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How to use fill method in Playwright Python

Best Python code snippet using playwright-python

sparse.py

Source:sparse.py

...300        return self.subtype301# ----------------------------------------------------------------------------302# Array303_sparray_doc_kwargs = dict(klass='SparseArray')304def _get_fill(arr):305    # type: (SparseArray) -> ndarray306    """307    Create a 0-dim ndarray containing the fill value308    Parameters309    ----------310    arr : SparseArray311    Returns312    -------313    fill_value : ndarray314        0-dim ndarray with just the fill value.315    Notes316    -----317    coerce fill_value to arr dtype if possible318    int64 SparseArray can have NaN as fill_value if there is no missing319    """320    try:321        return np.asarray(arr.fill_value, dtype=arr.dtype.subtype)322    except ValueError:323        return np.asarray(arr.fill_value)324def _sparse_array_op(left, right, op, name):325    """326    Perform a binary operation between two arrays.327    Parameters328    ----------329    left : Union[SparseArray, ndarray]330    right : Union[SparseArray, ndarray]331    op : Callable332        The binary operation to perform333    name str334        Name of the callable.335    Returns336    -------337    SparseArray338    """339    # type: (SparseArray, SparseArray, Callable, str) -> Any340    if name.startswith('__'):341        # For lookups in _libs.sparse we need non-dunder op name342        name = name[2:-2]343    # dtype used to find corresponding sparse method344    ltype = left.dtype.subtype345    rtype = right.dtype.subtype346    if not is_dtype_equal(ltype, rtype):347        subtype = find_common_type([ltype, rtype])348        ltype = SparseDtype(subtype, left.fill_value)349        rtype = SparseDtype(subtype, right.fill_value)350        # TODO(GH-23092): pass copy=False. Need to fix astype_nansafe351        left = left.astype(ltype)352        right = right.astype(rtype)353        dtype = ltype.subtype354    else:355        dtype = ltype356    # dtype the result must have357    result_dtype = None358    if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:359        with np.errstate(all='ignore'):360            result = op(left.get_values(), right.get_values())361            fill = op(_get_fill(left), _get_fill(right))362        if left.sp_index.ngaps == 0:363            index = left.sp_index364        else:365            index = right.sp_index366    elif left.sp_index.equals(right.sp_index):367        with np.errstate(all='ignore'):368            result = op(left.sp_values, right.sp_values)369            fill = op(_get_fill(left), _get_fill(right))370        index = left.sp_index371    else:372        if name[0] == 'r':373            left, right = right, left374            name = name[1:]375        if name in ('and', 'or') and dtype == 'bool':376            opname = 'sparse_{name}_uint8'.format(name=name)377            # to make template simple, cast here378            left_sp_values = left.sp_values.view(np.uint8)379            right_sp_values = right.sp_values.view(np.uint8)380            result_dtype = np.bool381        else:382            opname = 'sparse_{name}_{dtype}'.format(name=name, dtype=dtype)383            left_sp_values = left.sp_values384            right_sp_values = right.sp_values385        sparse_op = getattr(splib, opname)386        with np.errstate(all='ignore'):387            result, index, fill = sparse_op(388                left_sp_values, left.sp_index, left.fill_value,389                right_sp_values, right.sp_index, right.fill_value)390    if result_dtype is None:391        result_dtype = result.dtype392    return _wrap_result(name, result, index, fill, dtype=result_dtype)393def _wrap_result(name, data, sparse_index, fill_value, dtype=None):394    """395    wrap op result to have correct dtype396    """397    if name.startswith('__'):398        # e.g. __eq__ --> eq399        name = name[2:-2]400    if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):401        dtype = np.bool402    fill_value = lib.item_from_zerodim(fill_value)403    if is_bool_dtype(dtype):404        # fill_value may be np.bool_405        fill_value = bool(fill_value)406    return SparseArray(data,407                       sparse_index=sparse_index,408                       fill_value=fill_value,409                       dtype=dtype)410class SparseArray(PandasObject, ExtensionArray, ExtensionOpsMixin):411    """412    An ExtensionArray for storing sparse data.413    .. versionchanged:: 0.24.0414       Implements the ExtensionArray interface.415    Parameters416    ----------417    data : array-like418        A dense array of values to store in the SparseArray. This may contain419        `fill_value`.420    sparse_index : SparseIndex, optional421    index : Index422    fill_value : scalar, optional423        Elements in `data` that are `fill_value` are not stored in the424        SparseArray. For memory savings, this should be the most common value425        in `data`. By default, `fill_value` depends on the dtype of `data`:426        =========== ==========427        data.dtype  na_value428        =========== ==========429        float       ``np.nan``430        int         ``0``431        bool        False432        datetime64  ``pd.NaT``433        timedelta64 ``pd.NaT``434        =========== ==========435        The fill value is potentiall specified in three ways. In order of436        precedence, these are437        1. The `fill_value` argument438        2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is439           a ``SparseDtype``440        3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`441           is not a ``SparseDtype`` and `data` is a ``SparseArray``.442    kind : {'integer', 'block'}, default 'integer'443        The type of storage for sparse locations.444        * 'block': Stores a `block` and `block_length` for each445          contiguous *span* of sparse values. This is best when446          sparse data tends to be clumped together, with large447          regsions of ``fill-value`` values between sparse values.448        * 'integer': uses an integer to store the location of449          each sparse value.450    dtype : np.dtype or SparseDtype, optional451        The dtype to use for the SparseArray. For numpy dtypes, this452        determines the dtype of ``self.sp_values``. For SparseDtype,453        this determines ``self.sp_values`` and ``self.fill_value``.454    copy : bool, default False455        Whether to explicitly copy the incoming `data` array.456    """457    __array_priority__ = 15458    _pandas_ftype = 'sparse'459    _subtyp = 'sparse_array'  # register ABCSparseArray460    def __init__(self, data, sparse_index=None, index=None, fill_value=None,461                 kind='integer', dtype=None, copy=False):462        from pandas.core.internals import SingleBlockManager463        if isinstance(data, SingleBlockManager):464            data = data.internal_values()465        if fill_value is None and isinstance(dtype, SparseDtype):466            fill_value = dtype.fill_value467        if isinstance(data, (type(self), ABCSparseSeries)):468            # disable normal inference on dtype, sparse_index, & fill_value469            if sparse_index is None:470                sparse_index = data.sp_index471            if fill_value is None:472                fill_value = data.fill_value473            if dtype is None:474                dtype = data.dtype475            # TODO: make kind=None, and use data.kind?476            data = data.sp_values477        # Handle use-provided dtype478        if