BoghdadyJR/codesearch_python · Datasets at Hugging Face

text stringlengths 75 104k
def _convert_listlike_indexer(self, keyarr, kind=None): """ Parameters ---------- keyarr : list-like Indexer to convert. Returns ------- indexer : numpy.ndarray or None Return an ndarray or None if cannot convert. keyarr : numpy.nd...
def _invalid_indexer(self, form, key): """ Consistent invalid indexer message. """ raise TypeError("cannot do {form} indexing on {klass} with these " "indexers [{key}] of {kind}".format( form=form, klass=type(self), key=key, ...
def reindex(self, target, method=None, level=None, limit=None, tolerance=None): """ Create index with target's values (move/add/delete values as necessary). Parameters ---------- target : an iterable Returns ------- new_index : pd...
def _reindex_non_unique(self, target): """ Create a new index with target's values (move/add/delete values as necessary) use with non-unique Index and a possibly non-unique target. Parameters ---------- target : an iterable Returns ------- new_in...
def _join_level(self, other, level, how='left', return_indexers=False, keep_order=True): """ The join method only affects the level of the resulting MultiIndex. Otherwise it just exactly aligns the Index data to the labels of the level in the MultiIndex. If...
def _try_convert_to_int_index(cls, data, copy, name, dtype): """ Attempt to convert an array of data into an integer index. Parameters ---------- data : The data to convert. copy : Whether to copy the data or not. name : The name of the index returned. R...
def _coerce_to_ndarray(cls, data): """ Coerces data to ndarray. Converts other iterables to list first and then to array. Does not touch ndarrays. Raises ------ TypeError When the data passed in is a scalar. """ if not isinstance(dat...
def _coerce_scalar_to_index(self, item): """ We need to coerce a scalar to a compat for our index type. Parameters ---------- item : scalar item to coerce """ dtype = self.dtype if self._is_numeric_dtype and isna(item): # We can't coerce to t...
def _assert_can_do_op(self, value): """ Check value is valid for scalar op. """ if not is_scalar(value): msg = "'value' must be a scalar, passed: {0}" raise TypeError(msg.format(type(value).__name__))
def _can_hold_identifiers_and_holds_name(self, name): """ Faster check for ``name in self`` when we know `name` is a Python identifier (e.g. in NDFrame.__getattr__, which hits this to support . key lookup). For indexes that can't hold identifiers (everything but object & categori...
def append(self, other): """ Append a collection of Index options together. Parameters ---------- other : Index or list/tuple of indices Returns ------- appended : Index """ to_concat = [self] if isinstance(other, (list, tuple))...
def putmask(self, mask, value): """ Return a new Index of the values set with the mask. See Also -------- numpy.ndarray.putmask """ values = self.values.copy() try: np.putmask(values, mask, self._convert_for_op(value)) return self....
def equals(self, other): """ Determine if two Index objects contain the same elements. """ if self.is_(other): return True if not isinstance(other, Index): return False if is_object_dtype(self) and not is_object_dtype(other): # if oth...
def identical(self, other): """ Similar to equals, but check that other comparable attributes are also equal. """ return (self.equals(other) and all((getattr(self, c, None) == getattr(other, c, None) for c in self._comparables)) and ...
def asof(self, label): """ Return the label from the index, or, if not present, the previous one. Assuming that the index is sorted, return the passed index label if it is in the index, or return the previous index label if the passed one is not in the index. Parameters...
def asof_locs(self, where, mask): """ Find the locations (indices) of the labels from the index for every entry in the `where` argument. As in the `asof` function, if the label (a particular entry in `where`) is not in the index, the latest index label upto the passed la...
def sort_values(self, return_indexer=False, ascending=True): """ Return a sorted copy of the index. Return a sorted copy of the index, and optionally return the indices that sorted the index itself. Parameters ---------- return_indexer : bool, default False ...
def argsort(self, args, kwargs): """ Return the integer indices that would sort the index. Parameters ---------- args Passed to `numpy.ndarray.argsort`. **kwargs Passed to `numpy.ndarray.argsort`. Returns ------- numpy...
def get_value(self, series, key): """ Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing. """ # if we have something that is Index-like, then # use this, e.g. DatetimeIndex # Things like `Series._get_value` (via .at) pass...
def set_value(self, arr, key, value): """ Fast lookup of value from 1-dimensional ndarray. Notes ----- Only use this if you know what you're doing. """ self._engine.set_value(com.values_from_object(arr), com.values_from_object(key),...
def get_indexer_for(self, target, kwargs): """ Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_nonunique as appropriate. """ if self.is_unique: return self.get_indexer(target, kwargs) indexer, _ ...
def groupby(self, values): """ Group the index labels by a given array of values. Parameters ---------- values : array Values used to determine the groups. Returns ------- groups : dict {group name -> group labels} """ ...
def map(self, mapper, na_action=None): """ Map values using input correspondence (a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series Mapping correspondence. na_action : {None, 'ignore'} If 'ignore', propagate NA...
