import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime
import pandas as pd
import seaborn as sns
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
def cv_metric_horizon():
# Data for Ridge
ridge_rmse_means = [1.5325, 2.2207, 2.5017, 2.5879, 2.6164]
ridge_mae_means = [1.1858, 1.7490, 1.9916, 2.0732, 2.0849]
# Data for LGBM
lgbm_rmse_means = [1.5587, 2.2607, 2.5045, 2.5682, 2.5987]
lgbm_mae_means = [1.2018, 1.7782, 1.9940, 2.0520, 2.0750]
# Horizons
horizons = [1, 2, 3, 4, 5]
# Colors
ridge_color = '#d63031' # Đỏ
lgbm_color = '#2E86AB' # Xanh dương
ridge_bg = '#ffe6e6' # Background nhạt cho Ridge
lgbm_bg = '#e6f0ff' # Background nhạt cho LGBM
# Create figure với 2 subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
# ===== PLOT 1: RMSE comparison =====
ax1.plot(horizons, ridge_rmse_means, color=ridge_color, linewidth=2.5, marker='o', markersize=6, label='Ridge Tuned')
ax1.plot(horizons, lgbm_rmse_means, color=lgbm_color, linewidth=2.5, marker='s', markersize=6, label='LGBM Tuned')
# Highlight horizon 3-5
ax1.axvspan(3, 5, color=ridge_bg, alpha=0.3)
ax1.axvspan(3, 5, color=lgbm_bg, alpha=0.3)
ax1.set_xlabel('Forecasting Horizon (Days)', fontsize=12, fontweight='bold')
ax1.set_title('CV RMSE', fontsize=14, fontweight='bold')
ax1.grid(True, alpha=0.3)
ax1.set_xticks(horizons)
# ===== PLOT 2: MAE comparison =====
ax2.plot(horizons, ridge_mae_means, color=ridge_color, linewidth=2.5, marker='o', markersize=6, label='Ridge Tuned')
ax2.plot(horizons, lgbm_mae_means, color=lgbm_color, linewidth=2.5, marker='s', markersize=6, label='LGBM Tuned')
# Highlight horizon 3-5
ax2.axvspan(3, 5, color=ridge_bg, alpha=0.3)
ax2.axvspan(3, 5, color=lgbm_bg, alpha=0.3)
ax2.set_xlabel('Forecasting Horizon (Days)', fontsize=12, fontweight='bold')
ax2.set_title('CV MAE', fontsize=14, fontweight='bold')
ax2.grid(True, alpha=0.3)
ax2.set_xticks(horizons)
# ===== LEGEND CHUNG =====
legend_elements = [
Line2D([0], [0], color=ridge_color, marker='o', linestyle='-', linewidth=2.5, markersize=8, label='Ridge Tuned'),
Line2D([0], [0], color=lgbm_color, marker='s', linestyle='-', linewidth=2.5, markersize=8, label='LGBM Tuned'),
Patch(facecolor=ridge_bg, edgecolor="#361236", alpha=0.3, label='Longer Horizon 3-5'),
]
fig.legend(handles=legend_elements, loc='upper right', bbox_to_anchor=(0.98, 0.95),
fontsize=12, frameon=True, fancybox=True, shadow=True, ncol=1)
# ===== KEY INSIGHTS TEXT BOX (English) =====
insight_text = """KEY INSIGHTS:
• Ridge performs better at short-term
forecasting (Day 1-2)
• LGBM performs better from horizon 3-5
- Lower RMSE than Ridge
- Lower MAE than Ridge
- Higher stability
→ LGBM is more suitable for
longer term forecasting (3-5 days)"""
fig.text(0.83, 0.75, insight_text, fontsize=11,
bbox=dict(boxstyle="round,pad=0.5", facecolor='#F8F9FA', edgecolor='#2E86AB', alpha=0.9),
verticalalignment='top', linespacing=1.5)
plt.tight_layout(rect=[0, 0, 0.8, 1]) # Để chỗ cho text box
plt.savefig('figures/per_horizon_cv_performance.png', dpi=300, bbox_inches='tight')
plt.show()
def ridge_lgbm():
# Data for tuned models
models = ['Ridge Tuned', 'LGBM Tuned']
rmse_values = [2.2919, 2.2992]
mae_values = [1.8169, 1.8211]
r2_values = [0.7814, 0.7813]
