The 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.

Should be as easy as running pip install tstoolbox or easy_install tstoolbox at 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.

Just 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,
 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, 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.
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 exit

The 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.

You 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)