def isin(self, values, level=None): """ Return a boolean array where the index values are in `values`. Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index. Param...
def slice_indexer(self, start=None, end=None, step=None, kind=None): """ For an ordered or unique index, compute the slice indexer for input labels and step. Parameters ---------- start : label, default None If None, defaults to the beginning end : la...
def _maybe_cast_indexer(self, key): """ If we have a float key and are not a floating index, then try to cast to an int if equivalent. """ if is_float(key) and not self.is_floating(): try: ckey = int(key) if ckey == key: ...
def _validate_indexer(self, form, key, kind): """ If we are positional indexer, validate that we have appropriate typed bounds must be an integer. """ assert kind in ['ix', 'loc', 'getitem', 'iloc'] if key is None: pass elif is_integer(key): ...
def get_slice_bound(self, label, side, kind): """ Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if ``side=='right'``) position of given label. Parameters ---------- label : object side : {'left', 'right'}...
def slice_locs(self, start=None, end=None, step=None, kind=None): """ Compute slice locations for input labels. Parameters ---------- start : label, default None If None, defaults to the beginning end : label, default None If None, defaults to the...
def delete(self, loc): """ Make new Index with passed location(-s) deleted. Returns ------- new_index : Index """ return self._shallow_copy(np.delete(self._data, loc))
def insert(self, loc, item): """ Make new Index inserting new item at location. Follows Python list.append semantics for negative values. Parameters ---------- loc : int item : object Returns ------- new_index : Index """ ...
def drop(self, labels, errors='raise'): """ Make new Index with passed list of labels deleted. Parameters ---------- labels : array-like errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. Return...
def _add_comparison_methods(cls): """ Add in comparison methods. """ cls.__eq__ = _make_comparison_op(operator.eq, cls) cls.__ne__ = _make_comparison_op(operator.ne, cls) cls.__lt__ = _make_comparison_op(operator.lt, cls) cls.__gt__ = _make_comparison_op(operator....
def _add_numeric_methods_add_sub_disabled(cls): """ Add in the numeric add/sub methods to disable. """ cls.__add__ = make_invalid_op('__add__') cls.__radd__ = make_invalid_op('__radd__') cls.__iadd__ = make_invalid_op('__iadd__') cls.__sub__ = make_invalid_op('__s...
def _add_numeric_methods_disabled(cls): """ Add in numeric methods to disable other than add/sub. """ cls.__pow__ = make_invalid_op('__pow__') cls.__rpow__ = make_invalid_op('__rpow__') cls.__mul__ = make_invalid_op('__mul__') cls.__rmul__ = make_invalid_op('__rmu...
def _validate_for_numeric_unaryop(self, op, opstr): """ Validate if we can perform a numeric unary operation. """ if not self._is_numeric_dtype: raise TypeError("cannot evaluate a numeric op " "{opstr} for type: {typ}" ....
def _validate_for_numeric_binop(self, other, op): """ Return valid other; evaluate or raise TypeError if we are not of the appropriate type. Notes ----- This is an internal method called by ops. """ opstr = '__{opname}__'.format(opname=op.__name__) ...
def _add_numeric_methods_binary(cls): """ Add in numeric methods. """ cls.__add__ = _make_arithmetic_op(operator.add, cls) cls.__radd__ = _make_arithmetic_op(ops.radd, cls) cls.__sub__ = _make_arithmetic_op(operator.sub, cls) cls.__rsub__ = _make_arithmetic_op(ops...
def _add_numeric_methods_unary(cls): """ Add in numeric unary methods. """ def _make_evaluate_unary(op, opstr): def _evaluate_numeric_unary(self): self._validate_for_numeric_unaryop(op, opstr) attrs = self._get_attributes_dict() ...
def _add_logical_methods(cls): """ Add in logical methods. """ _doc = """ %(desc)s Parameters ---------- args These parameters will be passed to numpy.%(outname)s. *kwargs These parameters will be passed to numpy.%(outnam...
def _get_grouper(obj, key=None, axis=0, level=None, sort=True, observed=False, mutated=False, validate=True): """ create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. This may be composed of multiple Grouping objects, indicating multip...
def _get_grouper(self, obj, validate=True): """ Parameters ---------- obj : the subject object validate : boolean, default True if True, validate the grouper Returns ------- a tuple of binner, grouper, obj (possibly sorted) """ ...
def _set_grouper(self, obj, sort=False): """ given an object and the specifications, setup the internal grouper for this particular specification Parameters ---------- obj : the subject object sort : bool, default False whether the resulting grouper s...
def to_pickle(obj, path, compression='infer', protocol=pickle.HIGHEST_PROTOCOL): """ Pickle (serialize) object to file. Parameters ---------- obj : any object Any python object. path : str File path where the pickled object will be stored. compression : {'infer...
def read_pickle(path, compression='infer'): """ Load pickled pandas object (or any object) from file. .. warning:: Loading pickled data received from untrusted sources can be unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__. Parameters ---------- path : str ...
def mask_missing(arr, values_to_mask): """ Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True """ dtype, values_to_mask = infer_dtype_from_array(values_to_mask) try: values_to_mask = np.array(values_to_mask, dtype=dtype) ...
def interpolate_1d(xvalues, yvalues, method='linear', limit=None, limit_direction='forward', limit_area=None, fill_value=None, bounds_error=False, order=None, **kwargs): """ Logic for the 1-d interpolation. The result should be 1-d, inputs xvalues and yvalues will each...