# Create subplots
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
# Colors
ridge_color = '#d63031'
lgbm_color = '#2E86AB'
# Plot 1: RMSE comparison
x = np.arange(len(models))
width = 0.6
bars1 = ax1.bar(x, rmse_values, width, color=[ridge_color, lgbm_color], alpha=0.8)
ax1.set_title('RMSE', fontsize=14, fontweight='bold')
ax1.set_xticks(x)
ax1.set_xticklabels(models) # Bỏ rotation
ax1.set_ylim(0, 3) # Tăng y-limit đến 3
ax1.grid(True, alpha=0.3)
# Add values on bars
for bar, value in zip(bars1, rmse_values):
ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,
f'{value:.4f}', ha='center', va='bottom', fontweight='bold', fontsize=11)
# Plot 2: MAE comparison
bars2 = ax2.bar(x, mae_values, width, color=[ridge_color, lgbm_color], alpha=0.8)
ax2.set_title('MAE', fontsize=14, fontweight='bold')
ax2.set_xticks(x)
ax2.set_xticklabels(models) # Bỏ rotation
ax2.set_ylim(0, 3) # Tăng y-limit đến 3
ax2.grid(True, alpha=0.3)
# Add values on bars
for bar, value in zip(bars2, mae_values):
ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,
f'{value:.4f}', ha='center', va='bottom', fontweight='bold', fontsize=11)
# Plot 3: R² comparison
bars3 = ax3.bar(x, r2_values, width, color=[ridge_color, lgbm_color], alpha=0.8)
ax3.set_title('R²', fontsize=14, fontweight='bold')
ax3.set_xticks(x)
ax3.set_xticklabels(models) # Bỏ rotation
ax3.set_ylim(0, 1) # R² vẫn giữ 0-1
ax3.grid(True, alpha=0.3)
# Add values on bars
for bar, value in zip(bars3, r2_values):
ax3.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,
f'{value:.4f}', ha='center', va='bottom', fontweight='bold', fontsize=11)
# Bỏ tất cả xlabel và ylabel
ax1.set_xlabel('')
ax1.set_ylabel('')
ax2.set_xlabel('')
ax2.set_ylabel('')
ax3.set_xlabel('')
ax3.set_ylabel('')
plt.tight_layout()
plt.savefig('figures/tuned_models_comparison.png', dpi=300, bbox_inches='tight')
plt.show()
def train_test_gap():
# --- Chart 2: Horizon-wise Gap ---
horizons = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']
baseline_gap = [0.6915, 1.0250, 1.1818, 1.3531, 1.4625]
optimized_gap = [0.2626, 0.3331, 0.5779, 0.3747, 0.7057]
plt.figure(figsize=(10, 5))
x = range(len(horizons))
plt.plot(x, baseline_gap, marker='o', label="Baseline Gap", linewidth=2, markersize=8, color='red')
plt.plot(x, optimized_gap, marker='s', label="Optimized Gap", linewidth=2, markersize=8, color='green')
plt.xticks(x, horizons, fontsize=11)
plt.xlabel("Forecasting Horizon", fontsize=12, fontweight='bold')
plt.title("Train-Test Generalization Gap: Baseline vs Optimized Model", fontsize=14, fontweight='bold')
plt.legend(fontsize=10)
plt.grid(True, alpha=0.3)
plt.ylim(0, 1.6) # Trục y bắt đầu từ 0
# Thêm giá trị trên các điểm
for i, (base, opt) in enumerate(zip(baseline_gap, optimized_gap)):
plt.text(i, base + 0.05, f'{base:.3f}', ha='center', va='bottom', fontsize=9, color='red')
plt.text(i, opt - 0.08, f'{opt:.3f}', ha='center', va='top', fontsize=9, color='green')
plt.tight_layout()
plt.savefig('figures/horizon_gap_comparison.png', dpi=300, bbox_inches='tight')
plt.show()
def cv_range():
# Data
metrics = ['RMSE', 'MAE', 'R²']
cv_means = [2.2992, 1.8211, 0.7813]
cv_stds = [0.1346, 0.1236, 0.0290]
test_values = [2.1866, 1.7246, 0.8149]
