Welcome to dictutils

Small, dependency-free utilities for nested dictionaries: merge, pivot, aggregate, reshape.

See it in action

Transform flat data into nested aggregations with nest_agg:

from dictutils.nestagg import nest_agg, Agg

# Sales data
sales = [
    {"region": "US", "product": "laptop", "revenue": 1200, "units": 1},
    {"region": "US", "product": "laptop", "revenue": 1200, "units": 1},
    {"region": "US", "product": "phone", "revenue": 800, "units": 1},
    {"region": "EU", "product": "laptop", "revenue": 1100, "units": 1},
    {"region": "EU", "product": "phone", "revenue": 750, "units": 1},
]

# Group by region → product with revenue totals and averages
result = nest_agg(sales, keys=["region", "product"], aggs={
    "total_revenue": Agg(map=lambda x: x["revenue"], zero=0),
    "avg_revenue": Agg(
        map=lambda x: (x["revenue"], 1),
        zero=(0, 0),
        reduce=lambda a, b: (a[0] + b[0], a[1] + b[1]),
        finalize=lambda x: x[0] / x[1]
    ),
    "units_sold": Agg(map=lambda x: x["units"], zero=0)
})

# Result:
# {
#   "US": {
#     "laptop": {"total_revenue": 2400, "avg_revenue": 1200.0, "units_sold": 2},
#     "phone": {"total_revenue": 800, "avg_revenue": 800.0, "units_sold": 1}
#   },
#   "EU": {
#     "laptop": {"total_revenue": 1100, "avg_revenue": 1100.0, "units_sold": 1},
#     "phone": {"total_revenue": 750, "avg_revenue": 750.0, "units_sold": 1}
#   }
# }

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