def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, bounds_error=False, order=None, **kwargs): """ Passed off to scipy.interpolate.interp1d. method is scipy's kind. Returns an array interpolated at new_x. Add any new methods to the list in _clean_interp_m...
def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False): """ Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : ...
def _akima_interpolate(xi, yi, x, der=0, axis=0): """ Convenience function for akima interpolation. xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. See `Akima1DInterpolator` for details. Parameters ---------- xi : array_like A sorted lis...
def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None): """ Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. """ transf = (lambda x: x) if axis == 0 else (lambda x: x.T) # resha...
def _cast_values_for_fillna(values, dtype): """ Cast values to a dtype that algos.pad and algos.backfill can handle. """ # TODO: for int-dtypes we make a copy, but for everything else this # alters the values in-place. Is this intentional? if (is_datetime64_dtype(dtype) or is_datetime64tz_dty...
def fill_zeros(result, x, y, name, fill): """ If this is a reversed op, then flip x,y If we have an integer value (or array in y) and we have 0's, fill them with the fill, return the result. Mask the nan's from x. """ if fill is None or is_float_dtype(result): return result ...
def mask_zero_div_zero(x, y, result, copy=False): """ Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes of the numerator or the denominator. Parameters ---------- x : ndarray y : ndarray result : ndarray copy : bool (default False) Whether to always create a...
def dispatch_missing(op, left, right, result): """ Fill nulls caused by division by zero, casting to a diffferent dtype if necessary. Parameters ---------- op : function (operator.add, operator.div, ...) left : object (Index for non-reversed ops) right : object (Index fof reversed ops) ...
def _interp_limit(invalid, fw_limit, bw_limit): """ Get indexers of values that won't be filled because they exceed the limits. Parameters ---------- invalid : boolean ndarray fw_limit : int or None forward limit to index bw_limit : int or None backward limit to index ...
def _rolling_window(a, window): """ [True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, False], ] """ # https://stackoverflow.com/a/6811241 shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) strides = a.stri...
def get_console_size(): """Return console size as tuple = (width, height). Returns (None,None) in non-interactive session. """ from pandas import get_option display_width = get_option('display.width') # deprecated. display_height = get_option('display.max_rows') # Consider # inter...
def in_interactive_session(): """ check if we're running in an interactive shell returns True if running under python/ipython interactive shell """ from pandas import get_option def check_main(): try: import __main__ as main except ModuleNotFoundError: retur...
def recode_for_groupby(c, sort, observed): """ Code the categories to ensure we can groupby for categoricals. If observed=True, we return a new Categorical with the observed categories only. If sort=False, return a copy of self, coded with categories as returned by .unique(), followed by any c...
def recode_from_groupby(c, sort, ci): """ Reverse the codes_to_groupby to account for sort / observed. Parameters ---------- c : Categorical sort : boolean The value of the sort parameter groupby was called with. ci : CategoricalIndex The codes / categories to recode Re...
def get_engine(engine): """ return our implementation """ if engine == 'auto': engine = get_option('io.parquet.engine') if engine == 'auto': # try engines in this order try: return PyArrowImpl() except ImportError: pass try: retu...
def to_parquet(df, path, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs): """ Write a DataFrame to the parquet format. Parameters ---------- path : str File path or Root Directory path. Will be used as Root Directory path while writing ...
def read_parquet(path, engine='auto', columns=None, **kwargs): """ Load a parquet object from the file path, returning a DataFrame. .. versionadded 0.21.0 Parameters ---------- path : string File path engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto' Parquet libra...
def generate_bins_generic(values, binner, closed): """ Generate bin edge offsets and bin labels for one array using another array which has bin edge values. Both arrays must be sorted. Parameters ---------- values : array of values binner : a comparable array of values representing bins int...
def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ splitter = self._get_splitter(data, axis=axis) keys = self._get_group_keys() for key, (i,...
def indices(self): """ dict {group name -> group indices} """ if len(self.groupings) == 1: return self.groupings[0].indices else: label_list = [ping.labels for ping in self.groupings] keys = [com.values_from_object(ping.group_index) for pin...
def size(self): """ Compute group sizes """ ids, _, ngroup = self.group_info ids = ensure_platform_int(ids) if ngroup: out = np.bincount(ids[ids != -1], minlength=ngroup) else: out = [] return Series(out, inde...
def groups(self): """ dict {group name -> group labels} """ if len(self.groupings) == 1: return self.groupings[0].groups else: to_groupby = lzip(*(ping.grouper for ping in self.groupings)) to_groupby = Index(to_groupby) return self.axis.groupby(to_...
def groups(self): """ dict {group name -> group labels} """ # this is mainly for compat # GH 3881 result = {key: value for key, value in zip(self.binlabels, self.bins) if key is not NaT} return result
