phW,SSKrSSKJr SSKJrJrJrJr S/r SS/r SS/r S r "S S\ 5rS rS rS rSrSrg)N) PatsyError) safe_isnansafe_scalar_isnan no_picklingassert_no_picklingNAActionNoneNaNraisedropcbSRUVs/sHn[U5PM sn5$s snf)Nz, )joinrepr)optionsopts eC:\Users\julio\OneDrive\Documentos\Trabajo\IdeasFrscas\Cabanna\env\Lib\site-packages\patsy/missing.py _desc_optionsr2s' 99737Cd3i73 443s,cL\rSrSrSrSSS/4SjrSrSrS rS r S r \ r S r g )r6aiAn :class:`NAAction` object defines a strategy for handling missing data. "NA" is short for "Not Available", and is used to refer to any value which is somehow unmeasured or unavailable. In the long run, it is devoutly hoped that numpy will gain first-class missing value support. Until then, we work around this lack as best we're able. There are two parts to this: First, we have to determine what counts as missing data. For numerical data, the default is to treat NaN values (e.g., ``numpy.nan``) as missing. For categorical data, the default is to treat NaN values, and also the Python object None, as missing. (This is consistent with how pandas does things, so if you're already using None/NaN to mark missing data in your pandas DataFrames, you're good to go.) Second, we have to decide what to do with any missing data when we encounter it. One option is to simply discard any rows which contain missing data from our design matrices (``drop``). Another option is to raise an error (``raise``). A third option would be to simply let the missing values pass through into the returned design matrices. However, this last option is not yet implemented, because of the lack of any standard way to represent missing values in arbitrary numpy matrices; we're hoping numpy will get this sorted out before we standardize on anything ourselves. You can control how patsy handles missing data through the ``NA_action=`` argument to functions like :func:`build_design_matrices` and :func:`dmatrix`. If all you want to do is to choose between ``drop`` and ``raise`` behaviour, you can pass one of those strings as the ``NA_action=`` argument directly. If you want more fine-grained control over how missing values are detected and handled, then you can create an instance of this class, or your own object that implements the same interface, and pass that as the ``NA_action=`` argument instead. r r r c VXlUR[;a![SU<S[[5<S35e[ U[ 5(a [S5e[ U5UlURH.nU[;dM[SU<S[[5<S35e g)aoThe :class:`NAAction` constructor takes the following arguments: :arg on_NA: How to handle missing values. The default is ``"drop"``, which removes all rows from all matrices which contain any missing values. Also available is ``"raise"``, which raises an exception when any missing values are encountered. :arg NA_types: Which rules are used to identify missing values, as a list of strings. Allowed values are: * ``"None"``: treat the ``None`` object as missing in categorical data. * ``"NaN"``: treat floating point NaN values as missing in categorical and numerical data. .. versionadded:: 0.2.0 zinvalid on_NA action z (should be one of )z$NA_types should be a list of stringszinvalid NA_type N) on_NA_valid_NA_responses ValueErrorr isinstancestrtupleNA_types_valid_NA_types)selfrrNA_types r__init__NAAction.__init__[s" ::0 0+0-@S2TV  h $ $CD Dh }}Go- /6 o8VX%cnSUR;a[U5(agSUR;aUcgg)z_Return True if `obj` is a categorical NA value. Note that here `obj` is a single scalar value.r Tr F)rr)r objs ris_categorical_NANAAction.is_categorical_NA|s4 DMM !&7&<&< T]] "s{r$c[R"UR[S9nSUR;aU[R "U5-nUR S:a[R"USS9nU$)zReturns a 1-d mask array indicating which rows in an array of numerical values contain at least one NA value. Note that here `arr` is a numpy array or pandas DataFrame.dtyper )axis)npzerosshapeboolrisnanndimany)r arrmasks ris_numerical_NANAAction.is_numerical_NAsV xx . DMM ! BHHSM !D 99q=66$Q'D r$c [U5[U5s=:Xa[U5:Xde e[U5S:XaU$URS:XaURXU5$URS:XaURXU5$e)a8Takes a set of factor values that may have NAs, and handles them appropriately. :arg values: A list of `ndarray` objects representing the data. These may be 1- or 2-dimensional, and may be of varying dtype. All will have the same number of rows (or entries, for 1-d arrays). :arg is_NAs: A list with the same number of entries as `values`, containing boolean `ndarray` objects that indicate which rows contain NAs in the corresponding entry in `values`. :arg origins: A list with the same number of entries as `values`, containing information on the origin of each value. If we encounter a problem with some particular value, we use the corresponding entry in `origins` as the origin argument when raising a :class:`PatsyError`. :returns: A list of new values (which may have a differing number of rows.) rr r )lenr_handle_NA_raise_handle_NA_drop)r valuesis_NAsoriginss r handle_NANAAction.handle_NAs}$6{c&k9S\99999 v;! 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