DataSetIQ Python Client

Official Python SDK for DataSetIQ — The Modern Economic Data Platform

---

🚀 Features

• 40M+ Time Series: Access FRED, BLS, Census, World Bank, IMF, OECD, and more
• Pandas-Ready: Returns clean DataFrames with date index
• Intelligent Caching: Disk-based caching with TTL (24h default)
• Automatic Retries: Exponential backoff with Retry-After support
• Free Tier: 25 requests/minute + 25 AI insights/month
• Type-Safe Errors: Helpful exception messages with upgrade paths

---

📦 Installation

pip install datasetiq

Requirements: Python 3.9+

---

🔑 Quick Start

1. Get Your Free API Key

Visit datasetiq.com/dashboard/api-keys to create a free account and generate your API key.

2. Fetch Economic Data

import datasetiq as iq

# Set your API key
iq.set_api_key("diq_your_key_here")

# Get time series data as a Pandas DataFrame
df = iq.get("fred-cpi")
print(df.head())

Output:

            value
date             
1947-01-01  21.48
1947-02-01  21.62
1947-03-01  22.00
1947-04-01  22.00
1947-05-01  21.95

3. Plot It

import matplotlib.pyplot as plt

df['value'].plot(title="Consumer Price Index", figsize=(12, 6))
plt.ylabel("CPI")
plt.show()

---

📖 API Reference

Core Functions

get(series_id, start=None, end=None, dropna=False)

Fetch time series data as a Pandas DataFrame.

Parameters:

• series_id (str): Series identifier (e.g., "fred-cpi", "bls-unemployment")
• start (str, optional): Start date in YYYY-MM-DD format
• end (str, optional): End date in YYYY-MM-DD format
• dropna (bool): Drop rows with NaN values (default: False)

Returns: pd.DataFrame with date index and value column

Example:

# Get recent data
df = iq.get("fred-gdp", start="2020-01-01", end="2023-12-31")

# Preserve data gaps (default)
df = iq.get("fred-cpi", dropna=False)

# Drop missing values
df = iq.get("fred-cpi", dropna=True)

---

search(query, limit=10, offset=0)

Search for datasets by keyword.

Parameters:

• query (str): Search term (searches titles, descriptions, IDs)
• limit (int): Max results to return (default: 10, max: 10)
• offset (int): Pagination offset (default: 0)

Returns: pd.DataFrame with columns: id, slug, title, description, provider, frequency, start_date, end_date, last_updated

Example:

results = iq.search("unemployment rate")
print(results[["id", "title", "provider"]])

# Output:
#              id                          title provider
# 0  fred-unrate        Unemployment Rate (U.S.)     FRED
# 1  bls-lns14000000  Labor Force: Unemployed       BLS

---

Configuration

set_api_key(api_key)

Set your DataSetIQ API key.

iq.set_api_key("diq_your_key_here")

---

configure(**options)

Customize client behavior.

Options:

• api_key (str): Your API key
• base_url (str): API base URL (default: https://www.datasetiq.com/api/public)
• timeout (tuple): (connect_timeout, read_timeout) in seconds (default: (3.05, 30))
• max_retries (int): Max retry attempts (default: 3)
• max_retry_sleep (int): Cap total backoff time in seconds (default: 20)
• anon_max_pages (int): Safety limit for anonymous pagination (default: 200)
• data_cache_ttl (int): Cache TTL for time series data in seconds (default: 86400 / 24h)
• search_cache_ttl (int): Cache TTL for search results in seconds (default: 900 / 15m)
• enable_cache (bool): Enable/disable disk caching (default: True)

Example:

iq.configure(
    api_key="diq_your_key_here",
    max_retries=5,
    data_cache_ttl=3600,  # 1 hour cache
    enable_cache=True
)

---

Cache Management

clear_cache()

Clear all cached data.

count = iq.clear_cache()
print(f"Cleared {count} cached files")

get_cache_size()

Get cache statistics.

file_count, total_bytes = iq.get_cache_size()
print(f"Cache: {file_count} files, {total_bytes / 1024 / 1024:.2f} MB")