# Create figure
fig, ax = plt.subplots(figsize=(10, 6))
x_pos = np.arange(len(metrics))
width = 0.6
# Plot CV means with error bars (representing ±1 std)
bars = ax.bar(x_pos, cv_means, width, yerr=cv_stds,
capsize=8, label='CV Mean ± Std',
color='lightblue', alpha=0.7, edgecolor='navy', linewidth=1.5)
# Plot test values as points
# ax.scatter(x_pos, test_values, color='red', s=100, zorder=3,
# label='Test Value', marker='o', edgecolor='darkred', linewidth=1)
# Customize plot
ax.set_xlabel('Metrics', fontsize=12, fontweight='bold')
ax.set_ylabel('Score', fontsize=12, fontweight='bold')
ax.set_title('Generalization Check: CV Performance vs Test Set',
fontsize=14, fontweight='bold', pad=20)
ax.set_xticks(x_pos)
ax.set_xticklabels(metrics, fontsize=11)
ax.legend(fontsize=10)
plt.tight_layout()
plt.savefig('figures/generalization_check.png', dpi=300, bbox_inches='tight')
plt.show()
def metric_overall():
# 1. Biểu đồ so sánh average metrics
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
# Biểu đồ 1: Average metrics comparison
metrics = ['RMSE', 'MAE', 'R²']
baseline_avg = [2.4121, 1.8246, 0.7642]
optimized_avg = [2.1866, 1.7246, 0.8149]
x = np.arange(len(metrics))
width = 0.35
bars1 = ax1.bar(x - width/2, baseline_avg, width, label='Baseline', color='gray', alpha=0.7)
bars2 = ax1.bar(x + width/2, optimized_avg, width, label='Optimized', color='#2E86AB', alpha=0.8)
ax1.set_xlabel('Metrics', fontsize=12, fontweight='bold')
ax1.set_title('Average Performance: Optimized vs Baseline', fontsize=14, fontweight='bold', pad=20)
ax1.set_xticks(x)
ax1.set_xticklabels(metrics, fontsize=11)
ax1.legend(fontsize=10)
ax1.grid(True, alpha=0.3)
# Thêm giá trị trên cột
for bars in [bars1, bars2]:
for bar in bars:
height = bar.get_height()
ax1.text(bar.get_x() + bar.get_width()/2., height + 0.02,
f'{height:.3f}', ha='center', va='bottom', fontsize=10, fontweight='bold')
# Biểu đồ 2: Horizon-wise RMSE comparison
horizons = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']
baseline_rmse = [1.4713, 2.2180, 2.5523, 2.8270, 2.9917]
optimized_rmse = [1.4682, 2.1102, 2.3645, 2.4761, 2.5139]
x2 = np.arange(len(horizons))
bars3 = ax2.bar(x2 - width/2, baseline_rmse, width, label='Baseline', color='gray', alpha=0.7)
bars4 = ax2.bar(x2 + width/2, optimized_rmse, width, label='Optimized', color='#2E86AB', alpha=0.8)
ax2.set_xlabel('Forecasting Horizon', fontsize=12, fontweight='bold')
ax2.set_title('RMSE by Horizon: Optimized vs Baseline', fontsize=14, fontweight='bold', pad=20)
ax2.set_xticks(x2)
ax2.set_xticklabels(horizons, fontsize=11)
ax2.legend(fontsize=10)
ax2.grid(True, alpha=0.3)
# Thêm giá trị trên cột
for bars in [bars3, bars4]:
for bar in bars:
height = bar.get_height()
ax2.text(bar.get_x() + bar.get_width()/2., height + 0.02,
f'{height:.3f}', ha='center', va='bottom', fontsize=10, fontweight='bold')
plt.tight_layout()
plt.savefig('figures/optimized_vs_baseline_comparison.png', dpi=300, bbox_inches='tight')
plt.show()
def cv_overview():
# Dữ liệu từ output của bạn
folds_data = {
'fold1': {
'train_start': '2015-10-20', 'train_end': '2017-01-19',
'val_start': '2017-02-19', 'val_end': '2018-05-21',
'train_samples': 458, 'val_samples': 457
},
