electricsheepafrica/africa-economic-calendar-2020-2025

Africa Economic Calendar Dataset

Dataset Description

This dataset contains 111,450 macroeconomic events from 18 African countries, spanning from 2020 to 2025.

The dataset mirrors professional economic calendars used by traders, economists, and analysts to track key economic indicators that move financial markets. Each event includes its scheduled release time, geographical zone, currency, importance level, and actual, forecast, and previous values when available.

Key Statistics

• Total Records: 111,450
• Countries Covered: 18 African nations
• Date Range: January 1, 2020 - October 1, 2025
• File Formats: CSV (6.4 MB), Parquet (0.13 MB)

Use Cases

You can use this dataset for:

📈 Economic forecasting and sentiment analysis

• Predict economic trends based on historical indicator releases
• Analyze market sentiment around key economic events

💹 Financial market event-driven modeling

• Build trading strategies based on economic data releases
• Study market reactions to economic announcements

🧠 Machine learning applications on macroeconomic signals

• Train models to predict economic indicators
• Classification tasks for event importance
• Time series forecasting

🌍 Comparative studies of country-level indicators

• Cross-country economic analysis
• Regional economic trend identification
• Policy impact studies

Dataset Structure

Columns

Column	Type	Description
id	integer	Unique event identifier
date	datetime	Date of the economic event (YYYY-MM-DD)
time	string	Time of event release or "All Day"
zone	string	Country name (lowercase)
currency	string	Currency code (e.g., USD, EUR, ZAR)
importance	string	Event importance level
event	string	Description of the economic event
actual	float	Actual value reported (when available)
forecast	float	Forecasted value (when available)
previous	float	Previous period value (when available)

Countries Included (18)

Country	Records	Percentage
Egypt	24,366	21.9%
Tunisia	21,221	19.0%
Rwanda	7,154	6.4%
Tanzania	6,564	5.9%
South Africa	6,087	5.5%
Zimbabwe	5,491	4.9%
Morocco	5,385	4.8%
Nigeria	5,215	4.7%
Botswana	5,145	4.6%
Uganda	5,045	4.5%
Malawi	4,476	4.0%
Namibia	4,060	3.6%
Kenya	3,967	3.6%
Zambia	3,517	3.2%
Mauritius	3,494	3.1%
Angola	124	0.1%
Ghana	99	0.1%
Mozambique	40	0.0%

Regional Coverage

• North Africa: Egypt, Tunisia, Morocco (50,972 records - 45.7%)
• East Africa: Kenya, Tanzania, Uganda, Rwanda (22,730 records - 20.4%)
• Southern Africa: South Africa, Botswana, Zimbabwe, Namibia, Malawi, Mauritius, Zambia (32,275 records - 29.0%)
• West Africa: Nigeria, Ghana (5,314 records - 4.8%)
• Central Africa: Angola, Mozambique (164 records - 0.1%)

Data Loading

Using Pandas (CSV)

import pandas as pd

# Load CSV
df = pd.read_csv("hf://datasets/electricsheepafrica/africa-economic-calendar/africa_economic_calendar.csv")

# Convert date column to datetime
df['date'] = pd.to_datetime(df['date'])

# View first few rows
print(df.head())

Using Pandas (Parquet - Recommended)

import pandas as pd

# Load Parquet (faster and more efficient)
df = pd.read_parquet("hf://datasets/electricsheepafrica/africa-economic-calendar/africa_economic_calendar.parquet")

# View dataset info
print(df.info())
print(f"Total events: {len(df):,}")

Using Hugging Face Datasets

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("electricsheepafrica/africa-economic-calendar", split="train")

# Convert to pandas
df = dataset.to_pandas()

Example Usage

Filter Events by Country

# Get all events for Egypt
egypt_events = df[df['zone'] == 'egypt']
print(f"Egypt has {len(egypt_events):,} events")

Analyze Event Importance

# Distribution of event importance
importance_dist = df['importance'].value_counts()
print(importance_dist)

Time Series Analysis

import matplotlib.pyplot as plt

# Events over time by country
events_by_month = df.groupby([df['date'].dt.to_period('M'), 'zone']).size()
events_by_month.unstack().plot(figsize=(14, 8))
plt.title('Economic Events Over Time by Country')
plt.xlabel('Month')
plt.ylabel('Number of Events')
plt.show()

Filter High-Impact Events

# Get events with actual values reported
events_with_actuals = df[df['actual'].notna()]
print(f"Events with actual values: {len(events_with_actuals):,}")

Data Source and Methodology

This dataset is extracted from a comprehensive global economic calendar dataset containing over 400,000 macroeconomic events across more than 90 countries and regions. The Africa-focused subset was created by filtering for the following African countries present in the original dataset:

• Countries were identified based on the zone column
• 18 African countries were found in the original dataset
• All events associated with these countries were extracted
• Data spans from 2020 to 2025

Limitations

• Coverage Variability: Data availability varies significantly by country. Egypt and Tunisia have the most comprehensive coverage, while countries like Mozambique, Ghana, and Angola have limited data.
• Missing Values: Many events have missing values for actual, forecast, or previous fields, particularly for holidays and non-quantitative events.
• Importance Levels: Event importance classifications may not be standardized across all countries.
• Time Zones: Event times may not be standardized to a single time zone.

Potential Applications

1. Economic Forecasting

• Predict GDP growth, inflation rates, and other macroeconomic indicators
• Build country-specific forecasting models

2. Event Study Analysis

• Analyze market reactions to economic data releases
• Study the impact of economic surprises (actual vs forecast)

3. Comparative Economics

• Compare economic indicators across African countries
• Identify regional economic trends and patterns

4. Machine Learning Projects

• Classification: Predict event importance or market impact
• Regression: Forecast actual values based on historical patterns
• Time Series: Model temporal dependencies in economic indicators

5. Data Journalism and Visualization

• Create interactive dashboards of African economic indicators
• Track economic recovery post-pandemic across the continent

Citation

If you use this dataset in your research or projects, please cite:

@dataset{africa_economic_calendar_2025,
  title={Africa Economic Calendar Dataset},
  author={Electric Sheep Africa},
  year={2025},
  url={https://huggingface.co/datasets/electricsheepafrica/africa-economic-calendar}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Contact

For questions, issues, or suggestions regarding this dataset, please open an issue on the Hugging Face dataset repository.

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Last Updated: November 2025