BoghdadyJR/codesearch_python · Datasets at Hugging Face

text stringlengths 75 104k
def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis Parameters ---------- i, j : int, str (can be mixed) Level of index to be swapped. Can pass level name as string. Returns ------- swapped : ...
def rename(self, args, *kwargs): """ Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` with a scalar...
def rename_axis(self, mapper=sentinel, **kwargs): """ Set the name of the axis for the index or columns. Parameters ---------- mapper : scalar, list-like, optional Value to set the axis name attribute. index, columns : scalar, list-like, dict-like or function...
def _set_axis_name(self, name, axis=0, inplace=False): """ Set the name(s) of the axis. Parameters ---------- name : str or list of str Name(s) to set. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to set the label. The value 0 or 'ind...
def equals(self, other): """ Test whether two objects contain the same elements. This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The column headers do...
def bool(self): """ Return the bool of a single element PandasObject. This must be a boolean scalar value, either True or False. Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean """ v = self.squeeze() ...
def _is_level_reference(self, key, axis=0): """ Test whether a key is a level reference for a given axis. To be considered a level reference, `key` must be a string that: - (axis=0): Matches the name of an index level and does NOT match a column label. - (axis=1)...
def _is_label_reference(self, key, axis=0): """ Test whether a key is a label reference for a given axis. To be considered a label reference, `key` must be a string that: - (axis=0): Matches a column label - (axis=1): Matches an index label Parameters ------...
def _is_label_or_level_reference(self, key, axis=0): """ Test whether a key is a label or level reference for a given axis. To be considered either a label or a level reference, `key` must be a string that: - (axis=0): Matches a column label or an index level - (axis...
def _check_label_or_level_ambiguity(self, key, axis=0): """ Check whether `key` is ambiguous. By ambiguous, we mean that it matches both a level of the input `axis` and a label of the other axis. Parameters ---------- key: str or object label or leve...
def _get_label_or_level_values(self, key, axis=0): """ Return a 1-D array of values associated with `key`, a label or level from the given `axis`. Retrieval logic: - (axis=0): Return column values if `key` matches a column label. Otherwise return index level values...
def _drop_labels_or_levels(self, keys, axis=0): """ Drop labels and/or levels for the given `axis`. For each key in `keys`: - (axis=0): If key matches a column label then drop the column. Otherwise if key matches an index level then drop the level. - (axis=1): If...
def empty(self): """ Indicator whether DataFrame is empty. True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0. Returns ------- bool If DataFrame is empty, return True, if not return False. See Also ...
def _repr_data_resource_(self): """ Not a real Jupyter special repr method, but we use the same naming convention. """ if config.get_option("display.html.table_schema"): data = self.head(config.get_option('display.max_rows')) payload = json.loads(data.to_j...
def to_json(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=True): """ Convert the object to a JSON string. Note NaN's and ...
def to_hdf(self, path_or_buf, key, **kwargs): """ Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can h...
def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs): """ Serialize object to input file path using msgpack format. THIS IS AN EXPERIMENTAL LIBRARY and the storage format may not be stable until a future release. Parameters ---------- path : string...
def to_sql(self, name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None): """ Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created,...
def to_pickle(self, path, compression='infer', protocol=pickle.HIGHEST_PROTOCOL): """ Pickle (serialize) object to file. Parameters ---------- path : str File path where the pickled object will be stored. compression : {'infer', 'gzip', 'bz2...
def to_clipboard(self, excel=True, sep=None, **kwargs): r""" Copy object to the system clipboard. Write a text representation of object to the system clipboard. This can be pasted into Excel, for example. Parameters ---------- excel : bool, default True ...
def to_xarray(self): """ Return an xarray object from the pandas object. Returns ------- xarray.DataArray or xarray.Dataset Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series. ...
def to_latex(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, deci...
def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, tupleize_cols=No...
def _create_indexer(cls, name, indexer): """Create an indexer like _name in the class.""" if getattr(cls, name, None) is None: _indexer = functools.partial(indexer, name) setattr(cls, name, property(_indexer, doc=indexer.__doc__))
def get(self, key, default=None): """ Get item from object for given key (DataFrame column, Panel slice, etc.). Returns default value if not found. Parameters ---------- key : object Returns ------- value : same type as items contained in object ...
def _get_item_cache(self, item): """Return the cached item, item represents a label indexer.""" cache = self._item_cache res = cache.get(item) if res is None: values = self._data.get(item) res = self._box_item_values(item, values) cache[item] = res ...
