DataSetIQ for Python

import datasetiq as iq

# Set your API key (one-time setup)
iq.set_api_key("your_api_key_here")

# Get data as pandas DataFrame
df = iq.get("FRED/CPIAUCSL")

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

# Basic analysis
latest = df.iloc[-1]
print(f"Latest CPI: {latest['value']} on {latest['date']}")

# Calculate year-over-year inflation
df['yoy_inflation'] = df['value'].pct_change(12) * 100
print(df.tail())

import datasetiq as iq
import matplotlib.pyplot as plt

iq.set_api_key("your_api_key")

# Get CPI data
cpi = iq.get("FRED/CPIAUCSL", start="2000-01-01")

# Calculate year-over-year inflation
cpi['yoy_inflation'] = cpi['value'].pct_change(12) * 100

# Plot
plt.figure(figsize=(12, 6))
plt.plot(cpi['date'], cpi['yoy_inflation'])
plt.title('US Inflation Rate (Year-over-Year)')
plt.xlabel('Date')
plt.ylabel('Inflation (%)')
plt.grid(True, alpha=0.3)
plt.show()

# Recent inflation
recent = cpi.tail()
print(recent[['date', 'yoy_inflation']])

import datasetiq as iq
import pandas as pd

iq.set_api_key("your_api_key")

# Get multiple related series
unemployment = iq.get("FRED/UNRATE", start="2010-01-01")
inflation = iq.get("FRED/CPIAUCSL", start="2010-01-01")

# Merge on date
merged = pd.merge(
    unemployment.rename(columns={'value': 'unemployment'}),
    inflation.rename(columns={'value': 'cpi'}),
    on='date'
)

# Calculate inflation rate
merged['inflation'] = merged['cpi'].pct_change(12) * 100

# Correlation analysis
corr = merged[['unemployment', 'inflation']].corr()
print("Correlation between unemployment and inflation:")
print(corr)

import datasetiq as iq

iq.set_api_key("your_api_key")

# Get data
df = iq.get("FRED/CPIAUCSL", start="2020-01-01")

# Export to CSV
df.to_csv("cpi_data.csv", index=False)
print(f"Exported {len(df)} observations to cpi_data.csv")

# Or Excel
df.to_excel("cpi_data.xlsx", index=False, sheet_name="CPI")

import datasetiq as iq
import pandas as pd

iq.set_api_key("your_api_key")

# Search for GDP indicators
results = iq.search("gdp growth quarterly", limit=5)

# Fetch all series and combine
all_data = {}
for _, row in results.iterrows():
    series_id = row['series_id']
    title = row['title']
    
    try:
        df = iq.get(series_id, start="2020-01-01")
        all_data[title] = df.set_index('date')['value']
    except Exception as e:
        print(f"Failed to fetch {series_id}: {e}")

# Combine into one DataFrame
combined = pd.DataFrame(all_data)
print(combined.head())
combined.to_csv("gdp_indicators.csv")