famafrench.utils.get_statsTable

famafrench.utils.get_statsTable(dType, dFreq, df, dates_as_index=True, ptiles=[0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99])

Construct detailed tables with summary statistics.

Parameters

• dType (str) –

  Dataset type of the portfolios. Possible choices are:

  • Returns

  • Factors

  • NumFirms

  • Characs

• dFreq (str) –

  Observation frequency of the portfolios. Possible choices are:

  • D : daily

  • W : weekly

  • M : monthly

  • Q : quarterly (3-months)

  • A : annual

• df (pandas.DataFrame) – Dataset w/ portfolio returns (which may include factor returns), number of firms in each portfolio, or average anomaly portfolio characteristics for a given portfolio sorting strategy.

• dates_as_index (bool) – Flag determining whether df has a pandas.DatetimeIndex index (dates_as_index = True). Otherwise, dates_as_index = False.

• ptiles (list, float, default [0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99]) – List of percentiles (in decimal format) included as part of output results.

Returns

statsTable –

Summary statistics of the dataset including the following:

• number of observations pandas.DataFrame.count()

• sample mean pandas.DataFrame.mean()

• sample standard deviation pandas.DataFrame.std()

• sample min pandas.DataFrame.min()

• sample max pandas.DataFrame.max()

• sample skewness pandas.DataFrame.skew()

• sample kurtosis pandas.DataFrame.kurtosis()

• sample mean absolute deviation pandas.DataFrame.mad()

• sample percentiles numpy.percentile()

If dates_as_index = True, then the table also includes the starting and ending date for each observation type.

Return type

pandas.DataFrame