'fold2': {
'train_start': '2015-10-20', 'train_end': '2018-05-21',
'val_start': '2018-06-21', 'val_end': '2019-09-20',
'train_samples': 945, 'val_samples': 457
},
'fold3': {
'train_start': '2015-10-20', 'train_end': '2019-09-20',
'val_start': '2019-10-21', 'val_end': '2021-01-19',
'train_samples': 1432, 'val_samples': 457
},
'fold4': {
'train_start': '2015-10-20', 'train_end': '2021-01-19',
'val_start': '2021-02-19', 'val_end': '2022-05-21',
'train_samples': 1919, 'val_samples': 457
},
'fold5': {
'train_start': '2015-10-20', 'train_end': '2022-05-21',
'val_start': '2022-06-21', 'val_end': '2023-09-20',
'train_samples': 2406, 'val_samples': 457
}
}
# Chuyển đổi ngày tháng
for fold in folds_data.values():
for key in ['train_start', 'train_end', 'val_start', 'val_end']:
fold[key] = datetime.strptime(fold[key], '%Y-%m-%d')
# Màu sắc
train_color = '#1f77b4' # blue
val_color = '#ff7f0e' # orange
gap_color = 'lightgray'
# Vẽ biểu đồ tổng quan
plt.figure(figsize=(14, 6))
# Tạo timeline tổng quan
all_dates = []
for fold_data in folds_data.values():
all_dates.extend([fold_data['train_start'], fold_data['train_end'],
fold_data['val_start'], fold_data['val_end']])
overall_start = min(all_dates)
overall_end = max(all_dates)
# Tạo danh sách các điểm mốc thời gian quan trọng cho trục X
important_dates = [
datetime(2016, 1, 1), datetime(2017, 1, 1), datetime(2018, 1, 1),
datetime(2019, 1, 1), datetime(2020, 1, 1), datetime(2021, 1, 1),
datetime(2022, 1, 1), datetime(2023, 1, 1)
]
# Vẽ từng fold trên cùng một timeline
for i, (fold_name, fold_data) in enumerate(folds_data.items()):
y_pos = i # Fold 1 ở trên cùng, fold 5 ở dưới cùng
# Tính positions dựa trên datetime
train_start_pos = fold_data['train_start']
train_end_pos = fold_data['train_end']
val_start_pos = fold_data['val_start']
val_end_pos = fold_data['val_end']
# Tính gap period
gap_start_pos = fold_data['train_end']
gap_end_pos = fold_data['val_start']
# Vẽ train period
plt.barh(y_pos, (train_end_pos - train_start_pos).days,
left=(train_start_pos - overall_start).days,
height=0.4, color=train_color, alpha=0.7, label='Train' if i==0 else "")
# Vẽ gap period
plt.barh(y_pos, (gap_end_pos - gap_start_pos).days,
left=(gap_start_pos - overall_start).days,
height=0.4, color=gap_color, alpha=0.5, label='Cutoff 30 days (lag/rolling)' if i==0 else "")
# Vẽ validation period
plt.barh(y_pos, (val_end_pos - val_start_pos).days,
left=(val_start_pos - overall_start).days,
height=0.4, color=val_color, alpha=0.7, label='Validation' if i==0 else "")
# Thêm thông tin samples - FONT SIZE LỚN HƠN
plt.text((train_start_pos - overall_start).days, y_pos,
f" {fold_data['train_samples']}", va='center', ha='left',
fontsize=12, fontweight='bold') # Tăng từ 8 lên 12
plt.text((val_start_pos - overall_start).days, y_pos,
f" {fold_data['val_samples']}", va='center', ha='left',
fontsize=12, fontweight='bold') # Tăng từ 8 lên 12
# Customize overview plot - FONT SIZE LỚN HƠN
plt.yticks(range(len(folds_data)), [f'Fold {i+1}' for i in range(len(folds_data))],
fontsize=12) # Tăng font size cho trục Y
plt.ylabel('Folds', fontweight='bold', fontsize=14) # Tăng font size
# Thiết lập trục X với timeline - NẰM NGANG
x_ticks = [(date - overall_start).days for date in important_dates]