isinstance(dtype, compat.string_types):479            # Two options: dtype='int', regular numpy dtype480            # or dtype='Sparse[int]', a sparse dtype481            try:482                dtype = SparseDtype.construct_from_string(dtype)483            except TypeError:484                dtype = pandas_dtype(dtype)485        if isinstance(dtype, SparseDtype):486            if fill_value is None:487                fill_value = dtype.fill_value488            dtype = dtype.subtype489        if index is not None and not is_scalar(data):490            raise Exception("must only pass scalars with an index ")491        if is_scalar(data):492            if index is not None:493                if data is None:494                    data = np.nan495            if index is not None:496                npoints = len(index)497            elif sparse_index is None:498                npoints = 1499            else:500                npoints = sparse_index.length501            dtype = infer_dtype_from_scalar(data)[0]502            data = construct_1d_arraylike_from_scalar(503                data, npoints, dtype504            )505        if dtype is not None:506            dtype = pandas_dtype(dtype)507        # TODO: disentangle the fill_value dtype inference from508        # dtype inference509        if data is None:510            # XXX: What should the empty dtype be? Object or float?511            data = np.array([], dtype=dtype)512        if not is_array_like(data):513            try:514                # probably shared code in sanitize_series515                from pandas.core.internals.construction import sanitize_array516                data = sanitize_array(data, index=None)517            except ValueError:518                # NumPy may raise a ValueError on data like [1, []]519                # we retry with object dtype here.520                if dtype is None:521                    dtype = object522                    data = np.atleast_1d(np.asarray(data, dtype=dtype))523                else:524                    raise525        if copy:526            # TODO: avoid double copy when dtype forces cast.527            data = data.copy()528        if fill_value is None:529            fill_value_dtype = data.dtype if dtype is None else dtype530            if fill_value_dtype is None:531                fill_value = np.nan532            else:533                fill_value = na_value_for_dtype(fill_value_dtype)534        if isinstance(data, type(self)) and sparse_index is None:535            sparse_index = data._sparse_index536            sparse_values = np.asarray(data.sp_values, dtype=dtype)537        elif sparse_index is None:538            sparse_values, sparse_index, fill_value = make_sparse(539                data, kind=kind, fill_value=fill_value, dtype=dtype540            )541        else:542            sparse_values = np.asarray(data, dtype=dtype)543            if len(sparse_values) != sparse_index.npoints:544                raise AssertionError("Non array-like type {type} must "545                                     "have the same length as the index"546                                     .format(type=type(sparse_values)))547        self._sparse_index = sparse_index548        self._sparse_values = sparse_values549        self._dtype = SparseDtype(sparse_values.dtype, fill_value)550    @classmethod551    def _simple_new(cls, sparse_array, sparse_index, dtype):552        # type: (np.ndarray, SparseIndex, SparseDtype) -> 'SparseArray'553        new = cls([])554        new._sparse_index = sparse_index555        new._sparse_values = sparse_array556        new._dtype = dtype557        return new558    def __array__(self, dtype=None, copy=True):559        fill_value = self.fill_value560        if self.sp_index.ngaps == 0:561            # Compat for na dtype and int values.562            return self.sp_values563        if dtype is None:564            # Can NumPy represent this type?565            # If not, `np.result_type` will raise. We catch that566            # and return object.567            if is_datetime64_any_dtype(self.sp_values.dtype):568                # However, we *do* special-case the common case of569                # a datetime64 with pandas NaT.570                if fill_value is NaT:571                    # Can't put pd.NaT in a datetime64[ns]572                    fill_value = np.datetime64('NaT')573            try:574                dtype = np.result_type(self.sp_values.dtype, type(fill_value))575            except TypeError:576                dtype = object577        out = np.full(self.shape, fill_value, dtype=dtype)578        out[self.sp_index.to_int_index().indices] = self.sp_values579        return out580    def __setitem__(self, key, value):581        # I suppose we could allow setting of non-fill_value elements.582        # TODO(SparseArray.__setitem__): remove special cases in583        # ExtensionBlock.where584        msg = "SparseArray does not support item assignment via setitem"585        raise TypeError(msg)586    @classmethod587    def _from_sequence(cls, scalars, dtype=None, copy=False):588        return cls(scalars, dtype=dtype)589    @classmethod590    def _from_factorized(cls, values, original):591        return cls(values, dtype=original.dtype)592    # ------------------------------------------------------------------------593    # Data594    # ------------------------------------------------------------------------595    @property596    def sp_index(self):597        """598        The SparseIndex containing the location of non- ``fill_value`` points.599        """600        return self._sparse_index601    @property602    def sp_values(self):603        """604        An ndarray containing the non- ``fill_value`` values.605        Examples606        --------607        >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)608        >>> s.sp_values609        array([1, 2])610        """611        return self._sparse_values612    @property613    def dtype(self):614        return self._dtype615    @property616    def fill_value(self):617        """618        Elements in `data` that are `fill_value` are not stored.619        For memory savings, this should be the most common value in the array.620        """621        return self.dtype.fill_value622    @fill_value.setter623    def fill_value(self, value):624        self._dtype = SparseDtype(self.dtype.subtype, value)625    @property626    def kind(self):627        """628        The kind of sparse index for this array. One of {'integer', 'block'}.629        """630        if isinstance(self.sp_index, IntIndex):631            return 'integer'632        else:633            return 'block'634    @property635    def _valid_sp_values(self):636        sp_vals = self.sp_values637        mask = notna(sp_vals)638        return sp_vals[mask]639    def __len__(self):640        return self.sp_index.length641    @property642    def _null_fill_value(self):643        return self._dtype._is_na_fill_value644    def _fill_value_matches(self, fill_value):645        if self._null_fill_value:646            return isna(fill_value)647        else:648            return self.fill_value == fill_value649    @property650    def nbytes(self):651        return self.sp_values.nbytes + self.sp_index.nbytes652    @property653    def density(self):654        """655        The percent of non- ``fill_value`` points, as decimal.656        Examples657        --------658        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)659        >>> s.density660        0.6661        """662        r = float(self.sp_index.npoints) / float(self.sp_index.length)663        return r664    @property665    def npoints(self):666        """667        