BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def astype(self, dtype=None, copy=True): """ Change the dtype of a SparseArray. The output will always be a SparseArray. To convert to a dense ndarray with a certain dtype, use :meth:`numpy.asarray`. Parameters ---------- dtype : np.dtype or ExtensionDtype ...
def map(self, mapper): """ Map categories using input correspondence (dict, Series, or function). Parameters ---------- mapper : dict, Series, callable The correspondence from old values to new. Returns ------- SparseArray The out...
def all(self, axis=None, args, *kwargs): """ Tests whether all elements evaluate True Returns ------- all : bool See Also -------- numpy.all """ nv.validate_all(args, kwargs) values = self.sp_values if len(values) != l...
def any(self, axis=0, args, *kwargs): """ Tests whether at least one of elements evaluate True Returns ------- any : bool See Also -------- numpy.any """ nv.validate_any(args, kwargs) values = self.sp_values if len(val...
def sum(self, axis=0, args, *kwargs): """ Sum of non-NA/null values Returns ------- sum : float """ nv.validate_sum(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() if self._null_fill_value: return s...
def cumsum(self, axis=0, args, *kwargs): """ Cumulative sum of non-NA/null values. When performing the cumulative summation, any non-NA/null values will be skipped. The resulting SparseArray will preserve the locations of NaN values, but the fill value will be `np.nan` regardl...
def mean(self, axis=0, args, *kwargs): """ Mean of non-NA/null values Returns ------- mean : float """ nv.validate_mean(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() ct = len(valid_vals) if self._nul...
def tokenize_string(source): """Tokenize a Python source code string. Parameters ---------- source : str A Python source code string """ line_reader = StringIO(source).readline token_generator = tokenize.generate_tokens(line_reader) # Loop over all tokens till a backtick (`) is...
def _replace_booleans(tok): """Replace ``&`` with ``and`` and ``\|`` with ``or`` so that bitwise precedence is changed to boolean precedence. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple ...
def _replace_locals(tok): """Replace local variables with a syntactically valid name. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple of int, str Either the input or token or the replac...
def _clean_spaces_backtick_quoted_names(tok): """Clean up a column name if surrounded by backticks. Backtick quoted string are indicated by a certain tokval value. If a string is a backtick quoted token it will processed by :func:`_remove_spaces_column_name` so that the parser can find this string ...
def _preparse(source, f=_compose(_replace_locals, _replace_booleans, _rewrite_assign, _clean_spaces_backtick_quoted_names)): """Compose a collection of tokenization functions Parameters ---------- source : str A Python source cod...
def _filter_nodes(superclass, all_nodes=_all_nodes): """Filter out AST nodes that are subclasses of ``superclass``.""" node_names = (node.__name__ for node in all_nodes if issubclass(node, superclass)) return frozenset(node_names)
def _node_not_implemented(node_name, cls): """Return a function that raises a NotImplementedError with a passed node name. """ def f(self, args, *kwargs): raise NotImplementedError("{name!r} nodes are not " "implemented".format(name=node_name)) return f
def disallow(nodes): """Decorator to disallow certain nodes from parsing. Raises a NotImplementedError instead. Returns ------- disallowed : callable """ def disallowed(cls): cls.unsupported_nodes = () for node in nodes: new_method = _node_not_implemented(node, c...
def _op_maker(op_class, op_symbol): """Return a function to create an op class with its symbol already passed. Returns ------- f : callable """ def f(self, node, args, *kwargs): """Return a partial function with an Op subclass with an operator already passed. Returns...
def add_ops(op_classes): """Decorator to add default implementation of ops.""" def f(cls): for op_attr_name, op_class in op_classes.items(): ops = getattr(cls, '{name}_ops'.format(name=op_attr_name)) ops_map = getattr(cls, '{name}_op_nodes_map'.format( name=op_att...
def names(self): """Get the names in an expression""" if is_term(self.terms): return frozenset([self.terms.name]) return frozenset(term.name for term in com.flatten(self.terms))
def _is_convertible_to_index(other): """ return a boolean whether I can attempt conversion to a TimedeltaIndex """ if isinstance(other, TimedeltaIndex): return True elif (len(other) > 0 and other.inferred_type not in ('floating', 'mixed-integer', 'integer', ...
def timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None): """ Return a fixed frequency TimedeltaIndex, with day as the default frequency Parameters ---------- start : string or timedelta-like, default None Left bound for generating t...
def union(self, other): """ Returns a FrozenList with other concatenated to the end of self. Parameters ---------- other : array-like The array-like whose elements we are concatenating. Returns ------- diff : FrozenList The collec...
def difference(self, other): """ Returns a FrozenList with elements from other removed from self. Parameters ---------- other : array-like The array-like whose elements we are removing self. Returns ------- diff : FrozenList The c...
def searchsorted(self, value, side="left", sorter=None): """ Find indices to insert `value` so as to maintain order. For full documentation, see `numpy.searchsorted` See Also -------- numpy.searchsorted : Equivalent function. """ # We are much more perf...
def arrays_to_mgr(arrays, arr_names, index, columns, dtype=None): """ Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. """ # figure out the index, if necessary if index is None: index = extract_index(arrays) else: index = e...
def masked_rec_array_to_mgr(data, index, columns, dtype, copy): """ Extract from a masked rec array and create the manager. """ # essentially process a record array then fill it fill_value = data.fill_value fdata = ma.getdata(data) if index is None: index = get_names_from_index(fdat...
def init_dict(data, index, columns, dtype=None): """ Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. """ if columns is not None: from pandas.core.series import Series arrays = Series(data, index=columns, dtype=object) data_...
