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perform_calculations

perform_calculations(
    df_in: pd.DataFrame, 
    calc: dict, 
    rename_map: dict = None
) -> pd.DataFrame
Perform calculations on a DataFrame based on a dictionary of calculation instructions. This function creates new fields by applying various operations to existing fields.
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df_in
pd.DataFrame
required
Input DataFrame.
​
calc
dict
required
Dictionary of calculation instructions where keys are new field names and values are calculation expressions (lists).
​
rename_map
dict
Optional mapping of original to renamed columns.
​
return
pd.DataFrame
DataFrame with calculations applied and new fields added.

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Supported Operations

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Arithmetic Operations

  • +, -, *, /: Basic arithmetic
  • //: Integer division
  • /0: Division with zero-safe handling
  • min, max: Element-wise minimum/maximum

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Type Conversions

  • asint, asfloat, asstr: Convert data types
  • floor, ceil, round: Rounding operations
  • abs: Absolute value

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String Operations

  • strip: Strip whitespace
  • striplzero: Strip leading zeros
  • stripkey: Strip whitespace and leading zeros
  • split_before, split_after: String splitting
  • substr: Substring extraction
  • contains, contains_case_insensitive: String contains
  • replace, replace_regex: String replacement

​
Logical Operations

  • ==, !=: Equality comparisons
  • and, or, not: Boolean logic
  • where: Conditional selection (ternary operator)
  • isin: Membership test

​
Date/Time Operations

  • datetime: Parse datetime with format string
  • datetimestr: Parse and convert to string

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Special Operations

  • set: Set a value
  • fillna, fillempty: Fill missing values
  • map: Map values using a dictionary
  • geo_area: Calculate geometric area
  • round_nearest: Round to nearest multiple
  • ?: Resolve filter expression

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Examples

# Calculate price per square foot
calc = {
    "price_per_sqft": ["/", "sale_price", "bldg_sqft"]
}

df = perform_calculations(df, calc)

# Create a flag for high-value properties
calc = {
    "is_high_value": [">", "sale_price", 500000]
}

df = perform_calculations(df, calc)

# Conditional calculation (ternary)
calc = {
    "adjusted_sqft": [
        "where",
        ["==", "is_vacant", True],
        "land_sqft",
        "bldg_sqft"
    ]
}

df = perform_calculations(df, calc)

# String manipulation
calc = {
    "property_id_clean": ["stripkey", "property_id"]
}

df = perform_calculations(df, calc)

# Date parsing
calc = {
    "sale_date_parsed": ["datetime", "sale_date_str", "%Y-%m-%d"]
}

df = perform_calculations(df, calc)

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perform_tweaks

perform_tweaks(
    df_in: pd.DataFrame, 
    tweak: list, 
    rename_map: dict = None
) -> pd.DataFrame
Perform tweaks on a DataFrame based on a list of tweak instructions. Tweaks allow you to override specific field values based on key-value mappings.
​
df_in
pd.DataFrame
required
Input DataFrame.
​
tweak
list
required
List of tweak instructions. Each entry should be a dict with:
  • field: The field to modify
  • key: The field to use for matching
  • values: Dict mapping key values to new field values
​
rename_map
dict
Optional mapping of original to renamed columns.
​
return
pd.DataFrame
DataFrame with tweaks applied.

​
Example

# Override neighborhood names for specific property IDs
tweak = [
    {
        "field": "neighborhood",
        "key": "property_id",
        "values": {
            "PROP001": "Downtown",
            "PROP002": "Midtown",
            "PROP003": "Uptown"
        }
    }
]

df = perform_tweaks(df, tweak)

# Correct zoning codes for specific parcels
tweak = [
    {
        "field": "zoning_code",
        "key": "parcel_id",
        "values": {
            "12-34-56-78": "R1",
            "23-45-67-89": "C2"
        }
    }
]

df = perform_tweaks(df, tweak)

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resolve_filter

resolve_filter(
    df: pd.DataFrame, 
    f: list, 
    rename_map: dict = None
) -> pd.Series
Resolve a filter list into a boolean Series. This function is imported from the filters module and used within calculations for conditional operations. See the Filters API documentation for detailed information.

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