While summary statistics are certainly helpful for exploring and quantifying a feature, we might find it hard to wrap our minds around a bunch of numbers. This is why data visualization is such a powerful element of EDA.

For quantitative variables, *boxplots* and *histograms* are two common visualizations. These plots are useful because they simultaneously communicate information about minimum and maximum values, central location, and spread. Histograms can additionally illuminate patterns that can impact an analysis (e.g., skew or multimodality).

Python’s `seaborn`

library, built on top of `matplotlib`

, offers the `boxplot()`

and `histplot()`

functions to easily plot data from a `pandas`

DataFrame:

import matplotlib.pyplot as plt import seaborn as sns # Boxplot for rent sns.boxplot(x='rent', data=rentals) plt.show() plt.close()

# Histogram for rent sns.histplot(x='rent', data=rentals) plt.show() plt.close()

### Instructions

**1.**

Using the `movies`

DataFrame, create a boxplot for `production_budget`

using the `boxplot()`

function from `seaborn`

. Don’t forget to display the plot using `plt.show()`

and close the plot using `plt.close()`

.

**2.**

Create a histogram for `production_budget`

using the `histplot()`

function from `seaborn`

.

From the plots, what do you notice about the distribution of movie budgets?