x_tick_labels = [date.strftime('%Y-%m') for date in important_dates]
plt.xticks(x_ticks, x_tick_labels, rotation=0, fontsize=11) # rotation=0 để nằm ngang, tăng font size
plt.xlabel('Timeline', fontweight='bold', fontsize=14)
plt.title('Time Series Cross-Validation - Overview of All Folds', fontsize=16, fontweight='bold')
# LEGEND TO HƠN
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=12) # Tăng font size legend
plt.grid(True, alpha=0.3, axis='x')
plt.tight_layout()
plt.show()
def plot_month_average(df):
df0 = df.copy()
# Tính trung bình nhiệt độ theo tháng
df0['datetime'] = pd.to_datetime(df0['datetime'])
monthly_avg = df0.groupby(df0['datetime'].dt.month)['temp'].mean()
# Vẽ biểu đồ đường
plt.figure(figsize=(14, 7))
plt.plot(monthly_avg.index, monthly_avg.values, color='#E74C3C', marker='o',
markerfacecolor='#C0392B', markersize=8, linestyle='-', linewidth=3,
label='Average Temperature')
# Làm nổi bật điểm peak (tháng 6) và điểm thấp nhất (tháng 1)
max_temp_idx = monthly_avg.idxmax()
min_temp_idx = monthly_avg.idxmin()
# Highlight peak point
plt.scatter(max_temp_idx, monthly_avg[max_temp_idx], color='red', s=150,
zorder=5, label=f'Hottest Month (Jun: {monthly_avg[max_temp_idx]:.1f}°C)',
edgecolors='darkred', linewidth=2)
# Highlight lowest point
plt.scatter(min_temp_idx, monthly_avg[min_temp_idx], color='blue', s=150,
zorder=5, label=f'Coldest Month (Jan: {monthly_avg[min_temp_idx]:.1f}°C)',
edgecolors='darkblue', linewidth=2)
# Ghi giá trị nhiệt độ bên trên mỗi điểm
for i, val in enumerate(monthly_avg.values):
plt.text(monthly_avg.index[i], val + 0.3, f"{val:.1f}°C",
ha='center', fontsize=10, fontweight='bold', color='#2C3E50')
# Trang trí biểu đồ
plt.title("Hanoi Average Monthly Temperature (2015–2025)", fontsize=16, fontweight='bold', pad=20)
plt.xlabel("Month", fontsize=12, fontweight='bold')
#plt.ylabel("Temperature (°C)", fontsize=12, fontweight='bold')
plt.grid(alpha=0.3, linestyle='--')
plt.xticks(range(1, 13),
['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
# Đặt legend và key insights gần nhau hơn
plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left')
# Thêm Key Insights - đặt sát dưới legend
insights_text = """KEY INSIGHTS:
• SUMMER: Jun-Aug hottest
(June: 30.3°C peak)
• WINTER: Dec-Feb coldest
(Jan: 17.9°C lowest)
• RAPID TRANSITIONS:
- Spring (Mar-May): Fast warming
- Autumn (Sep-Nov): Fast cooling
• STABLE: Strong 10-year pattern"""
plt.figtext(0.83, 0.76, insights_text, fontsize=10,
bbox=dict(boxstyle="round,pad=0.6", facecolor="lightyellow", alpha=0.9),
verticalalignment='top')
plt.tight_layout()
plt.show()
def plot_volatility_customized(df1):
df = df1.copy()
df["datetime"] = pd.to_datetime(df["datetime"], errors="coerce")
df = df.sort_values("datetime").set_index("datetime")
df_2024 = df[df.index.year == 2024].copy()
df_2024['temp_rolling_std_5'] = df_2024['temp'].rolling(window=5).std()
# Tạo figure với layout tốt hơn
plt.figure(figsize=(15, 8))
# Tạo vùng màu theo mùa với độ nổi bật khác nhau
winter_mask = (df_2024.index.month >= 1) & (df_2024.index.month <= 3)
spring_mask = (df_2024.index.month >= 4) & (df_2024.index.month <= 5)