The number of non- ``fill_value`` points.668        Examples669        --------670        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)671        >>> s.npoints672        3673        """674        return self.sp_index.npoints675    @property676    def values(self):677        """678        Dense values679        """680        return self.to_dense()681    def isna(self):682        from pandas import isna683        # If null fill value, we want SparseDtype[bool, true]684        # to preserve the same memory usage.685        dtype = SparseDtype(bool, self._null_fill_value)686        return type(self)._simple_new(isna(self.sp_values),687                                      self.sp_index, dtype)688    def fillna(self, value=None, method=None, limit=None):689        """690        Fill missing values with `value`.691        Parameters692        ----------693        value : scalar, optional694        method : str, optional695            .. warning::696               Using 'method' will result in high memory use,697               as all `fill_value` methods will be converted to698               an in-memory ndarray699        limit : int, optional700        Returns701        -------702        SparseArray703        Notes704        -----705        When `value` is specified, the result's ``fill_value`` depends on706        ``self.fill_value``. The goal is to maintain low-memory use.707        If ``self.fill_value`` is NA, the result dtype will be708        ``SparseDtype(self.dtype, fill_value=value)``. This will preserve709        amount of memory used before and after filling.710        When ``self.fill_value`` is not NA, the result dtype will be711        ``self.dtype``. Again, this preserves the amount of memory used.712        """713        if ((method is None and value is None) or714                (method is not None and value is not None)):715            raise ValueError("Must specify one of 'method' or 'value'.")716        elif method is not None:717            msg = "fillna with 'method' requires high memory usage."718            warnings.warn(msg, PerformanceWarning)719            filled = interpolate_2d(np.asarray(self), method=method,720                                    limit=limit)721            return type(self)(filled, fill_value=self.fill_value)722        else:723            new_values = np.where(isna(self.sp_values), value, self.sp_values)724            if self._null_fill_value:725                # This is essentially just updating the dtype.726                new_dtype = SparseDtype(self.dtype.subtype, fill_value=value)727            else:728                new_dtype = self.dtype729        return self._simple_new(new_values, self._sparse_index, new_dtype)730    def shift(self, periods=1, fill_value=None):731        if not len(self) or periods == 0:732            return self.copy()733        if isna(fill_value):734            fill_value = self.dtype.na_value735        subtype = np.result_type(fill_value, self.dtype.subtype)736        if subtype != self.dtype.subtype:737            # just coerce up front738            arr = self.astype(SparseDtype(subtype, self.fill_value))739        else:740            arr = self741        empty = self._from_sequence(742            [fill_value] * min(abs(periods), len(self)),743            dtype=arr.dtype744        )745        if periods > 0:746            a = empty747            b = arr[:-periods]748        else:749            a = arr[abs(periods):]750            b = empty751        return arr._concat_same_type([a, b])752    def _first_fill_value_loc(self):753        """754        Get the location of the first missing value.755        Returns756        -------757        int758        """759        if len(self) == 0 or self.sp_index.npoints == len(self):760            return -1761        indices = self.sp_index.to_int_index().indices762        if not len(indices) or indices[0] > 0:763            return 0764        diff = indices[1:] - indices[:-1]765        return np.searchsorted(diff, 2) + 1766    def unique(self):767        uniques = list(algos.unique(self.sp_values))768        fill_loc = self._first_fill_value_loc()769        if fill_loc >= 0:770            uniques.insert(fill_loc, self.fill_value)771        return type(self)._from_sequence(uniques, dtype=self.dtype)772    def _values_for_factorize(self):773        # Still override this for hash_pandas_object774        return np.asarray(self), self.fill_value775    def factorize(self, na_sentinel=-1):776        # Currently, ExtensionArray.factorize -> Tuple[ndarray, EA]777        # The sparsity on this is backwards from what Sparse would want. Want778        # ExtensionArray.factorize -> Tuple[EA, EA]779        # Given that we have to return a dense array of labels, why bother780        # implementing an efficient factorize?781        labels, uniques = algos.factorize(np.asarray(self),782                                          na_sentinel=na_sentinel)783        uniques = SparseArray(uniques, dtype=self.dtype)784        return labels, uniques785    def value_counts(self, dropna=True):786        """787        Returns a Series containing counts of unique values.788        Parameters789        ----------790        dropna : boolean, default True791            Don't include counts of NaN, even if NaN is in sp_values.792        Returns793        -------794        counts : Series795        """796        from pandas import Index, Series797        keys, counts = algos._value_counts_arraylike(self.sp_values,798                                                     dropna=dropna)799        fcounts = self.sp_index.ngaps800        if fcounts > 0:801            if self._null_fill_value and dropna:802                pass803            else:804                if self._null_fill_value:805                    mask = isna(keys)806                else:807                    mask = keys == self.fill_value808                if mask.any():809                    counts[mask] += fcounts810                else:811                    keys = np.insert(keys, 0, self.fill_value)812                    counts = np.insert(counts, 0, fcounts)813        if not isinstance(keys, ABCIndexClass):814            keys = Index(keys)815        result = Series(counts, index=keys)816        return result817    # --------818    # Indexing819    # --------820    def __getitem__(self, key):821        if isinstance(key, tuple):822            if len(key) > 1:823                raise IndexError("too many indices for array.")824            key = key[0]825        if is_integer(key):826            return self._get_val_at(key)827        elif isinstance(key, tuple):828            data_slice = self.values[key]829        elif isinstance(key, slice):830            # special case to preserve dtypes831            if key == slice(None):832                return self.copy()833            # TODO: this logic is surely elsewhere834            # TODO: this could be more efficient835            indices = np.arange(len(self), dtype=np.int32)[key]836            return self.take(indices)837        else:838            # TODO: I think we can avoid densifying when masking a839            # boolean SparseArray with another. Need to look at the840            # key's fill_value for True / False, and then do an intersection841            # on the indicies of the sp_values.842            if isinstance(key, SparseArray):843                if is_bool_dtype(key):844                    key = key.to_dense()845                else:846                    key = np.asarray(key)847            if com.is_bool_indexer(key) and len(self) == len(key):848                return self.take(np.arange(len(key), dtype=np.int32)[key])849            elif