def to_arrays(data, columns, coerce_float=False, dtype=None): """ Return list of arrays, columns. """ if isinstance(data, ABCDataFrame): if columns is not None: arrays = [data._ixs(i, axis=1).values for i, col in enumerate(data.columns) if col in columns] ...
def sanitize_index(data, index, copy=False): """ Sanitize an index type to return an ndarray of the underlying, pass through a non-Index. """ if index is None: return data if len(data) != len(index): raise ValueError('Length of values does not match length of index') if is...
def sanitize_array(data, index, dtype=None, copy=False, raise_cast_failure=False): """ Sanitize input data to an ndarray, copy if specified, coerce to the dtype if specified. """ if dtype is not None: dtype = pandas_dtype(dtype) if isinstance(data, ma.MaskedArray): ...
def _check_engine(engine): """Make sure a valid engine is passed. Parameters ---------- engine : str Raises ------ KeyError * If an invalid engine is passed ImportError * If numexpr was requested but doesn't exist Returns ------- string engine """ from...
def _check_parser(parser): """Make sure a valid parser is passed. Parameters ---------- parser : str Raises ------ KeyError * If an invalid parser is passed """ from pandas.core.computation.expr import _parsers if parser not in _parsers: raise KeyError('Invalid p...
def eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False): """Evaluate a Python expression as a string using various backends. The following arithmetic operations are supported: ``+``, ``-``, ``*``, ``/...
def _codes_to_ints(self, codes): """ Transform combination(s) of uint64 in one uint64 (each), in a strictly monotonic way (i.e. respecting the lexicographic order of integer combinations): see BaseMultiIndexCodesEngine documentation. Parameters ---------- codes :...
def from_arrays(cls, arrays, sortorder=None, names=None): """ Convert arrays to MultiIndex. Parameters ---------- arrays : list / sequence of array-likes Each array-like gives one level's value for each data point. len(arrays) is the number of levels. ...
def from_tuples(cls, tuples, sortorder=None, names=None): """ Convert list of tuples to MultiIndex. Parameters ---------- tuples : list / sequence of tuple-likes Each tuple is the index of one row/column. sortorder : int or None Level of sortednes...
def from_product(cls, iterables, sortorder=None, names=None): """ Make a MultiIndex from the cartesian product of multiple iterables. Parameters ---------- iterables : list / sequence of iterables Each iterable has unique labels for each level of the index. s...
def from_frame(cls, df, sortorder=None, names=None): """ Make a MultiIndex from a DataFrame. .. versionadded:: 0.24.0 Parameters ---------- df : DataFrame DataFrame to be converted to MultiIndex. sortorder : int, optional Level of sortedn...
def set_levels(self, levels, level=None, inplace=False, verify_integrity=True): """ Set new levels on MultiIndex. Defaults to returning new index. Parameters ---------- levels : sequence or list of sequence new level(s) to apply lev...
def set_codes(self, codes, level=None, inplace=False, verify_integrity=True): """ Set new codes on MultiIndex. Defaults to returning new index. .. versionadded:: 0.24.0 New name for deprecated method `set_labels`. Parameters ---------- ...
def copy(self, names=None, dtype=None, levels=None, codes=None, deep=False, _set_identity=False, **kwargs): """ Make a copy of this object. Names, dtype, levels and codes can be passed and will be set on new copy. Parameters ---------- names : sequence, opti...
def view(self, cls=None): """ this is defined as a copy with the same identity """ result = self.copy() result._id = self._id return result
def _is_memory_usage_qualified(self): """ return a boolean if we need a qualified .info display """ def f(l): return 'mixed' in l or 'string' in l or 'unicode' in l return any(f(l) for l in self._inferred_type_levels)
def _nbytes(self, deep=False): """ return the number of bytes in the underlying data deeply introspect the level data if deep=True include the engine hashtable this is in internal routine """ # for implementations with no useful getsizeof (PyPy) objs...
def _format_attrs(self): """ Return a list of tuples of the (attr,formatted_value) """ attrs = [ ('levels', ibase.default_pprint(self._levels, max_seq_items=False)), ('codes', ibase.default_pprint(self._codes, ...
def _set_names(self, names, level=None, validate=True): """ Set new names on index. Each name has to be a hashable type. Parameters ---------- values : str or sequence name(s) to set level : int, level name, or sequence of int/level names (default None) ...
def is_monotonic_increasing(self): """ return if the index is monotonic increasing (only equal or increasing) values. """ # reversed() because lexsort() wants the most significant key last. values = [self._get_level_values(i).values for i in reversed(ra...
def _hashed_indexing_key(self, key): """ validate and return the hash for the provided key this is internal for use for the cython routines Parameters ---------- key : string or tuple Returns ------- np.uint64 Notes ----- ...
def _get_level_values(self, level, unique=False): """ Return vector of label values for requested level, equal to the length of the index this is an internal method Parameters ---------- level : int level unique : bool, default False if T...
def get_level_values(self, level): """ Return vector of label values for requested level, equal to the length of the index. Parameters ---------- level : int or str ``level`` is either the integer position of the level in the MultiIndex, or the na...
def to_frame(self, index=True, name=None): """ Create a DataFrame with the levels of the MultiIndex as columns. Column ordering is determined by the DataFrame constructor with data as a dict. .. versionadded:: 0.24.0 Parameters ---------- index : boolea...
def to_hierarchical(self, n_repeat, n_shuffle=1): """ Return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle. .. deprecated:: 0.24.0 Useful to replicate and rearrange a MultiIndex for combination with another Index with n_repeat items. ...
def _sort_levels_monotonic(self): """ .. versionadded:: 0.20.0 This is an internal function. Create a new MultiIndex from the current to monotonically sorted items IN the levels. This does not actually make the entire MultiIndex monotonic, JUST the levels. Th...