summer_mask = (df_2024.index.month >= 6) & (df_2024.index.month <= 8)
autumn_mask = (df_2024.index.month >= 9) & (df_2024.index.month <= 12)
plt.fill_between(df_2024[winter_mask].index,
df_2024[winter_mask]['temp'] - df_2024[winter_mask]['temp_rolling_std_5'],
df_2024[winter_mask]['temp'] + df_2024[winter_mask]['temp_rolling_std_5'],
alpha=0.7, color='#FF8A65', label='High Volatility (Winter)') # Cam đậm
# Spring - Medium-High volatility
plt.fill_between(df_2024[spring_mask].index,
df_2024[spring_mask]['temp'] - df_2024[spring_mask]['temp_rolling_std_5'],
df_2024[spring_mask]['temp'] + df_2024[spring_mask]['temp_rolling_std_5'],
alpha=0.6, color='#FF9800', label='Medium-High Volatility (Spring)') # Cam vừa
# Autumn - Medium volatility
plt.fill_between(df_2024[autumn_mask].index,
df_2024[autumn_mask]['temp'] - df_2024[autumn_mask]['temp_rolling_std_5'],
df_2024[autumn_mask]['temp'] + df_2024[autumn_mask]['temp_rolling_std_5'],
alpha=0.5, color='#FFB74D', label='Medium Volatility (Autumn)') # Cam nhạt
# Summer - Lowest volatility - màu nhạt nhất
plt.fill_between(df_2024[summer_mask].index,
df_2024[summer_mask]['temp'] - df_2024[summer_mask]['temp_rolling_std_5'],
df_2024[summer_mask]['temp'] + df_2024[summer_mask]['temp_rolling_std_5'],
alpha=0.4, color='#FFE0B2', label='Low Volatility (Summer)') # Cam rất nhạt
# Vẽ đường nhiệt độ
plt.plot(df_2024.index, df_2024['temp'], color='darkred', linewidth=2,
label='Daily Temperature', marker='o', markersize=2, alpha=0.8)
# Đánh dấu các điểm volatility cao nhất
high_vol_threshold = df_2024['temp_rolling_std_5'].quantile(0.95)
high_vol_points = df_2024[df_2024['temp_rolling_std_5'] > high_vol_threshold]
plt.scatter(high_vol_points.index, high_vol_points['temp'],
color='darkblue', s=30, zorder=5, label='Peak Volatility Days')
# Customize biểu đồ
# plt.ylabel('Temperature (°C)', fontsize=12, fontweight='bold')
plt.xlabel('Date', fontsize=12, fontweight='bold')
plt.title('Winter Shows Highest Volatility (2024)',
fontsize=18, fontweight='bold', pad=20)
plt.grid(True, alpha=0.3)
# Đặt legend bên ngoài
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
# Thêm text box với key insights - đặt ngay dưới legend
insights_text = """KEY INSIGHTS:
• WINTER (Jan-Mar): Highest volatility
- Frequent cold fronts & temperature swings
- Hardest to forecast accurately
• SUMMER (Jun-Aug): Most stable period
- Consistent high temperatures
- Easiest for 5-day forecasting
• SPRING/AUTUMN: Moderate volatility
- Transition seasons with mixed patterns
• ⚠️ PEAK VOLATILITY:
- Sudden weather events like 'Gio Mua Dong Bac'"""
plt.figtext(1.05, 0.7, insights_text, fontsize=10,
bbox=dict(boxstyle="round,pad=0.65", facecolor="lightyellow", alpha=0.9),
verticalalignment='top',
transform=plt.gca().transAxes)
plt.tight_layout()
plt.show()
def plot_yearly_temperature(df):
"""
Plot yearly temperature trend for Hanoi with detailed annotations
Parameters:
-----------
df : pandas.DataFrame
DataFrame containing 'datetime' and 'temp' columns
"""
# Prepare data
df["datetime"] = pd.to_datetime(df["datetime"])
yearly_temp = df.groupby(df['datetime'].dt.year)['temp'].mean()
plt.figure(figsize=(14, 7))
# Plot main line
plt.plot(yearly_temp.index, yearly_temp.values,
