BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def take_nd(self, indexer, allow_fill=None, fill_value=None): """ Take elements from the Categorical. Parameters ---------- indexer : sequence of int The indices in `self` to take. The meaning of negative values in `indexer` depends on the value of `allow...
def _slice(self, slicer): """ Return a slice of myself. For internal compatibility with numpy arrays. """ # only allow 1 dimensional slicing, but can # in a 2-d case be passd (slice(None),....) if isinstance(slicer, tuple) and len(slicer) == 2: if no...
def _tidy_repr(self, max_vals=10, footer=True): """ a short repr displaying only max_vals and an optional (but default footer) """ num = max_vals // 2 head = self[:num]._get_repr(length=False, footer=False) tail = self[-(max_vals - num):]._get_repr(length=False, footer=Fa...
def _repr_categories(self): """ return the base repr for the categories """ max_categories = (10 if get_option("display.max_categories") == 0 else get_option("display.max_categories")) from pandas.io.formats import format as fmt if len(self.categ...
def _repr_categories_info(self): """ Returns a string representation of the footer. """ category_strs = self._repr_categories() dtype = getattr(self.categories, 'dtype_str', str(self.categories.dtype)) levheader = "Categories ({length}, {dtype}):...
def _maybe_coerce_indexer(self, indexer): """ return an indexer coerced to the codes dtype """ if isinstance(indexer, np.ndarray) and indexer.dtype.kind == 'i': indexer = indexer.astype(self._codes.dtype) return indexer
def _reverse_indexer(self): """ Compute the inverse of a categorical, returning a dict of categories -> indexers. This is an internal function Returns ------- dict of categories -> indexers Example ------- In [1]: c = pd.Categorical(li...
def min(self, numeric_only=None, **kwargs): """ The minimum value of the object. Only ordered `Categoricals` have a minimum! Raises ------ TypeError If the `Categorical` is not `ordered`. Returns ------- min : the minimum of this `Ca...
def mode(self, dropna=True): """ Returns the mode(s) of the Categorical. Always returns `Categorical` even if only one value. Parameters ---------- dropna : bool, default True Don't consider counts of NaN/NaT. .. versionadded:: 0.24.0 R...
def unique(self): """ Return the ``Categorical`` which ``categories`` and ``codes`` are unique. Unused categories are NOT returned. - unordered category: values and categories are sorted by appearance order. - ordered category: values are sorted by appearance order, ca...
def equals(self, other): """ Returns True if categorical arrays are equal. Parameters ---------- other : `Categorical` Returns ------- bool """ if self.is_dtype_equal(other): if self.categories.equals(other.categories): ...
def is_dtype_equal(self, other): """ Returns True if categoricals are the same dtype same categories, and same ordered Parameters ---------- other : Categorical Returns ------- bool """ try: return hash(self.dtype) ...
def describe(self): """ Describes this Categorical Returns ------- description: `DataFrame` A dataframe with frequency and counts by category. """ counts = self.value_counts(dropna=False) freqs = counts / float(counts.sum()) from pand...
def isin(self, values): """ Check whether `values` are contained in Categorical. Return a boolean NumPy Array showing whether each element in the Categorical matches an element in the passed sequence of `values` exactly. Parameters ---------- values : se...
def to_timedelta(arg, unit='ns', box=True, errors='raise'): """ Convert argument to timedelta. Timedeltas are absolute differences in times, expressed in difference units (e.g. days, hours, minutes, seconds). This method converts an argument from a recognized timedelta format / value into a Tim...
def _coerce_scalar_to_timedelta_type(r, unit='ns', box=True, errors='raise'): """Convert string 'r' to a timedelta object.""" try: result = Timedelta(r, unit) if not box: # explicitly view as timedelta64 for case when result is pd.NaT result = result.asm8.view('timedelta...
def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None): """Convert a list of objects to a timedelta index object.""" if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'): # This is needed only to ensure that in the case where we end up # returning arg (errors == "...
def generate_range(start=None, end=None, periods=None, offset=BDay()): """ Generates a sequence of dates corresponding to the specified time offset. Similar to dateutil.rrule except uses pandas DateOffset objects to represent time increments. Parameters ---------- start : datetime (default ...
def apply_index(self, i): """ Vectorized apply of DateOffset to DatetimeIndex, raises NotImplentedError for offsets without a vectorized implementation. Parameters ---------- i : DatetimeIndex Returns ------- y : DatetimeIndex """...
def rollback(self, dt): """ Roll provided date backward to next offset only if not on offset. """ dt = as_timestamp(dt) if not self.onOffset(dt): dt = dt - self.__class__(1, normalize=self.normalize, **self.kwds) return dt
def rollforward(self, dt): """ Roll provided date forward to next offset only if not on offset. """ dt = as_timestamp(dt) if not self.onOffset(dt): dt = dt + self.__class__(1, normalize=self.normalize, **self.kwds) return dt
def next_bday(self): """ Used for moving to next business day. """ if self.n >= 0: nb_offset = 1 else: nb_offset = -1 if self._prefix.startswith('C'): # CustomBusinessHour return CustomBusinessDay(n=nb_offset, ...
def _next_opening_time(self, other): """ If n is positive, return tomorrow's business day opening time. Otherwise yesterday's business day's opening time. Opening time always locates on BusinessDay. Otherwise, closing time may not if business hour extends over midnight. ...
def _get_business_hours_by_sec(self): """ Return business hours in a day by seconds. """ if self._get_daytime_flag: # create dummy datetime to calculate businesshours in a day dtstart = datetime(2014, 4, 1, self.start.hour, self.start.minute) until = d...
def rollback(self, dt): """ Roll provided date backward to next offset only if not on offset. """ if not self.onOffset(dt): businesshours = self._get_business_hours_by_sec if self.n >= 0: dt = self._prev_opening_time( dt) + time...
