tstoolbox.tstoolbox.aggregate

tstoolbox.tstoolbox.aggregate(input_ts='-', groupby=None, statistic='mean', columns=None, start_date=None, end_date=None, dropna='no', clean=False, agg_interval=None, ninterval=None, round_index=None, skiprows=None, index_type='datetime', names=None, source_units=None, target_units=None, print_input=False, min_count=0)

Take a time series and aggregate to specified frequency.

Parameters:

• statistic (str) –

  [optional, defaults to ‘mean’, transformation]

  Any string in the following table of list of same to calculate on each groupby group.

  statistic	Allow kwd	Description
  count		Compute count of group, excluding missing values.
  nunique		Return number of unique elements in the group.
  first	min_count	Return first value within each group.
  last	min_count	Return last value within each group.
  max	min_count	Compute max of group values.
  mean		Compute mean of groups, excluding missing values.
  median		Compute median of groups, excluding missing values.
  min	min_count	Compute min of group values.
  ohlc		Compute open, high, low and close values of a group, excluding missing values.
  prod	min_count	Compute prod of group values.
  size		Compute group sizes.
  sem		Compute standard error of the mean of groups, excluding missing values.
  std		Compute standard deviation of groups, excluding missing values.
  sum	min_count	Compute sum of group values.
  var		Compute variance of groups, excluding missing values.

  Python example::

  statistic=[‘mean’, ‘max’, ‘first’]

  Command line example::

  –statistic=mean,max,first

• groupby (Optional[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 monthly categories across the entire time-series. The groupby keyword has a special option ‘all’ which will aggregate all records.

input_tsstr

[optional though required if using within Python, default is ‘-’ (stdin)]

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

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

Comma-separated values (CSV) files or tab-separated values (TSV):

File separators will be automatically detected.

Columns can be selected by name or index, where the index for
data columns starts at 1.

Command line examples:

Keyword Example	Description
–input_ts=fn.csv	read all columns from ‘fn.csv’
–input_ts=fn.csv,2,1	read data columns 2 and 1 from ‘fn.csv’
–input_ts=fn.csv,2,skiprows=2	read data column 2 from ‘fn.csv’, skipping first 2 rows so header is read from third row
–input_ts=fn.xlsx,2,Sheet21	read all data from 2nd sheet all data from “Sheet21” of ‘fn.xlsx’
–input_ts=fn.hdf5,Table12,T2	read all data from table “Table12” then all data from table “T2” of ‘fn.hdf5’
–input_ts=fn.wdm,210,110	read DSNs 210, then 110 from ‘fn.wdm’
–input_ts=’-’	read all columns from standard input (stdin)
–input_ts=’-’ –columns=4,1	read column 4 and 1 from standard input (stdin)

If working with CSV or TSV files you can use redirection rather than use –input_ts=fname.csv. The following are identical:

From a file:

command subcmd –input_ts=fname.csv

From standard input (since ‘–input_ts=-’ is the default:

command subcmd < fname.csv

Can also combine commands by piping:

command subcmd < filein.csv | command subcmd1 > fileout.csv

Python library examples:

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].

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 toolbox_utils pick command.

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

start_datestr

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

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

end_datestr

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

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

dropnastr

[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’.

clean

[optional, default is False, input filter]

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

round_index

[optional, default is None which will do nothing to the index, output format]

Round the index to the nearest time point. Can significantly improve the performance since can cut down on memory and processing requirements, however be cautious about rounding to a very course interval from a small one. This could lead to duplicate values in the index.

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) if a list or number of lines to skip at the start of the file if an integer.

If used in Python can be a 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_typestr

[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: str

[optional, default is None, transformation]

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

MUST include a name for all columns in the input dataset, including the index column.

source_units: str

[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: str

[optional, default is None, transformation]

The purpose of this option is to specify target units for unit conversion. The source units are specified in the header line of the input or using the ‘source_units’ keyword.

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.

tablefmtstr

[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’.

min_count:

The required number of valid values to perform the operation. If fewer than min_count non-NA values are present the result will be NA. Default is 0.

Only available for the following statistic methods: “first”, “last”, “max”, “min”, “prod”, and “sum”.

agg_interval :

DEPRECATED: Use the ‘groupby’ option instead.

ninterval :

DEPRECATED: Just prefix the number in front of the ‘groupby’ pandas offset code.