quantec 0.3.0

A Python package for working with Quantec EasyData.

Quantec

A Python package for working with Quantec EasyData API. Fetch economic time series data with support for caching and advanced filtering.

⚠️ Early Development Notice: This package is in early development and may undergo breaking changes without backwards compatibility until version 1.0 is reached.

Features

• 🎯 Multiple Data Access: Time series codes, selections, and grid/pivot data
• 📊 Format Support: CSV for time series, CSV/Parquet for grid data
• 📈 Multiple Frequencies: Monthly (M), Quarterly (Q), and Annual (A) frequencies
• 🔍 Advanced Filtering: Dimension-based filtering for grid data
• ⚡ Performance Caching: Optional caching for grid data
• 🛡️ Error Handling: Comprehensive network and API error handling
• 🔧 Flexible Configuration: Environment variables and parameter setup

Installation

pip install quantec

Quick Start

from quantec.easydata.client import Client

# Initialize client
client = Client()

# Get time series data (CSV format only)
data = client.get_data(time_series_codes="NMS-EC_BUS,NMS-GA_BUS")
print(data.head())

Configuration

Environment Variables

export EASYDATA_API_KEY="your-api-key-here"
export EASYDATA_API_URL="https://www.easydata.co.za/api/v3/"

Client Options

from quantec.easydata.client import Client

# Basic client (uses environment variables)
client = Client()

# With caching enabled
client = Client(
    use_cache=True,          # Enable caching for grid data
    cache_dir="./cache"      # Cache directory path
)

Time Series Data

Direct Access with Codes

# Single time series
data = client.get_data(time_series_codes="NMS-EC_BUS")

# Multiple time series
data = client.get_data(time_series_codes="NMS-EC_BUS,NMS-GA_BUS")

# With date filtering and frequency
data = client.get_data(
    time_series_codes="NMS-EC_BUS,NMS-GA_BUS",
    freq="Q",           # Quarterly data
    start_year="2020",  # Year format only
    end_year="2023"
)

Discovery-Based Access

# Find available selections
selections = client.get_selections(status="PSO")  # Private, Shared, Open

# Use selection for data retrieval (returns DataFrame)
if len(selections) > 0:
    selection_pk = selections.iloc[0]['pk']
    data = client.get_data(selection_pk=selection_pk)

Grid/Pivot Data

Basic Grid Data Access

# Get available recipes
recipes = client.get_recipes()

# Basic grid data retrieval
if len(recipes) > 0:
    recipe_id = recipes.iloc[0]['id']
    grid_data = client.get_grid_data(recipe_pk=recipe_id)

Grid Data with Filtering

# Filter by dimension levels only
filters = {"dimension": "d3", "levels": [2], "codes": []}
grid_data = client.get_grid_data(recipe_pk=1066, selectdimensionnodes=filters)

# Filter by levels and specific codes
filters = {"dimension": "d3", "levels": [1], "codes": ["TRD01-R_FI"]}
grid_data = client.get_grid_data(recipe_pk=1066, selectdimensionnodes=filters)

Format Options (CSV/Parquet only)

# DataFrame format (default)
df_data = client.get_grid_data(recipe_pk=1066)

# CSV format 
csv_data = client.get_grid_data(recipe_pk=1066, resp_format="csv")

# Parquet format (recommended for large datasets)
parquet_data = client.get_grid_data(recipe_pk=1066, resp_format="parquet")

Caching (Grid Data Only)

# Initialize client with caching
cached_client = Client(use_cache=True, cache_dir="./cache")

# First call - fetches from API and caches
grid_data = cached_client.get_grid_data(recipe_pk=1066)

# Subsequent calls - loads from cache (faster)
grid_data = cached_client.get_grid_data(recipe_pk=1066)

Error Handling

import requests
from quantec.easydata.client import Client

client = Client()

try:
    data = client.get_data(time_series_codes="INVALID_CODE")
except requests.HTTPError as e:
    print(f"API Error: {e}")
except ValueError as e:
    print(f"Parameter Error: {e}")

Complete Example

from quantec.easydata.client import Client

# Initialize client with caching
client = Client(use_cache=True, cache_dir="./cache")

# 1. Get time series data
ts_data = client.get_data(
    time_series_codes="NMS-EC_BUS,NMS-GA_BUS",
    freq="Q",
    start_year="2020"
)

# 2. Get available recipes and grid data
recipes = client.get_recipes()
if len(recipes) > 0:
    grid_data = client.get_grid_data(
        recipe_pk=recipes.iloc[0]['id'],
        resp_format="parquet"
    )

# 3. Get selections for discovery
selections = client.get_selections(status="PSO")

Important Notes

• Time series data: Only supports CSV format
• Grid data: Supports DataFrame (default), CSV and Parquet formats
• Date parameters: Use year format only (e.g., "2020", not "2020-01-01")
• Caching: Only available for grid data
• Dimension filtering: Must provide at least one of: codes, levels, children, or children_include_self

License

MIT License - see the LICENSE file for details.

Support

For support, contact Quantec