toucan_data_sdk/utils/generic/compute_evolution.py
def compute_evolution_by_frequency(
    df,
    id_cols: List[str],
    date_col: Union[str, Dict[str, str]],
    value_col: str,
    freq=1,
    method: str = 'abs',
    format: str = 'column',
    offseted_suffix: str = '_offseted',
    evolution_col_name: str = 'evolution_computed',
    missing_date_as_zero: bool = False,
    raise_duplicate_error: bool = True
):
    """
    This function answers the question: how has a value changed on a weekly, monthly, yearly basis ?

    ---

    ### Parameters

    *mandatory :*
    - `id_cols` (*list*): name of the columns used to create each group.
    - `date_col` (*str or dict*): either directly the name of the column containing the date or a dictionary with:
      - `selector` (*str*): the name of the column
      - `format` (*str*): the format of the date (see [pandas doc](
        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `value_col` (*str*): name of the column containing the value to compare.

    *optional :*
    - `freq` (*int/pd.DateOffset/pd.Serie/dict*): the frequency at which we calculate evolutions
    - `method` (*str*): either `"abs"` for absolute values or `"pct"` for the evolution in percentage of previous value.
    - `offseted_suffix` (*str*): suffix of the offseted column. By default, `"_offseted"`.
    - `evolution_col_name` (*str*): name given to the evolution column. By default, `"evolution_computed"`.
    - `missing_date_as_zero` (*boolean*): add missing date with zero value.
    - `raise_duplicate_error` (*boolean*): raise an error when the dataset has duplicated values with the given `id_cols`.
    - `format` (*str*): `'df'` # Do not change it !!!

    ---

    ### Example

    **Input**

    |   id_cols |    value_col |    date_col|
    |:---------:|:------------:|:----------:|
    |         A |           20 |        2010|
    |           |            7 |        2011|
    |         B |          200 |        2010|
    |           |          220 |        2011|
    |         C |          100 |        2011|

    ```cson
    compute_evolution_by_frequency:
      id_cols: "id_cols"
      date_col: "date_col"
      value_col: "value_col"
    ```

    **Output**

    |   id_cols |    value_col |    date_col|  evolution|
    |:---------:|:------------:|:----------:|:---------:|
    |         A |           20 |        2010|       null|
    |           |            7 |        2011|        -13|
    |         B |          200 |        2010|       null|
    |           |          220 |        2011|         20|
    |         C |          100 |        2011|       null|
    """
    if missing_date_as_zero:
        how = 'outer'
        fillna = 0
    else:
        how = 'left'
        fillna = None

    return __compute_evolution(
        df=df,
        id_cols=id_cols,
        value_col=value_col,
        date_col=date_col,
        freq=freq,
        method=method,
        format=format,
        offseted_suffix=offseted_suffix,
        evolution_col_name=evolution_col_name,
        how=how,
        fillna=fillna,
        raise_duplicate_error=raise_duplicate_error
    )
def compute_evolution_by_frequency(
    df,
    id_cols: List[str],
    date_col: Union[str, Dict[str, str]],
    value_col: str,
    freq=1,
    method: str = 'abs',
    format: str = 'column',
    offseted_suffix: str = '_offseted',
    evolution_col_name: str = 'evolution_computed',
    missing_date_as_zero: bool = False,
    raise_duplicate_error: bool = True
):
    """
    This function answers the question: how has a value changed on a weekly, monthly, yearly basis ?

    ---

    ### Parameters

    *mandatory :*
    - `id_cols` (*list*): name of the columns used to create each group.
    - `date_col` (*str or dict*): either directly the name of the column containing the date or a dictionary with:
      - `selector` (*str*): the name of the column
      - `format` (*str*): the format of the date (see [pandas doc](
        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `value_col` (*str*): name of the column containing the value to compare.

    *optional :*
    - `freq` (*int/pd.DateOffset/pd.Serie/dict*): the frequency at which we calculate evolutions
    - `method` (*str*): either `"abs"` for absolute values or `"pct"` for the evolution in percentage of previous value.
    - `offseted_suffix` (*str*): suffix of the offseted column. By default, `"_offseted"`.
    - `evolution_col_name` (*str*): name given to the evolution column. By default, `"evolution_computed"`.
    - `missing_date_as_zero` (*boolean*): add missing date with zero value.
    - `raise_duplicate_error` (*boolean*): raise an error when the dataset has duplicated values with the given `id_cols`.
    - `format` (*str*): `'df'` # Do not change it !!!