def rollforward(self, dt): """ Roll provided date forward to next offset only if not on offset. """ if not self.onOffset(dt): if self.n >= 0: return self._next_opening_time(dt) else: return self._prev_opening_time(dt) return...
def _onOffset(self, dt, businesshours): """ Slight speedups using calculated values. """ # if self.normalize and not _is_normalized(dt): # return False # Valid BH can be on the different BusinessDay during midnight # Distinguish by the time spent from previous...
def cbday_roll(self): """ Define default roll function to be called in apply method. """ cbday = CustomBusinessDay(n=self.n, normalize=False, **self.kwds) if self._prefix.endswith('S'): # MonthBegin roll_func = cbday.rollforward else: ...
def month_roll(self): """ Define default roll function to be called in apply method. """ if self._prefix.endswith('S'): # MonthBegin roll_func = self.m_offset.rollback else: # MonthEnd roll_func = self.m_offset.rollforward r...
def _apply_index_days(self, i, roll): """ Add days portion of offset to DatetimeIndex i. Parameters ---------- i : DatetimeIndex roll : ndarray[int64_t] Returns ------- result : DatetimeIndex """ nanos = (roll % 2) * Timedelta(day...
def _end_apply_index(self, dtindex): """ Add self to the given DatetimeIndex, specialized for case where self.weekday is non-null. Parameters ---------- dtindex : DatetimeIndex Returns ------- result : DatetimeIndex """ off = dtin...
def _get_offset_day(self, other): """ Find the day in the same month as other that has the same weekday as self.weekday and is the self.week'th such day in the month. Parameters ---------- other : datetime Returns ------- day : int """ ...
def _get_offset_day(self, other): """ Find the day in the same month as other that has the same weekday as self.weekday and is the last such day in the month. Parameters ---------- other: datetime Returns ------- day: int """ dim ...
def _rollback_to_year(self, other): """ Roll `other` back to the most recent date that was on a fiscal year end. Return the date of that year-end, the number of full quarters elapsed between that year-end and other, and the remaining Timedelta since the most recent quart...
def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=None, copy=True): """ Concatenate pandas objects along a particular axis with optional set logic along the other axes. Can also add a layer o...
def _get_concat_axis(self): """ Return index to be used along concatenation axis. """ if self._is_series: if self.axis == 0: indexes = [x.index for x in self.objs] elif self.ignore_index: idx = ibase.default_index(len(self.objs)) ...
def _in(x, y): """Compute the vectorized membership of ``x in y`` if possible, otherwise use Python. """ try: return x.isin(y) except AttributeError: if is_list_like(x): try: return y.isin(x) except AttributeError: pass ...
def _not_in(x, y): """Compute the vectorized membership of ``x not in y`` if possible, otherwise use Python. """ try: return ~x.isin(y) except AttributeError: if is_list_like(x): try: return ~y.isin(x) except AttributeError: pas...
def _cast_inplace(terms, acceptable_dtypes, dtype): """Cast an expression inplace. Parameters ---------- terms : Op The expression that should cast. acceptable_dtypes : list of acceptable numpy.dtype Will not cast if term's dtype in this list. .. versionadded:: 0.19.0 ...
def update(self, value): """ search order for local (i.e., @variable) variables: scope, key_variable [('locals', 'local_name'), ('globals', 'local_name'), ('locals', 'key'), ('globals', 'key')] """ key = self.name # if it's a variable ...
def evaluate(self, env, engine, parser, term_type, eval_in_python): """Evaluate a binary operation before being passed to the engine. Parameters ---------- env : Scope engine : str parser : str term_type : type eval_in_python : list Returns ...
def convert_values(self): """Convert datetimes to a comparable value in an expression. """ def stringify(value): if self.encoding is not None: encoder = partial(pprint_thing_encoded, encoding=self.encoding) else: ...
def crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=True, normalize=False): """ Compute a simple cross tabulation of two (or more) factors. By default computes a frequency table of the factors unless an arr...
def _shape(self, df): """ Calculate table chape considering index levels. """ row, col = df.shape return row + df.columns.nlevels, col + df.index.nlevels
def _get_cells(self, left, right, vertical): """ Calculate appropriate figure size based on left and right data. """ if vertical: # calculate required number of cells vcells = max(sum(self._shape(l)[0] for l in left), self._shape(right)[0...
def plot(self, left, right, labels=None, vertical=True): """ Plot left / right DataFrames in specified layout. Parameters ---------- left : list of DataFrames before operation is applied right : DataFrame of operation result labels : list of str to be drawn as ti...
def _conv(self, data): """Convert each input to appropriate for table outplot""" if isinstance(data, pd.Series): if data.name is None: data = data.to_frame(name='') else: data = data.to_frame() data = data.fillna('NaN') return data
def cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise'): """ Bin values into discrete intervals. Use `cut` when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a ...
def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'): """ Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles. For example 1000 values for 10 quantiles would produce a Categorical object indicating qua...
def _coerce_to_type(x): """ if the passed data is of datetime/timedelta type, this method converts it to numeric so that cut method can handle it """ dtype = None if is_datetime64tz_dtype(x): dtype = x.dtype elif is_datetime64_dtype(x): x = to_datetime(x) dtype =...
def _convert_bin_to_numeric_type(bins, dtype): """ if the passed bin is of datetime/timedelta type, this method converts it to integer Parameters ---------- bins : list-like of bins dtype : dtype of data Raises ------ ValueError if bins are not of a compat dtype to dtype ""...
def _convert_bin_to_datelike_type(bins, dtype): """ Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is datelike Parameters ---------- bins : list-like of bins dtype : dtype of data Returns ------- bins : Array-like of bins, DatetimeIndex or TimedeltaIndex...
