tstoolbox.tstoolbox.rolling_window

tstoolbox.tstoolbox.rolling_window(statistic, groupby=None, window=None, input_ts='-', columns=None, start_date=None, end_date=None, dropna='no', skiprows=None, index_type='datetime', names=None, clean=False, span=None, min_periods=None, center=False, win_type=None, on=None, closed=None, source_units=None, target_units=None, print_input=False)

Calculate a rolling window statistic.

Parameters
  • statistic (str) –

    corr

    correlation

    count

    count of numbers

    cov

    covariance

    kurt

    kurtosis

    max

    maximum

    mean

    mean

    median

    median

    min

    minimum

    quantile

    quantile

    skew

    skew

    std

    standard deviation

    sum

    sum

    var

    variance

  • groupby (str) –

    [optional, default is None, transformation]

    The pandas offset code to group the time-series data into. A special code is also available to group ‘months_across_years’ that will group into twelve categories for each month.

  • window

    [optional, default = 2]

    Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size.

    If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes.

  • min_periods (int) –

    [optional, default is None]

    Minimum number of observations in window required to have a value (otherwise result is NA). For a window that is specified by an offset, this will default to 1.

  • center (boolean) –

    [optional, default is False]

    Set the labels at the center of the window.

  • win_type (str) –

    [optional, default is None]

    Provide a window type. If None, all points are evenly weighted. See the notes below for further information.

  • on (str) –

    [optional, default is None]

    For a DataFrame, column on which to calculate the rolling window, rather than the index

  • closed (str) –

    [optional, default is None]

    Make the interval closed on the ‘right’, ‘left’, ‘both’ or ‘neither’ endpoints. For offset-based windows, it defaults to ‘right’. For fixed windows, defaults to ‘both’. Remaining cases not implemented for fixed windows.

  • span

    [optional, default = 2]

    DEPRECATED: Changed to ‘window’ to be consistent with pandas.

  • input_ts (str) –

    [optional, required if using Python API, default is ‘-‘ (stdin)]

    Whether from a file or standard input, data requires a header of column names. The default header is the first line of the input, but this can be changed using the ‘skiprows’ option.

    Most separators will be automatically detected. Most common date formats can be used, but the closer to ISO 8601 date/time standard the better.

    Command line:

    +-------------------------+------------------------+
    | --input_ts=filename.csv | to read 'filename.csv' |
    +-------------------------+------------------------+
    | --input_ts='-'          | to read from standard  |
    |                         | input (stdin).         |
    +-------------------------+------------------------+
    
    In many cases it is better to use redirection rather that use
    `--input_ts=filename.csv`.  The following are identical:
    
    From a file:
    
        command subcmd --input_ts=filename.csv
    
    From standard input:
    
        command subcmd --input_ts=- < filename.csv
    
    The BEST way since you don't have to include `--input_ts=-` because
    that is the default:
    
        command subcmd < filename.csv
    
    Can also combine commands by piping:
    
        command subcmd < filename.csv | command subcmd1 > fileout.csv
    

    As Python Library:

    You MUST use the `input_ts=...` option where `input_ts` can be one
    of a [pandas DataFrame, pandas Series, dict, tuple,
    list, StringIO, or file name].
    
    If result is a time series, returns a pandas DataFrame.
    

  • columns

    [optional, defaults to all columns, input filter]

    Columns to select out of input. Can use column names from the first line header or column numbers. If using numbers, column number 1 is the first data column. To pick multiple columns; separate by commas with no spaces. As used in tstoolbox pick command.

    This solves a big problem so that you don’t have to create a data set with a certain order, you can rearrange columns when data is read in.

  • start_date (str) –

    [optional, defaults to first date in time-series, input filter]

    The start_date of the series in ISOdatetime format, or ‘None’ for beginning.

  • end_date (str) –

    [optional, defaults to last date in time-series, input filter]

    The end_date of the series in ISOdatetime format, or ‘None’ for end.

  • dropna (str) –

    [optional, defauls it ‘no’, input filter]

    Set dropna to ‘any’ to have records dropped that have NA value in any column, or ‘all’ to have records dropped that have NA in all columns. Set to ‘no’ to not drop any records. The default is ‘no’.

  • skiprows (list-like or integer or callable) –

    [optional, default is None which will infer header from first line, input filter]

    Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file.

    If callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be

    lambda x: x in [0, 2].

  • index_type (str) –

    [optional, default is ‘datetime’, output format]

    Can be either ‘number’ or ‘datetime’. Use ‘number’ with index values that are Julian dates, or other epoch reference.

  • names

    [optional, default is None, input filter]

    If None, the column names are taken from the first row after ‘skiprows’ from the input dataset.

  • clean

    [optional, default is False, input filter]

    The ‘clean’ command will repair an index, removing duplicate index values and sorting.

  • source_units

    [optional, default is None, transformation]

    If unit is specified for the column as the second field of a ‘:’ delimited column name, then the specified units and the ‘source_units’ must match exactly.

    Any unit string compatible with the ‘pint’ library can be used.

  • target_units

    [optional, default is None, transformation]

    The main purpose of this option is to convert units from those specified in the header line of the input into ‘target_units’.

    The units of the input time-series or values are specified as the second field of a ‘:’ delimited name in the header line of the input or in the ‘source_units’ keyword.

    Any unit string compatible with the ‘pint’ library can be used.

    This option will also add the ‘target_units’ string to the column names.

  • print_input

    [optional, default is False, output format]

    If set to ‘True’ will include the input columns in the output table.

  • tablefmt (str) –

    [optional, default is ‘csv’, output format]

    The table format. Can be one of ‘csv’, ‘tsv’, ‘plain’, ‘simple’, ‘grid’, ‘pipe’, ‘orgtbl’, ‘rst’, ‘mediawiki’, ‘latex’, ‘latex_raw’ and ‘latex_booktabs’.