    ---

    ### Example

    **Input**

    |   id_cols |    value_col |    date_col|
    |:---------:|:------------:|:----------:|
    |         A |           20 |        2010|
    |           |            7 |        2011|
    |         B |          200 |        2010|
    |           |          220 |        2011|
    |         C |          100 |        2011|

    ```cson
    compute_evolution_by_frequency:
      id_cols: "id_cols"
      date_col: "date_col"
      value_col: "value_col"
    ```

    **Output**

    |   id_cols |    value_col |    date_col|  evolution|
    |:---------:|:------------:|:----------:|:---------:|
    |         A |           20 |        2010|       null|
    |           |            7 |        2011|        -13|
    |         B |          200 |        2010|       null|
    |           |          220 |        2011|         20|
    |         C |          100 |        2011|       null|
    """
    if missing_date_as_zero:
        how = 'outer'
        fillna = 0
    else:
        how = 'left'
        fillna = None

    return __compute_evolution(
        df=df,
        id_cols=id_cols,
        value_col=value_col,
        date_col=date_col,
        freq=freq,
        method=method,
        format=format,
        offseted_suffix=offseted_suffix,
        evolution_col_name=evolution_col_name,
        how=how,
        fillna=fillna,
        raise_duplicate_error=raise_duplicate_error
    )
def compute_evolution_by_frequency ( df , id_cols : List [ str ] , date_col : Union [ str , Dict [ str , str ] ] , value_col : str , freq = 1 , method : str = 'abs' , format : str = 'column' , offseted_suffix : str = '_offseted' , evolution_col_name : str = 'evolution_computed' , missing_date_as_zero : bool = False , raise_duplicate_error : bool = True ) : if missing_date_as_zero : how = 'outer' fillna = 0 else : how = 'left' fillna = None return __compute_evolution ( df = df , id_cols = id_cols , value_col = value_col , date_col = date_col , freq = freq , method = method , format = format , offseted_suffix = offseted_suffix , evolution_col_name = evolution_col_name , how = how , fillna = fillna , raise_duplicate_error = raise_duplicate_error )
This function answers the question: how has a value changed on a weekly, monthly, yearly basis ?

    ---

    ### Parameters

    *mandatory :*
    - `id_cols` (*list*): name of the columns used to create each group.
    - `date_col` (*str or dict*): either directly the name of the column containing the date or a dictionary with:
      - `selector` (*str*): the name of the column
      - `format` (*str*): the format of the date (see [pandas doc](
        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `value_col` (*str*): name of the column containing the value to compare.

    *optional :*
    - `freq` (*int/pd.DateOffset/pd.Serie/dict*): the frequency at which we calculate evolutions
    - `method` (*str*): either `"abs"` for absolute values or `"pct"` for the evolution in percentage of previous value.
    - `offseted_suffix` (*str*): suffix of the offseted column. By default, `"_offseted"`.
    - `evolution_col_name` (*str*): name given to the evolution column. By default, `"evolution_computed"`.
    - `missing_date_as_zero` (*boolean*): add missing date with zero value.
    - `raise_duplicate_error` (*boolean*): raise an error when the dataset has duplicated values with the given `id_cols`.
    - `format` (*str*): `'df'` # Do not change it !!!

    ---

    ### Example

    **Input**

    |   id_cols |    value_col |    date_col|
    |:---------:|:------------:|:----------:|
    |         A |           20 |        2010|
    |           |            7 |        2011|
    |         B |          200 |        2010|
    |           |          220 |        2011|
    |         C |          100 |        2011|

    ```cson
    compute_evolution_by_frequency:
      id_cols: "id_cols"
      date_col: "date_col"
      value_col: "value_col"
    ```

    **Output**

    |   id_cols |    value_col |    date_col|  evolution|
    |:---------:|:------------:|:----------:|:---------:|
    |         A |           20 |        2010|       null|
    |           |            7 |        2011|        -13|
    |         B |          200 |        2010|       null|
    |           |          220 |        2011|         20|
    |         C |          100 |        2011|       null|
This function answers the question : how has a value changed on a weekly monthly yearly basis ?