def _set_as_cached(self, item, cacher): """Set the _cacher attribute on the calling object with a weakref to cacher. """ self._cacher = (item, weakref.ref(cacher))
def _iget_item_cache(self, item): """Return the cached item, item represents a positional indexer.""" ax = self._info_axis if ax.is_unique: lower = self._get_item_cache(ax[item]) else: lower = self._take(item, axis=self._info_axis_number) return lower
def _maybe_update_cacher(self, clear=False, verify_is_copy=True): """ See if we need to update our parent cacher if clear, then clear our cache. Parameters ---------- clear : boolean, default False clear the item cache verify_is_copy : boolean, defaul...
def _slice(self, slobj, axis=0, kind=None): """ Construct a slice of this container. kind parameter is maintained for compatibility with Series slicing. """ axis = self._get_block_manager_axis(axis) result = self._constructor(self._data.get_slice(slobj, axis=axis)) ...
def _check_is_chained_assignment_possible(self): """ Check if we are a view, have a cacher, and are of mixed type. If so, then force a setitem_copy check. Should be called just near setting a value Will return a boolean if it we are a view and are cached, but a single-d...
def _take(self, indices, axis=0, is_copy=True): """ Return the elements in the given positional indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the e...
def take(self, indices, axis=0, convert=None, is_copy=True, *kwargs): """ Return the elements in the given positional* indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the a...
def xs(self, key, axis=0, level=None, drop_level=True): """ Return cross-section from the Series/DataFrame. This method takes a `key` argument to select data at a particular level of a MultiIndex. Parameters ---------- key : label or tuple of label L...
def select(self, crit, axis=0): """ Return data corresponding to axis labels matching criteria. .. deprecated:: 0.21.0 Use df.loc[df.index.map(crit)] to select via labels Parameters ---------- crit : function To be called on each index (label). S...
def reindex_like(self, other, method=None, copy=True, limit=None, tolerance=None): """ Return an object with matching indices as other object. Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value ...
def _drop_axis(self, labels, axis, level=None, errors='raise'): """ Drop labels from specified axis. Used in the ``drop`` method internally. Parameters ---------- labels : single label or list-like axis : int or axis name level : int or level name, defaul...
def _update_inplace(self, result, verify_is_copy=True): """ Replace self internals with result. Parameters ---------- verify_is_copy : boolean, default True provide is_copy checks """ # NOTE: This does not call __finalize__ and that's an explicit ...
def add_prefix(self, prefix): """ Prefix labels with string `prefix`. For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed. Parameters ---------- prefix : str The string to add before each label. Returns ...
def add_suffix(self, suffix): """ Suffix labels with string `suffix`. For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed. Parameters ---------- suffix : str The string to add after each label. Returns ...
def sort_values(self, by=None, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last'): """ Sort by the values along either axis. Parameters ----------%(optional_by)s axis : %(axes_single_arg)s, default 0 Axis to be sorted. ...
def sort_index(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True): """ Sort object by labels (along an axis). Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 Th...
def reindex(self, args, *kwargs): """ Conform %(klass)s to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and ``copy=False``. Param...
def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy): """Perform the reindex for all the axes.""" obj = self for a in self._AXIS_ORDERS: labels = axes[a] if labels is None: continue ax = self._...
def _needs_reindex_multi(self, axes, method, level): """Check if we do need a multi reindex.""" return ((com.count_not_none(*axes.values()) == self._AXIS_LEN) and method is None and level is None and not self._is_mixed_type)
def _reindex_with_indexers(self, reindexers, fill_value=None, copy=False, allow_dups=False): """allow_dups indicates an internal call here """ # reindex doing multiple operations on different axes if indicated new_data = self._data for axis in sorted(reind...
def filter(self, items=None, like=None, regex=None, axis=None): """ Subset rows or columns of dataframe according to labels in the specified index. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Para...
def sample(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None): """ Return a random sample of items from an axis of object. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional ...
def _dir_additions(self): """ add the string-like attributes from the info_axis. If info_axis is a MultiIndex, it's first level values are used. """ additions = {c for c in self._info_axis.unique(level=0)[:100] if isinstance(c, str) and c.isidentifier()} retu...
def _protect_consolidate(self, f): """Consolidate _data -- if the blocks have changed, then clear the cache """ blocks_before = len(self._data.blocks) result = f() if len(self._data.blocks) != blocks_before: self._clear_item_cache() return result