def _format_labels(bins, precision, right=True, include_lowest=False, dtype=None): """ based on the dtype, return our labels """ closed = 'right' if right else 'left' if is_datetime64tz_dtype(dtype): formatter = partial(Timestamp, tz=dtype.tz) adjust = lambda x: x - Time...
def _preprocess_for_cut(x): """ handles preprocessing for cut where we convert passed input to array, strip the index information and store it separately """ x_is_series = isinstance(x, Series) series_index = None name = None if x_is_series: series_index = x.index na...
def _postprocess_for_cut(fac, bins, retbins, x_is_series, series_index, name, dtype): """ handles post processing for the cut method where we combine the index information if the originally passed datatype was a series """ if x_is_series: fac = Series(fac, index=...
def _round_frac(x, precision): """ Round the fractional part of the given number """ if not np.isfinite(x) or x == 0: return x else: frac, whole = np.modf(x) if whole == 0: digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision else: digi...
def _infer_precision(base_precision, bins): """Infer an appropriate precision for _round_frac """ for precision in range(base_precision, 20): levels = [_round_frac(b, precision) for b in bins] if algos.unique(levels).size == bins.size: return precision return base_precision
def detect_console_encoding(): """ Try to find the most capable encoding supported by the console. slightly modified from the way IPython handles the same issue. """ global _initial_defencoding encoding = None try: encoding = sys.stdout.encoding or sys.stdin.encoding except (Att...
def _check_arg_length(fname, args, max_fname_arg_count, compat_args): """ Checks whether 'args' has length of at most 'compat_args'. Raises a TypeError if that is not the case, similar to in Python when a function is called with too many arguments. """ if max_fname_arg_count < 0: raise ...
def _check_for_default_values(fname, arg_val_dict, compat_args): """ Check that the keys in `arg_val_dict` are mapped to their default values as specified in `compat_args`. Note that this function is to be called only when it has been checked that arg_val_dict.keys() is a subset of compat_args ...
def validate_args(fname, args, max_fname_arg_count, compat_args): """ Checks whether the length of the `*args` argument passed into a function has at most `len(compat_args)` arguments and whether or not all of these elements in `args` are set to their default values. fname: str The name of ...
def _check_for_invalid_keys(fname, kwargs, compat_args): """ Checks whether 'kwargs' contains any keys that are not in 'compat_args' and raises a TypeError if there is one. """ # set(dict) --> set of the dictionary's keys diff = set(kwargs) - set(compat_args) if diff: bad_arg = lis...
def validate_kwargs(fname, kwargs, compat_args): """ Checks whether parameters passed to the *kwargs argument in a function `fname` are valid parameters as specified in `compat_args` and whether or not they are set to their default values. Parameters ---------- fname: str The name...
def validate_args_and_kwargs(fname, args, kwargs, max_fname_arg_count, compat_args): """ Checks whether parameters passed to the args and kwargs argument in a function `fname` are valid parameters as specified in `compat_args` and whether or ...
def validate_bool_kwarg(value, arg_name): """ Ensures that argument passed in arg_name is of type bool. """ if not (is_bool(value) or value is None): raise ValueError('For argument "{arg}" expected type bool, received ' 'type {typ}.'.format(arg=arg_name, ...
def validate_axis_style_args(data, args, kwargs, arg_name, method_name): """Argument handler for mixed index, columns / axis functions In an attempt to handle both `.method(index, columns)`, and `.method(arg, axis=.)`, we have to do some bad things to argument parsing. This translates all arguments to ...
def validate_fillna_kwargs(value, method, validate_scalar_dict_value=True): """Validate the keyword arguments to 'fillna'. This checks that exactly one of 'value' and 'method' is specified. If 'method' is specified, this validates that it's a valid method. Parameters ---------- value, method :...
def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None): """ Potentially we might have a deprecation warning, show it but call the appropriate methods anyhow. """ if how is not None: # .resample(..., how='sum') if isinstance(how, str): method = "{0}()...
def resample(obj, kind=None, kwds): """ Create a TimeGrouper and return our resampler. """ tg = TimeGrouper(kwds) return tg._get_resampler(obj, kind=kind)
def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None, limit=None, kind=None, **kwargs): """ Return our appropriate resampler when grouping as well. """ # .resample uses 'on' similar to how .groupby uses 'key' kwargs['key'] = kwargs.pop('on', None) ...
def _get_timestamp_range_edges(first, last, offset, closed='left', base=0): """ Adjust the `first` Timestamp to the preceeding Timestamp that resides on the provided offset. Adjust the `last` Timestamp to the following Timestamp that resides on the provided offset. Input Timestamps that already resi...
def _get_period_range_edges(first, last, offset, closed='left', base=0): """ Adjust the provided `first` and `last` Periods to the respective Period of the given offset that encompasses them. Parameters ---------- first : pd.Period The beginning Period of the range to be adjusted. l...
def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None): """ Utility frequency conversion method for Series/DataFrame. """ if isinstance(obj.index, PeriodIndex): if method is not None: raise NotImplementedError("'method' argument is not supported") if ...
def _from_selection(self): """ Is the resampling from a DataFrame column or MultiIndex level. """ # upsampling and PeriodIndex resampling do not work # with selection, this state used to catch and raise an error return (self.groupby is not None and (self.g...
def _set_binner(self): """ Setup our binners. Cache these as we are an immutable object """ if self.binner is None: self.binner, self.grouper = self._get_binner()
