Skip to content

Minimum

Minimum(dataset, column_list=None, filter_condition_dict=None, dataset_filter_by_data_type=None, duckdb_connection=None, decimal_places=2)

Calculate minimum values for specified numeric columns in a dataset using DuckDB.

This function analyzes numeric columns to find their minimum values. It supports filtering by column names and/or data types, and can process the data through specified conditions before calculating minimums. Only numeric-type columns (INTEGER, DECIMAL, FLOAT, etc.) will be processed.

Returns:

Type Description
List[Dict[str, Union[str, float, dict, None]]]

List[Dict[str, Union[str, float, dict, None]]]: A list of dictionaries with the following keys: - column_name (str): Name of the analyzed column - min_value (float): Minimum value found in the column, rounded to specified decimal places - table_name (str): Name of the analyzed table - execution_timestamp_utc (str): Timestamp of execution in UTC - filter_conditions (dict|None): Applied filter conditions if any - filtered_by_data_type (list|None): Data types used for filtering if any

Parameters:

Name Type Description Default
dataset Any

Input dataset that can be either: - A DataFrame (pandas, polars) or other DuckDB-compatible data structure - A string representing an existing table name in the DuckDB connection When providing a DataFrame along with duckdb_connection, the DataFrame will be registered as a temporary table in that connection.

required
column_list Optional[List[str]]

List of column names to analyze. Can be used together with dataset_filter_by_data_type. Only numeric columns from this list will be processed. Defaults to None. Example: ['price', 'quantity', 'weight']

None
filter_condition_dict Optional[Dict[str, Union[str, int, float]]]

Dictionary of filter conditions to apply before calculating minimum values. Format: {'column_name': value}. Supports string, integer, and float values. Example: {'category': 'electronics', 'status': 'active'} Defaults to None.

None
dataset_filter_by_data_type Optional[List[str]]

Data type(s) to filter columns. Can be used together with column_list. The function will analyze all numeric columns that match these data types. Case-insensitive. Defaults to None. Example: ['INTEGER'] or ['DECIMAL', 'FLOAT']

None
duckdb_connection Optional[DuckDBPyConnection]

Existing DuckDB connection. If None, a new connection will be created and closed after execution. Can be used with either a table name string or a DataFrame input. Defaults to None.

None
decimal_places int

Number of decimal places to round the minimum values. Applies to all numeric results. Defaults to 2. Example: With decimal_places=2, 10.543 becomes 10.54

2

Raises:

Type Description
ValueError

In the following cases: - Neither column_list nor dataset_filter_by_data_type is provided - Invalid column names in column_list or filter_condition_dict - No columns match the specified data types - Failed to register or access the dataset - Non-numeric columns specified for analysis

TypeError

If decimal_places is not an integer

Source code in src/whistlingduck/analyzers/Minimum.py
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
def Minimum(dataset: Any, 
           column_list: Optional[List[str]] = None, 
           filter_condition_dict: Optional[Dict[str, Union[str, int, float]]] = None,
           dataset_filter_by_data_type: Optional[List[str]] = None,
           duckdb_connection: Optional[DuckDBPyConnection] = None,
           decimal_places: int = 2
          ) -> List[Dict[str, Union[str, float, dict, None]]]:
    """
    Calculate minimum values for specified numeric columns in a dataset using DuckDB.

    This function analyzes numeric columns to find their minimum values. It supports filtering
    by column names and/or data types, and can process the data through specified conditions
    before calculating minimums. Only numeric-type columns (INTEGER, DECIMAL, FLOAT, etc.) 
    will be processed.


    Returns:
        List[Dict[str, Union[str, float, dict, None]]]: A list of dictionaries with the following keys:
            - column_name (str): Name of the analyzed column
            - min_value (float): Minimum value found in the column, rounded to specified decimal places
            - table_name (str): Name of the analyzed table
            - execution_timestamp_utc (str): Timestamp of execution in UTC
            - filter_conditions (dict|None): Applied filter conditions if any
            - filtered_by_data_type (list|None): Data types used for filtering if any

    Args:
        dataset (Any): Input dataset that can be either:
            - A DataFrame (pandas, polars) or other DuckDB-compatible data structure
            - A string representing an existing table name in the DuckDB connection
            When providing a DataFrame along with duckdb_connection, the DataFrame will be
            registered as a temporary table in that connection.

        column_list (Optional[List[str]], optional): List of column names to analyze.
            Can be used together with dataset_filter_by_data_type. Only numeric columns
            from this list will be processed. Defaults to None.
            Example: ['price', 'quantity', 'weight']

        filter_condition_dict (Optional[Dict[str, Union[str, int, float]]], optional): 
            Dictionary of filter conditions to apply before calculating minimum values.
            Format: {'column_name': value}. Supports string, integer, and float values.
            Example: {'category': 'electronics', 'status': 'active'}
            Defaults to None.

        dataset_filter_by_data_type (Optional[List[str]], optional): 
            Data type(s) to filter columns. Can be used together with column_list.
            The function will analyze all numeric columns that match these data types.
            Case-insensitive. Defaults to None.
            Example: ['INTEGER'] or ['DECIMAL', 'FLOAT']

        duckdb_connection (Optional[DuckDBPyConnection], optional): Existing DuckDB connection.
            If None, a new connection will be created and closed after execution.
            Can be used with either a table name string or a DataFrame input.
            Defaults to None.

        decimal_places (int, optional): Number of decimal places to round the minimum values.
            Applies to all numeric results. Defaults to 2.
            Example: With decimal_places=2, 10.543 becomes 10.54