def _consolidate_inplace(self): """Consolidate data in place and return None""" def f(): self._data = self._data.consolidate() self._protect_consolidate(f)
def _consolidate(self, inplace=False): """ Compute NDFrame with "consolidated" internals (data of each dtype grouped together in a single ndarray). Parameters ---------- inplace : boolean, default False If False return new object, otherwise modify existing ob...
def _check_inplace_setting(self, value): """ check whether we allow in-place setting with this type of value """ if self._is_mixed_type: if not self._is_numeric_mixed_type: # allow an actual np.nan thru try: if np.isnan(value): ...
def as_matrix(self, columns=None): """ Convert the frame to its Numpy-array representation. .. deprecated:: 0.23.0 Use :meth:`DataFrame.values` instead. Parameters ---------- columns : list, optional, default:None If None, return all columns, oth...
def values(self): """ Return a Numpy representation of the DataFrame. .. warning:: We recommend using :meth:`DataFrame.to_numpy` instead. Only the values in the DataFrame will be returned, the axes labels will be removed. Returns ------- num...
def get_ftype_counts(self): """ Return counts of unique ftypes in this object. .. deprecated:: 0.23.0 This is useful for SparseDataFrame or for DataFrames containing sparse arrays. Returns ------- dtype : Series Series with the count of colu...
def dtypes(self): """ Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the ``object`` dtype. See :ref:`the User Guide <basics.dtyp...
def ftypes(self): """ Return the ftypes (indication of sparse/dense and dtype) in DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the ``object`` dtype. See ...
def as_blocks(self, copy=True): """ Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. .. deprecated:: 0.21.0 NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in as_matrix) Parameters --------...
def _to_dict_of_blocks(self, copy=True): """ Return a dict of dtype -> Constructor Types that each is a homogeneous dtype. Internal ONLY """ return {k: self._constructor(v).__finalize__(self) for k, v, in self._data.to_dict(copy=copy).items()}
def astype(self, dtype, copy=True, errors='raise', **kwargs): """ Cast a pandas object to a specified dtype ``dtype``. Parameters ---------- dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to ...
def copy(self, deep=True): """ Make a copy of this object's indices and data. When ``deep=True`` (default), a new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the or...
def _convert(self, datetime=False, numeric=False, timedelta=False, coerce=False, copy=True): """ Attempt to infer better dtype for object columns Parameters ---------- datetime : boolean, default False If True, convert to date where possible. ...
def convert_objects(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True): """ Attempt to infer better dtype for object columns. .. deprecated:: 0.21.0 Parameters ---------- convert_dates : boolean, default True ...
def infer_objects(self): """ Attempt to infer better dtypes for object columns. Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction. ...
def fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None): """ Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alt...
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. """ inplace = validate_bool_kwarg(inplace, 'inpla...
def asof(self, where, subset=None): """ Return the last row(s) without any NaNs before `where`. The last row (for each element in `where`, if list) without any NaN is taken. In case of a :class:`~pandas.DataFrame`, the last row without NaN considering only the subset of ...
def clip(self, lower=None, upper=None, axis=None, inplace=False, args, *kwargs): """ Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is perf...
def clip_upper(self, threshold, axis=None, inplace=False): """ Trim values above a given threshold. .. deprecated:: 0.24.0 Use clip(upper=threshold) instead. Elements above the `threshold` will be changed to match the `threshold` value(s). Threshold can be a single ...
def clip_lower(self, threshold, axis=None, inplace=False): """ Trim values below a given threshold. .. deprecated:: 0.24.0 Use clip(lower=threshold) instead. Elements below the `threshold` will be changed to match the `threshold` value(s). Threshold can be a single ...
def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs): """ Group DataFrame or Series using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the obje...
def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None): """ Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Returns the original data conformed to a new index with the specified ...
def at_time(self, time, asof=False, axis=None): """ Select values at particular time of day (e.g. 9:30AM). Parameters ---------- time : datetime.time or str axis : {0 or 'index', 1 or 'columns'}, default 0 .. versionadded:: 0.24.0 Returns --...
def between_time(self, start_time, end_time, include_start=True, include_end=True, axis=None): """ Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting ``start_time`` to be later than ``end_time``, you can get the times that are not b...
def resample(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None): """ Resample time-series data. Convenience method for frequency conversion and resamplin...