hasattr(key, '__len__'):850                return self.take(key)851            else:852                raise ValueError("Cannot slice with '{}'".format(key))853        return type(self)(data_slice, kind=self.kind)854    def _get_val_at(self, loc):855        n = len(self)856        if loc < 0:857            loc += n858        if loc >= n or loc < 0:859            raise IndexError('Out of bounds access')860        sp_loc = self.sp_index.lookup(loc)861        if sp_loc == -1:862            return self.fill_value863        else:864            return libindex.get_value_at(self.sp_values, sp_loc)865    def take(self, indices, allow_fill=False, fill_value=None):866        if is_scalar(indices):867            raise ValueError("'indices' must be an array, not a "868                             "scalar '{}'.".format(indices))869        indices = np.asarray(indices, dtype=np.int32)870        if indices.size == 0:871            result = []872            kwargs = {'dtype': self.dtype}873        elif allow_fill:874            result = self._take_with_fill(indices, fill_value=fill_value)875            kwargs = {}876        else:877            result = self._take_without_fill(indices)878            kwargs = {'dtype': self.dtype}879        return type(self)(result, fill_value=self.fill_value, kind=self.kind,880                          **kwargs)881    def _take_with_fill(self, indices, fill_value=None):882        if fill_value is None:883            fill_value = self.dtype.na_value884        if indices.min() < -1:885            raise ValueError("Invalid value in 'indices'. Must be between -1 "886                             "and the length of the array.")887        if indices.max() >= len(self):888            raise IndexError("out of bounds value in 'indices'.")889        if len(self) == 0:890            # Empty... Allow taking only if all empty891            if (indices == -1).all():892                dtype = np.result_type(self.sp_values, type(fill_value))893                taken = np.empty_like(indices, dtype=dtype)894                taken.fill(fill_value)895                return taken896            else:897                raise IndexError('cannot do a non-empty take from an empty '898                                 'axes.')899        sp_indexer = self.sp_index.lookup_array(indices)900        if self.sp_index.npoints == 0:901            # Avoid taking from the empty self.sp_values902            taken = np.full(sp_indexer.shape, fill_value=fill_value,903                            dtype=np.result_type(type(fill_value)))904        else:905            taken = self.sp_values.take(sp_indexer)906            # sp_indexer may be -1 for two reasons907            # 1.) we took for an index of -1 (new)908            # 2.) we took a value that was self.fill_value (old)909            new_fill_indices = indices == -1910            old_fill_indices = (sp_indexer == -1) & ~new_fill_indices911            # Fill in two steps.912            # Old fill values913            # New fill values914            # potentially coercing to a new dtype at each stage.915            m0 = sp_indexer[old_fill_indices] < 0916            m1 = sp_indexer[new_fill_indices] < 0917            result_type = taken.dtype918            if m0.any():919                result_type = np.result_type(result_type,920                                             type(self.fill_value))921                taken = taken.astype(result_type)922                taken[old_fill_indices] = self.fill_value923            if m1.any():924                result_type = np.result_type(result_type, type(fill_value))925                taken = taken.astype(result_type)926                taken[new_fill_indices] = fill_value927        return taken928    def _take_without_fill(self, indices):929        to_shift = indices < 0930        indices = indices.copy()931        n = len(self)932        if (indices.max() >= n) or (indices.min() < -n):933            if n == 0:934                raise IndexError("cannot do a non-empty take from an "935                                 "empty axes.")936            else:937                raise IndexError("out of bounds value in 'indices'.")938        if to_shift.any():939            indices[to_shift] += n940        if self.sp_index.npoints == 0:941            # edge case in take...942            # I think just return943            out = np.full(indices.shape, self.fill_value,944                          dtype=np.result_type(type(self.fill_value)))945            arr, sp_index, fill_value = make_sparse(out,946                                                    fill_value=self.fill_value)947            return type(self)(arr, sparse_index=sp_index,948                              fill_value=fill_value)949        sp_indexer = self.sp_index.lookup_array(indices)950        taken = self.sp_values.take(sp_indexer)951        fillable = (sp_indexer < 0)952        if fillable.any():953            # TODO: may need to coerce array to fill value954            result_type = np.result_type(taken, type(self.fill_value))955            taken = taken.astype(result_type)956            taken[fillable] = self.fill_value957        return taken958    def searchsorted(self, v, side="left", sorter=None):959        msg = "searchsorted requires high memory usage."960        warnings.warn(msg, PerformanceWarning, stacklevel=2)961        if not is_scalar(v):962            v = np.asarray(v)963        v = np.asarray(v)964        return np.asarray(self, dtype=self.dtype.subtype).searchsorted(965            v, side, sorter966        )967    def copy(self, deep=False):968        if deep:969            values = self.sp_values.copy()970        else:971            values = self.sp_values972        return self._simple_new(values, self.sp_index, self.dtype)973    @classmethod974    def _concat_same_type(cls, to_concat):975        fill_values = [x.fill_value for x in to_concat]976        fill_value = fill_values[0]977        # np.nan isn't a singleton, so we may end up with multiple978        # NaNs here, so we ignore tha all NA case too.979        if not (len(set(fill_values)) == 1 or isna(fill_values).all()):980            warnings.warn("Concatenating sparse arrays with multiple fill "981                          "values: '{}'. Picking the first and "982                          "converting the rest.".format(fill_values),983                          PerformanceWarning,984                          stacklevel=6)985            keep = to_concat[0]986            to_concat2 = [keep]987            for arr in to_concat[1:]:988                to_concat2.append(cls(np.asarray(arr), fill_value=fill_value))989            to_concat = to_concat2990        values = []991        length = 0992        if to_concat:993            sp_kind = to_concat[0].kind994        else:995            sp_kind = 'integer'996        if sp_kind == 'integer':997            indices = []998            for arr in to_concat:999                idx = arr.sp_index.to_int_index().indices.copy()1000                idx += length  # TODO: wraparound1001                length += arr.sp_index.length1002                values.append(arr.sp_values)1003                indices.append(idx)1004            data = np.concatenate(values)1005            indices = np.concatenate(indices)1006            sp_index = IntIndex(length, indices)1007        else:1008            # when concatentating block indices, we don't claim that you'll1009            # get an identical index as concating the values and then1010            # creating a new index. We don't want to spend the time trying1011            # to merge blocks across arrays in `to_concat`, so the resulting1012            # BlockIndex may have more