The timeops module provides helpful functionality for timeseries datasets
set_timeseries(df: DataFrame, time_column: str) → DataFrame
Transforms the dataframe to a timeseries enabled dataframe by applying a DateTimeIndex to the given column
- `df` (pd.DataFrame): The DataFrame that should be time-indexed
- `pd.DataFrame`: The DataFrame, including the DatetimeIndex
add_time_reference( sorted_df: DataFrame, n: int, reference_column: str, new_column: str) → DataFrame
This method will add a reference column to the DataFrame that contains the value of reference column of n items before
- `sorted_df` (pd.DataFrame): a DataFrame that is sorted by the time
- `n` (int): the number of records to look back for the referencing columns
- `new_column` (str): the name of the new column
- `pd.DataFrame`: the dataframe with the new column created
time_slice( df: DataFrame, time_column: str, start: datetime = None, end: datetime = None)
This method takes a time series DataFrame and only returns the time slice, based on the start & end date
- `df` (pd.DataFrame): The indexed Data Frame , which should be a DatetimeIndex
- `start` (datetime): The start time of the time slice. When skipped, the time slice begins at the beginning of the index
- `end` (datetime): The end time of the time slice. When skipped, the time slice end at the end of the index
- `pd.DataFrame`: the dataframe only containing the records inside the time slice
get_windows( sorted_df: DataFrame, window_size: int, window_stride: int = 1, group_column: str = None, zero_padding: bool = False, remove_group_column: bool = False, target_column: str = None) → <built-in function array>
This method take a DataFrame and returns a set of time windows of a specific length and a given column, eventually grouped by another column
- `sorted_df` (pd.DataFrame): A sorted Data Frame by a DatetimeIndex, that contains all time values
- `window_size` (int): The size of a window. How much record values should be added in every window. Consider this as a slice of the time series.
- `window_stride` (int): How much records should be between the different windows? (Default: 1)
- `group_column` (str): The name of the column on which you should group the time windows. This could be something like device_id. Optional.
- `zero_padding` (bool): If True, zeros will be added in the first time windows, to fill the array, prior to the first values.
- `remove_group_column` (bool): Indicates if the actual group column should be removed from the destination data frame
- `target_column` (str): Used to return a related array of values, taking from the column with this name. Commonly used to specify classes in training sets.
Returns: a tuple with the following objects:
- `np.array`: A multi dimensional array with all the windows, eventuall grouped
- `np.array`: An array, with all the linked target values
This method combines multiple timeseries (as DataFrame) and removes the overlapping time sections
- `*args (pd.DataFrame)`: A list of DataFrames, containing time series and the same layout.
- `pd.DataFrame`: A DataFrame, containing all unique, ordered time series data from the given DataFrames
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