def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ if isinstance(data, NDFrame): slicer = lambda start, edge: data._slice( slice...
def json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None, errors='raise', sep='.'): """ Normalize semi-structured JSON data into a flat table. Parameters ---------- data : dict or list of d...
def lreshape(data, groups, dropna=True, label=None): """ Reshape long-format data to wide. Generalized inverse of DataFrame.pivot Parameters ---------- data : DataFrame groups : dict {new_name : list_of_columns} dropna : boolean, default True Examples -------- >>> data ...
def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'): r""" Wide panel to long format. Less flexible but more user-friendly than melt. With stubnames ['A', 'B'], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,... You ...
def _get_indices(self, names): """ Safe get multiple indices, translate keys for datelike to underlying repr. """ def get_converter(s): # possibly convert to the actual key types # in the indices, could be a Timestamp or a np.datetime64 if isi...
def _set_group_selection(self): """ Create group based selection. Used when selection is not passed directly but instead via a grouper. NOTE: this should be paired with a call to _reset_group_selection """ grp = self.grouper if not (self.as_index and ...
def get_group(self, name, obj=None): """ Construct NDFrame from group with provided name. Parameters ---------- name : object the name of the group to get as a DataFrame obj : NDFrame, default None the NDFrame to take the DataFrame out of. If ...
def _cumcount_array(self, ascending=True): """ Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=T...
def _try_cast(self, result, obj, numeric_only=False): """ Try to cast the result to our obj original type, we may have roundtripped through object in the mean-time. If numeric_only is True, then only try to cast numerics and not datetimelikes. """ if obj.ndim > ...
def _transform_should_cast(self, func_nm): """ Parameters: ----------- func_nm: str The name of the aggregation function being performed Returns: -------- bool Whether transform should attempt to cast the result of aggregation """ ...
def _bool_agg(self, val_test, skipna): """ Shared func to call any / all Cython GroupBy implementations. """ def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]: if is_object_dtype(vals): vals = np.array([bool(x) for x in vals]) else: ...
def mean(self, args, *kwargs): """ Compute mean of groups, excluding missing values. Returns ------- pandas.Series or pandas.DataFrame %(see_also)s Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B'...
def median(self, kwargs): """ Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex """ try: return self._cython_agg_general('median', kwargs) except GroupByError: raise excep...
def std(self, ddof=1, args, *kwargs): """ Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom """ ...
def var(self, ddof=1, args, *kwargs): """ Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom """ nv.validat...
def sem(self, ddof=1): """ Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom """ return s...
def size(self): """ Compute group sizes. """ result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result
def _add_numeric_operations(cls): """ Add numeric operations to the GroupBy generically. """ def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_count=-1): _local_template = "Compute %(f)...
def resample(self, rule, args, *kwargs): """ Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string "string" -> "frequency". See the :ref:`frequency aliases <timeseries.offset_aliases>` documentation for more deta...
def rolling(self, args, kwargs): """ Return a rolling grouper, providing rolling functionality per group. """ from pandas.core.window import RollingGroupby return RollingGroupby(self, args, **kwargs)
def expanding(self, args, kwargs): """ Return an expanding grouper, providing expanding functionality per group. """ from pandas.core.window import ExpandingGroupby return ExpandingGroupby(self, args, **kwargs)
def _fill(self, direction, limit=None): """ Shared function for `pad` and `backfill` to call Cython method. Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwar...
def nth(self, n, dropna=None): """ Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints. If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or 'all', 'any' (if a DataFrame); this is equivalent to callin...
def quantile(self, q=0.5, interpolation='linear'): """ Return group values at the given quantile, a la numpy.percentile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value(s) between 0 and 1 providing the quantile(s) to compute. i...
def ngroup(self, ascending=True): """ Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not th...
def cumcount(self, ascending=True): """ Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True...
def rank(self, method='average', ascending=True, na_option='keep', pct=False, axis=0): """ Provide the rank of values within each group. Parameters ---------- method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank...
def cumprod(self, axis=0, args, kwargs): """ Cumulative product for each group. """ nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumprod(axis=axis, *kwarg...
def cummin(self, axis=0, **kwargs): """ Cumulative min for each group. """ if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False)
def cummax(self, axis=0, **kwargs): """ Cumulative max for each group. """ if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False)