def first(self, offset): """ Convenience method for subsetting initial periods of time series data based on a date offset. Parameters ---------- offset : string, DateOffset, dateutil.relativedelta Returns ------- subset : same type as caller ...
def last(self, offset): """ Convenience method for subsetting final periods of time series data based on a date offset. Parameters ---------- offset : string, DateOffset, dateutil.relativedelta Returns ------- subset : same type as caller ...
def rank(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False): """ Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values. Parameters -------...
def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False): """ Equivalent to public method `where`, except that `other` is not applied as a function even if callable. Used in __setitem__. """ inplace = validate_bool_...
def slice_shift(self, periods=1, axis=0): """ Equivalent to `shift` without copying data. The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. Parameters ---------- periods : int Number of perio...
def tshift(self, periods=1, freq=None, axis=0): """ Shift the time index, using the index's frequency if available. Parameters ---------- periods : int Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, d...
def truncate(self, before=None, after=None, axis=None, copy=True): """ Truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds. Parameters ---------- ...
def tz_convert(self, tz, axis=0, level=None, copy=True): """ Convert tz-aware axis to target time zone. Parameters ---------- tz : string or pytz.timezone object axis : the axis to convert level : int, str, default None If axis ia a MultiIndex, conver...
def tz_localize(self, tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise'): """ Localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. To localize the values in a timezone-naive Series, use :met...
def describe(self, percentiles=None, include=None, exclude=None): """ Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding ``NaN`` values. Analyzes both numeric and object series, as well as ``Da...
def _check_percentile(self, q): """ Validate percentiles (used by describe and quantile). """ msg = ("percentiles should all be in the interval [0, 1]. " "Try {0} instead.") q = np.asarray(q) if q.ndim == 0: if not 0 <= q <= 1: ...
def _add_numeric_operations(cls): """ Add the operations to the cls; evaluate the doc strings again """ axis_descr, name, name2 = _doc_parms(cls) cls.any = _make_logical_function( cls, 'any', name, name2, axis_descr, _any_desc, nanops.nanany, _any_see_al...
def _add_series_only_operations(cls): """ Add the series only operations to the cls; evaluate the doc strings again. """ axis_descr, name, name2 = _doc_parms(cls) def nanptp(values, axis=0, skipna=True): nmax = nanops.nanmax(values, axis, skipna) ...
def _add_series_or_dataframe_operations(cls): """ Add the series or dataframe only operations to the cls; evaluate the doc strings again. """ from pandas.core import window as rwindow @Appender(rwindow.rolling.__doc__) def rolling(self, window, min_periods=None,...
def _find_valid_index(self, how): """ Retrieves the index of the first valid value. Parameters ---------- how : {'first', 'last'} Use this parameter to change between the first or last valid index. Returns ------- idx_first_valid : type of in...
def _reset_cache(self, key=None): """ Reset cached properties. If ``key`` is passed, only clears that key. """ if getattr(self, '_cache', None) is None: return if key is None: self._cache.clear() else: self._cache.pop(key, None)
def _try_aggregate_string_function(self, arg, args, *kwargs): """ if arg is a string, then try to operate on it: - try to find a function (or attribute) on ourselves - try to find a numpy function - raise """ assert isinstance(arg, str) f = getattr(sel...
def _aggregate(self, arg, args, kwargs): """ provide an implementation for the aggregators Parameters ---------- arg : string, dict, function args : args to pass on to the function **kwargs : kwargs to pass on to the function Returns ------- ...
def _shallow_copy(self, obj=None, obj_type=None, **kwargs): """ return a new object with the replacement attributes """ if obj is None: obj = self._selected_obj.copy() if obj_type is None: obj_type = self._constructor if isinstance(obj, obj_type): ...
def itemsize(self): """ Return the size of the dtype of the item of the underlying data. .. deprecated:: 0.23.0 """ warnings.warn("{obj}.itemsize is deprecated and will be removed " "in a future version".format(obj=type(self).__name__), ...
def base(self): """ Return the base object if the memory of the underlying data is shared. .. deprecated:: 0.23.0 """ warnings.warn("{obj}.base is deprecated and will be removed " "in a future version".format(obj=type(self).__name__), ...
def array(self) -> ExtensionArray: """ The ExtensionArray of the data backing this Series or Index. .. versionadded:: 0.24.0 Returns ------- ExtensionArray An ExtensionArray of the values stored within. For extension types, this is the actual arr...
def to_numpy(self, dtype=None, copy=False): """ A NumPy ndarray representing the values in this Series or Index. .. versionadded:: 0.24.0 Parameters ---------- dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray` copy : boo...
def _ndarray_values(self) -> np.ndarray: """ The data as an ndarray, possibly losing information. The expectation is that this is cheap to compute, and is primarily used for interacting with our indexers. - categorical -> codes """ if is_extension_array_dtype(se...

