Line Graph

# === IMPORTS ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime

# === USER-EDITABLE PARAMETERS ===
x_col = 'Date'          # Change: column name for x-axis (datetime)
y_col = 'Temperature'   # Change: column name for y-axis (numeric)
group_col = 'City'      # Change: column name for grouping variable
figsize = (12, 6)       # Change: figure size in inches
title_fontsize = 18     # Change: title font size
label_fontsize = 16     # Change: axis label and tick font size
colors = ['#1f77b4', '#ff7f0e', '#2ca02c']  # Change: hex colors for each city
markers = ['o', 's', '^']  # Change: markers for each city
line_width = 2          # Change: line width
show_legend = True      # Change: whether to show legend

# === DATA PREPARATION ===
# Generate example dataset: monthly average temperatures for 3 cities over one year
np.random.seed(0)
months = pd.date_range(start='2023-01-01', periods=12, freq='MS')
cities = ['City A', 'City B', 'City C']
data = []

for city in cities:
    if city == 'City A':
        base = 20
        trend = 5
    elif city == 'City B':
        base = 15
        trend = 3
    else:  # City C
        base = 10
        trend = 1
    temps = base + trend * np.sin(np.linspace(0, 2*np.pi, 12)) + np.random.normal(0, 2, 12)
    for date, temp in zip(months, temps):
        data.append({'Date': date, 'City': city, 'Temperature': temp})

df = pd.DataFrame(data)

# === ANALYSIS ===
# Compute max temperature per city
max_temps = df.groupby(group_col)[y_col].max()
max_months = df.loc[df.groupby(group_col)[y_col].idxmax()][['Date', group_col, y_col]]
mean_temps = df.groupby(group_col)[y_col].mean()

# Print analysis results
print("Maximum temperatures per city:")
for city in cities:
    month_str = max_months.loc[max_months[group_col]==city, 'Date'].dt.strftime('%b %Y').values[0]
    print(f"  {city}: {max_temps[city]:.1f}°C on {month_str}")
print("\nMean temperatures per city:")
for city in cities:
    print(f"  {city}: {mean_temps[city]:.1f}°C")

# Determine city with highest max temperature for title
top_city = max_temps.idxmax()
top_temp = max_temps[top_city]
top_month = max_months.loc[max_months[group_col]==top_city, 'Date'].dt.strftime('%b').values[0]

# === PLOT CREATION ===
fig, ax = plt.subplots(figsize=figsize)

# Plot each city's temperature trend
for i, city in enumerate(cities):
    city_data = df[df[group_col]==city].sort_values(x_col)
    ax.plot(city_data[x_col], city_data[y_col],
            label=city,
            color=colors[i],
            marker=markers[i],
            linewidth=line_width,
            markersize=6)

# Formatting
ax.set_title(f"{top_city} reaches highest temperatures of {top_temp:.1f}°C in {top_month}",
             fontsize=title_fontsize)
ax.set_xlabel('Month', fontsize=label_fontsize)
ax.set_ylabel('Temperature (°C)', fontsize=label_fontsize)
ax.tick_params(axis='both', which='major', labelsize=label_fontsize)

# Legend
if show_legend:
    ax.legend(loc='upper left', fontsize=label_fontsize)

# Annotation of max temperatures
max_temps_str = ', '.join([f"{city}: {max_temps[city]:.1f}°C" for city in cities])
ax.text(0.95, 0.85, f"Max temps: {max_temps_str}",
        transform=ax.transAxes,
        ha='right', va='top',
        bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))

# Layout adjustments
plt.subplots_adjust(top=0.90, bottom=0.12, left=0.12, right=0.95)
plt.tight_layout()

# Display plot
plt.show()
# END-OF-CODE

Annual Temperature Statistics:
New York: min=30°F, max=82°F, avg=54.6°F
Miami: min=65°F, max=90°F, avg=79.8°F
Chicago: min=25°F, max=80°F, avg=52.3°F

Comfortable Months (60-75°F): NYC=7, Miami=4, Chicago=5