pyhampel/utils/ts_utils.py
def gen_list_of_frames(df_ts: pd.DataFrame, grp_keys: list):
    """
    This function takes a single dataframe of time-series data and groups them based
    upon a key value for each distinct time series.

    For example, if you have time series for 10 company's stocks, you can separate these 10 company's stock
    time-series into a list of dataframes, where each dataframe is an individual time-series for each stock.

    Parameters
    ----------
    df_ts: pd.DataFrame
        Dataframe containing many different time series that can be grouped by some key.
    grp_keys: list
        A list of one or mor keys on which to uniquely extract a time series.  For example,
        if we were processing time series of many stock tickers, the stock ticker
        could be the key.

    Returns
    -------
        grp_list: list
            A list of dataframes with each dataframe containing a uniquely identified time series.
    """

    grp_list = []
    df_groups = df_ts.groupby(grp_keys)

    for name, df in df_groups:
        grp_list.append(df)

    return grp_list

    This function takes a single dataframe of time-series data and groups them based
    upon a key value for each distinct time series.

    For example, if you have time series for 10 company's stocks, you can separate these 10 company's stock
    time-series into a list of dataframes, where each dataframe is an individual time-series for each stock.

    Parameters
    ----------
    df_ts: pd.DataFrame
        Dataframe containing many different time series that can be grouped by some key.
    grp_keys: list
        A list of one or mor keys on which to uniquely extract a time series.  For example,
        if we were processing time series of many stock tickers, the stock ticker
        could be the key.

    Returns
    -------
        grp_list: list
            A list of dataframes with each dataframe containing a uniquely identified time series.
    
This function takes a single dataframe of time-series data and groups them based
upon a key value for each distinct time series.

For example, if you have time series for 10 company's stocks, you can separate these 10 company's stock
time-series into a list of dataframes, where each dataframe is an individual time-series for each stock.

Parameters

pd.DataFrame
Dataframe containing many different time series that can be grouped by some key.
grp_keys: list
A list of one or mor keys on which to uniquely extract a time series.  For example,
if we were processing time series of many stock tickers, the stock ticker
could be the key.

Returns

list
A list of dataframes with each dataframe containing a uniquely identified time series.
This function takes a single dataframe of time - series data and groups them based upon a key value for each distinct time series . For example if you have time series for 10 company ' s stocks you can separate these 10 company ' s stock time - series into a list of dataframes where each dataframe is an individual time - series for each stock . Parameters pd . DataFrame Dataframe containing many different time series that can be grouped by some key . grp_keys : list A list of one or mor keys on which to uniquely extract a time series . For example if we were processing time series of many stock tickers the stock ticker could be the key . Returns list A list of dataframes with each dataframe containing a uniquely identified time series .
def gen_list_of_frames(df_ts: pd.DataFrame, grp_keys: list):
    grp_list = []
    df_groups = df_ts.groupby(grp_keys)
    for name, df in df_groups:
        grp_list.append(df)
    return grp_list
def gen_list_of_frames ( df_ts : pd . DataFrame , grp_keys : list ) : grp_list = [ ] df_groups = df_ts . groupby ( grp_keys ) for name , df in df_groups : grp_list . append ( df ) return grp_list
This function takes a single dataframe of time-series data and groups them based
upon a key value for each distinct time series.
This function takes a single dataframe of time - series data and groups them based upon a key value for each distinct time series .
"""
    This function takes a single dataframe of time-series data and groups them based
    upon a key value for each distinct time series.

    For example, if you have time series for 10 company's stocks, you can separate these 10 company's stock
    time-series into a list of dataframes, where each dataframe is an individual time-series for each stock.

    Parameters
    ----------
    df_ts: pd.DataFrame
        Dataframe containing many different time series that can be grouped by some key.
    grp_keys: list
        A list of one or mor keys on which to uniquely extract a time series.  For example,
        if we were processing time series of many stock tickers, the stock ticker
        could be the key.

    Returns
    -------
        grp_list: list
            A list of dataframes with each dataframe containing a uniquely identified time series.
    """
df_ts
pd.DataFrame
grp_keys
list