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- import seaborn as sns
- import pandas as pd
- import matplotlib.pyplot as plt
- from scipy.stats import f_oneway
- iris_df = sns.load_dataset('iris')
- plt.figure(figsize=(7,4))
- plt.title("Comparison on various species")
- sns.scatterplot(x=iris_df['sepal_length'], y=iris_df['sepal_width'], hue=iris_df['species'], s=50)
- plt.figure(figsize=(6,4))
- iris_corr = iris_df.select_dtypes(include=['number']).corr()
- sns.heatmap(iris_corr, annot=True)
- grouped_data = [iris_df[iris_df['species'] == species]['sepal_length'] for species in iris_df['species'].unique()]
- f_stat, p_value = f_oneway(*grouped_data)
- print("F-statistic:", f_stat)
- print("P-value:", p_value)
- boston = pd.read_csv('BostonHousing.csv')
- #display(boston)
- boston_corr = boston.corr()
- plt.figure(figsize=(12,8))
- sns.heatmap(boston_corr,annot=True)
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