hist(math_scores,bins=10,fill="blue" color="black", main="Math Scores Distribution")

The provided code snippets demonstrate how to create visualizations of distributions in Python using seaborn library. Here's a brief explanation and demonstration of creating a histogram:

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# Let's assume math_scores is a list or pandas Series with mathematics scores
math_scores = ... # Replace with your actual data

# Plotting a histogram using Seaborn
fig, ax = plt.subplots(1, 1, figsize=(10, 6))  # Create a new figure with one subplot
sns.histplot(data=math_scores, bins=10,  # Use histplot function from seaborn
             fill="blue",  # Fill the bars with blue color
             color="black",  # Set the edge color to black
             kde=False,  # Since we want a histogram, not a density plot
             ax=ax,  # Pass the axis object to set the plot within it
             element="stepfilled",  # Make it filled bars (not steps)
             alpha=0.7,  # Reduce opacity for better visibility
             label="Math Scores")  # Add a label for the legend

# Customizing the plot
ax.set_title("Math Scores Distribution")  # Set the title
ax.set_xlabel("Score")  # Label the x-axis
ax.set_ylabel("Frequency")  # Label the y-axis
ax.legend()  # Display the legend

plt.show()  # Display the plot

This code creates a histogram of `math_scores` with 10 bins, fills the bars in blue, and adds a title and labels for clarity.