def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis Parameters ---------- i, j : int, str (can be mixed) Level of index to be swapped. Can pass level name as string. Returns ------- swapped : ...

def rename(self, *args, **kwargs): """ Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` with a scalar...

def rename_axis(self, mapper=sentinel, **kwargs): """ Set the name of the axis for the index or columns. Parameters ---------- mapper : scalar, list-like, optional Value to set the axis name attribute. index, columns : scalar, list-like, dict-like or function...

def _set_axis_name(self, name, axis=0, inplace=False): """ Set the name(s) of the axis. Parameters ---------- name : str or list of str Name(s) to set. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to set the label. The value 0 or 'ind...

def equals(self, other): """ Test whether two objects contain the same elements. This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The column headers do...

def bool(self): """ Return the bool of a single element PandasObject. This must be a boolean scalar value, either True or False. Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean """ v = self.squeeze() ...

def _is_level_reference(self, key, axis=0): """ Test whether a key is a level reference for a given axis. To be considered a level reference, `key` must be a string that: - (axis=0): Matches the name of an index level and does NOT match a column label. - (axis=1)...

def _is_label_reference(self, key, axis=0): """ Test whether a key is a label reference for a given axis. To be considered a label reference, `key` must be a string that: - (axis=0): Matches a column label - (axis=1): Matches an index label Parameters ------...

def _is_label_or_level_reference(self, key, axis=0): """ Test whether a key is a label or level reference for a given axis. To be considered either a label or a level reference, `key` must be a string that: - (axis=0): Matches a column label or an index level - (axis...

def _check_label_or_level_ambiguity(self, key, axis=0): """ Check whether `key` is ambiguous. By ambiguous, we mean that it matches both a level of the input `axis` and a label of the other axis. Parameters ---------- key: str or object label or leve...

def _get_label_or_level_values(self, key, axis=0): """ Return a 1-D array of values associated with `key`, a label or level from the given `axis`. Retrieval logic: - (axis=0): Return column values if `key` matches a column label. Otherwise return index level values...

def _drop_labels_or_levels(self, keys, axis=0): """ Drop labels and/or levels for the given `axis`. For each key in `keys`: - (axis=0): If key matches a column label then drop the column. Otherwise if key matches an index level then drop the level. - (axis=1): If...