blocs.1013            blengths = []1014            blocs = []1015            for arr in to_concat:1016                idx = arr.sp_index.to_block_index()1017                values.append(arr.sp_values)1018                blocs.append(idx.blocs.copy() + length)1019                blengths.append(idx.blengths)1020                length += arr.sp_index.length1021            data = np.concatenate(values)1022            blocs = np.concatenate(blocs)1023            blengths = np.concatenate(blengths)1024            sp_index = BlockIndex(length, blocs, blengths)1025        return cls(data, sparse_index=sp_index, fill_value=fill_value)1026    def astype(self, dtype=None, copy=True):1027        """1028        Change the dtype of a SparseArray.1029        The output will always be a SparseArray. To convert to a dense1030        ndarray with a certain dtype, use :meth:`numpy.asarray`.1031        Parameters1032        ----------1033        dtype : np.dtype or ExtensionDtype1034            For SparseDtype, this changes the dtype of1035            ``self.sp_values`` and the ``self.fill_value``.1036            For other dtypes, this only changes the dtype of1037            ``self.sp_values``.1038        copy : bool, default True1039            Whether to ensure a copy is made, even if not necessary.1040        Returns1041        -------1042        SparseArray1043        Examples1044        --------1045        >>> arr = SparseArray([0, 0, 1, 2])1046        >>> arr1047        [0, 0, 1, 2]1048        Fill: 01049        IntIndex1050        Indices: array([2, 3], dtype=int32)1051        >>> arr.astype(np.dtype('int32'))1052        [0, 0, 1, 2]1053        Fill: 01054        IntIndex1055        Indices: array([2, 3], dtype=int32)1056        Using a NumPy dtype with a different kind (e.g. float) will coerce1057        just ``self.sp_values``.1058        >>> arr.astype(np.dtype('float64'))1059        ... # doctest: +NORMALIZE_WHITESPACE1060        [0, 0, 1.0, 2.0]1061        Fill: 01062        IntIndex1063        Indices: array([2, 3], dtype=int32)1064        Use a SparseDtype if you wish to be change the fill value as well.1065        >>> arr.astype(SparseDtype("float64", fill_value=np.nan))1066        ... # doctest: +NORMALIZE_WHITESPACE1067        [nan, nan, 1.0, 2.0]1068        Fill: nan1069        IntIndex1070        Indices: array([2, 3], dtype=int32)1071        """1072        dtype = self.dtype.update_dtype(dtype)1073        subtype = dtype._subtype_with_str1074        sp_values = astype_nansafe(self.sp_values,1075                                   subtype,1076                                   copy=copy)1077        if sp_values is self.sp_values and copy:1078            sp_values = sp_values.copy()1079        return self._simple_new(sp_values,1080                                self.sp_index,1081                                dtype)1082    def map(self, mapper):1083        """1084        Map categories using input correspondence (dict, Series, or function).1085        Parameters1086        ----------1087        mapper : dict, Series, callable1088            The correspondence from old values to new.1089        Returns1090        -------1091        SparseArray1092            The output array will have the same density as the input.1093            The output fill value will be the result of applying the1094            mapping to ``self.fill_value``1095        Examples1096        --------1097        >>> arr = pd.SparseArray([0, 1, 2])1098        >>> arr.apply(lambda x: x + 10)1099        [10, 11, 12]1100        Fill: 101101        IntIndex1102        Indices: array([1, 2], dtype=int32)1103        >>> arr.apply({0: 10, 1: 11, 2: 12})1104        [10, 11, 12]1105        Fill: 101106        IntIndex1107        Indices: array([1, 2], dtype=int32)1108        >>> arr.apply(pd.Series([10, 11, 12], index=[0, 1, 2]))1109        [10, 11, 12]1110        Fill: 101111        IntIndex1112        Indices: array([1, 2], dtype=int32)1113        """1114        # this is used in apply.1115        # We get hit since we're an "is_extension_type" but regular extension1116        # types are not hit. This may be worth adding to the interface.1117        if isinstance(mapper, ABCSeries):1118            mapper = mapper.to_dict()1119        if isinstance(mapper, compat.Mapping):1120            fill_value = mapper.get(self.fill_value, self.fill_value)1121            sp_values = [mapper.get(x, None) for x in self.sp_values]1122        else:1123            fill_value = mapper(self.fill_value)1124            sp_values = [mapper(x) for x in self.sp_values]1125        return type(self)(sp_values, sparse_index=self.sp_index,1126                          fill_value=fill_value)1127    def to_dense(self):1128        """1129        Convert SparseArray to a NumPy array.1130        Returns1131        -------1132        arr : NumPy array1133        """1134        return np.asarray(self, dtype=self.sp_values.dtype)1135    # TODO: Look into deprecating this in favor of `to_dense`.1136    get_values = to_dense1137    # ------------------------------------------------------------------------1138    # IO1139    # ------------------------------------------------------------------------1140    def __setstate__(self, state):1141        """Necessary for making this object picklable"""1142        if isinstance(state, tuple):1143            # Compat for pandas < 0.24.01144            nd_state, (fill_value, sp_index) = state1145            sparse_values = np.array([])1146            sparse_values.__setstate__(nd_state)1147            self._sparse_values = sparse_values1148            self._sparse_index = sp_index1149            self._dtype = SparseDtype(sparse_values.dtype, fill_value)1150        else:1151            self.__dict__.update(state)1152    def nonzero(self):1153        if self.fill_value == 0:1154            return self.sp_index.to_int_index().indices,1155        else:1156            return self.sp_index.to_int_index().indices[self.sp_values != 0],1157    # ------------------------------------------------------------------------1158    # Reductions1159    # ------------------------------------------------------------------------1160    def _reduce(self, name, skipna=True, **kwargs):1161        method = getattr(self, name, None)1162        if method is None:1163            raise TypeError("cannot perform {name} with type {dtype}".format(1164                name=name, dtype=self.dtype))1165        if skipna:1166            arr = self1167        else:1168            arr = self.dropna()1169        # we don't support these kwargs.1170        # They should only be present when called via pandas, so do it here.1171        # instead of in `any` / `all` (which will raise if they're present,1172        # thanks to nv.validate1173        kwargs.pop('filter_type', None)1174        kwargs.pop('numeric_only', None)1175        kwargs.pop('op', None)1176        return getattr(arr, name)(**kwargs)1177    def all(self, axis=None, *args, **kwargs):1178        """1179        Tests whether all elements evaluate True1180        Returns1181        -------1182        all : bool1183        See Also1184        --------1185        numpy.all1186        """1187        nv.validate_all(args, kwargs)1188        values = self.sp_values1189        if len(values) != len(self) and not np.all(self.fill_value):1190            return False1191        return values.all()1192    def any(self, axis=0, *args, **kwargs):1193        """1194        Tests whether at least one of elements evaluate True1195        Returns1196        -------1197        any : bool1198        See Also1199        --------1200        numpy.any1201        """1202        