def remove_unused_levels(self): """ Create a new MultiIndex from the current that removes unused levels, meaning that they are not expressed in the labels. The resulting MultiIndex will have the same outward appearance, meaning the same .values and ordering. It will also ...
def _assert_take_fillable(self, values, indices, allow_fill=True, fill_value=None, na_value=None): """ Internal method to handle NA filling of take """ # only fill if we are passing a non-None fill_value if allow_fill and fill_value is not None: if (indi...
def append(self, other): """ Append a collection of Index options together Parameters ---------- other : Index or list/tuple of indices Returns ------- appended : Index """ if not isinstance(other, (list, tuple)): other = [oth...
def drop(self, codes, level=None, errors='raise'): """ Make new MultiIndex with passed list of codes deleted Parameters ---------- codes : array-like Must be a list of tuples level : int or level name, default None Returns ------- dro...
def swaplevel(self, i=-2, j=-1): """ Swap level i with level j. Calling this method does not change the ordering of the values. Parameters ---------- i : int, str, default -2 First level of index to be swapped. Can pass level name as string. Type...
def reorder_levels(self, order): """ Rearrange levels using input order. May not drop or duplicate levels Parameters ---------- """ order = [self._get_level_number(i) for i in order] if len(order) != self.nlevels: raise AssertionError('Length of order...
def _get_codes_for_sorting(self): """ we categorizing our codes by using the available categories (all, not just observed) excluding any missing ones (-1); this is in preparation for sorting, where we need to disambiguate that -1 is not a valid valid """ f...
def sortlevel(self, level=0, ascending=True, sort_remaining=True): """ Sort MultiIndex at the requested level. The result will respect the original ordering of the associated factor at that level. Parameters ---------- level : list-like, int or str, default 0 ...
def _convert_listlike_indexer(self, keyarr, kind=None): """ Parameters ---------- keyarr : list-like Indexer to convert. Returns ------- tuple (indexer, keyarr) indexer is an ndarray or None if cannot convert keyarr are tuple-s...
def reindex(self, target, method=None, level=None, limit=None, tolerance=None): """ Create index with target's values (move/add/delete values as necessary) Returns ------- new_index : pd.MultiIndex Resulting index indexer : np.ndarray or None ...
def slice_locs(self, start=None, end=None, step=None, kind=None): """ For an ordered MultiIndex, compute the slice locations for input labels. The input labels can be tuples representing partial levels, e.g. for a MultiIndex with 3 levels, you can pass a single value (correspond...
def get_loc(self, key, method=None): """ Get location for a label or a tuple of labels as an integer, slice or boolean mask. Parameters ---------- key : label or tuple of labels (one for each level) method : None Returns ------- loc : int...
def get_loc_level(self, key, level=0, drop_level=True): """ Get both the location for the requested label(s) and the resulting sliced index. Parameters ---------- key : label or sequence of labels level : int/level name or list thereof, optional drop_leve...
def get_locs(self, seq): """ Get location for a given label/slice/list/mask or a sequence of such as an array of integers. Parameters ---------- seq : label/slice/list/mask or a sequence of such You should use one of the above for each level. If a l...
def truncate(self, before=None, after=None): """ Slice index between two labels / tuples, return new MultiIndex Parameters ---------- before : label or tuple, can be partial. Default None None defaults to start after : label or tuple, can be partial. Default ...
def equals(self, other): """ Determines if two MultiIndex objects have the same labeling information (the levels themselves do not necessarily have to be the same) See Also -------- equal_levels """ if self.is_(other): return True if ...
def equal_levels(self, other): """ Return True if the levels of both MultiIndex objects are the same """ if self.nlevels != other.nlevels: return False for i in range(self.nlevels): if not self.levels[i].equals(other.levels[i]): return Fa...
def union(self, other, sort=None): """ Form the union of two MultiIndex objects Parameters ---------- other : MultiIndex or array / Index of tuples sort : False or None, default None Whether to sort the resulting Index. * None : Sort the result, ...
def intersection(self, other, sort=False): """ Form the intersection of two MultiIndex objects. Parameters ---------- other : MultiIndex or array / Index of tuples sort : False or None, default False Sort the resulting MultiIndex if possible .. v...
def difference(self, other, sort=None): """ Compute set difference of two MultiIndex objects Parameters ---------- other : MultiIndex sort : False or None, default None Sort the resulting MultiIndex if possible .. versionadded:: 0.24.0 ...
def insert(self, loc, item): """ Make new MultiIndex inserting new item at location Parameters ---------- loc : int item : tuple Must be same length as number of levels in the MultiIndex Returns ------- new_index : Index """ ...
def delete(self, loc): """ Make new index with passed location deleted Returns ------- new_index : MultiIndex """ new_codes = [np.delete(level_codes, loc) for level_codes in self.codes] return MultiIndex(levels=self.levels, codes=new_codes, ...
def _ensure_data(values, dtype=None): """ routine to ensure that our data is of the correct input dtype for lower-level routines This will coerce: - ints -> int64 - uint -> uint64 - bool -> uint64 (TODO this should be uint8) - datetimelike -> i8 - datetime64tz -> i8 (in local tz) ...
def _reconstruct_data(values, dtype, original): """ reverse of _ensure_data Parameters ---------- values : ndarray dtype : pandas_dtype original : ndarray-like Returns ------- Index for extension types, otherwise ndarray casted to dtype """ from pandas import Index ...
def _ensure_arraylike(values): """ ensure that we are arraylike if not already """ if not is_array_like(values): inferred = lib.infer_dtype(values, skipna=False) if inferred in ['mixed', 'string', 'unicode']: if isinstance(values, tuple): values = list(values)...
