Challenge The objective of this exercise is to discriminating between three species of Iris from the...

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Challenge The objective of this exercise is to discriminating between three species of Iris from the measurements of their petals and sepals. Your challenge will be to write your code in 20 lines or less, not including comment lines. Out [2]: array([[1., 5.1, 3.5, 1.4, 0.2, 1. ], [2., 4.9, 3., 1.4, 0.2, 1. ], [3., 4.7, 3.2, 1.3, 0.2, 1. ], 4.6, 3.1, 1.5, 0.2, 1. 1, 5. 3.6, 1.4, 0.2, 1. ]]) In [ ]: In [ ]: In [ ]: [4. [5. 2 2 # The name of each column is placed in the following list: column_name = ['num', 'sepal length', 'sepal Width', 'petal length', 'petal width', 'species'] # Using a for Loop and the NumPy Library: # print the minimum value of each column # print the maximum value of each column # print the mean of each column # Create a histogram showing the number of flowers for each species # Create a subplot that shows 4 histograms # The first histogram shows the categories of sepal Lengths # The second histogram shows the categories of sepal widths # The third histogram shows the categories of petal Lengths # The fourth histogram shows the categories of petal widths #Label the x-axis of each histogram by the name of the column you are looking at #make sure the the Labels do not overlap with the figures In [ ]: # Create a subplot show two plots on the same row. # for the first subplot, use plt.scatter to graph the sepal width as a function of the sepal Length # for the second subplot, use plt.scatter to graph the petal width as a function of the petal Length # Label the x and y axis Challenge The objective of this exercise is to discriminating between three species of Iris from the measurements of their petals and sepals. Your challenge will be to write your code in 20 lines or less, not including comment lines. Out [2]: array([[1., 5.1, 3.5, 1.4, 0.2, 1. ], [2., 4.9, 3., 1.4, 0.2, 1. ], [3., 4.7, 3.2, 1.3, 0.2, 1. ], 4.6, 3.1, 1.5, 0.2, 1. 1, 5. 3.6, 1.4, 0.2, 1. ]]) In [ ]: In [ ]: In [ ]: [4. [5. 2 2 # The name of each column is placed in the following list: column_name = ['num', 'sepal length', 'sepal Width', 'petal length', 'petal width', 'species'] # Using a for Loop and the NumPy Library: # print the minimum value of each column # print the maximum value of each column # print the mean of each column # Create a histogram showing the number of flowers for each species # Create a subplot that shows 4 histograms # The first histogram shows the categories of sepal Lengths # The second histogram shows the categories of sepal widths # The third histogram shows the categories of petal Lengths # The fourth histogram shows the categories of petal widths #Label the x-axis of each histogram by the name of the column you are looking at #make sure the the Labels do not overlap with the figures In [ ]: # Create a subplot show two plots on the same row. # for the first subplot, use plt.scatter to graph the sepal width as a function of the sepal Length # for the second subplot, use plt.scatter to graph the petal width as a function of the petal Length # Label the x and y axis

Answer rating: 100% (QA)

import numpy as np import matplotlibpyplot as plt data nparray1 51 35 14 02 1 2 49 3 14 02 1 3 47 32 ... View the full answer