---

🔐 Authentication Modes

Authenticated Mode (Recommended)

With API Key:

• ✅ Full CSV exports (all observations)
• ✅ Higher rate limits (25-500 RPM based on plan)
• ✅ Access to AI insights and premium features
• ✅ Date filtering support

iq.set_api_key("diq_your_key_here")
df = iq.get("fred-cpi")  # Full dataset

Anonymous Mode

Without API Key:

• ⚠️ Returns latest 100 observations only (most recent data)
• ⚠️ Lower rate limits (5 RPM)
• ⚠️ Metadata-only for some datasets
• ⚠️ No date filtering support

# No API key set
df = iq.get("fred-cpi")  # Latest 100 observations only
print(df.tail())  # Most recent data points

---

🛡️ Error Handling

All errors include helpful marketing messages to guide you toward solutions.

Authentication Required (401)

try:
    df = iq.get("fred-cpi")
except iq.AuthenticationError as e:
    print(e)
    # Output:
    # [UNAUTHORIZED] Authentication required
    #
    # 🔑 GET YOUR FREE API KEY:
    #    → https://www.datasetiq.com/dashboard/api-keys
    # ...

Rate Limit Exceeded (429)

try:
    df = iq.get("fred-cpi")
except iq.RateLimitError as e:
    print(e)
    # Output:
    # [RATE_LIMITED] Rate limit exceeded: 26/25 requests this minute
    #
    # ⚡ RATE LIMIT REACHED:
    #    26/25 requests this minute
    #
    # 🚀 INCREASE YOUR LIMITS:
    #    → https://www.datasetiq.com/pricing
    # ...

Quota Exceeded (429)

try:
    # Generate 26th basic insight on free plan
    pass
except iq.QuotaExceededError as e:
    print(e.metric)  # "insight_basic"
    print(e.current)  # 26
    print(e.limit)  # 25

Series Not Found (404)

try:
    df = iq.get("invalid-series-id")
except iq.NotFoundError as e:
    print(e)
    # Output:
    # [NOT_FOUND] Series not found
    #
    # 🔍 SERIES NOT FOUND
    #
    # 💡 TIP: Search for series first:
    #    import datasetiq as iq
    #    results = iq.search('unemployment rate')
    # ...

---

📊 Advanced Examples

Comparing Multiple Series

import datasetiq as iq
import pandas as pd

# Fetch multiple series
cpi = iq.get("fred-cpi", start="2020-01-01")
gdp = iq.get("fred-gdp", start="2020-01-01")

# Merge on date
df = pd.merge(
    cpi.rename(columns={"value": "CPI"}),
    gdp.rename(columns={"value": "GDP"}),
    left_index=True,
    right_index=True,
    how="outer"
)

print(df.head())

Calculate Year-over-Year Change

df = iq.get("fred-cpi", start="2015-01-01")

# Calculate YoY % change
df['yoy_change'] = df['value'].pct_change(periods=12) * 100

print(df.tail())

Export to Excel

df = iq.get("fred-gdp")
df.to_excel("gdp_data.xlsx")

---

🧪 Development

Setup

git clone https://github.com/DataSetIQ/datasetiq-python.git
cd datasetiq-python
pip install -e ".[dev]"

Run Tests

pytest

Code Formatting

black datasetiq tests
ruff check datasetiq tests

---

🗺️ Roadmap

• Add get_insight() for AI-generated analysis
• Support batch requests: iq.get_many(["fred-cpi", "fred-gdp"])
• Async support: await iq.get_async("fred-cpi")
• Streaming for large datasets
• Jupyter notebook integration (progress bars)

---

📚 Resources

• Homepage: datasetiq.com
• API Keys: datasetiq.com/dashboard/api-keys
• Documentation: datasetiq.com/docs
• Pricing: datasetiq.com/pricing
• GitHub: github.com/DataSetIQ/datasetiq-python
• Support: support@datasetiq.com

---

📄 License

MIT License — See LICENSE for details.

---

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

---

Made with ❤️ by DataSetIQ