Package: tstoolbox
tstoolbox - Quick GuideThe tstoolbox is a Python script to manipulate time-series on the command line
or by function calls within Python.  Uses pandas (http://pandas.pydata.org/)
or numpy (http://numpy.scipy.org) for any heavy lifting.InstallationShould be as easy as runningpip install tstoolboxoreasy_install tstoolboxat any command line.  Not sure on Windows whether
this will bring in pandas, but as mentioned above, if you start with scientific
Python distribution then you shouldn’t have a problem.Usage - Command LineJust run ‘tstoolbox –help’ to get a list of subcommands:usage: tstoolbox [-h]
                 {accumulate, add_trend, aggregate, calculate_fdc,
                 calculate_kde, clip, convert, convert_index,
                 convert_index_to_julian, converttz, lag, correlation,
                 createts, date_offset, date_slice, describe, dtw,
                 equation, ewm_window, expanding_window, fill, filter, fit,
                 forecast, read, gof, normalization, pca, pct_change,
                 peak_detection, pick, plot, rank, regression,
                 remove_trend, replace, rolling_window, stack, stdtozrxp,
                 tstopickle, unstack, about} ...

positional arguments:
  {accumulate, add_trend, aggregate, calculate_fdc, calculate_kde, clip,
  convert, convert_index, convert_index_to_julian, converttz, lag,
  correlation, createts, date_offset, date_slice, describe, dtw, equation,
  ewm_window, expanding_window, fill, filter, fit, forecast, read, gof,
  normalization, pca, pct_change, peak_detection, pick, plot, rank,
  regression, remove_trend, replace, rolling_window, stack, stdtozrxp,
  tstopickle, unstack, about}

accumulate
    Calculate accumulating statistics.
add_trend
    Add a trend.
aggregate
    Take a time series and aggregate to specified frequency.
calculate_fdc
    Return the frequency distribution curve.
calculate_kde
    Return the kernel density estimation (KDE) curve.
clip
    Return a time-series with values limited to [a_min, a_max].
convert
    Convert values of a time series by applying a factor and offset.
convert_index
    Convert datetime to/from Julian dates from different epochs.
convert_index_to_julian
    DEPRECATED: Use convert_index instead.
converttz
    Convert the time zone of the index.
lag
    Create a series of lagged time-series.
correlation
    Develop a correlation between time-series and potentially lags.
createts
    Create empty time series, optionally fill with a value.
date_offset
    Apply a date offset to a time-series index.
date_slice
    Print out data to the screen between start_date and end_date.
describe
    Print out statistics for the time-series.
dtw
    Dynamic Time Warping.
equation
    Apply <equation_str> cto the time series data.
ewm_window
    Calculate exponential weighted functions.
expanding_window
    Calculate an expanding window statistic.
fill
    Fill missing values (NaN) with different methods.
filter
    Apply different filters to the time-series.
fit
    Fit model to data.
forecast
    Forecast algorithms
read
    Combines time-series from a list of pickle or csv files.
gof
    Will calculate goodness of fit statistics between two time-series.
normalization
    Return the normalization of the time series.
pca
    Return the principal components analysis of the time series.
pct_change
    Return the percent change between times.
peak_detection
    Peak and valley detection.
pick
    DEPRECATED: Will pick a column or list of columns from input
plot
    Plot data.
rank
    Compute numerical data ranks (1 through n) along axis.
regression
    Regression of one or more time-series or indices to a time-series.
remove_trend
    Remove a 'trend'.
replace
    Return a time-series replacing values with others.
rolling_window
    Calculate a rolling window statistic.
stack
    Return the stack of the input table.
stdtozrxp
    Print out data to the screen in a WISKI ZRXP format.
tstopickle
    Pickle the data into a Python pickled file.
unstack
    Return the unstack of the input table.
about
    Display version number and system information.

optional arguments:
  -h, --help            show this help message and exitThe default for all of the subcommands is to accept data from stdin (typically
a pipe).  If a subcommand accepts an input file for an argument, you can use
“–input_ts=input_file_name.csv”, or to explicitly specify from stdin (the
default) “–input_ts=’-‘”.For the subcommands that output data it is printed to the screen and you can
then redirect to a file.Usage - APIYou can use all of the command line subcommands as functions.  The function
signature is identical to the command line subcommands.  The return is always
a PANDAS DataFrame.  Input can be a CSV or TAB separated file, or a PANDAS
DataFrame and is supplied to the function via the ‘input_ts’ keyword.Simply import tstoolbox:import tstoolbox

# Then you could call the functions
ntsd = tstoolbox.fill(method='linear', input_ts='tests/test_fill_01.csv')

# Once you have a PANDAS DataFrame you can use that as input to other
# tstoolbox functions.
ntsd = tstoolbox.aggregate(statistic='mean', groupby='D', input_ts=ntsd)