def _get_hashtable_algo(values): """ Parameters ---------- values : arraylike Returns ------- tuples(hashtable class, vector class, values, dtype, ndtype) """ values, dtype, ndtype = _ensure_data(values) if ndtype == 'object': ...
def match(to_match, values, na_sentinel=-1): """ Compute locations of to_match into values Parameters ---------- to_match : array-like values to find positions of values : array-like Unique set of values na_sentinel : int, default -1 Value to mark "not found" Ex...
def unique(values): """ Hash table-based unique. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique. Includes NA values. Parameters ---------- values : 1d array-like Returns ------- numpy.ndarray or ExtensionArray T...
def isin(comps, values): """ Compute the isin boolean array Parameters ---------- comps : array-like values : array-like Returns ------- boolean array same length as comps """ if not is_list_like(comps): raise TypeError("only list-like objects are allowed to be pas...
def _factorize_array(values, na_sentinel=-1, size_hint=None, na_value=None): """Factorize an array-like to labels and uniques. This doesn't do any coercion of types or unboxing before factorization. Parameters ---------- values : ndarray na_sentinel : int, default -1 s...
def value_counts(values, sort=True, ascending=False, normalize=False, bins=None, dropna=True): """ Compute a histogram of the counts of non-null values. Parameters ---------- values : ndarray (1-d) sort : boolean, default True Sort by values ascending : boolean, def...
def _value_counts_arraylike(values, dropna): """ Parameters ---------- values : arraylike dropna : boolean Returns ------- (uniques, counts) """ values = _ensure_arraylike(values) original = values values, dtype, ndtype = _ensure_data(values) if needs_i8_conversion...
def duplicated(values, keep='first'): """ Return boolean ndarray denoting duplicate values. .. versionadded:: 0.19.0 Parameters ---------- values : ndarray-like Array over which to check for duplicate values. keep : {'first', 'last', False}, default 'first' - ``first`` : Ma...
def mode(values, dropna=True): """ Returns the mode(s) of an array. Parameters ---------- values : array-like Array over which to check for duplicate values. dropna : boolean, default True Don't consider counts of NaN/NaT. .. versionadded:: 0.24.0 Returns -----...
def rank(values, axis=0, method='average', na_option='keep', ascending=True, pct=False): """ Rank the values along a given axis. Parameters ---------- values : array-like Array whose values will be ranked. The number of dimensions in this array must not exceed 2. axis :...
def checked_add_with_arr(arr, b, arr_mask=None, b_mask=None): """ Perform array addition that checks for underflow and overflow. Performs the addition of an int64 array and an int64 integer (or array) but checks that they do not result in overflow first. For elements that are indicated to be NaN, w...
def quantile(x, q, interpolation_method='fraction'): """ Compute sample quantile or quantiles of the input array. For example, q=0.5 computes the median. The `interpolation_method` parameter supports three values, namely `fraction` (default), `lower` and `higher`. Interpolation is done only, if...
def take(arr, indices, axis=0, allow_fill=False, fill_value=None): """ Take elements from an array. .. versionadded:: 0.23.0 Parameters ---------- arr : sequence Non array-likes (sequences without a dtype) are coerced to an ndarray. indices : sequence of integers In...
def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, allow_fill=True): """ Specialized Cython take which sets NaN values in one pass This dispatches to ``take`` defined on ExtensionArrays. It does not currently dispatch to ``SparseArray.take`` for sparse ``arr``. ...
def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, allow_fill=True): """ Specialized Cython take which sets NaN values in one pass """ if indexer is None or (indexer[0] is None and indexer[1] is None): row_idx = np.arange(arr.shape[0], dtype=np.int64) ...
def searchsorted(arr, value, side="left", sorter=None): """ Find indices where elements should be inserted to maintain order. .. versionadded:: 0.25.0 Find the indices into a sorted array `arr` (a) such that, if the corresponding elements in `value` were inserted before the indices, the order ...
def diff(arr, n, axis=0): """ difference of n between self, analogous to s-s.shift(n) Parameters ---------- arr : ndarray n : int number of periods axis : int axis to shift on Returns ------- shifted """ n = int(n) na = np.nan dtype = arr.d...
def _to_ijv(ss, row_levels=(0, ), column_levels=(1, ), sort_labels=False): """ For arbitrary (MultiIndexed) SparseSeries return (v, i, j, ilabels, jlabels) where (v, (i, j)) is suitable for passing to scipy.sparse.coo constructor. """ # index and column levels must be a partition of the index _check...
def _sparse_series_to_coo(ss, row_levels=(0, ), column_levels=(1, ), sort_labels=False): """ Convert a SparseSeries to a scipy.sparse.coo_matrix using index levels row_levels, column_levels as the row and column labels respectively. Returns the sparse_matrix, row and column lab...
def _coo_to_sparse_series(A, dense_index=False): """ Convert a scipy.sparse.coo_matrix to a SparseSeries. Use the defaults given in the SparseSeries constructor. """ s = Series(A.data, MultiIndex.from_arrays((A.row, A.col))) s = s.sort_index() s = s.to_sparse() # TODO: specify kind? if ...
def _to_M8(key, tz=None): """ Timestamp-like => dt64 """ if not isinstance(key, Timestamp): # this also converts strings key = Timestamp(key) if key.tzinfo is not None and tz is not None: # Don't tz_localize(None) if key is already tz-aware key = key.tz_co...
def _dt_array_cmp(cls, op): """ Wrap comparison operations to convert datetime-like to datetime64 """ opname = '__{name}__'.format(name=op.__name__) nat_result = opname == '__ne__' def wrapper(self, other): if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)): retu...
def sequence_to_dt64ns(data, dtype=None, copy=False, tz=None, dayfirst=False, yearfirst=False, ambiguous='raise', int_as_wall_time=False): """ Parameters ---------- data : list-like dtype : dtype, str, or None, default None cop...

