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pythonscatter-plotiris-dataset

scatter subplot for iris dataset

I'm new to data science. I wrote this script for plotting all different kinds of iris data set scatter plot. trying not to plot something with itself . how can I optimize my code ?

'''python

from sklearn.datasets import load_iris

import numpy as np

import pandas as pd

iris=load_iris()

list1=[]

fig, ax =plt.subplots(nrows=3,ncols=2,figsize=(10,10))

for ii in range(4):
  for jj in range(1,4):
    if ii==jj:
      break
    if ii*jj not in list1[1::2]:
      list1.extend((ii+jj,ii*jj))
    elif ii+jj in list1[::2]:
      break
    a=ii
    b=jj
    x_index=ii
    y_index=jj
    colors=['blue','red','green']
    if ii==0:
      b=b-1
    elif jj==1:
      a=a-2
      b,a=a,b
    elif ii==3:
      a=a-1
      b=b-1
      a,b=b,a
    for label , color in zip(range(len(iris.target_names)),colors):
      ax[b,a].scatter(iris.data[iris.target==label,x_index] 
              , iris.data[iris.target==label,y_index]
              , label=iris.target_names[label]
              , color=color)

    ax[b,a].set_xlabel(iris.feature_names[x_index])
    ax[b,a].set_ylabel(iris.feature_names[y_index])
    ax[b,a].legend(loc="upper right")
    fig.tight_layout()
    fig.show()

''' enter image description here this is the output

how would you write it if it was you?

I appreciate any help.

Solution

  • I would have use either pandas' visualization or seaborn's.

    The followings would do the work in much less space but remember that by calling it efficient , you are making a mistake. Because effiency is not an important matter in plotting a data set especially in python (correct me if I'm wrong).

    import seaborn as sns
import matplotlib.pyplot as plt
from pandas.plotting import parallel_coordinates
import pandas as pd
# Parallel Coordinates
# Load the data set
iris = sns.load_dataset("iris")
parallel_coordinates(iris, 'species', color=('#556270', '#4ECDC4', '#C7F464'))
plt.show()

    and Result is as follow:

    enter image description here

    from pandas.plotting import andrews_curves
# Andrew Curves
a_c = andrews_curves(iris, 'species')
a_c.plot()
plt.show()

    and its plot is shown below: enter image description here

    from seaborn import pairplot
# Pair Plot
pairplot(iris, hue='species')
plt.show()

    which would plot the following fig: enter image description here

    and also another plot which is I think the least used and the most important is the following one:

    from plotly.express import scatter_3d
# Plotting in 3D by plotly.express that would show the plot with capability of zooming,
# changing the orientation, and rotating
scatter_3d(iris, x='sepal_length', y='sepal_width', z='petal_length', size="petal_width",
                   color="species", color_discrete_map={"Joly": "blue", "Bergeron": "violet", "Coderre": "pink"})\
            .show()

    This one would plot into your browser and demands HTML5 and you can see as you wish with it. The next figure is the one. Remember that It is a SCATTERING plot and the size of each ball is showing data of the petal_width so all four features are in one single plot.

    enter image description here