def _convert_listlike_indexer(self, keyarr, kind=None): """ Parameters ---------- keyarr : list-like Indexer to convert. Returns ------- indexer : numpy.ndarray or None Return an ndarray or None if cannot convert. keyarr : numpy.nd...

def _invalid_indexer(self, form, key): """ Consistent invalid indexer message. """ raise TypeError("cannot do {form} indexing on {klass} with these " "indexers [{key}] of {kind}".format( form=form, klass=type(self), key=key, ...

def reindex(self, target, method=None, level=None, limit=None, tolerance=None): """ Create index with target's values (move/add/delete values as necessary). Parameters ---------- target : an iterable Returns ------- new_index : pd...

def _reindex_non_unique(self, target): """ Create a new index with target's values (move/add/delete values as necessary) use with non-unique Index and a possibly non-unique target. Parameters ---------- target : an iterable Returns ------- new_in...

def _join_level(self, other, level, how='left', return_indexers=False, keep_order=True): """ The join method *only* affects the level of the resulting MultiIndex. Otherwise it just exactly aligns the Index data to the labels of the level in the MultiIndex. If...

def _try_convert_to_int_index(cls, data, copy, name, dtype): """ Attempt to convert an array of data into an integer index. Parameters ---------- data : The data to convert. copy : Whether to copy the data or not. name : The name of the index returned. R...

def _coerce_to_ndarray(cls, data): """ Coerces data to ndarray. Converts other iterables to list first and then to array. Does not touch ndarrays. Raises ------ TypeError When the data passed in is a scalar. """ if not isinstance(dat...

def _coerce_scalar_to_index(self, item): """ We need to coerce a scalar to a compat for our index type. Parameters ---------- item : scalar item to coerce """ dtype = self.dtype if self._is_numeric_dtype and isna(item): # We can't coerce to t...

def _assert_can_do_op(self, value): """ Check value is valid for scalar op. """ if not is_scalar(value): msg = "'value' must be a scalar, passed: {0}" raise TypeError(msg.format(type(value).__name__))

def _can_hold_identifiers_and_holds_name(self, name): """ Faster check for ``name in self`` when we know `name` is a Python identifier (e.g. in NDFrame.__getattr__, which hits this to support . key lookup). For indexes that can't hold identifiers (everything but object & categori...

def append(self, other): """ Append a collection of Index options together. Parameters ---------- other : Index or list/tuple of indices Returns ------- appended : Index """ to_concat = [self] if isinstance(other, (list, tuple))...

def putmask(self, mask, value): """ Return a new Index of the values set with the mask. See Also -------- numpy.ndarray.putmask """ values = self.values.copy() try: np.putmask(values, mask, self._convert_for_op(value)) return self....

def equals(self, other): """ Determine if two Index objects contain the same elements. """ if self.is_(other): return True if not isinstance(other, Index): return False if is_object_dtype(self) and not is_object_dtype(other): # if oth...

def identical(self, other): """ Similar to equals, but check that other comparable attributes are also equal. """ return (self.equals(other) and all((getattr(self, c, None) == getattr(other, c, None) for c in self._comparables)) and ...

def asof(self, label): """ Return the label from the index, or, if not present, the previous one. Assuming that the index is sorted, return the passed index label if it is in the index, or return the previous index label if the passed one is not in the index. Parameters...

def asof_locs(self, where, mask): """ Find the locations (indices) of the labels from the index for every entry in the `where` argument. As in the `asof` function, if the label (a particular entry in `where`) is not in the index, the latest index label upto the passed la...

def sort_values(self, return_indexer=False, ascending=True): """ Return a sorted copy of the index. Return a sorted copy of the index, and optionally return the indices that sorted the index itself. Parameters ---------- return_indexer : bool, default False ...

def argsort(self, *args, **kwargs): """ Return the integer indices that would sort the index. Parameters ---------- *args Passed to `numpy.ndarray.argsort`. **kwargs Passed to `numpy.ndarray.argsort`. Returns ------- numpy...

def get_value(self, series, key): """ Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing. """ # if we have something that is Index-like, then # use this, e.g. DatetimeIndex # Things like `Series._get_value` (via .at) pass...

def set_value(self, arr, key, value): """ Fast lookup of value from 1-dimensional ndarray. Notes ----- Only use this if you know what you're doing. """ self._engine.set_value(com.values_from_object(arr), com.values_from_object(key),...

def get_indexer_for(self, target, **kwargs): """ Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_nonunique as appropriate. """ if self.is_unique: return self.get_indexer(target, **kwargs) indexer, _ ...

def groupby(self, values): """ Group the index labels by a given array of values. Parameters ---------- values : array Values used to determine the groups. Returns ------- groups : dict {group name -> group labels} """ ...

def map(self, mapper, na_action=None): """ Map values using input correspondence (a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series Mapping correspondence. na_action : {None, 'ignore'} If 'ignore', propagate NA...