def astype(self, dtype=None, copy=True): """ Change the dtype of a SparseArray. The output will always be a SparseArray. To convert to a dense ndarray with a certain dtype, use :meth:`numpy.asarray`. Parameters ---------- dtype : np.dtype or ExtensionDtype ...

def map(self, mapper): """ Map categories using input correspondence (dict, Series, or function). Parameters ---------- mapper : dict, Series, callable The correspondence from old values to new. Returns ------- SparseArray The out...

def all(self, axis=None, *args, **kwargs): """ Tests whether all elements evaluate True Returns ------- all : bool See Also -------- numpy.all """ nv.validate_all(args, kwargs) values = self.sp_values if len(values) != l...

def any(self, axis=0, *args, **kwargs): """ Tests whether at least one of elements evaluate True Returns ------- any : bool See Also -------- numpy.any """ nv.validate_any(args, kwargs) values = self.sp_values if len(val...

def sum(self, axis=0, *args, **kwargs): """ Sum of non-NA/null values Returns ------- sum : float """ nv.validate_sum(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() if self._null_fill_value: return s...

def cumsum(self, axis=0, *args, **kwargs): """ Cumulative sum of non-NA/null values. When performing the cumulative summation, any non-NA/null values will be skipped. The resulting SparseArray will preserve the locations of NaN values, but the fill value will be `np.nan` regardl...

def mean(self, axis=0, *args, **kwargs): """ Mean of non-NA/null values Returns ------- mean : float """ nv.validate_mean(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() ct = len(valid_vals) if self._nul...

def tokenize_string(source): """Tokenize a Python source code string. Parameters ---------- source : str A Python source code string """ line_reader = StringIO(source).readline token_generator = tokenize.generate_tokens(line_reader) # Loop over all tokens till a backtick (`) is...

def _replace_booleans(tok): """Replace ``&`` with ``and`` and ``|`` with ``or`` so that bitwise precedence is changed to boolean precedence. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple ...

def _replace_locals(tok): """Replace local variables with a syntactically valid name. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple of int, str Either the input or token or the replac...

def _clean_spaces_backtick_quoted_names(tok): """Clean up a column name if surrounded by backticks. Backtick quoted string are indicated by a certain tokval value. If a string is a backtick quoted token it will processed by :func:`_remove_spaces_column_name` so that the parser can find this string ...

def _preparse(source, f=_compose(_replace_locals, _replace_booleans, _rewrite_assign, _clean_spaces_backtick_quoted_names)): """Compose a collection of tokenization functions Parameters ---------- source : str A Python source cod...

def _filter_nodes(superclass, all_nodes=_all_nodes): """Filter out AST nodes that are subclasses of ``superclass``.""" node_names = (node.__name__ for node in all_nodes if issubclass(node, superclass)) return frozenset(node_names)

def _node_not_implemented(node_name, cls): """Return a function that raises a NotImplementedError with a passed node name. """ def f(self, *args, **kwargs): raise NotImplementedError("{name!r} nodes are not " "implemented".format(name=node_name)) return f

def disallow(nodes): """Decorator to disallow certain nodes from parsing. Raises a NotImplementedError instead. Returns ------- disallowed : callable """ def disallowed(cls): cls.unsupported_nodes = () for node in nodes: new_method = _node_not_implemented(node, c...

def _op_maker(op_class, op_symbol): """Return a function to create an op class with its symbol already passed. Returns ------- f : callable """ def f(self, node, *args, **kwargs): """Return a partial function with an Op subclass with an operator already passed. Returns...

def add_ops(op_classes): """Decorator to add default implementation of ops.""" def f(cls): for op_attr_name, op_class in op_classes.items(): ops = getattr(cls, '{name}_ops'.format(name=op_attr_name)) ops_map = getattr(cls, '{name}_op_nodes_map'.format( name=op_att...

def names(self): """Get the names in an expression""" if is_term(self.terms): return frozenset([self.terms.name]) return frozenset(term.name for term in com.flatten(self.terms))

def _is_convertible_to_index(other): """ return a boolean whether I can attempt conversion to a TimedeltaIndex """ if isinstance(other, TimedeltaIndex): return True elif (len(other) > 0 and other.inferred_type not in ('floating', 'mixed-integer', 'integer', ...

def timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None): """ Return a fixed frequency TimedeltaIndex, with day as the default frequency Parameters ---------- start : string or timedelta-like, default None Left bound for generating t...

def union(self, other): """ Returns a FrozenList with other concatenated to the end of self. Parameters ---------- other : array-like The array-like whose elements we are concatenating. Returns ------- diff : FrozenList The collec...

def difference(self, other): """ Returns a FrozenList with elements from other removed from self. Parameters ---------- other : array-like The array-like whose elements we are removing self. Returns ------- diff : FrozenList The c...

def searchsorted(self, value, side="left", sorter=None): """ Find indices to insert `value` so as to maintain order. For full documentation, see `numpy.searchsorted` See Also -------- numpy.searchsorted : Equivalent function. """ # We are much more perf...

def arrays_to_mgr(arrays, arr_names, index, columns, dtype=None): """ Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. """ # figure out the index, if necessary if index is None: index = extract_index(arrays) else: index = e...

def masked_rec_array_to_mgr(data, index, columns, dtype, copy): """ Extract from a masked rec array and create the manager. """ # essentially process a record array then fill it fill_value = data.fill_value fdata = ma.getdata(data) if index is None: index = get_names_from_index(fdat...

def init_dict(data, index, columns, dtype=None): """ Segregate Series based on type and coerce into matrices. Needs to handle a lot of exceptional cases. """ if columns is not None: from pandas.core.series import Series arrays = Series(data, index=columns, dtype=object) data_...

def to_arrays(data, columns, coerce_float=False, dtype=None): """ Return list of arrays, columns. """ if isinstance(data, ABCDataFrame): if columns is not None: arrays = [data._ixs(i, axis=1).values for i, col in enumerate(data.columns) if col in columns] ...

