toucan_data_sdk/utils/generic/date_requester.py
def date_requester_generator(
        df: pd.DataFrame,
        date_column: str,
        frequency: str,
        date_column_format: str = None,
        format: str = '%Y-%m-%d',
        granularities: Dict[str, str] = None,
        others_format: Dict[str, str] = None,
        times_delta: Dict[str, str] = None
) -> pd.DataFrame:
    """
    From a dataset containing dates in a column, return a dataset
    with at least 3 columns :
    - "DATE" : Label of date
    - "DATETIME" : Date in datetime dtype
    - "GRANULARITY" : Granularity of date

    ---

    ### Parameters

    *mandatory :*
    - `date_column` (*str*): name of column containing the date in the dataframe
    - `frequency` (*str*): see [pandas doc](
    http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)

    *optional :*
    - `date_column_format` (*str*): format of the date in date_column
    - `format` (*str*): format of the date (see [pandas doc](
        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
        By default, the format is set to `'%d/%m/%Y'`
        **WARNING**: only use if `granularities` is None.
    - `granularities` (*dict*):
        - key (*str*): name of the granularity
        - value (*str*): Format of the granularity e.g. '%d/%m/%Y' (see [pandas doc](
            https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `others_format` (*dict*) : Add new columns for each key
        - key (*str*) : name of the column
        - value (*str*): format of the granularity e.g. '%d/%m/%Y' (see [pandas doc](
            https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `times_delta` (*dict*) : Add new columns for each key
        - key (*str*) : name of the column
        - value (*str*): time delta (e.g. '+1 day', '+3 day', '-4 month')

    ---

    ### Example

    **Input**

    date | kpi
    :---:|:-----:
    2018-01-01 | 1
    2018-01-05 | 2
    2018-01-04 | 3
    2018-01-03 | 4
    2018-01-02 | 5

    ```cson
    date_requester_generator:
      date_column: 'date'
      frequency: 'D'
      granularities:
        'day': '%d/%m/%Y'
        'Semaine': '%W'
      others_format:
        'year': '%Y'
    ```

    **Ouput**

    DATE  |  DATETIME  | GRANULARITY | year
    :---------:|:----------:|:-----------:|:---:
    01/01/2018 | 2018-01-01 |         day | 2018
    02/01/2018 | 2018-01-02 |         day | 2018
    03/01/2018 | 2018-01-03 |         day | 2018
    04/01/2018 | 2018-01-04 |         day | 2018
    05/01/2018 | 2018-01-05 |         day | 2018
    01 | 2018-01-01 |     Semaine | 2018
    """

    start_date = pd.to_datetime(df[date_column], format=date_column_format).min()
    end_date = pd.to_datetime(df[date_column], format=date_column_format).max()

    granularities = granularities or {'date': format}
    others_format = others_format or {}
    times_delta = times_delta or {}

    # Base DataFrame
    columns_list = ['DATE', 'DATETIME', 'GRANULARITY', *others_format, *times_delta]
    result_df = {col_name: [] for col_name in columns_list}

    # Generate the range
    date_range = pd.date_range(start=start_date, end=end_date, freq=frequency)

    for granularity_name, granularity_format in granularities.items():
        date_range_label = date_range.strftime(granularity_format)
        a = list(set(date_range_label))
        index_unique = list(set([a.index(x) for x in date_range_label]))
        date_range_datetime = date_range[index_unique]
        date_range_label = date_range_label.unique()

        result_df['DATE'] += list(date_range_label)
        result_df['DATETIME'] += list(date_range_datetime)
        result_df['GRANULARITY'] += [granularity_name] * len(date_range_label)

        for col_name, other_format in others_format.items():
            result_df[col_name] += list(date_range_datetime.strftime(other_format))

        for col_name, time_delta in times_delta.items():
            result_df[col_name] += list((date_range_datetime + pd.Timedelta(time_delta))
                                        .strftime(granularity_format))

    return pd.DataFrame(result_df)
def date_requester_generator(
        df: pd.DataFrame,
        date_column: str,
        frequency: str,
        date_column_format: str = None,
        format: str = '%Y-%m-%d',
        granularities: Dict[str, str] = None,
        others_format: Dict[str, str] = None,
        times_delta: Dict[str, str] = None
) -> pd.DataFrame:
    """
    From a dataset containing dates in a column, return a dataset
    with at least 3 columns :
    - "DATE" : Label of date
    - "DATETIME" : Date in datetime dtype
    - "GRANULARITY" : Granularity of date

