(python)

Step (1): Import the pandas, numpy, and seaborn libraries.

import pandas as pd

import numpy as np

import seaborn as sns

Step (2): Use the code line below to load the "iris" dataset and store it as a data frame.

iris = sns.load_dataset('iris')

Step (3): Drop the "species" column in the iris data frame.

	sepal_length	sepal_width	petal_length	petal_width
0	5.1	3.5	1.4	0.2
1	4.9	3	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5	3.6	1.4	0.2

Step (4): Add two more columns to the iris data frame, "sepal_sum" and "petal_sum". The "sepal_sum" is the sum of the values from the "sepal_length" and "sepal_width" columns. On the other hand, The "petal_sum" is the sum of the values from the "petal_length" and "petal_width" columns.

	sepal_length	sepal_width	petal_length	petal_width	sepal_sum	petal_sum
0	5.1	3.5	1.4	0.2	8.6	1.6
1	4.9	3	1.4	0.2	7.9	1.6
2	4.7	3.2	1.3	0.2	7.9	1.5
3	4.6	3.1	1.5	0.2	7.7	1.7
4	5	3.6	1.4	0.2	8.6	1.6

Step (5): Create a new data frame with the summary stats for the iris dataset. For this summary, compute each column's mean, standard deviation, and minimum and maximum values.

Note: Decide the final layout for the data frame. Use as a reference the below example.

	mean	std	min	max
sepal_length	5.843333	0.825301	4.3	7.9
sepal_width	3.057333	0.434411	2	4.4
sepal_sum	8.900667	0.886303	6.8	11.7
petal_length	3.758	1.759404	1	6.9
petal_width	1.199333	0.759693	0.1	2.5
petal_sum	4.957333	2.499316	1.2	9.2