def sanitize_index(data, index, copy=False): """ Sanitize an index type to return an ndarray of the underlying, pass through a non-Index. """ if index is None: return data if len(data) != len(index): raise ValueError('Length of values does not match length of index') if is...

def sanitize_array(data, index, dtype=None, copy=False, raise_cast_failure=False): """ Sanitize input data to an ndarray, copy if specified, coerce to the dtype if specified. """ if dtype is not None: dtype = pandas_dtype(dtype) if isinstance(data, ma.MaskedArray): ...

def _check_engine(engine): """Make sure a valid engine is passed. Parameters ---------- engine : str Raises ------ KeyError * If an invalid engine is passed ImportError * If numexpr was requested but doesn't exist Returns ------- string engine """ from...

def _check_parser(parser): """Make sure a valid parser is passed. Parameters ---------- parser : str Raises ------ KeyError * If an invalid parser is passed """ from pandas.core.computation.expr import _parsers if parser not in _parsers: raise KeyError('Invalid p...

def eval(expr, parser='pandas', engine=None, truediv=True, local_dict=None, global_dict=None, resolvers=(), level=0, target=None, inplace=False): """Evaluate a Python expression as a string using various backends. The following arithmetic operations are supported: ``+``, ``-``, ``*``, ``/...

def _codes_to_ints(self, codes): """ Transform combination(s) of uint64 in one uint64 (each), in a strictly monotonic way (i.e. respecting the lexicographic order of integer combinations): see BaseMultiIndexCodesEngine documentation. Parameters ---------- codes :...

def from_arrays(cls, arrays, sortorder=None, names=None): """ Convert arrays to MultiIndex. Parameters ---------- arrays : list / sequence of array-likes Each array-like gives one level's value for each data point. len(arrays) is the number of levels. ...

def from_tuples(cls, tuples, sortorder=None, names=None): """ Convert list of tuples to MultiIndex. Parameters ---------- tuples : list / sequence of tuple-likes Each tuple is the index of one row/column. sortorder : int or None Level of sortednes...

def from_product(cls, iterables, sortorder=None, names=None): """ Make a MultiIndex from the cartesian product of multiple iterables. Parameters ---------- iterables : list / sequence of iterables Each iterable has unique labels for each level of the index. s...

def from_frame(cls, df, sortorder=None, names=None): """ Make a MultiIndex from a DataFrame. .. versionadded:: 0.24.0 Parameters ---------- df : DataFrame DataFrame to be converted to MultiIndex. sortorder : int, optional Level of sortedn...

def set_levels(self, levels, level=None, inplace=False, verify_integrity=True): """ Set new levels on MultiIndex. Defaults to returning new index. Parameters ---------- levels : sequence or list of sequence new level(s) to apply lev...

def set_codes(self, codes, level=None, inplace=False, verify_integrity=True): """ Set new codes on MultiIndex. Defaults to returning new index. .. versionadded:: 0.24.0 New name for deprecated method `set_labels`. Parameters ---------- ...

def copy(self, names=None, dtype=None, levels=None, codes=None, deep=False, _set_identity=False, **kwargs): """ Make a copy of this object. Names, dtype, levels and codes can be passed and will be set on new copy. Parameters ---------- names : sequence, opti...

def view(self, cls=None): """ this is defined as a copy with the same identity """ result = self.copy() result._id = self._id return result

def _is_memory_usage_qualified(self): """ return a boolean if we need a qualified .info display """ def f(l): return 'mixed' in l or 'string' in l or 'unicode' in l return any(f(l) for l in self._inferred_type_levels)

def _nbytes(self, deep=False): """ return the number of bytes in the underlying data deeply introspect the level data if deep=True include the engine hashtable *this is in internal routine* """ # for implementations with no useful getsizeof (PyPy) objs...

def _format_attrs(self): """ Return a list of tuples of the (attr,formatted_value) """ attrs = [ ('levels', ibase.default_pprint(self._levels, max_seq_items=False)), ('codes', ibase.default_pprint(self._codes, ...

def _set_names(self, names, level=None, validate=True): """ Set new names on index. Each name has to be a hashable type. Parameters ---------- values : str or sequence name(s) to set level : int, level name, or sequence of int/level names (default None) ...

def is_monotonic_increasing(self): """ return if the index is monotonic increasing (only equal or increasing) values. """ # reversed() because lexsort() wants the most significant key last. values = [self._get_level_values(i).values for i in reversed(ra...

def _hashed_indexing_key(self, key): """ validate and return the hash for the provided key *this is internal for use for the cython routines* Parameters ---------- key : string or tuple Returns ------- np.uint64 Notes ----- ...

def _get_level_values(self, level, unique=False): """ Return vector of label values for requested level, equal to the length of the index **this is an internal method** Parameters ---------- level : int level unique : bool, default False if T...

def get_level_values(self, level): """ Return vector of label values for requested level, equal to the length of the index. Parameters ---------- level : int or str ``level`` is either the integer position of the level in the MultiIndex, or the na...

def to_frame(self, index=True, name=None): """ Create a DataFrame with the levels of the MultiIndex as columns. Column ordering is determined by the DataFrame constructor with data as a dict. .. versionadded:: 0.24.0 Parameters ---------- index : boolea...

def to_hierarchical(self, n_repeat, n_shuffle=1): """ Return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle. .. deprecated:: 0.24.0 Useful to replicate and rearrange a MultiIndex for combination with another Index with n_repeat items. ...

def _sort_levels_monotonic(self): """ .. versionadded:: 0.20.0 This is an *internal* function. Create a new MultiIndex from the current to monotonically sorted items IN the levels. This does not actually make the entire MultiIndex monotonic, JUST the levels. Th...

def remove_unused_levels(self): """ Create a new MultiIndex from the current that removes unused levels, meaning that they are not expressed in the labels. The resulting MultiIndex will have the same outward appearance, meaning the same .values and ordering. It will also ...