linewidth=3, color='#2E86AB', marker='o',
markersize=8, markerfacecolor='#A23B72',
markeredgecolor='white', markeredgewidth=2,
label='Yearly Average Temperature')
# Find hottest year and significant dip year (2022)
hottest_year = yearly_temp.idxmax()
hottest_temp = yearly_temp.max()
dip_year = 2022
dip_temp = yearly_temp.loc[dip_year]
# Highlight hottest year
plt.annotate(f'HOTTEST: {hottest_temp:.1f}°C',
xy=(hottest_year, hottest_temp),
xytext=(hottest_year-0.5, hottest_temp+0.3),
fontsize=11, fontweight='bold', color='#F24236',
arrowprops=dict(arrowstyle='->', color='#F24236', lw=1.5),
bbox=dict(boxstyle="round,pad=0.3", facecolor='#FFE5E5', edgecolor='#F24236'))
# Highlight significant dip year (2022)
plt.annotate(f'SHARP DROP\n{dip_temp:.1f}°C',
xy=(dip_year, dip_temp),
xytext=(dip_year-0.8, dip_temp-0.5),
fontsize=10, fontweight='bold', color='#5D6D7E',
arrowprops=dict(arrowstyle='->', color='#5D6D7E', lw=1.5),
bbox=dict(boxstyle="round,pad=0.3", facecolor='#F8F9F9', edgecolor='#5D6D7E'))
# Fill area under the line
plt.fill_between(yearly_temp.index, yearly_temp.values, alpha=0.2, color='#2E86AB')
# Customize title and labels
plt.title("HANOI YEARLY AVERAGE TEMPERATURE TREND (2015-2025)",
fontsize=16, fontweight='bold', pad=20, color='#2C3E50')
# X-axis (không rotate)
plt.xlabel("YEAR", fontsize=12, fontweight='bold', color='#2C3E50', labelpad=10)
plt.xticks(range(2015, 2026), fontsize=11)
plt.xlim(2014.5, 2025.5)
# Y-axis
plt.ylim(23.2, 25.8)
plt.yticks(np.arange(23.5, 26.0, 0.5), fontsize=11)
# Background
plt.gca().set_facecolor('#FDFEFE')
# Add trend line to show clear trend
z = np.polyfit(yearly_temp.index, yearly_temp.values, 1)
p = np.poly1d(z)
plt.plot(yearly_temp.index, p(yearly_temp.index), "--",
color="#E74C3C", alpha=0.7, linewidth=2,
label=f'Trend Line')
# Add analysis text box (KEY INSIGHTS)
analysis_text = """KEY INSIGHTS:
• 2019: Hottest year (25.3°C)
• 2022: Significant temperature drop
• Trend: +{:.1f}°C per decade
• 2023-2025: Stabilized at high level""".format(z[0]*10)
plt.text(0.02, 0.98, analysis_text, transform=plt.gca().transAxes,
fontsize=10, verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='#EBF5FB', alpha=0.8, edgecolor='#3498DB'))
# Add legend ngay dưới KEY INSIGHTS
plt.legend(loc='upper left', bbox_to_anchor=(0.01, 0.8), fontsize=11, framealpha=0.9)
# Display values on each point
for year, temp in yearly_temp.items():
plt.text(year, temp-0.15, f'{temp:.1f}°C',
ha='center', va='top', fontsize=9, fontweight='bold',
bbox=dict(boxstyle="round,pad=0.2", facecolor='white', alpha=0.8))
# Adjust layout
plt.tight_layout()
plt.show()
def get_temperature_statistics(df):
"""
Get detailed temperature statistics
Parameters:
-----------
df : pandas.DataFrame
DataFrame containing 'datetime' and 'temp' columns
Returns:
--------
dict : Dictionary containing temperature statistics
"""
df["datetime"] = pd.to_datetime(df["datetime"])
yearly_temp = df.groupby(df['datetime'].dt.year)['temp'].mean()
z = np.polyfit(yearly_temp.index, yearly_temp.values, 1)
trend_per_decade = z[0] * 10
stats = {
'yearly_temperatures': yearly_temp.to_dict(),
'hottest_year': yearly_temp.idxmax(),
'hottest_temp': yearly_temp.max(),