def isin(self, values, level=None): """ Return a boolean array where the index values are in `values`. Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index. Param...

def slice_indexer(self, start=None, end=None, step=None, kind=None): """ For an ordered or unique index, compute the slice indexer for input labels and step. Parameters ---------- start : label, default None If None, defaults to the beginning end : la...

def _maybe_cast_indexer(self, key): """ If we have a float key and are not a floating index, then try to cast to an int if equivalent. """ if is_float(key) and not self.is_floating(): try: ckey = int(key) if ckey == key: ...

def _validate_indexer(self, form, key, kind): """ If we are positional indexer, validate that we have appropriate typed bounds must be an integer. """ assert kind in ['ix', 'loc', 'getitem', 'iloc'] if key is None: pass elif is_integer(key): ...

def get_slice_bound(self, label, side, kind): """ Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if ``side=='right'``) position of given label. Parameters ---------- label : object side : {'left', 'right'}...

def slice_locs(self, start=None, end=None, step=None, kind=None): """ Compute slice locations for input labels. Parameters ---------- start : label, default None If None, defaults to the beginning end : label, default None If None, defaults to the...

def delete(self, loc): """ Make new Index with passed location(-s) deleted. Returns ------- new_index : Index """ return self._shallow_copy(np.delete(self._data, loc))

def insert(self, loc, item): """ Make new Index inserting new item at location. Follows Python list.append semantics for negative values. Parameters ---------- loc : int item : object Returns ------- new_index : Index """ ...

def drop(self, labels, errors='raise'): """ Make new Index with passed list of labels deleted. Parameters ---------- labels : array-like errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. Return...

def _add_comparison_methods(cls): """ Add in comparison methods. """ cls.__eq__ = _make_comparison_op(operator.eq, cls) cls.__ne__ = _make_comparison_op(operator.ne, cls) cls.__lt__ = _make_comparison_op(operator.lt, cls) cls.__gt__ = _make_comparison_op(operator....

def _add_numeric_methods_add_sub_disabled(cls): """ Add in the numeric add/sub methods to disable. """ cls.__add__ = make_invalid_op('__add__') cls.__radd__ = make_invalid_op('__radd__') cls.__iadd__ = make_invalid_op('__iadd__') cls.__sub__ = make_invalid_op('__s...

def _add_numeric_methods_disabled(cls): """ Add in numeric methods to disable other than add/sub. """ cls.__pow__ = make_invalid_op('__pow__') cls.__rpow__ = make_invalid_op('__rpow__') cls.__mul__ = make_invalid_op('__mul__') cls.__rmul__ = make_invalid_op('__rmu...

def _validate_for_numeric_unaryop(self, op, opstr): """ Validate if we can perform a numeric unary operation. """ if not self._is_numeric_dtype: raise TypeError("cannot evaluate a numeric op " "{opstr} for type: {typ}" ....

def _validate_for_numeric_binop(self, other, op): """ Return valid other; evaluate or raise TypeError if we are not of the appropriate type. Notes ----- This is an internal method called by ops. """ opstr = '__{opname}__'.format(opname=op.__name__) ...

def _add_numeric_methods_binary(cls): """ Add in numeric methods. """ cls.__add__ = _make_arithmetic_op(operator.add, cls) cls.__radd__ = _make_arithmetic_op(ops.radd, cls) cls.__sub__ = _make_arithmetic_op(operator.sub, cls) cls.__rsub__ = _make_arithmetic_op(ops...

def _add_numeric_methods_unary(cls): """ Add in numeric unary methods. """ def _make_evaluate_unary(op, opstr): def _evaluate_numeric_unary(self): self._validate_for_numeric_unaryop(op, opstr) attrs = self._get_attributes_dict() ...

def _add_logical_methods(cls): """ Add in logical methods. """ _doc = """ %(desc)s Parameters ---------- *args These parameters will be passed to numpy.%(outname)s. **kwargs These parameters will be passed to numpy.%(outnam...

def _get_grouper(obj, key=None, axis=0, level=None, sort=True, observed=False, mutated=False, validate=True): """ create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. This may be composed of multiple Grouping objects, indicating multip...

def _get_grouper(self, obj, validate=True): """ Parameters ---------- obj : the subject object validate : boolean, default True if True, validate the grouper Returns ------- a tuple of binner, grouper, obj (possibly sorted) """ ...

def _set_grouper(self, obj, sort=False): """ given an object and the specifications, setup the internal grouper for this particular specification Parameters ---------- obj : the subject object sort : bool, default False whether the resulting grouper s...

def to_pickle(obj, path, compression='infer', protocol=pickle.HIGHEST_PROTOCOL): """ Pickle (serialize) object to file. Parameters ---------- obj : any object Any python object. path : str File path where the pickled object will be stored. compression : {'infer...

