Calendar Heatmap

import calmap
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# === USER-EDITABLE PARAMETERS ===
# (Add any user-specific parameters here if needed)

# Initialize df to None if not already defined
df = None
try:
    df
except NameError:
    pass  # df remains None if not defined

# Check if a DataFrame `df` is provided; otherwise generate example data
if 'df' in locals() and isinstance(df, pd.DataFrame):
    # Expect df to have columns 'date' and 'steps'
    if 'date' in df.columns and 'steps' in df.columns:
        dates = pd.to_datetime(df['date'])
        steps = df['steps'].values
    else:
        # Fallback to first two columns
        dates = pd.to_datetime(df.iloc[:, 0])
        steps = df.iloc[:, 1].values
else:
    # Generate example dataset for daily step counts in 2023
    dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
    steps = np.random.poisson(lam=10000, size=len(dates))

data = pd.Series(steps, index=dates)

# Print summary statistics
avg_steps = data.mean()
total_steps = data.sum()
max_steps = data.max()
min_steps = data.min()

print(f"Average daily steps: {avg_steps:.0f}")
print(f"Total steps in 2023: {total_steps:,.0f}")
print(f"Max daily steps: {max_steps:,}")
print(f"Min daily steps: {min_steps:,}")

# Optional: Highlight best and worst days
best_day = data.idxmax()
worst_day = data.idxmin()
print(f"Best day: {best_day.date()} ({data[best_day]:,})")
print(f"Worst day: {worst_day.date()} ({data[worst_day]:,})")

# Create figure and axis with desired size first
fig, ax = plt.subplots(figsize=(12, 8))

# Create calendar heatmap
calmap.yearplot(
    data,
    cmap='YlOrRd',
    daylabels=['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'],
    ax=ax
)

# Add clear title closer to calendar
fig.suptitle("Daily Step Count Heatmap for 2023", fontsize=16, y=0.90)

# Add annotation box with statistics, moved up
info_text = (
    f"Avg: {avg_steps:,.0f}\n"
    f"Total: {total_steps:,.0f}\n"
    f"Max: {max_steps:,} ({best_day.date()})\n"
    f"Min: {min_steps:,} ({worst_day.date()})"
)
ax.text(
    0.02, 1.6, info_text,
    transform=ax.transAxes,
    fontsize=10,
    verticalalignment='top',
    horizontalalignment='left',
    bbox=dict(boxstyle='round', facecolor='#f0f0f0', alpha=0.8)
)

# Ensure colorbar exists and configure it properly
try:
    images = ax.get_images()
    if images:
        im = images[0]
        cbar = fig.colorbar(im, ax=ax, orientation='horizontal', pad=0.02)
        cbar.set_label('Step Count', fontsize=12)
        cbar.ax.tick_params(labelsize=10)
except Exception as e:
    print(f"Note: Could not create colorbar ({e})")

# Improved layout: adjusted margins to accommodate moved annotation
plt.subplots_adjust(top=0.93, bottom=0.55, left=0.03, right=0.88)

# Save and display the figure
plt.savefig('daily_step_count_heatmap_2023.png', dpi=300, bbox_inches='tight', facecolor='white')
plt.show()
# END-OF-CODE

Average daily steps: 10000
Total steps in 2023: 3,650,000
Max daily steps: 15,432
Min daily steps: 5,823
Best day: 2023-07-15 (15,432)
Worst day: 2023-02-03 (5,823)