nv.validate_any(args, kwargs)1203        values = self.sp_values1204        if len(values) != len(self) and np.any(self.fill_value):1205            return True1206        return values.any().item()1207    def sum(self, axis=0, *args, **kwargs):1208        """1209        Sum of non-NA/null values1210        Returns1211        -------1212        sum : float1213        """1214        nv.validate_sum(args, kwargs)1215        valid_vals = self._valid_sp_values1216        sp_sum = valid_vals.sum()1217        if self._null_fill_value:1218            return sp_sum1219        else:1220            nsparse = self.sp_index.ngaps1221            return sp_sum + self.fill_value * nsparse1222    def cumsum(self, axis=0, *args, **kwargs):1223        """1224        Cumulative sum of non-NA/null values.1225        When performing the cumulative summation, any non-NA/null values will1226        be skipped. The resulting SparseArray will preserve the locations of1227        NaN values, but the fill value will be `np.nan` regardless.1228        Parameters1229        ----------1230        axis : int or None1231            Axis over which to perform the cumulative summation. If None,1232            perform cumulative summation over flattened array.1233        Returns1234        -------1235        cumsum : SparseArray1236        """1237        nv.validate_cumsum(args, kwargs)1238        if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.1239            raise ValueError("axis(={axis}) out of bounds".format(axis=axis))1240        if not self._null_fill_value:1241            return SparseArray(self.to_dense()).cumsum()1242        return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index,1243                           fill_value=self.fill_value)1244    def mean(self, axis=0, *args, **kwargs):1245        """1246        Mean of non-NA/null values1247        Returns1248        -------1249        mean : float1250        """1251        nv.validate_mean(args, kwargs)1252        valid_vals = self._valid_sp_values1253        sp_sum = valid_vals.sum()1254        ct = len(valid_vals)1255        if self._null_fill_value:1256            return sp_sum / ct1257        else:1258            nsparse = self.sp_index.ngaps1259            return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)1260    def transpose(self, *axes):1261        """1262        Returns the SparseArray.1263        """1264        return self1265    @property1266    def T(self):1267        """1268        Returns the SparseArray.1269        """1270        return self1271    # ------------------------------------------------------------------------1272    # Ufuncs1273    # ------------------------------------------------------------------------1274    def __array_wrap__(self, array, context=None):1275        from pandas.core.dtypes.generic import ABCSparseSeries1276        ufunc, inputs, _ = context1277        inputs = tuple(x.values if isinstance(x, ABCSparseSeries) else x1278                       for x in inputs)1279        return self.__array_ufunc__(ufunc, '__call__', *inputs)1280    _HANDLED_TYPES = (np.ndarray, numbers.Number)1281    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):1282        out = kwargs.get('out', ())1283        for x in inputs + out:1284            if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):1285                return NotImplemented1286        special = {'add', 'sub', 'mul', 'pow', 'mod', 'floordiv', 'truediv',1287                   'divmod', 'eq', 'ne', 'lt', 'gt', 'le', 'ge', 'remainder'}1288        if compat.PY2:1289            special.add('div')1290        aliases = {1291            'subtract': 'sub',1292            'multiply': 'mul',1293            'floor_divide': 'floordiv',1294            'true_divide': 'truediv',1295            'power': 'pow',1296            'remainder': 'mod',1297            'divide': 'div',1298            'equal': 'eq',1299            'not_equal': 'ne',1300            'less': 'lt',1301            'less_equal': 'le',1302            'greater': 'gt',1303            'greater_equal': 'ge',1304        }1305        flipped = {1306            'lt': '__gt__',1307            'le': '__ge__',1308            'gt': '__lt__',1309            'ge': '__le__',1310            'eq': '__eq__',1311            'ne': '__ne__',1312        }1313        op_name = ufunc.__name__1314        op_name = aliases.get(op_name, op_name)1315        if op_name in special and kwargs.get('out') is None:1316            if isinstance(inputs[0], type(self)):1317                return getattr(self, '__{}__'.format(op_name))(inputs[1])1318            else:1319                name = flipped.get(op_name, '__r{}__'.format(op_name))1320                return getattr(self, name)(inputs[0])1321        if len(inputs) == 1:1322            # No alignment necessary.1323            sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)1324            fill_value = getattr(ufunc, method)(self.fill_value, **kwargs)1325            return self._simple_new(sp_values,1326                                    self.sp_index,1327                                    SparseDtype(sp_values.dtype, fill_value))1328        result = getattr(ufunc, method)(*[np.asarray(x) for x in inputs],1329                                        **kwargs)1330        if out:1331            if len(out) == 1:1332                out = out[0]1333            return out1334        if type(result) is tuple:1335            return tuple(type(self)(x) for x in result)1336        elif method == 'at':1337            # no return value1338            return None1339        else:1340            return type(self)(result)1341    def __abs__(self):1342        return np.abs(self)1343    # ------------------------------------------------------------------------1344    # Ops1345    # ------------------------------------------------------------------------1346    @classmethod1347    def _create_unary_method(cls, op):1348        def sparse_unary_method(self):1349            fill_value = op(np.array(self.fill_value)).item()1350            values = op(self.sp_values)1351            dtype = SparseDtype(values.dtype, fill_value)1352            return cls._simple_new(values, self.sp_index, dtype)1353        name = '__{name}__'.format(name=op.__name__)1354        return compat.set_function_name(sparse_unary_method, name, cls)1355    @classmethod1356    def _create_arithmetic_method(cls, op):1357        def sparse_arithmetic_method(self, other):1358            op_name = op.__name__1359            if isinstance(other, (ABCSeries, ABCIndexClass)):1360                # Rely on pandas to dispatch to us.1361                return NotImplemented1362            if isinstance(other, SparseArray):1363                return _sparse_array_op(self, other, op, op_name)1364            elif is_scalar(other):1365                with np.errstate(all='ignore'):1366                    fill = op(_get_fill(self), np.asarray(other))1367                    result = op(self.sp_values, other)1368                if op_name == 'divmod':1369                    left, right = result1370                    lfill, rfill = fill1371                    return (_wrap_result(op_name, left, self.sp_index, lfill),1372                            _wrap_result(op_name, right, self.sp_index, rfill))1373                return _wrap_result(op_name, result, self.sp_index, fill)1374            else:1375                other = np.asarray(other)1376                with np.errstate(all='ignore'):1377                    # TODO: delete sparse stuff in core/ops.py1378                    # TODO: look into _wrap_result1379                    if len(self) != len(other):1380                        raise AssertionError(...