def empty(self): """ Indicator whether DataFrame is empty. True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0. Returns ------- bool If DataFrame is empty, return True, if not return False. See Also ...

def _repr_data_resource_(self): """ Not a real Jupyter special repr method, but we use the same naming convention. """ if config.get_option("display.html.table_schema"): data = self.head(config.get_option('display.max_rows')) payload = json.loads(data.to_j...

def to_json(self, path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=True): """ Convert the object to a JSON string. Note NaN's and ...

def to_hdf(self, path_or_buf, key, **kwargs): """ Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can h...

def to_msgpack(self, path_or_buf=None, encoding='utf-8', **kwargs): """ Serialize object to input file path using msgpack format. THIS IS AN EXPERIMENTAL LIBRARY and the storage format may not be stable until a future release. Parameters ---------- path : string...

def to_sql(self, name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None): """ Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created,...

def to_pickle(self, path, compression='infer', protocol=pickle.HIGHEST_PROTOCOL): """ Pickle (serialize) object to file. Parameters ---------- path : str File path where the pickled object will be stored. compression : {'infer', 'gzip', 'bz2...

def to_clipboard(self, excel=True, sep=None, **kwargs): r""" Copy object to the system clipboard. Write a text representation of object to the system clipboard. This can be pasted into Excel, for example. Parameters ---------- excel : bool, default True ...

def to_xarray(self): """ Return an xarray object from the pandas object. Returns ------- xarray.DataArray or xarray.Dataset Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series. ...

def to_latex(self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, deci...

def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, tupleize_cols=No...

def _create_indexer(cls, name, indexer): """Create an indexer like _name in the class.""" if getattr(cls, name, None) is None: _indexer = functools.partial(indexer, name) setattr(cls, name, property(_indexer, doc=indexer.__doc__))

def get(self, key, default=None): """ Get item from object for given key (DataFrame column, Panel slice, etc.). Returns default value if not found. Parameters ---------- key : object Returns ------- value : same type as items contained in object ...

def _get_item_cache(self, item): """Return the cached item, item represents a label indexer.""" cache = self._item_cache res = cache.get(item) if res is None: values = self._data.get(item) res = self._box_item_values(item, values) cache[item] = res ...

def _set_as_cached(self, item, cacher): """Set the _cacher attribute on the calling object with a weakref to cacher. """ self._cacher = (item, weakref.ref(cacher))

def _iget_item_cache(self, item): """Return the cached item, item represents a positional indexer.""" ax = self._info_axis if ax.is_unique: lower = self._get_item_cache(ax[item]) else: lower = self._take(item, axis=self._info_axis_number) return lower

def _maybe_update_cacher(self, clear=False, verify_is_copy=True): """ See if we need to update our parent cacher if clear, then clear our cache. Parameters ---------- clear : boolean, default False clear the item cache verify_is_copy : boolean, defaul...

def _slice(self, slobj, axis=0, kind=None): """ Construct a slice of this container. kind parameter is maintained for compatibility with Series slicing. """ axis = self._get_block_manager_axis(axis) result = self._constructor(self._data.get_slice(slobj, axis=axis)) ...

def _check_is_chained_assignment_possible(self): """ Check if we are a view, have a cacher, and are of mixed type. If so, then force a setitem_copy check. Should be called just near setting a value Will return a boolean if it we are a view and are cached, but a single-d...

def _take(self, indices, axis=0, is_copy=True): """ Return the elements in the given *positional* indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the e...

def take(self, indices, axis=0, convert=None, is_copy=True, **kwargs): """ Return the elements in the given *positional* indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the a...

def xs(self, key, axis=0, level=None, drop_level=True): """ Return cross-section from the Series/DataFrame. This method takes a `key` argument to select data at a particular level of a MultiIndex. Parameters ---------- key : label or tuple of label L...

def select(self, crit, axis=0): """ Return data corresponding to axis labels matching criteria. .. deprecated:: 0.21.0 Use df.loc[df.index.map(crit)] to select via labels Parameters ---------- crit : function To be called on each index (label). S...

