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using plotly to boxplot every 4 columns alone

I have a dataset size similar for the following code:

import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from plotly.subplots import make_subplots
import plotly.graph_objs as go
from plotly.offline import plot


 X, y = datasets.make_classification(n_samples=300, n_features=70, n_classes=3, n_redundant=0,
                                n_clusters_per_class=1, weights=[0.5, 0.3, 0.2], random_state=42)

data=[]
for i in range(1,X.shape[1]+1,5):
data.append(go.Box(y = X[i],
                    name = i,
                    showlegend = False))

plot(data)

i am not getting the desired result, I want to plot every 5 columns alone in each figure

hopefully my question is understandable

Solution

  • You can use make_subplots to create each 5 figures along with a for loop.

    Full code:

        import numpy as np
    from sklearn import datasets
    import matplotlib.pyplot as plt
    from plotly.subplots import make_subplots
    import plotly.graph_objs as go
    from plotly.offline import plot
    
    X, y = datasets.make_classification(n_samples=300, n_features=70, n_classes=3, n_redundant=0,
                                    n_clusters_per_class=1, weights=[0.5, 0.3, 0.2], random_state=42)
    
    data=[]
    count = 1
    
    fig = make_subplots(rows=1, cols=int(X.shape[1]/5))
    
    for i in range(1,X.shape[1]+1):
        fig.add_trace(go.Box(y = X[i],
                        name = i,
                        showlegend = False), row=1, col=count)
        
        if i % 5 == 0:
            count += 1
    
    plot(fig)

**Output:** 
[![enter image description here][1]][1]

    To save every 5 plots in the one figure and export it use the following code:

    import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from plotly.subplots import make_subplots
import plotly.graph_objs as go
from plotly.offline import plot
import plotly.io as pio

X, y = datasets.make_classification(n_samples=300, n_features=70, n_classes=3, n_redundant=0,
                                n_clusters_per_class=1, weights=[0.5, 0.3, 0.2], random_state=42)

colors = ['rgba(93, 164, 214, 0.5)', 'rgba(255, 144, 14, 0.5)', 'rgba(44, 160, 101, 0.5)',
          'rgba(255, 65, 54, 0.5)', 'rgba(207, 114, 255, 0.5)']

image_count = 1

fig = go.Figure()

for i in range(1,X.shape[1]+1):
    
    fig.add_trace(go.Box(y = X[i],
                    name = i,
                    showlegend = False,
                    boxpoints='all',
                    jitter=0.5,
                    whiskerwidth=0.2,
                    fillcolor=colors[i%5],
                    marker_size=2,
                    line_width=1))
    
    fig.update_layout(
         title=f'Figure {image_count}',
         yaxis=dict(
            autorange=True,
            showgrid=True,
            zeroline=True,
            gridcolor='rgb(255, 255, 255)',
            gridwidth=1,
            zerolinecolor='rgb(255, 255, 255)',
            zerolinewidth=2,
             ),
        margin=dict(
            l=40,
            r=30,
            b=80,
            t=100,
            ),
        paper_bgcolor='rgb(243, 243, 243)',
        plot_bgcolor='rgb(243, 243, 243)',
        )

    
    if  i % 5 == 0:
        fig.write_html(f"image{image_count}.html")
        fig = go.Figure()
        image_count += 1

    enter image description here