0002_auto_20210108_1807.py

Source:0002_auto_20210108_1807.py

1# Generated by Django 3.1.4 on 2021-01-08 18:072from django.db import migrations, models3class Migration(migrations.Migration):4    dependencies = [5        ('module', '0001_initial'),6    ]7    operations = [8        migrations.RemoveField(9            model_name='module',10            name='icon',11        ),12        migrations.AddField(13            model_name='module',14            name='icon_name',15            field=models.CharField(blank=True, choices=[('house-door.svg', 'house-door.svg'), ('graph-up.svg', 'graph-up.svg'), ('sort-alpha-up-alt.svg', 'sort-alpha-up-alt.svg'), ('eye-slash-fill.svg', 'eye-slash-fill.svg'), ('arrows-angle-expand.svg', 'arrows-angle-expand.svg'), ('layout-text-window.svg', 'layout-text-window.svg'), ('textarea.svg', 'textarea.svg'), ('slack.svg', 'slack.svg'), ('tv-fill.svg', 'tv-fill.svg'), ('hash.svg', 'hash.svg'), ('file-medical-fill.svg', 'file-medical-fill.svg'), ('calculator-fill.svg', 'calculator-fill.svg'), ('twitter.svg', 'twitter.svg'), ('bookmark-heart.svg', 'bookmark-heart.svg'), ('star.svg', 'star.svg'), ('at.svg', 'at.svg'), ('type-italic.svg', 'type-italic.svg'), ('hand-index.svg', 'hand-index.svg'), ('volume-off.svg', 'volume-off.svg'), ('telephone-plus-fill.svg', 'telephone-plus-fill.svg'), ('arrow-down-left.svg', 'arrow-down-left.svg'), ('hand-index-thumb.svg', 'hand-index-thumb.svg'), ('calendar2-range.svg', 'calendar2-range.svg'), ('dice-4.svg', 'dice-4.svg'), ('inboxes-fill.svg', 'inboxes-fill.svg'), ('bag-x-fill.svg', 'bag-x-fill.svg'), ('caret-down-square-fill.svg', 'caret-down-square-fill.svg'), ('thermometer-half.svg', 'thermometer-half.svg'), ('calendar2-day-fill.svg', 'calendar2-day-fill.svg'), ('alt.svg', 'alt.svg'), ('heart-half.svg', 'heart-half.svg'), ('pause-btn.svg', 'pause-btn.svg'), ('plus-circle-fill.svg', 'plus-circle-fill.svg'), ('mic.svg', 'mic.svg'), ('person-lines-fill.svg', 'person-lines-fill.svg'), ('markdown.svg', 'markdown.svg'), ('shield-minus.svg', 'shield-minus.svg'), ('file-earmark-richtext-fill.svg', 'file-earmark-richtext-fill.svg'), ('trophy-fill.svg', 'trophy-fill.svg'), ('hdd.svg', 'hdd.svg'), ('shop.svg', 'shop.svg'), ('skip-backward-circle.svg', 'skip-backward-circle.svg'), ('bar-chart.svg', 'bar-chart.svg'), ('phone-landscape.svg', 'phone-landscape.svg'), ('suit-spade.svg', 'suit-spade.svg'), ('wrench.svg', 'wrench.svg'), ('badge-vo.svg', 'badge-vo.svg'), ('house-door-fill.svg', 'house-door-fill.svg'), ('sort-down.svg', 'sort-down.svg'), ('plus-circle.svg', 'plus-circle.svg'), ('skip-end-circle.svg', 'skip-end-circle.svg'), ('inboxes.svg', 'inboxes.svg'), ('envelope-open-fill.svg', 'envelope-open-fill.svg'), ('file-arrow-down.svg', 'file-arrow-down.svg'), ('file-x.svg', 'file-x.svg'), ('record-btn.svg', 'record-btn.svg'), ('basket.svg', 'basket.svg'), ('award-fill.svg', 'award-fill.svg'), ('badge-8k.svg', 'badge-8k.svg'), ('flower3.svg', 'flower3.svg'), ('skip-start-circle-fill.svg', 'skip-start-circle-fill.svg'), ('voicemail.svg', 'voicemail.svg'), ('arrow-up-down.svg', 'arrow-up-down.svg'), ('ui-checks-grid.svg', 'ui-checks-grid.svg'), ('upc.svg', 'upc.svg'), ('tablet.svg', 'tablet.svg'), ('bag-plus.svg', 'bag-plus.svg'), ('calendar3.svg', 'calendar3.svg'), ('calendar2-x-fill.svg', 'calendar2-x-fill.svg'), ('arrow-up-left-circle.svg', 'arrow-up-left-circle.svg'), ('chat-square-text.svg', 'chat-square-text.svg'), ('toggle-on.svg', 'toggle-on.svg'), ('patch-plus.svg', 'patch-plus.svg'), ('peace.svg', 'peace.svg'), ('file-word.svg', 'file-word.svg'), ('chat-right-quote.svg', 'chat-right-quote.svg'), ('file-earmark-person.svg', 'file-earmark-person.svg'), ('bell.svg', 'bell.svg'), ('share.svg', 'share.svg'), ('easel.svg', 'easel.svg'), ('layout-text-window-reverse.svg', 'layout-text-window-reverse.svg'), ('file-bar-graph-fill.svg', 'file-bar-graph-fill.svg'), ('pentagon.svg', 'pentagon.svg'), ('table.svg', 'table.svg'), ('patch-question.svg', 'patch-question.svg'), ('gem.svg', 'gem.svg'), ('person-fill.svg', 'person-fill.svg'), ('file-earmark-excel-fill.svg', 'file-earmark-excel-fill.svg'), ('bookmark-star-fill.svg', 'bookmark-star-fill.svg'), ('option.svg', 'option.svg'), ('stop-circle.svg', 'stop-circle.svg'), ('layout-sidebar-inset-reverse.svg', 'layout-sidebar-inset-reverse.svg'), ('bag-check.svg', 'bag-check.svg'), ('badge-cc.svg', 'badge-cc.svg'), ('octagon-fill.svg', 'octagon-fill.svg'), ('pause-circle.svg', 'pause-circle.svg'), ('arrow-down-square.svg', 'arrow-down-square.svg'), ('hdd-network-fill.svg', 'hdd-network-fill.svg'), ('chevron-right.svg', 'chevron-right.svg'), ('peace-fill.svg', 'peace-fill.svg'), ('journal-bookmark-fill.svg', 'journal-bookmark-fill.svg'), ('collection.svg', 'collection.svg'), ('headphones.svg', 'headphones.svg'), ('chat-square.svg', 'chat-square.svg'), ('filter-square.svg', 'filter-square.svg'), ('blockquote-left.svg', 'blockquote-left.svg'), ('folder-fill.svg', 'folder-fill.svg'), ('crop.svg', 'crop.svg'), ('play-circle.svg', 'play-circle.svg'), 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0003_auto_20210110_1957.py

