Schools Budget In USA Data Visualization

In this data visualization project, our focus will be on examining the impact and distribution of funds from the US School Improvement Grants in 2010. This program allocated a substantial amount, nearly $4 billion, to schools with the aim of supporting renovations and enhancements to their educational programs. By delving into the dataset associated with these grants, we can employ various visualization techniques to effectively communicate key insights. Potential visualizations may include bar charts or choropleth maps to showcase the distribution of funding across different regions or states. We can also explore line charts to illustrate trends in funding over time or pie charts to represent the allocation of funds across different improvement categories. This project aims to provide a comprehensive visual narrative, offering stakeholders and the general public a clearer understanding of how these grants were distributed and utilized to improve educational facilities and programs across the United States in 2010.

Source:

<https://campus.datacamp.com/courses/supervised-learning-with-scikit-learn>

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('schoolimprovement2010grants.csv')
print(df.head())

sns.set_palette("rocket")

# Create a displot of the Award Amount
sns.displot(df['Award_Amount'],
             kind='kde',
             rug=True,
             fill=True)

# Plot the results
plt.show()

# Create a barplot with each Region shown as a different color
sns.barplot(data=df,
         y='Award_Amount',
         x='Model Selected',
         hue='Region')

plt.show()
plt.clf()

Reference:

https://www.datacamp.com/