'coolest_year': yearly_temp.idxmin(),
'coolest_temp': yearly_temp.min(),
'trend_per_decade': trend_per_decade,
'average_temperature': yearly_temp.mean()
}
return stats
def create_data_types_table(df):
"""
Create a styled table for data types overview
"""
# Analyze data types
dtype_counts = df.dtypes.value_counts()
dtype_details = []
for dtype, count in dtype_counts.items():
if 'datetime' in str(dtype):
dtype_details.append({'Data Type': 'datetime', 'Count': count, 'Examples': 'DateTime index'})
elif 'float' in str(dtype) or 'int' in str(dtype):
examples = df.select_dtypes(include=[dtype]).columns[:3].tolist()
dtype_details.append({'Data Type': 'numeric', 'Count': count, 'Examples': ', '.join(examples)})
elif 'object' in str(dtype):
examples = df.select_dtypes(include=[dtype]).columns[:3].tolist()
dtype_details.append({'Data Type': 'object', 'Count': count, 'Examples': ', '.join(examples)})
else:
examples = df.select_dtypes(include=[dtype]).columns[:3].tolist()
dtype_details.append({'Data Type': str(dtype), 'Count': count, 'Examples': ', '.join(examples)})
# Create DataFrame
dtype_df = pd.DataFrame(dtype_details)
# Apply styling
styled_dtype_table = dtype_df.style\
.set_properties(**{
'border': '1px solid #dee2e6',
'text-align': 'left',
'padding': '8px'
})\
.set_table_styles([
{'selector': 'th', 'props': [('background-color', '#6c757d'),
('color', 'white'),
('font-weight', 'bold'),
('padding', '12px')]},
{'selector': 'tr:hover', 'props': [('background-color', '#f8f9fa')]}
])\
.set_caption('📋 Data Types Overview
')\
.hide(axis='index')
return styled_dtype_table
def create_detailed_data_types_table(df):
"""
Create a more detailed table showing each column's data type
"""
dtype_info = []
for col in df.columns:
dtype = df[col].dtype
if 'datetime' in str(dtype):
category = 'datetime'
elif 'float' in str(dtype) or 'int' in str(dtype):
category = 'numeric'
elif 'object' in str(dtype):
category = 'object'
else:
category = str(dtype)
dtype_info.append({
'Column': col,
'Data Type': category,
'Specific Type': str(dtype),
'Null Count': df[col].isnull().sum(),
'Unique Values': df[col].nunique()
})
detailed_dtype_df = pd.DataFrame(dtype_info)
# Color coding for data types
def color_data_type(val):
if val == 'datetime':
return 'background-color: #e8f5e8; color: #2e7d32; font-weight: bold'
elif val == 'numeric':
return 'background-color: #e3f2fd; color: #1565c0; font-weight: bold'
elif val == 'object':
return 'background-color: #f3e5f5; color: #7b1fa2; font-weight: bold'
else:
return 'background-color: #fff3e0; color: #ef6c00; font-weight: bold'
styled_detailed_table = detailed_dtype_df.style\
.applymap(color_data_type, subset=['Data Type'])\
.background_gradient(subset=['Null Count'], cmap='Reds')\
.background_gradient(subset=['Unique Values'], cmap='Blues')\
.set_properties(**{
'border': '1px solid #dee2e6',
'text-align': 'left',
'padding': '8px'
})\
.set_table_styles([
{'selector': 'th', 'props': [('background-color', '#495057'),
('color', 'white'),
('font-weight', 'bold'),
('padding', '12px')]},
{'selector': 'tr:hover', 'props': [('background-color', '#f8f9fa')]}
])\
.set_caption('📋 Detailed Data Types Information
')
return styled_detailed_table