def reindex_like(self, other, method=None, copy=True, limit=None, tolerance=None): """ Return an object with matching indices as other object. Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value ...

def _drop_axis(self, labels, axis, level=None, errors='raise'): """ Drop labels from specified axis. Used in the ``drop`` method internally. Parameters ---------- labels : single label or list-like axis : int or axis name level : int or level name, defaul...

def _update_inplace(self, result, verify_is_copy=True): """ Replace self internals with result. Parameters ---------- verify_is_copy : boolean, default True provide is_copy checks """ # NOTE: This does *not* call __finalize__ and that's an explicit ...

def add_prefix(self, prefix): """ Prefix labels with string `prefix`. For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed. Parameters ---------- prefix : str The string to add before each label. Returns ...

def add_suffix(self, suffix): """ Suffix labels with string `suffix`. For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed. Parameters ---------- suffix : str The string to add after each label. Returns ...

def sort_values(self, by=None, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last'): """ Sort by the values along either axis. Parameters ----------%(optional_by)s axis : %(axes_single_arg)s, default 0 Axis to be sorted. ...

def sort_index(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True): """ Sort object by labels (along an axis). Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 Th...

def reindex(self, *args, **kwargs): """ Conform %(klass)s to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and ``copy=False``. Param...

def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy): """Perform the reindex for all the axes.""" obj = self for a in self._AXIS_ORDERS: labels = axes[a] if labels is None: continue ax = self._...

def _needs_reindex_multi(self, axes, method, level): """Check if we do need a multi reindex.""" return ((com.count_not_none(*axes.values()) == self._AXIS_LEN) and method is None and level is None and not self._is_mixed_type)

def _reindex_with_indexers(self, reindexers, fill_value=None, copy=False, allow_dups=False): """allow_dups indicates an internal call here """ # reindex doing multiple operations on different axes if indicated new_data = self._data for axis in sorted(reind...

def filter(self, items=None, like=None, regex=None, axis=None): """ Subset rows or columns of dataframe according to labels in the specified index. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Para...

def sample(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None): """ Return a random sample of items from an axis of object. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional ...

def _dir_additions(self): """ add the string-like attributes from the info_axis. If info_axis is a MultiIndex, it's first level values are used. """ additions = {c for c in self._info_axis.unique(level=0)[:100] if isinstance(c, str) and c.isidentifier()} retu...

def _protect_consolidate(self, f): """Consolidate _data -- if the blocks have changed, then clear the cache """ blocks_before = len(self._data.blocks) result = f() if len(self._data.blocks) != blocks_before: self._clear_item_cache() return result

def _consolidate_inplace(self): """Consolidate data in place and return None""" def f(): self._data = self._data.consolidate() self._protect_consolidate(f)

def _consolidate(self, inplace=False): """ Compute NDFrame with "consolidated" internals (data of each dtype grouped together in a single ndarray). Parameters ---------- inplace : boolean, default False If False return new object, otherwise modify existing ob...

def _check_inplace_setting(self, value): """ check whether we allow in-place setting with this type of value """ if self._is_mixed_type: if not self._is_numeric_mixed_type: # allow an actual np.nan thru try: if np.isnan(value): ...

def as_matrix(self, columns=None): """ Convert the frame to its Numpy-array representation. .. deprecated:: 0.23.0 Use :meth:`DataFrame.values` instead. Parameters ---------- columns : list, optional, default:None If None, return all columns, oth...

def values(self): """ Return a Numpy representation of the DataFrame. .. warning:: We recommend using :meth:`DataFrame.to_numpy` instead. Only the values in the DataFrame will be returned, the axes labels will be removed. Returns ------- num...

def get_ftype_counts(self): """ Return counts of unique ftypes in this object. .. deprecated:: 0.23.0 This is useful for SparseDataFrame or for DataFrames containing sparse arrays. Returns ------- dtype : Series Series with the count of colu...

def dtypes(self): """ Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the ``object`` dtype. See :ref:`the User Guide <basics.dtyp...

def ftypes(self): """ Return the ftypes (indication of sparse/dense and dtype) in DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the ``object`` dtype. See ...

