This code defines a function `show_correlation(df)` that performs the following...

This code defines a function show_correlation(df) that performs the following actions:

1. Input: It accepts a DataFrame df as input.

2. Calculates Correlation Matrix:

   • It calculates the correlation matrix between numeric columns of the provided DataFrame using df.corr(numeric_only=True) (only numeric columns are considered for correlation).

3. Prints Correlation Matrix:

   • It prints a neatly formatted correlation matrix to the console, rounding the values to 2 decimal places. The matrix is visualized with horizontal separators made of equals (=) signs to improve readability.

4. Generates a Heatmap:

   • Using the correlation matrix, it generates a heatmap using seaborn (sns.heatmap).
   • The options include:
     • annot=True: Annotates the heatmap cells with correlation values.
     • fmt=".2f": Ensures the correlation values are displayed with 2 decimal places.
     • cmap='coolwarm': Uses a color scale ranging from blue (negative correlations) to red (positive correlations).
     • center=0: Centers the colormap at 0 (neutral correlation).
     • vmin=-1 and vmax=1: Correlation values range between -1 and 1.
     • linewidths=0.5: Adds small lines around each cell for better visual separation.
   • The heatmap's size is set to 10x8 inches and the layout is adjusted using plt.tight_layout().

5. Displays the Heatmap:

   • The heatmap is displayed using plt.show().

Intended Purpose:
This function helps in analyzing and visualizing the correlation between numeric columns of a DataFrame by:

• Printing the raw correlation matrix for numeric inspection.
• Providing a heatmap for intuitive visual analysis of correlations.

Usage:
You can call show_correlation(df) with any pandas DataFrame df containing numeric columns to analyze and visualize correlations.

Note: The libraries matplotlib.pyplot as plt and seaborn as sns need to be imported for this code to work.