def _assert_take_fillable(self, values, indices, allow_fill=True, fill_value=None, na_value=None): """ Internal method to handle NA filling of take """ # only fill if we are passing a non-None fill_value if allow_fill and fill_value is not None: if (indi...

def append(self, other): """ Append a collection of Index options together Parameters ---------- other : Index or list/tuple of indices Returns ------- appended : Index """ if not isinstance(other, (list, tuple)): other = [oth...

def drop(self, codes, level=None, errors='raise'): """ Make new MultiIndex with passed list of codes deleted Parameters ---------- codes : array-like Must be a list of tuples level : int or level name, default None Returns ------- dro...

def swaplevel(self, i=-2, j=-1): """ Swap level i with level j. Calling this method does not change the ordering of the values. Parameters ---------- i : int, str, default -2 First level of index to be swapped. Can pass level name as string. Type...

def reorder_levels(self, order): """ Rearrange levels using input order. May not drop or duplicate levels Parameters ---------- """ order = [self._get_level_number(i) for i in order] if len(order) != self.nlevels: raise AssertionError('Length of order...

def _get_codes_for_sorting(self): """ we categorizing our codes by using the available categories (all, not just observed) excluding any missing ones (-1); this is in preparation for sorting, where we need to disambiguate that -1 is not a valid valid """ f...

def sortlevel(self, level=0, ascending=True, sort_remaining=True): """ Sort MultiIndex at the requested level. The result will respect the original ordering of the associated factor at that level. Parameters ---------- level : list-like, int or str, default 0 ...

def _convert_listlike_indexer(self, keyarr, kind=None): """ Parameters ---------- keyarr : list-like Indexer to convert. Returns ------- tuple (indexer, keyarr) indexer is an ndarray or None if cannot convert keyarr are tuple-s...

def reindex(self, target, method=None, level=None, limit=None, tolerance=None): """ Create index with target's values (move/add/delete values as necessary) Returns ------- new_index : pd.MultiIndex Resulting index indexer : np.ndarray or None ...

def slice_locs(self, start=None, end=None, step=None, kind=None): """ For an ordered MultiIndex, compute the slice locations for input labels. The input labels can be tuples representing partial levels, e.g. for a MultiIndex with 3 levels, you can pass a single value (correspond...

def get_loc(self, key, method=None): """ Get location for a label or a tuple of labels as an integer, slice or boolean mask. Parameters ---------- key : label or tuple of labels (one for each level) method : None Returns ------- loc : int...

def get_loc_level(self, key, level=0, drop_level=True): """ Get both the location for the requested label(s) and the resulting sliced index. Parameters ---------- key : label or sequence of labels level : int/level name or list thereof, optional drop_leve...

def get_locs(self, seq): """ Get location for a given label/slice/list/mask or a sequence of such as an array of integers. Parameters ---------- seq : label/slice/list/mask or a sequence of such You should use one of the above for each level. If a l...

def truncate(self, before=None, after=None): """ Slice index between two labels / tuples, return new MultiIndex Parameters ---------- before : label or tuple, can be partial. Default None None defaults to start after : label or tuple, can be partial. Default ...

def equals(self, other): """ Determines if two MultiIndex objects have the same labeling information (the levels themselves do not necessarily have to be the same) See Also -------- equal_levels """ if self.is_(other): return True if ...

def equal_levels(self, other): """ Return True if the levels of both MultiIndex objects are the same """ if self.nlevels != other.nlevels: return False for i in range(self.nlevels): if not self.levels[i].equals(other.levels[i]): return Fa...

def union(self, other, sort=None): """ Form the union of two MultiIndex objects Parameters ---------- other : MultiIndex or array / Index of tuples sort : False or None, default None Whether to sort the resulting Index. * None : Sort the result, ...

def intersection(self, other, sort=False): """ Form the intersection of two MultiIndex objects. Parameters ---------- other : MultiIndex or array / Index of tuples sort : False or None, default False Sort the resulting MultiIndex if possible .. v...

def difference(self, other, sort=None): """ Compute set difference of two MultiIndex objects Parameters ---------- other : MultiIndex sort : False or None, default None Sort the resulting MultiIndex if possible .. versionadded:: 0.24.0 ...

def insert(self, loc, item): """ Make new MultiIndex inserting new item at location Parameters ---------- loc : int item : tuple Must be same length as number of levels in the MultiIndex Returns ------- new_index : Index """ ...

def delete(self, loc): """ Make new index with passed location deleted Returns ------- new_index : MultiIndex """ new_codes = [np.delete(level_codes, loc) for level_codes in self.codes] return MultiIndex(levels=self.levels, codes=new_codes, ...

def _ensure_data(values, dtype=None): """ routine to ensure that our data is of the correct input dtype for lower-level routines This will coerce: - ints -> int64 - uint -> uint64 - bool -> uint64 (TODO this should be uint8) - datetimelike -> i8 - datetime64tz -> i8 (in local tz) ...

def _reconstruct_data(values, dtype, original): """ reverse of _ensure_data Parameters ---------- values : ndarray dtype : pandas_dtype original : ndarray-like Returns ------- Index for extension types, otherwise ndarray casted to dtype """ from pandas import Index ...

def _ensure_arraylike(values): """ ensure that we are arraylike if not already """ if not is_array_like(values): inferred = lib.infer_dtype(values, skipna=False) if inferred in ['mixed', 'string', 'unicode']: if isinstance(values, tuple): values = list(values)...

def _get_hashtable_algo(values): """ Parameters ---------- values : arraylike Returns ------- tuples(hashtable class, vector class, values, dtype, ndtype) """ values, dtype, ndtype = _ensure_data(values) if ndtype == 'object': ...

