Live Data Feeds Active
Nigerian High-Frequency
Retail Pricing Index
Ground-truth alternative data tracking fast-moving consumer goods (FMCG) and construction staples across Nigerian digital storefronts. Engineered for quantitative inflation models.
Built for Quantitative Ingestion
Our master panels are pre-cleaned and standardized. Drop them directly into your Python/pandas environment without writing a single line of regex or cleaning logic.
Zero missing values
Strict Float casting
backtest_model.py
import pandas as pd
import matplotlib.pyplot as plt
# 1. Load the LagosMetrics Master Panel
df = pd.read_csv('lagosmetrics_master_panel.csv')
# 2. Convert timestamps to datetime index
df['timestamp_iso'] = pd.to_datetime(df['timestamp_iso'])
df.set_index('timestamp_iso', inplace=True)
# 3. Isolate construction staples for trend analysis
cement_df = df[df['category'] == 'construction_cement']
monthly_avg = cement_df['price_ngn'].resample('ME').mean()
print(f"Data loaded: {len(df)} rows ready for analysis.")
Data Dictionary
Standardized schema for seamless quantitative ingestion.
| Column Name | Data Type | Description | Sample Value |
|---|---|---|---|
| item_id | String | Unique identifier assigned to the scraped item | JUM-STAPLES_RICE-1 |
| category | String | Broad product classification mapped to index | staples_rice |
| product_name | String | Exact string title of the product listing as scraped | Dangote Cement 50kg |
| price_ngn | Float | Numerical retail price normalized to Nigerian Naira | 10500.0 |
| currency | String | Base currency of the numerical price | NGN |
| stock_status | String | Current availability status on the host platform | In Stock |
| source_platform | String | Origin marketplace where data was extracted | Jumia NG |
| timestamp_iso | Datetime | Exact extraction time in ISO 8601 format (UTC) | 2026-08-01T12:34:59Z |