SimpleTable.as_text()

statsmodels.iolib.table.SimpleTable.as_text SimpleTable.as_text(**fmt_dict) [source] Return string, the table as text.

SimpleTable.as_csv()

statsmodels.iolib.table.SimpleTable.as_csv SimpleTable.as_csv(**fmt_dict) [source] Return string, the table in CSV format. Currently only supports comma separator.

SimpleTable.as_latex_tabular()

statsmodels.iolib.table.SimpleTable.as_latex_tabular SimpleTable.as_latex_tabular(center=True, **fmt_dict) [source] Return string, the table as a LaTeX tabular environment. Note: will require the booktabs package.

SimpleTable.as_html()

statsmodels.iolib.table.SimpleTable.as_html SimpleTable.as_html(**fmt_dict) [source] Return string. This is the default formatter for HTML tables. An HTML table formatter must accept as arguments a table and a format dictionary.

Sem2SLS.fit()

statsmodels.sandbox.sysreg.Sem2SLS.fit Sem2SLS.fit() [source]

SimpleTable.append()

statsmodels.iolib.table.SimpleTable.append SimpleTable.append() L.append(object) ? append object to end

Sem2SLS.whiten()

statsmodels.sandbox.sysreg.Sem2SLS.whiten Sem2SLS.whiten(Y) [source] Runs the first stage of the 2SLS. Returns the RHS variables that include the instruments.

sandbox.tsa.movstat.movvar()

statsmodels.sandbox.tsa.movstat.movvar statsmodels.sandbox.tsa.movstat.movvar(x, windowsize=3, lag='lagged') [source] moving window variance Parameters: x : array time series data windsize : int window size lag : ?lagged?, ?centered?, or ?leading? location of window relative to current position Returns: mk : array moving variance, with same shape as x

sandbox.tsa.movstat.movmoment()

statsmodels.sandbox.tsa.movstat.movmoment statsmodels.sandbox.tsa.movstat.movmoment(x, k, windowsize=3, lag='lagged') [source] non-central moment Parameters: x : array time series data windsize : int window size lag : ?lagged?, ?centered?, or ?leading? location of window relative to current position Returns: mk : array k-th moving non-central moment, with same shape as x Notes If data x is 2d, then moving moment is calculated for each column.

sandbox.tsa.movstat.movorder()

statsmodels.sandbox.tsa.movstat.movorder statsmodels.sandbox.tsa.movstat.movorder(x, order='med', windsize=3, lag='lagged') [source] moving order statistics Parameters: x : array time series data order : float or ?med?, ?min?, ?max? which order statistic to calculate windsize : int window size lag : ?lagged?, ?centered?, or ?leading? location of window relative to current position Returns: filtered array :