    Raises:
        ValueError: In the following cases:
            - Neither column_list nor dataset_filter_by_data_type is provided
            - Invalid column names in column_list or filter_condition_dict
            - No columns match the specified data types
            - Failed to register or access the dataset
            - Non-numeric columns specified for analysis
        TypeError: If decimal_places is not an integer

    """

    if decimal_places < 0:
        raise ValueError("decimal_places must be non-negative")

    # Generate UUID for table name and get UTC timestamp
    unique_id = str(uuid.uuid4()).replace('-', '_')
    timestamp = datetime.now(timezone.utc)
    temp_table_name = f"minimum_{unique_id}"

    if column_list is None and dataset_filter_by_data_type is None:
        raise ValueError(
            "Please provide either a list of columns using column_list or specify "
            "data type(s) using dataset_filter_by_data_type."
        )

    # Handle DuckDB connection and table registration
    if duckdb_connection is None:
        con = duckdb.connect()
        try:
            con.register(temp_table_name, dataset)
            source_table = temp_table_name
        except Exception as e:
            con.close()
            raise ValueError(f"Failed to register dataset: {str(e)}. Please ensure the dataset is in a DuckDB-compatible format.")
    else:
        con = duckdb_connection
        if isinstance(dataset, str):
            try:
                con.sql(f"PRAGMA table_info('{dataset}')")
                source_table = dataset
            except duckdb.CatalogException:
                raise ValueError(f"Table '{dataset}' does not exist in the DuckDB connection")
        else:
            try:
                con.register(temp_table_name, dataset)
                source_table = temp_table_name
            except Exception as e:
                raise ValueError(
                    f"Failed to register dataset with existing connection: {str(e)}. "
                    "Please ensure the dataset is in a DuckDB-compatible format."
                )

    # Get table info
    dtype_info = con.sql(f"PRAGMA table_info('{source_table}')").pl()
    dataset_columns = dtype_info['name'].to_list()

    # Initialize final column list
    final_column_list = set()

    # Validate column list if provided
    if column_list:
        if not isinstance(column_list, list):
            raise ValueError(
                "column_list must be a list of strings. "
                "For single column, use ['column_name'] instead of 'column_name'."
            )
        invalid_cols = set(column_list) - set(dataset_columns)
        if invalid_cols:
            raise ValueError(
                f"These columns were not found in the dataset: {', '.join(invalid_cols)}. "
                "Please verify the column names."
            )
        final_column_list.update(column_list)

    # Handle data type filtering
    numeric_types = {'INTEGER', 'BIGINT', 'DOUBLE', 'REAL', 'DECIMAL', 'NUMERIC', 'TINYINT', 'SMALLINT', 'FLOAT'}
    if dataset_filter_by_data_type:
        if not isinstance(dataset_filter_by_data_type, list):
            raise ValueError(
                "dataset_filter_by_data_type must be a list of strings. "
                "For single data type, use ['INTEGER'] instead of 'INTEGER'."
            )

        data_type_columns = dtype_info.filter(
            pl.col("type").str.to_uppercase().is_in([dt.upper() for dt in dataset_filter_by_data_type])
        )['name'].to_list()

        if not data_type_columns:
            raise ValueError(
                f"No columns found of types {dataset_filter_by_data_type}. "
                "Please check the data types or specify columns directly using column_list."
            )

        final_column_list.update(data_type_columns)

    # Convert set back to list
    final_column_list = list(final_column_list)

    # Validate numeric types for selected columns
    for column in final_column_list:
        column_type = dtype_info.filter(pl.col("name") == column)['type'].item().upper()
        if not any(numeric_type in column_type for numeric_type in numeric_types):
            raise ValueError(
                f"Column '{column}' is of type {column_type}. Minimum operation requires numeric columns. "
                f"Supported types: {', '.join(sorted(numeric_types))}"
            )

    # Handle filter conditions
    if filter_condition_dict:
        if not isinstance(filter_condition_dict, dict):
            raise ValueError(
                "filter_condition_dict must be a dictionary. "
                "For single filter condition, use {'column_name': value} instead of a single value."
            )
        invalid_filter_cols = list(set(filter_condition_dict.keys()) - set(dataset_columns))
        if invalid_filter_cols:
            raise ValueError(
                f"These columns were not found in your dataset: {', '.join(invalid_filter_cols)}. "
                "Please verify the column names in your filter conditions."
            )

        where_clause = "WHERE " + " AND ".join(
            f"{col} = '{val}'" if isinstance(val, str) else f"{col} = {val}"
            for col, val in filter_condition_dict.items()
        )
    else:
        where_clause = ""

    # Generate and execute SQL queries
    sql_statements = [
        f"""
        SELECT 
            '{column}' as column_name,
            ROUND(CAST(MIN({column}) AS DOUBLE), {decimal_places}) as min_value
        FROM {source_table}
        {where_clause}
        """
        for column in final_column_list
    ]

    sql_query = " UNION ALL ".join(sql_statements)
    result = con.sql(sql_query).pl()

    if duckdb_connection is None:
        con.close()

    results = result.select([
        pl.col('column_name'),
        pl.col('min_value').cast(pl.Float64)
    ]).to_dicts()

    for result in results:
        result.update({
            'table_name': source_table,
            'execution_timestamp_utc': timestamp.strftime("%Y-%m-%d %H:%M:%S"),
            'filter_conditions': filter_condition_dict if filter_condition_dict else None,
            'filtered_by_data_type': dataset_filter_by_data_type if dataset_filter_by_data_type else None
        })

    return results