💾 Exporting & Summarizing Data

Every response returned by the PyDoge SDK — whether a Pydantic model or plain dictionary — supports:

• .export() — Save data to CSV, Excel, or JSON
• .to_dataframe() — Get a Pandas DataFrame
• .summary() — Generate analytic summaries: rows, nulls, types, stats

These methods are available on all major responses: - Grants - Payments - Contracts - Leases

---

📤 .export()

with DogeAPI(fetch_all=True) as api:
    grants = api.savings.get_grants()
    path = grants.export("grants_q1", format="csv")
    print(f"Saved to: {path}")

This saves a timestamped file like:

grants_q2_20250410_172308.csv

Supported Formats - csv → grants_q1_YYYYMMDD_HHMMSS.csv - xlsx → Excel spreadsheet - json → JSON array (records-style)

📊 .to_dataframe()

Convert any result into a Pandas DataFrame for analysis:

with DogeAPI() as api:
    df = api.savings.get_contracts().to_dataframe()
    print(df.head())
    print(df.shape)
    df.plot("date_closed", "savings")

This is helpful for:

• Analytics
• Plotting
• Custom filters

📈 .summary(verbose=True)

Get a full analytical breakdown of your dataset:

with DogeAPI() as api:
    grants.summary()

    # Show summary in terminal
    grants.summary(verbose=True)

    # Save the summary as markdown
    grants.summary(save_as="logs/grants_summary.md")

Output:

• Total rows and columns
• Nulls by column
• Dtypes
• Numeric stats (mean, min, max)
• Top categories
• Optional .head() preview

🧪 Example Output

📊 PyDoge Data Summary
========================================
🧾 Rows       : 2450
🧬 Columns    : 8
🕳️  Total NaNs : 17

📑 Column Data Types:
id              int64
agency         object
savings       float64

📉 Nulls by Column:
savings     7
status      10

📈 Numeric Column Stats:
         count      mean      std     min       max
savings  2443.0  151234.3  98231.4  100.0   998500.0

🔠 Top Categories:
[status]
completed     1300
cancelled      900
pending        250

📋 Sample Preview:
   id agency status savings
0   1   NASA   completed  100000.0
...