def read_pickle(path, compression='infer'): """ Load pickled pandas object (or any object) from file. .. warning:: Loading pickled data received from untrusted sources can be unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__. Parameters ---------- path : str ...

def mask_missing(arr, values_to_mask): """ Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True """ dtype, values_to_mask = infer_dtype_from_array(values_to_mask) try: values_to_mask = np.array(values_to_mask, dtype=dtype) ...

def interpolate_1d(xvalues, yvalues, method='linear', limit=None, limit_direction='forward', limit_area=None, fill_value=None, bounds_error=False, order=None, **kwargs): """ Logic for the 1-d interpolation. The result should be 1-d, inputs xvalues and yvalues will each...

def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, bounds_error=False, order=None, **kwargs): """ Passed off to scipy.interpolate.interp1d. method is scipy's kind. Returns an array interpolated at new_x. Add any new methods to the list in _clean_interp_m...

def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False): """ Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : ...

def _akima_interpolate(xi, yi, x, der=0, axis=0): """ Convenience function for akima interpolation. xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. See `Akima1DInterpolator` for details. Parameters ---------- xi : array_like A sorted lis...

def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None): """ Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. """ transf = (lambda x: x) if axis == 0 else (lambda x: x.T) # resha...

def _cast_values_for_fillna(values, dtype): """ Cast values to a dtype that algos.pad and algos.backfill can handle. """ # TODO: for int-dtypes we make a copy, but for everything else this # alters the values in-place. Is this intentional? if (is_datetime64_dtype(dtype) or is_datetime64tz_dty...

def fill_zeros(result, x, y, name, fill): """ If this is a reversed op, then flip x,y If we have an integer value (or array in y) and we have 0's, fill them with the fill, return the result. Mask the nan's from x. """ if fill is None or is_float_dtype(result): return result ...

def mask_zero_div_zero(x, y, result, copy=False): """ Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes of the numerator or the denominator. Parameters ---------- x : ndarray y : ndarray result : ndarray copy : bool (default False) Whether to always create a...

def dispatch_missing(op, left, right, result): """ Fill nulls caused by division by zero, casting to a diffferent dtype if necessary. Parameters ---------- op : function (operator.add, operator.div, ...) left : object (Index for non-reversed ops) right : object (Index fof reversed ops) ...

def _interp_limit(invalid, fw_limit, bw_limit): """ Get indexers of values that won't be filled because they exceed the limits. Parameters ---------- invalid : boolean ndarray fw_limit : int or None forward limit to index bw_limit : int or None backward limit to index ...

def _rolling_window(a, window): """ [True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, False], ] """ # https://stackoverflow.com/a/6811241 shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) strides = a.stri...

def get_console_size(): """Return console size as tuple = (width, height). Returns (None,None) in non-interactive session. """ from pandas import get_option display_width = get_option('display.width') # deprecated. display_height = get_option('display.max_rows') # Consider # inter...

def in_interactive_session(): """ check if we're running in an interactive shell returns True if running under python/ipython interactive shell """ from pandas import get_option def check_main(): try: import __main__ as main except ModuleNotFoundError: retur...

def recode_for_groupby(c, sort, observed): """ Code the categories to ensure we can groupby for categoricals. If observed=True, we return a new Categorical with the observed categories only. If sort=False, return a copy of self, coded with categories as returned by .unique(), followed by any c...

def recode_from_groupby(c, sort, ci): """ Reverse the codes_to_groupby to account for sort / observed. Parameters ---------- c : Categorical sort : boolean The value of the sort parameter groupby was called with. ci : CategoricalIndex The codes / categories to recode Re...

def get_engine(engine): """ return our implementation """ if engine == 'auto': engine = get_option('io.parquet.engine') if engine == 'auto': # try engines in this order try: return PyArrowImpl() except ImportError: pass try: retu...

def to_parquet(df, path, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs): """ Write a DataFrame to the parquet format. Parameters ---------- path : str File path or Root Directory path. Will be used as Root Directory path while writing ...

def read_parquet(path, engine='auto', columns=None, **kwargs): """ Load a parquet object from the file path, returning a DataFrame. .. versionadded 0.21.0 Parameters ---------- path : string File path engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto' Parquet libra...

def generate_bins_generic(values, binner, closed): """ Generate bin edge offsets and bin labels for one array using another array which has bin edge values. Both arrays must be sorted. Parameters ---------- values : array of values binner : a comparable array of values representing bins int...

def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ splitter = self._get_splitter(data, axis=axis) keys = self._get_group_keys() for key, (i,...

def indices(self): """ dict {group name -> group indices} """ if len(self.groupings) == 1: return self.groupings[0].indices else: label_list = [ping.labels for ping in self.groupings] keys = [com.values_from_object(ping.group_index) for pin...

def size(self): """ Compute group sizes """ ids, _, ngroup = self.group_info ids = ensure_platform_int(ids) if ngroup: out = np.bincount(ids[ids != -1], minlength=ngroup) else: out = [] return Series(out, inde...

