In [88]:
# Visualize the quarterly time series data
fig, axes = plt.subplots(2, 2, figsize=(16, 12))
fig.suptitle('Kachin State Quarterly Time Series Analysis', fontsize=16, fontweight='bold')
# 1. Basic quarterly events line chart
axes[0, 0].plot(range(len(kachin_time_series)), kachin_time_series['Events'],
linewidth=3, marker='o', markersize=6, color='red', alpha=0.8)
axes[0, 0].set_title('Quarterly Events in Kachin State', fontweight='bold')
axes[0, 0].set_xlabel('Quarter Index')
axes[0, 0].set_ylabel('Number of Events')
axes[0, 0].grid(True, alpha=0.3)
axes[0, 0].tick_params(axis='x', rotation=45)
# Add quarter labels
axes[0, 0].set_xticks(range(len(kachin_time_series)))
axes[0, 0].set_xticklabels(kachin_time_series['Time'], rotation=45, ha='right')
# 2. Quarterly fatalities
axes[0, 1].plot(range(len(extended_time_series)), extended_time_series['Fatalities'],
linewidth=3, marker='s', markersize=6, color='darkred', alpha=0.8)
axes[0, 1].set_title('Quarterly Fatalities in Kachin State', fontweight='bold')
axes[0, 1].set_xlabel('Quarter Index')
axes[0, 1].set_ylabel('Number of Fatalities')
axes[0, 1].grid(True, alpha=0.3)
axes[0, 1].set_xticks(range(len(extended_time_series)))
axes[0, 1].set_xticklabels(extended_time_series['Time'], rotation=45, ha='right')
# 3. Events vs Fatalities scatter plot
axes[1, 0].scatter(extended_time_series['Events'], extended_time_series['Fatalities'],
s=100, color='blue', alpha=0.7, edgecolors='black')
axes[1, 0].set_title('Events vs Fatalities Correlation', fontweight='bold')
axes[1, 0].set_xlabel('Number of Events')
axes[1, 0].set_ylabel('Number of Fatalities')
axes[1, 0].grid(True, alpha=0.3)
# Add correlation coefficient
correlation = extended_time_series['Events'].corr(extended_time_series['Fatalities'])
axes[1, 0].text(0.05, 0.95, f'Correlation: {correlation:.3f}',
transform=axes[1, 0].transAxes, fontsize=12,
bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
# 4. Bar chart of quarterly events
bars = axes[1, 1].bar(range(len(kachin_time_series)), kachin_time_series['Events'],
color='orange', alpha=0.7, edgecolor='black', linewidth=1)
axes[1, 1].set_title('Quarterly Events Bar Chart', fontweight='bold')
axes[1, 1].set_xlabel('Quarter')
axes[1, 1].set_ylabel('Number of Events')
axes[1, 1].grid(True, alpha=0.3, axis='y')
axes[1, 1].set_xticks(range(len(kachin_time_series)))
axes[1, 1].set_xticklabels(kachin_time_series['Time'], rotation=45, ha='right')
# Add value labels on bars
for i, bar in enumerate(bars):
height = bar.get_height()
axes[1, 1].text(bar.get_x() + bar.get_width()/2., height + 0.1,
f'{int(height)}', ha='center', va='bottom', fontweight='bold')
plt.tight_layout()
plt.show()
# Display the final clean dataset
print("\nFINAL TIME SERIES DATASET FOR ANALYSIS:")
print("=" * 50)
print("Two-column dataset (Time, Events):")
print(kachin_time_series)
print(f"\nDataset ready for time series analysis!")
print(f"Use 'kachin_time_series' variable or load from 'kachin_quarterly_events.csv'")
FINAL TIME SERIES DATASET FOR ANALYSIS:
==================================================
Two-column dataset (Time, Events):
Time Events
0 2010Q2 1
1 2011Q2 16
2 2011Q3 6
3 2011Q4 34
4 2012Q1 5
5 2012Q2 11
6 2012Q3 5
7 2012Q4 10
8 2013Q1 7
9 2013Q3 5
10 2013Q4 9
11 2014Q1 4
12 2014Q2 2
13 2014Q3 1
14 2014Q4 2
15 2015Q1 2
16 2015Q2 8
17 2015Q3 2
18 2015Q4 9
19 2016Q2 8
20 2016Q3 9
21 2016Q4 27
22 2017Q1 11
23 2017Q2 3
24 2017Q3 9
25 2017Q4 8
26 2018Q1 12
27 2018Q2 15
28 2018Q3 10
29 2018Q4 3
30 2019Q1 2
31 2019Q2 2
32 2020Q3 1
33 2021Q1 7
34 2021Q2 65
35 2021Q3 20
36 2021Q4 13
37 2022Q1 15
38 2022Q2 9
39 2022Q3 23
40 2022Q4 27
41 2023Q1 32
42 2023Q2 32
43 2023Q3 50
44 2023Q4 19
45 2024Q1 33
46 2024Q2 53
47 2024Q3 22
48 2024Q4 17
49 2025Q1 56
50 2025Q2 43
51 2025Q3 34
Dataset ready for time series analysis!
Use 'kachin_time_series' variable or load from 'kachin_quarterly_events.csv'