    ---

    ### Parameters

    *mandatory :*
    - `date_column` (*str*): name of column containing the date in the dataframe
    - `frequency` (*str*): see [pandas doc](
    http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)

    *optional :*
    - `date_column_format` (*str*): format of the date in date_column
    - `format` (*str*): format of the date (see [pandas doc](
        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
        By default, the format is set to `'%d/%m/%Y'`
        **WARNING**: only use if `granularities` is None.
    - `granularities` (*dict*):
        - key (*str*): name of the granularity
        - value (*str*): Format of the granularity e.g. '%d/%m/%Y' (see [pandas doc](
            https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `others_format` (*dict*) : Add new columns for each key
        - key (*str*) : name of the column
        - value (*str*): format of the granularity e.g. '%d/%m/%Y' (see [pandas doc](
            https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `times_delta` (*dict*) : Add new columns for each key
        - key (*str*) : name of the column
        - value (*str*): time delta (e.g. '+1 day', '+3 day', '-4 month')

    ---

    ### Example

    **Input**

    date | kpi
    :---:|:-----:
    2018-01-01 | 1
    2018-01-05 | 2
    2018-01-04 | 3
    2018-01-03 | 4
    2018-01-02 | 5

    ```cson
    date_requester_generator:
      date_column: 'date'
      frequency: 'D'
      granularities:
        'day': '%d/%m/%Y'
        'Semaine': '%W'
      others_format:
        'year': '%Y'
    ```

    **Ouput**

    DATE  |  DATETIME  | GRANULARITY | year
    :---------:|:----------:|:-----------:|:---:
    01/01/2018 | 2018-01-01 |         day | 2018
    02/01/2018 | 2018-01-02 |         day | 2018
    03/01/2018 | 2018-01-03 |         day | 2018
    04/01/2018 | 2018-01-04 |         day | 2018
    05/01/2018 | 2018-01-05 |         day | 2018
    01 | 2018-01-01 |     Semaine | 2018
    """

    start_date = pd.to_datetime(df[date_column], format=date_column_format).min()
    end_date = pd.to_datetime(df[date_column], format=date_column_format).max()

    granularities = granularities or {'date': format}
    others_format = others_format or {}
    times_delta = times_delta or {}

    # Base DataFrame
    columns_list = ['DATE', 'DATETIME', 'GRANULARITY', *others_format, *times_delta]
    result_df = {col_name: [] for col_name in columns_list}

    # Generate the range
    date_range = pd.date_range(start=start_date, end=end_date, freq=frequency)

    for granularity_name, granularity_format in granularities.items():
        date_range_label = date_range.strftime(granularity_format)
        a = list(set(date_range_label))
        index_unique = list(set([a.index(x) for x in date_range_label]))
        date_range_datetime = date_range[index_unique]
        date_range_label = date_range_label.unique()

        result_df['DATE'] += list(date_range_label)
        result_df['DATETIME'] += list(date_range_datetime)
        result_df['GRANULARITY'] += [granularity_name] * len(date_range_label)

        for col_name, other_format in others_format.items():
            result_df[col_name] += list(date_range_datetime.strftime(other_format))

        for col_name, time_delta in times_delta.items():
            result_df[col_name] += list((date_range_datetime + pd.Timedelta(time_delta))
                                        .strftime(granularity_format))

