We are using the Iris dataset with errors for our analysis:

Import the iris_errors file into a dataframe named "df" (sample done, below)

import numpy as np

import pandas as pd # Reading the CSV file df = pd.read_csv("iris_errors2.csv") # Printing top 5 rows df.head()

For this Kindly answer the following questions in python programming language by implementing this in Jupyter Notebook. Kindly attach the output with the code:

1. Change the data type of the PetalWidthCm column to numeric.

2. How many rows and columns does our new dataframe have?

3. How balanced is our data now?

4. Import the Seaborn and matplotib libraries and draw pairplots for all the data columns (colored by species)

5. Do there appear to be any outliers in the data?

6. Draw stripplots for each species for sepal length and sepal width

***Draw violinplots for each species for petal length and petal width.

7. Eliminate the obvious outlier by replacing it with the average for that species in that column.

8. Draw pairplots for all data columns again (still colored by species).

9. Draw boxplots for all data columns for each species.

10. Display Pearson Correlation and heatmap for all data columns.

11. Compare the data in this homework with the data in the original, 'clean' iris.csv dataset. Do you see any significant differences?