import pandas as pd
 import seaborn as sns
 
 data = sns.load_dataset('mpg')
 correlation_matrix = data.corr(numeric_only=True)
 correlation_matrix
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import numpy as np
 from sklearn.datasets import load_iris
 
 iris = load_iris()
 np.corrcoef(iris["data"])
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import seaborn as sns
 
 data = sns.load_dataset('mpg')
 correlation_matrix = data.corr()
 
 sns.heatmap(data.corr(), 
            annot=True, 
            cmap='coolwarm')
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import statsmodels.api as sm
 
 correlation_matrix = sm.graphics.plot_corr(
    data.corr(), 
    xnames=data.columns.tolist())
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import plotly.offline as pyo
 pyo.init_notebook_mode(cnotallow=True)
 
 import plotly.figure_factory as ff
 
 correlation_matrix = data.corr()
 
 fig = ff.create_annotated_heatmap(
    z=correlation_matrix.values, 
    x=list(correlation_matrix.columns), 
    y=list(correlation_matrix.index), 
    colorscale='Blues')
 
 fig.show()
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sns.pairplot(df[['mpg','weight','horsepower','acceleration']])
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import matplotlib.pyplot as plt
 
 pd.plotting.scatter_matrix(
    data, alpha=0.2, 
    figsize=(6, 6), 
    diagnotallow='hist')
 
 plt.show()
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from scipy.stats import pearsonr
 import pandas as pd
 import seaborn as sns
 
 def corr_full(df, numeric_notallow=True, rows=['corr', 'p-value', 'obs']):
    """
    Generates a correlation matrix with correlation coefficients, 
    p-values, and observation count.
     
    Args:
    - df:                 Input dataframe
    - numeric_only (bool): Whether to consider only numeric columns for 
                            correlation. Default is True.
    - rows:               Determines the information to show. 
                            Default is ['corr', 'p-value', 'obs'].
     
    Returns:
    - formatted_table: The correlation matrix with the specified rows.
    """
     
    # Calculate Pearson correlation coefficients
    corr_matrix = df.corr(
        numeric_notallow=numeric_only)
     
    # Calculate the p-values using scipy's pearsonr
    pvalue_matrix = df.corr(
        numeric_notallow=numeric_only, 
        method=lambda x, y: pearsonr(x, y)[1])
     
    # Calculate the non-null observation count for each column
    obs_count = df.apply(lambda x: x.notnull().sum())
     
    # Calculate observation count for each pair of columns
    obs_matrix = pd.DataFrame(
        index=corr_matrix.columns, columns=corr_matrix.columns)
    for col1 in obs_count.index:
        for col2 in obs_count.index:
            obs_matrix.loc[col1, col2] = min(obs_count[col1], obs_count[col2])
         
    # Create a multi-index dataframe to store the formatted correlations
    formatted_table = pd.DataFrame(
        index=pd.MultiIndex.from_product([corr_matrix.columns, rows]), 
        columns=corr_matrix.columns
    )
     
    # Assign values to the appropriate cells in the formatted table
    for col1 in corr_matrix.columns:
        for col2 in corr_matrix.columns:
            if 'corr' in rows:
                formatted_table.loc[
                    (col1, 'corr'), col2] = corr_matrix.loc[col1, col2]
             
            if 'p-value' in rows:
                # Avoid p-values for diagonal they correlate perfectly
                if col1 != col2:
                    formatted_table.loc[
                        (col1, 'p-value'), col2] = f"({pvalue_matrix.loc[col1, col2]:.4f})"
            if 'obs' in rows:
                formatted_table.loc[
                    (col1, 'obs'), col2] = obs_matrix.loc[col1, col2]
     
    return(formatted_table.fillna('')
            .style.set_properties(**{'text-align': 'center'}))
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df = sns.load_dataset('mpg')
 result = corr_full(df, rows=['corr', 'p-value'])
 result
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