    return pd.DataFrame(result_df)
def date_requester_generator ( df : pd . DataFrame , date_column : str , frequency : str , date_column_format : str = None , format : str = '%Y-%m-%d' , granularities : Dict [ str , str ] = None , others_format : Dict [ str , str ] = None , times_delta : Dict [ str , str ] = None ) -> pd . DataFrame : start_date = pd . to_datetime ( df [ date_column ] , format = date_column_format ) . min ( ) end_date = pd . to_datetime ( df [ date_column ] , format = date_column_format ) . max ( ) granularities = granularities or { 'date' : format } others_format = others_format or { } times_delta = times_delta or { } # Base DataFrame columns_list = [ 'DATE' , 'DATETIME' , 'GRANULARITY' , * others_format , * times_delta ] result_df = { col_name : [ ] for col_name in columns_list } # Generate the range date_range = pd . date_range ( start = start_date , end = end_date , freq = frequency ) for granularity_name , granularity_format in granularities . items ( ) : date_range_label = date_range . strftime ( granularity_format ) a = list ( set ( date_range_label ) ) index_unique = list ( set ( [ a . index ( x ) for x in date_range_label ] ) ) date_range_datetime = date_range [ index_unique ] date_range_label = date_range_label . unique ( ) result_df [ 'DATE' ] += list ( date_range_label ) result_df [ 'DATETIME' ] += list ( date_range_datetime ) result_df [ 'GRANULARITY' ] += [ granularity_name ] * len ( date_range_label ) for col_name , other_format in others_format . items ( ) : result_df [ col_name ] += list ( date_range_datetime . strftime ( other_format ) ) for col_name , time_delta in times_delta . items ( ) : result_df [ col_name ] += list ( ( date_range_datetime + pd . Timedelta ( time_delta ) ) . strftime ( granularity_format ) ) return pd . DataFrame ( result_df )
From a dataset containing dates in a column, return a dataset
    with at least 3 columns :
    - "DATE" : Label of date
    - "DATETIME" : Date in datetime dtype
    - "GRANULARITY" : Granularity of date

    ---

    ### Parameters

    *mandatory :*
    - `date_column` (*str*): name of column containing the date in the dataframe
    - `frequency` (*str*): see [pandas doc](
    http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)

    *optional :*
    - `date_column_format` (*str*): format of the date in date_column
    - `format` (*str*): format of the date (see [pandas doc](
        https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
        By default, the format is set to `'%d/%m/%Y'`
        **WARNING**: only use if `granularities` is None.
    - `granularities` (*dict*):
        - key (*str*): name of the granularity
        - value (*str*): Format of the granularity e.g. '%d/%m/%Y' (see [pandas doc](
            https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `others_format` (*dict*) : Add new columns for each key
        - key (*str*) : name of the column
        - value (*str*): format of the granularity e.g. '%d/%m/%Y' (see [pandas doc](
            https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior))
    - `times_delta` (*dict*) : Add new columns for each key
        - key (*str*) : name of the column
        - value (*str*): time delta (e.g. '+1 day', '+3 day', '-4 month')

    ---

    ### Example

    **Input**

    date | kpi
    :---:|:-----:
    2018-01-01 | 1
    2018-01-05 | 2
    2018-01-04 | 3
    2018-01-03 | 4
    2018-01-02 | 5

    ```cson
    date_requester_generator:
      date_column: 'date'
      frequency: 'D'
      granularities:
        'day': '%d/%m/%Y'
        'Semaine': '%W'
      others_format:
        'year': '%Y'
    ```

    **Ouput**

    DATE  |  DATETIME  | GRANULARITY | year
    :---------:|:----------:|:-----------:|:---:
    01/01/2018 | 2018-01-01 |         day | 2018
    02/01/2018 | 2018-01-02 |         day | 2018
    03/01/2018 | 2018-01-03 |         day | 2018
    04/01/2018 | 2018-01-04 |         day | 2018
    05/01/2018 | 2018-01-05 |         day | 2018
    01 | 2018-01-01 |     Semaine | 2018
From a dataset containing dates in a column return a dataset with at least 3 columns : - DATE : Label of date - DATETIME : Date in datetime dtype - GRANULARITY : Granularity of date