Source:0003_auto_20210110_1957.py

1# Generated by Django 3.1.4 on 2021-01-10 19:572from django.db import migrations, models3class Migration(migrations.Migration):4    dependencies = [5        ('module', '0002_auto_20210108_1807'),6    ]7    operations = [8        migrations.AlterField(9            model_name='module',10            name='icon_name',11            field=models.CharField(blank=True, choices=[('alarm-fill.svg', 'alarm-fill.svg'), ('alarm.svg', 'alarm.svg'), ('align-bottom.svg', 'align-bottom.svg'), ('align-center.svg', 'align-center.svg'), ('align-end.svg', 'align-end.svg'), ('align-middle.svg', 'align-middle.svg'), ('align-start.svg', 'align-start.svg'), ('align-top.svg', 'align-top.svg'), ('alt.svg', 'alt.svg'), ('app-indicator.svg', 'app-indicator.svg'), ('app.svg', 'app.svg'), ('archive-fill.svg', 'archive-fill.svg'), ('archive.svg', 'archive.svg'), ('arrow-90deg-down.svg', 'arrow-90deg-down.svg'), ('arrow-90deg-left.svg', 'arrow-90deg-left.svg'), ('arrow-90deg-right.svg', 'arrow-90deg-right.svg'), 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'text-left.svg'), ('text-paragraph.svg', 'text-paragraph.svg'), ('text-right.svg', 'text-right.svg'), ('textarea-resize.svg', 'textarea-resize.svg'), ('textarea-t.svg', 'textarea-t.svg'), ('textarea.svg', 'textarea.svg'), ('thermometer-half.svg', 'thermometer-half.svg'), ('thermometer.svg', 'thermometer.svg'), ('three-dots-vertical.svg', 'three-dots-vertical.svg'), ('three-dots.svg', 'three-dots.svg'), ('toggle-off.svg', 'toggle-off.svg'), ('toggle-on.svg', 'toggle-on.svg'), ('toggle2-off.svg', 'toggle2-off.svg'), ('toggle2-on.svg', 'toggle2-on.svg'), ('toggles.svg', 'toggles.svg'), ('toggles2.svg', 'toggles2.svg'), ('tools.svg', 'tools.svg'), ('trash-fill.svg', 'trash-fill.svg'), ('trash.svg', 'trash.svg'), ('trash2-fill.svg', 'trash2-fill.svg'), ('trash2.svg', 'trash2.svg'), ('tree-fill.svg', 'tree-fill.svg'), ('tree.svg', 'tree.svg'), ('triangle-fill.svg', 'triangle-fill.svg'), ('triangle-half.svg', 'triangle-half.svg'), ('triangle.svg', 'triangle.svg'), ('trophy-fill.svg', 'trophy-fill.svg'), ('trophy.svg', 'trophy.svg'), ('truck-flatbed.svg', 'truck-flatbed.svg'), ('truck.svg', 'truck.svg'), ('tv-fill.svg', 'tv-fill.svg'), ('tv.svg', 'tv.svg'), ('twitch.svg', 'twitch.svg'), ('twitter.svg', 'twitter.svg'), ('type-bold.svg', 'type-bold.svg'), ('type-h1.svg', 'type-h1.svg'), ('type-h2.svg', 'type-h2.svg'), ('type-h3.svg', 'type-h3.svg'), ('type-italic.svg', 'type-italic.svg'), ('type-strikethrough.svg', 'type-strikethrough.svg'), ('type-underline.svg', 'type-underline.svg'), ('type.svg', 'type.svg'), ('ui-checks-grid.svg', 'ui-checks-grid.svg'), ('ui-checks.svg', 'ui-checks.svg'), ('ui-radios-grid.svg', 'ui-radios-grid.svg'), ('ui-radios.svg', 'ui-radios.svg'), ('union.svg', 'union.svg'), ('unlock-fill.svg', 'unlock-fill.svg'), ('unlock.svg', 'unlock.svg'), ('upc-scan.svg', 'upc-scan.svg'), ('upc.svg', 'upc.svg'), ('upload.svg', 'upload.svg'), ('vector-pen.svg', 'vector-pen.svg'), ('view-list.svg', 'view-list.svg'), ('view-stacked.svg', 'view-stacked.svg'), ('vinyl-fill.svg', 'vinyl-fill.svg'), ('vinyl.svg', 'vinyl.svg'), ('voicemail.svg', 'voicemail.svg'), ('volume-down-fill.svg', 'volume-down-fill.svg'), ('volume-down.svg', 'volume-down.svg'), ('volume-mute-fill.svg', 'volume-mute-fill.svg'), ('volume-mute.svg', 'volume-mute.svg'), ('volume-off-fill.svg', 'volume-off-fill.svg'), ('volume-off.svg', 'volume-off.svg'), ('volume-up-fill.svg', 'volume-up-fill.svg'), ('volume-up.svg', 'volume-up.svg'), ('vr.svg', 'vr.svg'), ('wallet-fill.svg', 'wallet-fill.svg'), ('wallet.svg', 'wallet.svg'), ('wallet2.svg', 'wallet2.svg'), ('watch.svg', 'watch.svg'), ('wifi-1.svg', 'wifi-1.svg'), ('wifi-2.svg', 'wifi-2.svg'), ('wifi-off.svg', 'wifi-off.svg'), ('wifi.svg', 'wifi.svg'), ('window.svg', 'window.svg'), ('wrench.svg', 'wrench.svg'), ('x-circle-fill.svg', 'x-circle-fill.svg'), ('x-circle.svg', 'x-circle.svg'), ('x-diamond-fill.svg', 'x-diamond-fill.svg'), ('x-diamond.svg', 'x-diamond.svg'), ('x-octagon-fill.svg', 'x-octagon-fill.svg'), ('x-octagon.svg', 'x-octagon.svg'), ('x-square-fill.svg', 'x-square-fill.svg'), ('x-square.svg', 'x-square.svg'), ('x.svg', 'x.svg'), ('youtube.svg', 'youtube.svg'), ('zoom-in.svg', 'zoom-in.svg'), ('zoom-out.svg', 'zoom-out.svg')], max_length=100, verbose_name='Ãcono'),12        ),...

test_array.py

Source:test_array.py

...937])938@pytest.mark.parametrize("fill_value", [939    np.nan, 0, 1940])941def test_unique_na_fill(arr, fill_value):942    a = pd.SparseArray(arr, fill_value=fill_value).unique()943    b = pd.Series(arr).unique()944    assert isinstance(a, SparseArray)945    a = np.asarray(a)946    tm.assert_numpy_array_equal(a, b)947def test_unique_all_sparse():948    # https://github.com/pandas-dev/pandas/issues/23168949    arr = SparseArray([0, 0])950    result = arr.unique()951    expected = SparseArray([0])952    tm.assert_sp_array_equal(result, expected)953def test_map():954    arr = SparseArray([0, 1, 2])955    expected = SparseArray([10, 11, 12], fill_value=10)...

Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

Chapters:

What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.