def _get_binner(self): """ Create the BinGrouper, assume that self.set_grouper(obj) has already been called. """ binner, bins, binlabels = self._get_binner_for_time() bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer) return binner, bin_group...
def transform(self, arg, args, *kwargs): """ Call function producing a like-indexed Series on each group and return a Series with the transformed values. Parameters ---------- arg : function To apply to each group. Should return a Series with the same index...
def _gotitem(self, key, ndim, subset=None): """ Sub-classes to define. Return a sliced object. Parameters ---------- key : string / list of selections ndim : 1,2 requested ndim of result subset : object, default None subset to act on ...
def _groupby_and_aggregate(self, how, grouper=None, args, *kwargs): """ Re-evaluate the obj with a groupby aggregation. """ if grouper is None: self._set_binner() grouper = self.grouper obj = self._selected_obj grouped = groupby(obj, by=None, ...
def _apply_loffset(self, result): """ If loffset is set, offset the result index. This is NOT an idempotent routine, it will be applied exactly once to the result. Parameters ---------- result : Series or DataFrame the result of resample """ ...
def _get_resampler_for_grouping(self, groupby, kwargs): """ Return the correct class for resampling with groupby. """ return self._resampler_for_grouping(self, groupby=groupby, kwargs)
def _wrap_result(self, result): """ Potentially wrap any results. """ if isinstance(result, ABCSeries) and self._selection is not None: result.name = self._selection if isinstance(result, ABCSeries) and result.empty: obj = self.obj if isinstan...
def interpolate(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', limit_area=None, downcast=None, **kwargs): """ Interpolate values according to different methods. .. versionadded:: 0.18.1 """ result = se...
def std(self, ddof=1, args, *kwargs): """ Compute standard deviation of groups, excluding missing values. Parameters ---------- ddof : integer, default 1 Degrees of freedom. """ nv.validate_resampler_func('std', args, kwargs) return self._do...
def var(self, ddof=1, args, *kwargs): """ Compute variance of groups, excluding missing values. Parameters ---------- ddof : integer, default 1 degrees of freedom """ nv.validate_resampler_func('var', args, kwargs) return self._downsample('v...
def _apply(self, f, grouper=None, args, *kwargs): """ Dispatch to _upsample; we are stripping all of the _upsample kwargs and performing the original function call on the grouped object. """ def func(x): x = self._shallow_copy(x, groupby=self.groupby) ...
def _downsample(self, how, kwargs): """ Downsample the cython defined function. Parameters ---------- how : string / cython mapped function kwargs : kw args passed to how function """ self._set_binner() how = self._is_cython_func(how) or how ...
def _adjust_binner_for_upsample(self, binner): """ Adjust our binner when upsampling. The range of a new index should not be outside specified range """ if self.closed == 'right': binner = binner[1:] else: binner = binner[:-1] return binne...
def _upsample(self, method, limit=None, fill_value=None): """ Parameters ---------- method : string {'backfill', 'bfill', 'pad', 'ffill', 'asfreq'} method for upsampling limit : int, default None Maximum size gap to fill when reindexing fill_value ...
def _downsample(self, how, kwargs): """ Downsample the cython defined function. Parameters ---------- how : string / cython mapped function kwargs : kw args passed to how function """ # we may need to actually resample as if we are timestamps ...
def _upsample(self, method, limit=None, fill_value=None): """ Parameters ---------- method : string {'backfill', 'bfill', 'pad', 'ffill'} method for upsampling limit : int, default None Maximum size gap to fill when reindexing fill_value : scalar, ...
def _get_resampler(self, obj, kind=None): """ Return my resampler or raise if we have an invalid axis. Parameters ---------- obj : input object kind : string, optional 'period','timestamp','timedelta' are valid Returns ------- a Resam...
def _combine_hash_arrays(arrays, num_items): """ Parameters ---------- arrays : generator num_items : int Should be the same as CPython's tupleobject.c """ try: first = next(arrays) except StopIteration: return np.array([], dtype=np.uint64) arrays = itertools.ch...
def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None, categorize=True): """ Return a data hash of the Index/Series/DataFrame .. versionadded:: 0.19.2 Parameters ---------- index : boolean, default True include the index in the hash (if Series/Da...
def hash_tuples(vals, encoding='utf8', hash_key=None): """ Hash an MultiIndex / list-of-tuples efficiently .. versionadded:: 0.20.0 Parameters ---------- vals : MultiIndex, list-of-tuples, or single tuple encoding : string, default 'utf8' hash_key : string key to encode, default to _de...
def hash_tuple(val, encoding='utf8', hash_key=None): """ Hash a single tuple efficiently Parameters ---------- val : single tuple encoding : string, default 'utf8' hash_key : string key to encode, default to _default_hash_key Returns ------- hash """ hashes = (_hash_sc...
def _hash_categorical(c, encoding, hash_key): """ Hash a Categorical by hashing its categories, and then mapping the codes to the hashes Parameters ---------- c : Categorical encoding : string, default 'utf8' hash_key : string key to encode, default to _default_hash_key Returns ...
def hash_array(vals, encoding='utf8', hash_key=None, categorize=True): """ Given a 1d array, return an array of deterministic integers. .. versionadded:: 0.19.2 Parameters ---------- vals : ndarray, Categorical encoding : string, default 'utf8' encoding for data & key when strings ...
def _hash_scalar(val, encoding='utf8', hash_key=None): """ Hash scalar value Returns ------- 1d uint64 numpy array of hash value, of length 1 """ if isna(val): # this is to be consistent with the _hash_categorical implementation return np.array([np.iinfo(np.uint64).max], dt...
def _process_single_doc(self, single_doc): """ Make sure the provided value for --single is a path to an existing .rst/.ipynb file, or a pandas object that can be imported. For example, categorial.rst or pandas.DataFrame.head. For the latter, return the corresponding file path ...