def match(to_match, values, na_sentinel=-1): """ Compute locations of to_match into values Parameters ---------- to_match : array-like values to find positions of values : array-like Unique set of values na_sentinel : int, default -1 Value to mark "not found" Ex...

def unique(values): """ Hash table-based unique. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique. Includes NA values. Parameters ---------- values : 1d array-like Returns ------- numpy.ndarray or ExtensionArray T...

def isin(comps, values): """ Compute the isin boolean array Parameters ---------- comps : array-like values : array-like Returns ------- boolean array same length as comps """ if not is_list_like(comps): raise TypeError("only list-like objects are allowed to be pas...

def _factorize_array(values, na_sentinel=-1, size_hint=None, na_value=None): """Factorize an array-like to labels and uniques. This doesn't do any coercion of types or unboxing before factorization. Parameters ---------- values : ndarray na_sentinel : int, default -1 s...

def value_counts(values, sort=True, ascending=False, normalize=False, bins=None, dropna=True): """ Compute a histogram of the counts of non-null values. Parameters ---------- values : ndarray (1-d) sort : boolean, default True Sort by values ascending : boolean, def...

def _value_counts_arraylike(values, dropna): """ Parameters ---------- values : arraylike dropna : boolean Returns ------- (uniques, counts) """ values = _ensure_arraylike(values) original = values values, dtype, ndtype = _ensure_data(values) if needs_i8_conversion...

def duplicated(values, keep='first'): """ Return boolean ndarray denoting duplicate values. .. versionadded:: 0.19.0 Parameters ---------- values : ndarray-like Array over which to check for duplicate values. keep : {'first', 'last', False}, default 'first' - ``first`` : Ma...

def mode(values, dropna=True): """ Returns the mode(s) of an array. Parameters ---------- values : array-like Array over which to check for duplicate values. dropna : boolean, default True Don't consider counts of NaN/NaT. .. versionadded:: 0.24.0 Returns -----...

def rank(values, axis=0, method='average', na_option='keep', ascending=True, pct=False): """ Rank the values along a given axis. Parameters ---------- values : array-like Array whose values will be ranked. The number of dimensions in this array must not exceed 2. axis :...

def checked_add_with_arr(arr, b, arr_mask=None, b_mask=None): """ Perform array addition that checks for underflow and overflow. Performs the addition of an int64 array and an int64 integer (or array) but checks that they do not result in overflow first. For elements that are indicated to be NaN, w...

def quantile(x, q, interpolation_method='fraction'): """ Compute sample quantile or quantiles of the input array. For example, q=0.5 computes the median. The `interpolation_method` parameter supports three values, namely `fraction` (default), `lower` and `higher`. Interpolation is done only, if...

def take(arr, indices, axis=0, allow_fill=False, fill_value=None): """ Take elements from an array. .. versionadded:: 0.23.0 Parameters ---------- arr : sequence Non array-likes (sequences without a dtype) are coerced to an ndarray. indices : sequence of integers In...

def take_nd(arr, indexer, axis=0, out=None, fill_value=np.nan, mask_info=None, allow_fill=True): """ Specialized Cython take which sets NaN values in one pass This dispatches to ``take`` defined on ExtensionArrays. It does not currently dispatch to ``SparseArray.take`` for sparse ``arr``. ...

def take_2d_multi(arr, indexer, out=None, fill_value=np.nan, mask_info=None, allow_fill=True): """ Specialized Cython take which sets NaN values in one pass """ if indexer is None or (indexer[0] is None and indexer[1] is None): row_idx = np.arange(arr.shape[0], dtype=np.int64) ...

def searchsorted(arr, value, side="left", sorter=None): """ Find indices where elements should be inserted to maintain order. .. versionadded:: 0.25.0 Find the indices into a sorted array `arr` (a) such that, if the corresponding elements in `value` were inserted before the indices, the order ...

def diff(arr, n, axis=0): """ difference of n between self, analogous to s-s.shift(n) Parameters ---------- arr : ndarray n : int number of periods axis : int axis to shift on Returns ------- shifted """ n = int(n) na = np.nan dtype = arr.d...

def _to_ijv(ss, row_levels=(0, ), column_levels=(1, ), sort_labels=False): """ For arbitrary (MultiIndexed) SparseSeries return (v, i, j, ilabels, jlabels) where (v, (i, j)) is suitable for passing to scipy.sparse.coo constructor. """ # index and column levels must be a partition of the index _check...

def _sparse_series_to_coo(ss, row_levels=(0, ), column_levels=(1, ), sort_labels=False): """ Convert a SparseSeries to a scipy.sparse.coo_matrix using index levels row_levels, column_levels as the row and column labels respectively. Returns the sparse_matrix, row and column lab...

def _coo_to_sparse_series(A, dense_index=False): """ Convert a scipy.sparse.coo_matrix to a SparseSeries. Use the defaults given in the SparseSeries constructor. """ s = Series(A.data, MultiIndex.from_arrays((A.row, A.col))) s = s.sort_index() s = s.to_sparse() # TODO: specify kind? if ...

def _to_M8(key, tz=None): """ Timestamp-like => dt64 """ if not isinstance(key, Timestamp): # this also converts strings key = Timestamp(key) if key.tzinfo is not None and tz is not None: # Don't tz_localize(None) if key is already tz-aware key = key.tz_co...

def _dt_array_cmp(cls, op): """ Wrap comparison operations to convert datetime-like to datetime64 """ opname = '__{name}__'.format(name=op.__name__) nat_result = opname == '__ne__' def wrapper(self, other): if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)): retu...

def sequence_to_dt64ns(data, dtype=None, copy=False, tz=None, dayfirst=False, yearfirst=False, ambiguous='raise', int_as_wall_time=False): """ Parameters ---------- data : list-like dtype : dtype, str, or None, default None cop...