Data Job Trends in New Zealand

from pytrends.request import TrendReq
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime, timedelta

# Initialize pytrends
pytrends = TrendReq(hl='en-NZ', tz=360)

# Define the time period (last 5 years)
end_date = datetime.now()
start_date = end_date - timedelta(days=365*5)
timeframe = f'{start_date.strftime("%Y-%m-%d")} {end_date.strftime("%Y-%m-%d")}'

# Search terms for data related roles in NZ
search_terms = [ 'data engineer', 'analytics engineer', 'ai engineer', 'product engineer']

# Fetch interest over time
print(f"Fetching Google Trends data for: {search_terms}")
print(f"Time period: {timeframe}")
pytrends.build_payload(kw_list=search_terms, timeframe=timeframe, geo='NZ')
df = pytrends.interest_over_time()

# Remove the 'isPartial' column if it exists
if 'isPartial' in df.columns:
    df = df.drop('isPartial', axis=1)

print(f"Data shape: {df.shape}")
print(f"Data range: {df.index.min()} to {df.index.max()}")

# Filter out search terms with all zero values
non_zero_columns = [col for col in df.columns if df[col].sum() > 0]
df_filtered = df[non_zero_columns]

print(f"\nSearch terms with data: {non_zero_columns}")
print(f"Search terms filtered out (no data): {[term for term in search_terms if term not in non_zero_columns]}")

# Define colorblind-friendly palette (ColorBrewer Dark2)
# These colors are distinguishable for people with color blindness
colors = ['#1b9e77', '#d95f02', '#7570b3', '#e7298a', '#66a61e']
term_colors = {term: colors[i % len(colors)] for i, term in enumerate(non_zero_columns)}

Fetching Google Trends data for: ['data engineer', 'analytics engineer', 'ai engineer', 'product engineer']
Time period: 2021-02-18 2026-02-17
Data shape: (262, 4)
Data range: 2021-02-14 00:00:00 to 2026-02-15 00:00:00

Search terms with data: ['data engineer', 'analytics engineer', 'ai engineer', 'product engineer']
Search terms filtered out (no data): []

# Create stacked bar chart over time with absolute values
fig, ax = plt.subplots(figsize=(14, 7))

# Create the stacked bar chart using raw filtered data (absolute values)
df_filtered.plot(
    kind='bar',
    stacked=True,
    ax=ax,
    color=[term_colors[term] for term in non_zero_columns],
    width=0.8,
    edgecolor='white',
    linewidth=0.5
)

ax.set_title('Search Trends for Data Roles in New Zealand - Absolute Interest Over Time (Last 5 Years)',
             fontsize=14, fontweight='bold', pad=20)
ax.set_xlabel('Date', fontsize=12)
ax.set_ylabel('Search Interest (Google Trends Index)', fontsize=12)
ax.legend(title='Job Role', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=10)
ax.grid(True, alpha=0.3, axis='y')

# Format x-axis to show fewer labels for readability
n_ticks = 12  # Show approximately 12 date labels
tick_positions = range(0, len(df_filtered), max(1, len(df_filtered) // n_ticks))
ax.set_xticks(tick_positions)
ax.set_xticklabels([df_filtered.index[i].strftime('%Y-%m') for i in tick_positions], rotation=45, ha='right')

plt.tight_layout()
plt.show()

# Create individual line charts for each job role
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
axes = axes.flatten()

for idx, term in enumerate(non_zero_columns[:4]):
    ax = axes[idx]
    ax.plot(df_filtered.index, df_filtered[term],
            color=term_colors[term], linewidth=2.5, marker='o', markersize=4)
    ax.fill_between(df_filtered.index, df_filtered[term], alpha=0.3, color=term_colors[term])

    ax.set_title(f'{term.title()}', fontsize=12, fontweight='bold')
    ax.set_xlabel('Date', fontsize=10)
    ax.set_ylabel('Search Interest (Google Trends Index)', fontsize=10)
    ax.grid(True, alpha=0.3)
    ax.tick_params(axis='x', rotation=45)

# Hide any unused subplots if fewer than 4 terms
for idx in range(len(non_zero_columns), 4):
    axes[idx].set_visible(False)

fig.suptitle('Individual Search Trends for Data Roles in New Zealand (Last 5 Years)',
             fontsize=14, fontweight='bold', y=1.00)
plt.tight_layout()
plt.show()