def take_nd(self, indexer, allow_fill=None, fill_value=None): """ Take elements from the Categorical. Parameters ---------- indexer : sequence of int The indices in `self` to take. The meaning of negative values in `indexer` depends on the value of `allow...

def _slice(self, slicer): """ Return a slice of myself. For internal compatibility with numpy arrays. """ # only allow 1 dimensional slicing, but can # in a 2-d case be passd (slice(None),....) if isinstance(slicer, tuple) and len(slicer) == 2: if no...

def _tidy_repr(self, max_vals=10, footer=True): """ a short repr displaying only max_vals and an optional (but default footer) """ num = max_vals // 2 head = self[:num]._get_repr(length=False, footer=False) tail = self[-(max_vals - num):]._get_repr(length=False, footer=Fa...

def _repr_categories(self): """ return the base repr for the categories """ max_categories = (10 if get_option("display.max_categories") == 0 else get_option("display.max_categories")) from pandas.io.formats import format as fmt if len(self.categ...

def _repr_categories_info(self): """ Returns a string representation of the footer. """ category_strs = self._repr_categories() dtype = getattr(self.categories, 'dtype_str', str(self.categories.dtype)) levheader = "Categories ({length}, {dtype}):...

def _maybe_coerce_indexer(self, indexer): """ return an indexer coerced to the codes dtype """ if isinstance(indexer, np.ndarray) and indexer.dtype.kind == 'i': indexer = indexer.astype(self._codes.dtype) return indexer

def _reverse_indexer(self): """ Compute the inverse of a categorical, returning a dict of categories -> indexers. *This is an internal function* Returns ------- dict of categories -> indexers Example ------- In [1]: c = pd.Categorical(li...

def min(self, numeric_only=None, **kwargs): """ The minimum value of the object. Only ordered `Categoricals` have a minimum! Raises ------ TypeError If the `Categorical` is not `ordered`. Returns ------- min : the minimum of this `Ca...

def mode(self, dropna=True): """ Returns the mode(s) of the Categorical. Always returns `Categorical` even if only one value. Parameters ---------- dropna : bool, default True Don't consider counts of NaN/NaT. .. versionadded:: 0.24.0 R...

def unique(self): """ Return the ``Categorical`` which ``categories`` and ``codes`` are unique. Unused categories are NOT returned. - unordered category: values and categories are sorted by appearance order. - ordered category: values are sorted by appearance order, ca...

def equals(self, other): """ Returns True if categorical arrays are equal. Parameters ---------- other : `Categorical` Returns ------- bool """ if self.is_dtype_equal(other): if self.categories.equals(other.categories): ...

def is_dtype_equal(self, other): """ Returns True if categoricals are the same dtype same categories, and same ordered Parameters ---------- other : Categorical Returns ------- bool """ try: return hash(self.dtype) ...

def describe(self): """ Describes this Categorical Returns ------- description: `DataFrame` A dataframe with frequency and counts by category. """ counts = self.value_counts(dropna=False) freqs = counts / float(counts.sum()) from pand...

def isin(self, values): """ Check whether `values` are contained in Categorical. Return a boolean NumPy Array showing whether each element in the Categorical matches an element in the passed sequence of `values` exactly. Parameters ---------- values : se...

def to_timedelta(arg, unit='ns', box=True, errors='raise'): """ Convert argument to timedelta. Timedeltas are absolute differences in times, expressed in difference units (e.g. days, hours, minutes, seconds). This method converts an argument from a recognized timedelta format / value into a Tim...

def _coerce_scalar_to_timedelta_type(r, unit='ns', box=True, errors='raise'): """Convert string 'r' to a timedelta object.""" try: result = Timedelta(r, unit) if not box: # explicitly view as timedelta64 for case when result is pd.NaT result = result.asm8.view('timedelta...

def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None): """Convert a list of objects to a timedelta index object.""" if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'): # This is needed only to ensure that in the case where we end up # returning arg (errors == "...

def generate_range(start=None, end=None, periods=None, offset=BDay()): """ Generates a sequence of dates corresponding to the specified time offset. Similar to dateutil.rrule except uses pandas DateOffset objects to represent time increments. Parameters ---------- start : datetime (default ...

def apply_index(self, i): """ Vectorized apply of DateOffset to DatetimeIndex, raises NotImplentedError for offsets without a vectorized implementation. Parameters ---------- i : DatetimeIndex Returns ------- y : DatetimeIndex """...

def rollback(self, dt): """ Roll provided date backward to next offset only if not on offset. """ dt = as_timestamp(dt) if not self.onOffset(dt): dt = dt - self.__class__(1, normalize=self.normalize, **self.kwds) return dt

def rollforward(self, dt): """ Roll provided date forward to next offset only if not on offset. """ dt = as_timestamp(dt) if not self.onOffset(dt): dt = dt + self.__class__(1, normalize=self.normalize, **self.kwds) return dt

def next_bday(self): """ Used for moving to next business day. """ if self.n >= 0: nb_offset = 1 else: nb_offset = -1 if self._prefix.startswith('C'): # CustomBusinessHour return CustomBusinessDay(n=nb_offset, ...

def _next_opening_time(self, other): """ If n is positive, return tomorrow's business day opening time. Otherwise yesterday's business day's opening time. Opening time always locates on BusinessDay. Otherwise, closing time may not if business hour extends over midnight. ...

def _get_business_hours_by_sec(self): """ Return business hours in a day by seconds. """ if self._get_daytime_flag: # create dummy datetime to calculate businesshours in a day dtstart = datetime(2014, 4, 1, self.start.hour, self.start.minute) until = d...

def rollback(self, dt): """ Roll provided date backward to next offset only if not on offset. """ if not self.onOffset(dt): businesshours = self._get_business_hours_by_sec if self.n >= 0: dt = self._prev_opening_time( dt) + time...

