1import pandas as pd2import matplotlib.pyplot as plt3import numpy as np4import requests5from io import StringIO67# Fetch the data8url = "https://hebbkx1anhila5yf.public.blob.vercel-storage.com/NY%202016%20Party%20Locations-gANytICbCtxX5hfeeGk21AnEZX6Hee.csv"9response = requests.get(url)10data = pd.read_csv(StringIO(response.text))1112# Convert 'num_calls' to numeric, replacing any non-numeric values with NaN13data['num_calls'] = pd.to_numeric(data['num_calls'], errors='coerce')1415# Set up the plot style16plt.style.use('default')1718# Create a 2x2 grid of subplots19fig, axs = plt.subplots(2, 2, figsize=(24, 24))20fig.suptitle('New York 2016 Party Locations Analysis', fontsize=16)2122# 1. Distribution of party locations by borough23borough_counts = data['Borough'].value_counts()24axs[0, 0].bar(borough_counts.index, borough_counts.values)25axs[0, 0].set_title('Distribution of Party Locations by Borough')26axs[0, 0].set_xlabel('Borough')27axs[0, 0].set_ylabel('Number of Locations')28axs[0, 0].tick_params(axis='x', rotation=45)2930# 2. Top 10 zip codes with the most party calls31top_zips = data.groupby('Incident Zip')['num_calls'].sum().sort_values(ascending=False).head(10)32axs[0, 1].bar(top_zips.index.astype(str), top_zips.values)33axs[0, 1].set_title('Top 10 Zip Codes with Most Party Calls')34axs[0, 1].set_xlabel('Zip Code')35axs[0, 1].set_ylabel('Number of Calls')36axs[0, 1].tick_params(axis='x', rotation=45)3738# 3. Pie chart of all zip codes by number of parties39zip_counts = data['Incident Zip'].value_counts()40pie_data = zip_counts4142# Create color map43colors = plt.cm.viridis(np.linspace(0, 1, len(pie_data)))4445# Sort pie_data by values in descending order46pie_data_sorted = pie_data.sort_values(ascending=False)4748# Calculate percentages49total = pie_data_sorted.sum()50percentages = pie_data_sorted / total * 1005152# Function to create labels - only show if >= 1%53def make_labels(percentages):54    return [f'{percentages.index[i]}' if p >= 1 else '' for i, p in enumerate(percentages)]5556def make_autopct(percentages):57    def my_autopct(pct):58        return f'{pct:.1f}%' if pct >= 1 else ''59    return my_autopct6061# Create pie chart with labels outside62wedges, texts, autotexts = axs[1, 0].pie(pie_data_sorted.values, 63                                        colors=colors,64                                        autopct=make_autopct(percentages),65                                        pctdistance=0.85,66                                        startangle=90,67                                        labels=make_labels(percentages),68                                        labeldistance=1.1)  # Move labels outside6970# Make percentage labels inside the donut71plt.setp(autotexts, size=8, weight="bold")72# Make zip code labels outside larger and rotated for better readability73plt.setp(texts, size=8)7475axs[1, 0].set_title('Distribution of All Zip Codes by Number of Parties')7677# Add a circle at the center to create a donut chart effect78center_circle = plt.Circle((0, 0), 0.70, fc='white')79axs[1, 0].add_artist(center_circle)8081# 4. Scatter plot of party locations on a map82axs[1, 1].scatter(data['Longitude'], data['Latitude'], alpha=0.6, c=data['Borough'].astype('category').cat.codes)83axs[1, 1].set_title('Party Locations in New York City')84axs[1, 1].set_xlabel('Longitude')85axs[1, 1].set_ylabel('Latitude')8687# Create a custom legend for the scatter plot88boroughs = data['Borough'].unique()89for i, borough in enumerate(boroughs):90    axs[1, 1].scatter([], [], c=[i], label=borough)91axs[1, 1].legend(title='Borough', loc='center left', bbox_to_anchor=(1, 0.5))9293# Adjust layout and display the plot94plt.tight_layout()95plt.show()9697print("Analysis complete. Four plots have been generated and displayed.")