tstoolbox.tstoolbox.fill

tstoolbox.tstoolbox.fill(input_ts='-', method='ffill', print_input=False, start_date=None, end_date=None, columns=None, clean=False, index_type='datetime', names=None, source_units=None, target_units=None, skiprows=None, from_columns=None, to_columns=None)

Fill missing values (NaN) with different methods.

Missing values can occur because of NaN, or because the time series is sparse.

Parameters
  • method (str) –

    [optional, default is ‘ffill’]

    String contained in single quotes or a number that defines the method to use for filling.

    method=

    fill missing values with…

    ffill

    …the last good value

    bfill

    …the next good value

    2.3

    …with this number

    linear

    …with linearly interpolated values

    nearest

    …nearest good value

    zero

    …zeroth order spline

    slinear

    …first order spline

    quadratic

    …second order spline

    cubic

    …third order spline

    mean

    …with mean

    median

    …with median

    max

    …with maximum

    min

    …with minimum

    from

    …with good values from other columns

  • print_input

    [optional, default is False, output format]

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

  • 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.
    

  • 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.

  • clean

    [optional, default is False, input filter]

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

  • 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.

  • 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.

  • 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.

  • from_columns (str or list) –

    [required if method=’from’, otherwise not used]

    List of column names/numbers from which good values will be taken to fill missing values in the to_columns keyword.

  • to_columns (str or list) –

    [required if method=’from’, otherwise not used]

    List of column names/numbers that missing values will be replaced in from good values in the from_columns keyword.

  • 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’.