Using Python to Visualize Data

import matplotlib.pyplot as plt
import pandas as pd

# Get user input for jurisdiction
jurisdiction = input("Enter jurisdiction: ")

# Read in data
data = pd.read_csv("data.csv")

# Filter data for chosen jurisdiction
jurisdiction_data = data[data["jurisdiction"] == jurisdiction]

# Extract murder_rate and rape_rate columns
murder_rate = jurisdiction_data["murder_rate"]
rape_rate = jurisdiction_data["rape_rate"]

# Create scatter plot
fig, ax = plt.subplots()
ax.scatter(murder_rate, rape_rate)
ax.set_title("Murder Rate vs Rape Rate in " + jurisdiction)
ax.set_xlabel("Murder Rate")
ax.set_ylabel("Rape Rate")
plt.show()

Enter jurisdiction: Ohio

import pandas as pd
import matplotlib.pyplot as plt

# Read in the data
df = pd.read_csv('data.csv')

# Prompt the user to enter a jurisdiction name
jurisdiction_name = input("Enter a jurisdiction name: ")

# Filter the data to only include the selected jurisdiction
selected_jurisdiction = df[df['jurisdiction'] == jurisdiction_name]

# Get the crime rates for the selected jurisdiction
murder_rate = selected_jurisdiction['murder_rate'].iloc[0]
robbery_rate = selected_jurisdiction['robbery_rate'].iloc[0]
rape_rate = selected_jurisdiction['rape_rate'].iloc[0]
agg_rate = selected_jurisdiction['agg_rate'].iloc[0]

# Create a bar chart of the crime rates
plt.bar(['Murder', 'Robbery', 'Rape', 'Aggravated Assault'], [murder_rate, robbery_rate, rape_rate, agg_rate])
plt.title(f'Crime rates for {jurisdiction_name}')
plt.xlabel('Crime Type')
plt.ylabel('Rate per 100,000 people')
plt.show()

Enter a jurisdiction name: Alabama

import pandas as pd
import matplotlib.pyplot as plt

# Load the dataset
df = pd.read_csv('data.csv')

# Get a list of all the available jurisdictions in the dataset
jurisdictions = df['jurisdiction'].unique()

# Prompt the user to select two jurisdictions to compare
print('Available jurisdictions:', jurisdictions)
jurisdiction1 = input('Enter the name of the first jurisdiction to compare: ')
jurisdiction2 = input('Enter the name of the second jurisdiction to compare: ')

# Filter the dataset to only include data for the two selected jurisdictions
df_jurisdictions = df[(df['jurisdiction']==jurisdiction1) | (df['jurisdiction']==jurisdiction2)]

# Create a scatter plot
plt.scatter(df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction1]['murder_rate'], 
            df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction2]['murder_rate'], 
            label='Murder Rate')

plt.scatter(df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction1]['robbery_rate'], 
            df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction2]['robbery_rate'], 
            label='Robbery Rate')

plt.scatter(df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction1]['rape_rate'], 
            df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction2]['rape_rate'], 
            label='Rape Rate')

plt.scatter(df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction1]['agg_rate'], 
            df_jurisdictions[df_jurisdictions['jurisdiction']==jurisdiction2]['agg_rate'], 
            label='Agg. Assault Rate')

plt.xlabel(jurisdiction1 + ' Crime Rate')
plt.ylabel(jurisdiction2 + ' Crime Rate')
plt.title('Comparison of ' + jurisdiction1 + ' and ' + jurisdiction2 + ' Crime Rates')

plt.legend()
plt.show()

Available jurisdictions: ['Alabama' 'Alaska' 'Arizona' 'Arkansas' 'California' 'Colorado'
 'Connecticut' 'Delaware' 'Florida' 'Georgia' 'Minnesota' 'Hawaii' 'Idaho'
 'Illinois' 'Indiana' 'Iowa' 'Kansas' 'Kentucky' 'Louisiana' 'Maine'
 'Maryland' 'Massachusetts' 'Michigan' 'Mississippi' 'Missouri' 'Montana'
 'Nebraska' 'Nevada' 'New Hampshire' 'New Jersey' 'New Mexico' 'New York'
 'North Carolina' 'North Dakota' 'Ohio' 'Oklahoma' 'Oregon' 'Pennsylvania'
 'Rhode Island' 'South Carolina' 'South Dakota' 'Tennessee' 'Texas' 'Utah'
 'Vermont' 'Virginia' 'Washington' 'West Virginia' 'Wisconsin' 'Wyoming']
Enter the name of the first jurisdiction to compare: New York
Enter the name of the second jurisdiction to compare: California

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