def rollforward(self, dt): """ Roll provided date forward to next offset only if not on offset. """ if not self.onOffset(dt): if self.n >= 0: return self._next_opening_time(dt) else: return self._prev_opening_time(dt) return...

def _onOffset(self, dt, businesshours): """ Slight speedups using calculated values. """ # if self.normalize and not _is_normalized(dt): # return False # Valid BH can be on the different BusinessDay during midnight # Distinguish by the time spent from previous...

def cbday_roll(self): """ Define default roll function to be called in apply method. """ cbday = CustomBusinessDay(n=self.n, normalize=False, **self.kwds) if self._prefix.endswith('S'): # MonthBegin roll_func = cbday.rollforward else: ...

def month_roll(self): """ Define default roll function to be called in apply method. """ if self._prefix.endswith('S'): # MonthBegin roll_func = self.m_offset.rollback else: # MonthEnd roll_func = self.m_offset.rollforward r...

def _apply_index_days(self, i, roll): """ Add days portion of offset to DatetimeIndex i. Parameters ---------- i : DatetimeIndex roll : ndarray[int64_t] Returns ------- result : DatetimeIndex """ nanos = (roll % 2) * Timedelta(day...

def _end_apply_index(self, dtindex): """ Add self to the given DatetimeIndex, specialized for case where self.weekday is non-null. Parameters ---------- dtindex : DatetimeIndex Returns ------- result : DatetimeIndex """ off = dtin...

def _get_offset_day(self, other): """ Find the day in the same month as other that has the same weekday as self.weekday and is the self.week'th such day in the month. Parameters ---------- other : datetime Returns ------- day : int """ ...

def _get_offset_day(self, other): """ Find the day in the same month as other that has the same weekday as self.weekday and is the last such day in the month. Parameters ---------- other: datetime Returns ------- day: int """ dim ...

def _rollback_to_year(self, other): """ Roll `other` back to the most recent date that was on a fiscal year end. Return the date of that year-end, the number of full quarters elapsed between that year-end and other, and the remaining Timedelta since the most recent quart...

def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=None, copy=True): """ Concatenate pandas objects along a particular axis with optional set logic along the other axes. Can also add a layer o...

def _get_concat_axis(self): """ Return index to be used along concatenation axis. """ if self._is_series: if self.axis == 0: indexes = [x.index for x in self.objs] elif self.ignore_index: idx = ibase.default_index(len(self.objs)) ...

def _in(x, y): """Compute the vectorized membership of ``x in y`` if possible, otherwise use Python. """ try: return x.isin(y) except AttributeError: if is_list_like(x): try: return y.isin(x) except AttributeError: pass ...

def _not_in(x, y): """Compute the vectorized membership of ``x not in y`` if possible, otherwise use Python. """ try: return ~x.isin(y) except AttributeError: if is_list_like(x): try: return ~y.isin(x) except AttributeError: pas...

def _cast_inplace(terms, acceptable_dtypes, dtype): """Cast an expression inplace. Parameters ---------- terms : Op The expression that should cast. acceptable_dtypes : list of acceptable numpy.dtype Will not cast if term's dtype in this list. .. versionadded:: 0.19.0 ...

def update(self, value): """ search order for local (i.e., @variable) variables: scope, key_variable [('locals', 'local_name'), ('globals', 'local_name'), ('locals', 'key'), ('globals', 'key')] """ key = self.name # if it's a variable ...

def evaluate(self, env, engine, parser, term_type, eval_in_python): """Evaluate a binary operation *before* being passed to the engine. Parameters ---------- env : Scope engine : str parser : str term_type : type eval_in_python : list Returns ...

def convert_values(self): """Convert datetimes to a comparable value in an expression. """ def stringify(value): if self.encoding is not None: encoder = partial(pprint_thing_encoded, encoding=self.encoding) else: ...

def crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=True, normalize=False): """ Compute a simple cross tabulation of two (or more) factors. By default computes a frequency table of the factors unless an arr...

def _shape(self, df): """ Calculate table chape considering index levels. """ row, col = df.shape return row + df.columns.nlevels, col + df.index.nlevels

def _get_cells(self, left, right, vertical): """ Calculate appropriate figure size based on left and right data. """ if vertical: # calculate required number of cells vcells = max(sum(self._shape(l)[0] for l in left), self._shape(right)[0...

def plot(self, left, right, labels=None, vertical=True): """ Plot left / right DataFrames in specified layout. Parameters ---------- left : list of DataFrames before operation is applied right : DataFrame of operation result labels : list of str to be drawn as ti...

def _conv(self, data): """Convert each input to appropriate for table outplot""" if isinstance(data, pd.Series): if data.name is None: data = data.to_frame(name='') else: data = data.to_frame() data = data.fillna('NaN') return data

def cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise'): """ Bin values into discrete intervals. Use `cut` when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a ...

def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'): """ Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles. For example 1000 values for 10 quantiles would produce a Categorical object indicating qua...

def _coerce_to_type(x): """ if the passed data is of datetime/timedelta type, this method converts it to numeric so that cut method can handle it """ dtype = None if is_datetime64tz_dtype(x): dtype = x.dtype elif is_datetime64_dtype(x): x = to_datetime(x) dtype =...

def _convert_bin_to_numeric_type(bins, dtype): """ if the passed bin is of datetime/timedelta type, this method converts it to integer Parameters ---------- bins : list-like of bins dtype : dtype of data Raises ------ ValueError if bins are not of a compat dtype to dtype ""...

def _convert_bin_to_datelike_type(bins, dtype): """ Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is datelike Parameters ---------- bins : list-like of bins dtype : dtype of data Returns ------- bins : Array-like of bins, DatetimeIndex or TimedeltaIndex...

