Drug Abuse Trends by County and Substance: 2010-2017

This dataset provides a comprehensive overview of drug abuse trends across various counties in multiple states from 2010 to 2017. It includes information on the number of reports for specific substances like Morphine, Methadone, and Heroin, offering insights into regional variations in drug abuse patterns.

Data Format

The data is presented in a tabular format with the following columns:

SubstanceName: The specific drug reported (e.g., 'Morphine', 'Methadone', 'Heroin').
COUNTY: The county where the drug abuse was reported.
State: The state where the county is located.
2010: The number of reports for the specified substance in the county during 2010.
2011: The number of reports for the specified substance in the county during 2011.
...
2017: The number of reports for the specified substance in the county during 2017.

Data Analysis

To analyze the data, we can utilize the Pandas library in Python. Here's an example of how to load the data and transform it for easier analysis:

import pandas as pd

# Read the data from a CSV file (replace 'data.csv' with your actual file name)
df = pd.read_csv('data.csv')

# Pivot the data to create a table with SubstanceName, COUNTY, and State as indexes and years as columns
df_pivot = df.pivot_table(index=['SubstanceName', 'COUNTY', 'State'], columns='YYYY', values='DrugReports', fill_value=0).reset_index()

# Sort the data by SubstanceName, COUNTY, and State
df_pivot_sorted = df_pivot.sort_values(by=['SubstanceName', 'COUNTY', 'State'])

# Display the sorted data
print(df_pivot_sorted)

This code will generate a table showing the number of reports for each substance in each county across all years. You can then use this data to explore trends, identify hotspots, and draw conclusions about drug abuse patterns in different regions.

Note:

This dataset serves as a starting point for analysis. You might need to further process and clean the data depending on your specific research questions and analysis goals.

Drug Abuse Trends by County and Substance: 2010-2017