search
LoginSignup
22
Help us understand the problem. What are the problem?

More than 1 year has passed since last update.

posted at

【散布図と3D plotと回帰平面】plotlyで動的な可視化をする【python,scatter,3D,surface,pair,joint】

33dd.gif

python==3.8
plotly==4.10.0

公式のギャラリーを参考にオプションを弄ってみる記事

scatter(散布図)

基本

import plotly.express as px

df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", title="iris scatter plot")
fig.show()

image.png

分割

facetで描画する図面を分けて
add_traceからrow,colを指定してどの図面に上書きするか決める

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", facet_col="species",
                 title="Add line subplot")

reference_line = go.Scatter(x=[2, 4],
                            y=[4, 8],
                            mode="lines",
                            line=go.scatter.Line(color="gray"),
                            showlegend=False)

fig.add_trace(reference_line, row=1, col=1)
fig.add_trace(reference_line, row=1, col=2)
fig.add_trace(reference_line, row=1, col=3)

fig.show()

image.png

異なるタイプのグラフオブジェクトを上書き

from plotly.subplots import make_subplots

fig = make_subplots(rows=1, cols=2)

fig.add_scatter(y=[4, 2, 3.5], mode="markers",
                marker=dict(size=20, color="LightSeaGreen"),
                name="a", row=1, col=1)

fig.add_bar(y=[2, 1, 3],
            marker=dict(color="MediumPurple"),
            name="b", row=1, col=1)

fig.add_scatter(y=[2, 3.5, 4], mode="markers",
                marker=dict(size=20, color="MediumPurple"),
                name="c", row=1, col=2)

fig.add_bar(y=[1, 3, 2],
            marker=dict(color="LightSeaGreen"),
            name="d", row=1, col=2)

fig.show()

image.png

値の大きさによってsizeを変える(bubble)

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
                 size='petal_length')
fig.show()

image.png

点ごとにテキストを振る

import plotly.express as px
fig = px.scatter(df, x="sepal_length", y="sepal_width", text="species", size_max=60)

fig.update_traces(textposition='top center')

fig.update_layout(
    height=800,
    title_text='iris label'
)

fig.show()

image.png

scatterでのjoin plot

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", marginal_y="violin",
           marginal_x="box", trendline="ols", template="simple_white")
fig.show()

image.png

pair plot

import plotly.express as px
df = px.data.iris()
fig = px.scatter_matrix(df, dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"], color="species")
fig.show()

image.png

3d scatter

import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
                    color='petal_length', symbol='species')
fig.show()

image.png

平面を追加する(surface)

import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.svm import SVR

df = px.data.iris()

#Xからの写像を作っておく

margin = 0
X = df[['sepal_width', 'sepal_length']]
y = df['petal_width']
model = SVR(C=1.)
model.fit(X, y)

#細かい点(メッシュ,グリッド)を発生

mesh_size = .02
x_min, x_max = X.sepal_width.min() - margin, X.sepal_width.max() + margin
y_min, y_max = X.sepal_length.min() - margin, X.sepal_length.max() + margin
xrange = np.arange(x_min, x_max, mesh_size)
yrange = np.arange(y_min, y_max, mesh_size)
xx, yy = np.meshgrid(xrange, yrange)

#メッシュのすべての点について予測

pred = model.predict(np.c_[xx.ravel(), yy.ravel()])
pred = pred.reshape(xx.shape)

#元の点をplotしてから、x1,x2によるgrid面をzによって押し上げる
#面は全ての点をつなぐsurfaceをつかって描く

fig = px.scatter_3d(df, x='sepal_width', y='sepal_length', z='petal_width')
fig.update_traces(marker=dict(size=5))
fig.add_traces(go.Surface(x=xrange, y=yrange, z=pred, name='pred_surface'))
fig.show()

image.png

prjection Zで三次元グラフの等高線を軸面に表示することもできる

fig = go.Figure(data=[go.Surface(z=pred)])
fig.update_traces(contours_z=dict(show=True, usecolormap=True,
                                  highlightcolor="limegreen", project_z=True))

fig.show()

image.png

平面図での等高線

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Contour(
    z=pred,
    colorscale="Cividis",
))

fig.show()

image.png

学習過程を可視化する

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Contour(
    z=pred,
    colorscale="Cividis",
))

fig.add_trace(
    go.Scatter(
        x=[20,40,60,70,80,100,90,80],
        y=[20,40,80,100,120,140,160,160],
        mode="markers+lines",
        name="steepest",
        line=dict(
            color="red"
        )
    )
)


fig.show()

image.png

モデルを置き換えてもいい

from sklearn.linear_model import LinearRegression

model_LR = LinearRegression()
model_LR.fit(X, y)


pred_LR = model_LR.predict(np.c_[xx.ravel(), yy.ravel()])
pred_LR = pred_LR.reshape(xx.shape)

fig = px.scatter_3d(df, x='sepal_width', y='sepal_length', z='petal_width',color='species')
fig.update_traces(marker=dict(size=5))
fig.add_traces(go.Surface(x=xrange, y=yrange, z=pred_LR, name='pred_LR_surface',colorscale='Viridis'))
fig.show()

33dd.gif

その他

type = line

scatterをtype=lineにしてstackを指定することで積み上げ面積グラフにする

import plotly.graph_objects as go

x=['Winter', 'Spring', 'Summer', 'Fall']

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=x, y=[30, 30, 30, 30],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(131, 90, 1)'),
        stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[20, 20, 20, 20],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(111, 1, 219)'),
        stackgroup='one'
))
fig.add_trace(go.Scatter(
    x=x, y=[10, 10, 10, 10],
    hoverinfo='x+y',
    mode='lines',
    line=dict(width=0.5, color='rgb(1, 247, 212)'),
        stackgroup='one'
))

fig.update_layout(yaxis_range=(0, 100))
fig.show()

image.png

pxから簡単に行うのがarea

import plotly.express as px

fig = px.area(x=['Winter', 'Spring', 'Summer', 'Fall'], 
              y=[[30, 30, 30, 30],
                [20, 20, 20, 20],
                [10, 10, 10, 10]]
             )
fig.update_layout(yaxis_range=(0, 100))
fig.show()

image.png

データフレームでareaに指定する場合

df = px.data.stocks()
fig = px.area(df,x='date', y=df.columns[1:6], title="6 company stocks plot")
fig.show()

image.png

ついでにline_3dも

import plotly.express as px
df = px.data.gapminder().query("country=='Brazil'")
fig = px.line_3d(df, x="gdpPercap", y="pop", z="year")
fig.show()

image.png

以上

Register as a new user and use Qiita more conveniently

  1. You get articles that match your needs
  2. You can efficiently read back useful information
What you can do with signing up
22
Help us understand the problem. What are the problem?