def _format_labels(bins, precision, right=True, include_lowest=False, dtype=None): """ based on the dtype, return our labels """ closed = 'right' if right else 'left' if is_datetime64tz_dtype(dtype): formatter = partial(Timestamp, tz=dtype.tz) adjust = lambda x: x - Time...

def _preprocess_for_cut(x): """ handles preprocessing for cut where we convert passed input to array, strip the index information and store it separately """ x_is_series = isinstance(x, Series) series_index = None name = None if x_is_series: series_index = x.index na...

def _postprocess_for_cut(fac, bins, retbins, x_is_series, series_index, name, dtype): """ handles post processing for the cut method where we combine the index information if the originally passed datatype was a series """ if x_is_series: fac = Series(fac, index=...

def _round_frac(x, precision): """ Round the fractional part of the given number """ if not np.isfinite(x) or x == 0: return x else: frac, whole = np.modf(x) if whole == 0: digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision else: digi...

def _infer_precision(base_precision, bins): """Infer an appropriate precision for _round_frac """ for precision in range(base_precision, 20): levels = [_round_frac(b, precision) for b in bins] if algos.unique(levels).size == bins.size: return precision return base_precision

def detect_console_encoding(): """ Try to find the most capable encoding supported by the console. slightly modified from the way IPython handles the same issue. """ global _initial_defencoding encoding = None try: encoding = sys.stdout.encoding or sys.stdin.encoding except (Att...

def _check_arg_length(fname, args, max_fname_arg_count, compat_args): """ Checks whether 'args' has length of at most 'compat_args'. Raises a TypeError if that is not the case, similar to in Python when a function is called with too many arguments. """ if max_fname_arg_count < 0: raise ...

def _check_for_default_values(fname, arg_val_dict, compat_args): """ Check that the keys in `arg_val_dict` are mapped to their default values as specified in `compat_args`. Note that this function is to be called only when it has been checked that arg_val_dict.keys() is a subset of compat_args ...

def validate_args(fname, args, max_fname_arg_count, compat_args): """ Checks whether the length of the `*args` argument passed into a function has at most `len(compat_args)` arguments and whether or not all of these elements in `args` are set to their default values. fname: str The name of ...

def _check_for_invalid_keys(fname, kwargs, compat_args): """ Checks whether 'kwargs' contains any keys that are not in 'compat_args' and raises a TypeError if there is one. """ # set(dict) --> set of the dictionary's keys diff = set(kwargs) - set(compat_args) if diff: bad_arg = lis...

def validate_kwargs(fname, kwargs, compat_args): """ Checks whether parameters passed to the **kwargs argument in a function `fname` are valid parameters as specified in `*compat_args` and whether or not they are set to their default values. Parameters ---------- fname: str The name...

def validate_args_and_kwargs(fname, args, kwargs, max_fname_arg_count, compat_args): """ Checks whether parameters passed to the *args and **kwargs argument in a function `fname` are valid parameters as specified in `*compat_args` and whether or ...

def validate_bool_kwarg(value, arg_name): """ Ensures that argument passed in arg_name is of type bool. """ if not (is_bool(value) or value is None): raise ValueError('For argument "{arg}" expected type bool, received ' 'type {typ}.'.format(arg=arg_name, ...

def validate_axis_style_args(data, args, kwargs, arg_name, method_name): """Argument handler for mixed index, columns / axis functions In an attempt to handle both `.method(index, columns)`, and `.method(arg, axis=.)`, we have to do some bad things to argument parsing. This translates all arguments to ...

def validate_fillna_kwargs(value, method, validate_scalar_dict_value=True): """Validate the keyword arguments to 'fillna'. This checks that exactly one of 'value' and 'method' is specified. If 'method' is specified, this validates that it's a valid method. Parameters ---------- value, method :...

def _maybe_process_deprecations(r, how=None, fill_method=None, limit=None): """ Potentially we might have a deprecation warning, show it but call the appropriate methods anyhow. """ if how is not None: # .resample(..., how='sum') if isinstance(how, str): method = "{0}()...

def resample(obj, kind=None, **kwds): """ Create a TimeGrouper and return our resampler. """ tg = TimeGrouper(**kwds) return tg._get_resampler(obj, kind=kind)

def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None, limit=None, kind=None, **kwargs): """ Return our appropriate resampler when grouping as well. """ # .resample uses 'on' similar to how .groupby uses 'key' kwargs['key'] = kwargs.pop('on', None) ...

def _get_timestamp_range_edges(first, last, offset, closed='left', base=0): """ Adjust the `first` Timestamp to the preceeding Timestamp that resides on the provided offset. Adjust the `last` Timestamp to the following Timestamp that resides on the provided offset. Input Timestamps that already resi...

def _get_period_range_edges(first, last, offset, closed='left', base=0): """ Adjust the provided `first` and `last` Periods to the respective Period of the given offset that encompasses them. Parameters ---------- first : pd.Period The beginning Period of the range to be adjusted. l...

def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None): """ Utility frequency conversion method for Series/DataFrame. """ if isinstance(obj.index, PeriodIndex): if method is not None: raise NotImplementedError("'method' argument is not supported") if ...

def _from_selection(self): """ Is the resampling from a DataFrame column or MultiIndex level. """ # upsampling and PeriodIndex resampling do not work # with selection, this state used to catch and raise an error return (self.groupby is not None and (self.g...

def _set_binner(self): """ Setup our binners. Cache these as we are an immutable object """ if self.binner is None: self.binner, self.grouper = self._get_binner()

def _get_binner(self): """ Create the BinGrouper, assume that self.set_grouper(obj) has already been called. """ binner, bins, binlabels = self._get_binner_for_time() bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer) return binner, bin_group...

