation_text=z_text, # 标注文本内容
    colorscale='Viridis',
    showscale=True
)

# 字体大小设置
for i in range(len(fig.layout.annotations)):
    fig.layout.annotations[i].font.size=12

fig.show()

效果：

AI图表

同时，Plotly也支持绘制一些简单的机器学习图表，不过都是依靠上面的基本图表实现的，如下述的线性回归。

import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression

df = px.data.tips()
X = df.total_bill.values.reshape(-1, 1)

model = LinearRegression()
model.fit(X, df.tip)

x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))

fig = px.scatter(df, x='total_bill', y='tip', opacity=0.65)
fig.add_traces(go.Scatter(x=x_range, y=y_range, name='Regression Fit'))
fig.show()

三维图

import plotly.graph_objects as go
import numpy as np
z1 = np.array([  # numpy数组形式
    [8.83,8.89,8.81,8.87,8.9,8.87],
    [8.89,8.94,8.85,8.94,8.96,8.92],
    [8.84,8.9,8.82,8.92,8.93,8.91],
    [8.79,8.85,8.79,8.9,8.94,8.92],
    [8.79,8.88,8.81,8.9,8.95,8.92],
    [8.8,8.82,8.78,8.91,8.94,8.92],
    [8.75,8.78,8.77,8.91,8.95,8.92],
    [8.8,8.8,8.77,8.91,8.95,8.94],
    [8.74,8.81,8.76,8.93,8.98,8.99]
])

z2 = z1 + 5
z3 = z1 - 5

fig = go.Figure(data=[
    go.Surface(z=z1),
    go.Surface(z=z2, showscale=False, opacity=0.9),
    go.Surface(z=z3, showscale=False, opacity=0.9)

])

fig.show()