def groups(self): """ dict {group name -> group labels} """ if len(self.groupings) == 1: return self.groupings[0].groups else: to_groupby = lzip(*(ping.grouper for ping in self.groupings)) to_groupby = Index(to_groupby) return self.axis.groupby(to_...

def groups(self): """ dict {group name -> group labels} """ # this is mainly for compat # GH 3881 result = {key: value for key, value in zip(self.binlabels, self.bins) if key is not NaT} return result

def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ if isinstance(data, NDFrame): slicer = lambda start, edge: data._slice( slice...

def json_normalize(data, record_path=None, meta=None, meta_prefix=None, record_prefix=None, errors='raise', sep='.'): """ Normalize semi-structured JSON data into a flat table. Parameters ---------- data : dict or list of d...

def lreshape(data, groups, dropna=True, label=None): """ Reshape long-format data to wide. Generalized inverse of DataFrame.pivot Parameters ---------- data : DataFrame groups : dict {new_name : list_of_columns} dropna : boolean, default True Examples -------- >>> data ...

def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'): r""" Wide panel to long format. Less flexible but more user-friendly than melt. With stubnames ['A', 'B'], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,... You ...

def _get_indices(self, names): """ Safe get multiple indices, translate keys for datelike to underlying repr. """ def get_converter(s): # possibly convert to the actual key types # in the indices, could be a Timestamp or a np.datetime64 if isi...

def _set_group_selection(self): """ Create group based selection. Used when selection is not passed directly but instead via a grouper. NOTE: this should be paired with a call to _reset_group_selection """ grp = self.grouper if not (self.as_index and ...

def get_group(self, name, obj=None): """ Construct NDFrame from group with provided name. Parameters ---------- name : object the name of the group to get as a DataFrame obj : NDFrame, default None the NDFrame to take the DataFrame out of. If ...

def _cumcount_array(self, ascending=True): """ Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=T...

def _try_cast(self, result, obj, numeric_only=False): """ Try to cast the result to our obj original type, we may have roundtripped through object in the mean-time. If numeric_only is True, then only try to cast numerics and not datetimelikes. """ if obj.ndim > ...

def _transform_should_cast(self, func_nm): """ Parameters: ----------- func_nm: str The name of the aggregation function being performed Returns: -------- bool Whether transform should attempt to cast the result of aggregation """ ...

def _bool_agg(self, val_test, skipna): """ Shared func to call any / all Cython GroupBy implementations. """ def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]: if is_object_dtype(vals): vals = np.array([bool(x) for x in vals]) else: ...

def mean(self, *args, **kwargs): """ Compute mean of groups, excluding missing values. Returns ------- pandas.Series or pandas.DataFrame %(see_also)s Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B'...

def median(self, **kwargs): """ Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex """ try: return self._cython_agg_general('median', **kwargs) except GroupByError: raise excep...

def std(self, ddof=1, *args, **kwargs): """ Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom """ ...

def var(self, ddof=1, *args, **kwargs): """ Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom """ nv.validat...

def sem(self, ddof=1): """ Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom """ return s...

def size(self): """ Compute group sizes. """ result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result

def _add_numeric_operations(cls): """ Add numeric operations to the GroupBy generically. """ def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_count=-1): _local_template = "Compute %(f)...

def resample(self, rule, *args, **kwargs): """ Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string "string" -> "frequency". See the :ref:`frequency aliases <timeseries.offset_aliases>` documentation for more deta...

def rolling(self, *args, **kwargs): """ Return a rolling grouper, providing rolling functionality per group. """ from pandas.core.window import RollingGroupby return RollingGroupby(self, *args, **kwargs)

def expanding(self, *args, **kwargs): """ Return an expanding grouper, providing expanding functionality per group. """ from pandas.core.window import ExpandingGroupby return ExpandingGroupby(self, *args, **kwargs)

def _fill(self, direction, limit=None): """ Shared function for `pad` and `backfill` to call Cython method. Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwar...

def nth(self, n, dropna=None): """ Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints. If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or 'all', 'any' (if a DataFrame); this is equivalent to callin...

def quantile(self, q=0.5, interpolation='linear'): """ Return group values at the given quantile, a la numpy.percentile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value(s) between 0 and 1 providing the quantile(s) to compute. i...

def ngroup(self, ascending=True): """ Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not th...

def cumcount(self, ascending=True): """ Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True...

def rank(self, method='average', ascending=True, na_option='keep', pct=False, axis=0): """ Provide the rank of values within each group. Parameters ---------- method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank...

def cumprod(self, axis=0, *args, **kwargs): """ Cumulative product for each group. """ nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumprod(axis=axis, **kwarg...

def cummin(self, axis=0, **kwargs): """ Cumulative min for each group. """ if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False)

def cummax(self, axis=0, **kwargs): """ Cumulative max for each group. """ if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False)