def transform(self, arg, *args, **kwargs): """ Call function producing a like-indexed Series on each group and return a Series with the transformed values. Parameters ---------- arg : function To apply to each group. Should return a Series with the same index...

def _gotitem(self, key, ndim, subset=None): """ Sub-classes to define. Return a sliced object. Parameters ---------- key : string / list of selections ndim : 1,2 requested ndim of result subset : object, default None subset to act on ...

def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs): """ Re-evaluate the obj with a groupby aggregation. """ if grouper is None: self._set_binner() grouper = self.grouper obj = self._selected_obj grouped = groupby(obj, by=None, ...

def _apply_loffset(self, result): """ If loffset is set, offset the result index. This is NOT an idempotent routine, it will be applied exactly once to the result. Parameters ---------- result : Series or DataFrame the result of resample """ ...

def _get_resampler_for_grouping(self, groupby, **kwargs): """ Return the correct class for resampling with groupby. """ return self._resampler_for_grouping(self, groupby=groupby, **kwargs)

def _wrap_result(self, result): """ Potentially wrap any results. """ if isinstance(result, ABCSeries) and self._selection is not None: result.name = self._selection if isinstance(result, ABCSeries) and result.empty: obj = self.obj if isinstan...

def interpolate(self, method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', limit_area=None, downcast=None, **kwargs): """ Interpolate values according to different methods. .. versionadded:: 0.18.1 """ result = se...

def std(self, ddof=1, *args, **kwargs): """ Compute standard deviation of groups, excluding missing values. Parameters ---------- ddof : integer, default 1 Degrees of freedom. """ nv.validate_resampler_func('std', args, kwargs) return self._do...

def var(self, ddof=1, *args, **kwargs): """ Compute variance of groups, excluding missing values. Parameters ---------- ddof : integer, default 1 degrees of freedom """ nv.validate_resampler_func('var', args, kwargs) return self._downsample('v...

def _apply(self, f, grouper=None, *args, **kwargs): """ Dispatch to _upsample; we are stripping all of the _upsample kwargs and performing the original function call on the grouped object. """ def func(x): x = self._shallow_copy(x, groupby=self.groupby) ...

def _downsample(self, how, **kwargs): """ Downsample the cython defined function. Parameters ---------- how : string / cython mapped function **kwargs : kw args passed to how function """ self._set_binner() how = self._is_cython_func(how) or how ...

def _adjust_binner_for_upsample(self, binner): """ Adjust our binner when upsampling. The range of a new index should not be outside specified range """ if self.closed == 'right': binner = binner[1:] else: binner = binner[:-1] return binne...

def _upsample(self, method, limit=None, fill_value=None): """ Parameters ---------- method : string {'backfill', 'bfill', 'pad', 'ffill', 'asfreq'} method for upsampling limit : int, default None Maximum size gap to fill when reindexing fill_value ...

def _downsample(self, how, **kwargs): """ Downsample the cython defined function. Parameters ---------- how : string / cython mapped function **kwargs : kw args passed to how function """ # we may need to actually resample as if we are timestamps ...

def _upsample(self, method, limit=None, fill_value=None): """ Parameters ---------- method : string {'backfill', 'bfill', 'pad', 'ffill'} method for upsampling limit : int, default None Maximum size gap to fill when reindexing fill_value : scalar, ...

def _get_resampler(self, obj, kind=None): """ Return my resampler or raise if we have an invalid axis. Parameters ---------- obj : input object kind : string, optional 'period','timestamp','timedelta' are valid Returns ------- a Resam...

def _combine_hash_arrays(arrays, num_items): """ Parameters ---------- arrays : generator num_items : int Should be the same as CPython's tupleobject.c """ try: first = next(arrays) except StopIteration: return np.array([], dtype=np.uint64) arrays = itertools.ch...

def hash_pandas_object(obj, index=True, encoding='utf8', hash_key=None, categorize=True): """ Return a data hash of the Index/Series/DataFrame .. versionadded:: 0.19.2 Parameters ---------- index : boolean, default True include the index in the hash (if Series/Da...

def hash_tuples(vals, encoding='utf8', hash_key=None): """ Hash an MultiIndex / list-of-tuples efficiently .. versionadded:: 0.20.0 Parameters ---------- vals : MultiIndex, list-of-tuples, or single tuple encoding : string, default 'utf8' hash_key : string key to encode, default to _de...

def hash_tuple(val, encoding='utf8', hash_key=None): """ Hash a single tuple efficiently Parameters ---------- val : single tuple encoding : string, default 'utf8' hash_key : string key to encode, default to _default_hash_key Returns ------- hash """ hashes = (_hash_sc...

def _hash_categorical(c, encoding, hash_key): """ Hash a Categorical by hashing its categories, and then mapping the codes to the hashes Parameters ---------- c : Categorical encoding : string, default 'utf8' hash_key : string key to encode, default to _default_hash_key Returns ...

def hash_array(vals, encoding='utf8', hash_key=None, categorize=True): """ Given a 1d array, return an array of deterministic integers. .. versionadded:: 0.19.2 Parameters ---------- vals : ndarray, Categorical encoding : string, default 'utf8' encoding for data & key when strings ...

def _hash_scalar(val, encoding='utf8', hash_key=None): """ Hash scalar value Returns ------- 1d uint64 numpy array of hash value, of length 1 """ if isna(val): # this is to be consistent with the _hash_categorical implementation return np.array([np.iinfo(np.uint64).max], dt...

def _process_single_doc(self, single_doc): """ Make sure the provided value for --single is a path to an existing .rst/.ipynb file, or a pandas object that can be imported. For example, categorial.rst or pandas.DataFrame.head. For the latter, return the corresponding file path ...