/Statsmodels

# statsmodels.stats.weightstats.DescrStatsW.quantile

`DescrStatsW.quantile(probs, return_pandas=True)` [source]

Compute quantiles for a weighted sample.

Parameters: probs (array-like) – A vector of probability points at which to calculate the quantiles. Each element of `probs` should fall in [0, 1]. return_pandas (bool) – If True, return value is a Pandas DataFrame or Series. Otherwise returns a ndarray. quantiles – `If return_pandas = True, returns one of the following:` data are 1d, `return_pandas` = True: a Series indexed by the probability points. data are 2d, `return_pandas` = True: a DataFrame with the probability points as row index and the variables as column index. If `return_pandas` = False, returns an ndarray containing the same values as the Series/DataFrame. Series, DataFrame, or ndarray

#### Notes

To compute the quantiles, first, the weights are summed over exact ties yielding distinct data values y_1 < y_2 < …, and corresponding weights w_1, w_2, …. Let s_j denote the sum of the first j weights, and let W denote the sum of all the weights. For a probability point p, if pW falls strictly between s_j and s_{j+1} then the estimated quantile is y_{j+1}. If pW = s_j then the estimated quantile is (y_j + y_{j+1})/2. If pW < p_1 then the estimated quantile is y_1.

#### References

SAS documentation for weighted quantiles:

https://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_univariate_sect028.htm

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© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.stats.weightstats.DescrStatsW.quantile.html