def as_blocks(self, copy=True): """ Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. .. deprecated:: 0.21.0 NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in as_matrix) Parameters --------...

def _to_dict_of_blocks(self, copy=True): """ Return a dict of dtype -> Constructor Types that each is a homogeneous dtype. Internal ONLY """ return {k: self._constructor(v).__finalize__(self) for k, v, in self._data.to_dict(copy=copy).items()}

def astype(self, dtype, copy=True, errors='raise', **kwargs): """ Cast a pandas object to a specified dtype ``dtype``. Parameters ---------- dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to ...

def copy(self, deep=True): """ Make a copy of this object's indices and data. When ``deep=True`` (default), a new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the or...

def _convert(self, datetime=False, numeric=False, timedelta=False, coerce=False, copy=True): """ Attempt to infer better dtype for object columns Parameters ---------- datetime : boolean, default False If True, convert to date where possible. ...

def convert_objects(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True): """ Attempt to infer better dtype for object columns. .. deprecated:: 0.21.0 Parameters ---------- convert_dates : boolean, default True ...

def infer_objects(self): """ Attempt to infer better dtypes for object columns. Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction. ...

def fillna(self, value=None, method=None, axis=None, inplace=False, limit=None, downcast=None): """ Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alt...

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. """ inplace = validate_bool_kwarg(inplace, 'inpla...

def asof(self, where, subset=None): """ Return the last row(s) without any NaNs before `where`. The last row (for each element in `where`, if list) without any NaN is taken. In case of a :class:`~pandas.DataFrame`, the last row without NaN considering only the subset of ...

def clip(self, lower=None, upper=None, axis=None, inplace=False, *args, **kwargs): """ Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is perf...

def clip_upper(self, threshold, axis=None, inplace=False): """ Trim values above a given threshold. .. deprecated:: 0.24.0 Use clip(upper=threshold) instead. Elements above the `threshold` will be changed to match the `threshold` value(s). Threshold can be a single ...

def clip_lower(self, threshold, axis=None, inplace=False): """ Trim values below a given threshold. .. deprecated:: 0.24.0 Use clip(lower=threshold) instead. Elements below the `threshold` will be changed to match the `threshold` value(s). Threshold can be a single ...

def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs): """ Group DataFrame or Series using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the obje...

def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None): """ Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Returns the original data conformed to a new index with the specified ...

def at_time(self, time, asof=False, axis=None): """ Select values at particular time of day (e.g. 9:30AM). Parameters ---------- time : datetime.time or str axis : {0 or 'index', 1 or 'columns'}, default 0 .. versionadded:: 0.24.0 Returns --...

def between_time(self, start_time, end_time, include_start=True, include_end=True, axis=None): """ Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting ``start_time`` to be later than ``end_time``, you can get the times that are *not* b...

def resample(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None): """ Resample time-series data. Convenience method for frequency conversion and resamplin...

def first(self, offset): """ Convenience method for subsetting initial periods of time series data based on a date offset. Parameters ---------- offset : string, DateOffset, dateutil.relativedelta Returns ------- subset : same type as caller ...

def last(self, offset): """ Convenience method for subsetting final periods of time series data based on a date offset. Parameters ---------- offset : string, DateOffset, dateutil.relativedelta Returns ------- subset : same type as caller ...

def rank(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False): """ Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values. Parameters -------...

def _where(self, cond, other=np.nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False): """ Equivalent to public method `where`, except that `other` is not applied as a function even if callable. Used in __setitem__. """ inplace = validate_bool_...

def slice_shift(self, periods=1, axis=0): """ Equivalent to `shift` without copying data. The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. Parameters ---------- periods : int Number of perio...

def tshift(self, periods=1, freq=None, axis=0): """ Shift the time index, using the index's frequency if available. Parameters ---------- periods : int Number of periods to move, can be positive or negative freq : DateOffset, timedelta, or time rule string, d...

def truncate(self, before=None, after=None, axis=None, copy=True): """ Truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds. Parameters ---------- ...

def tz_convert(self, tz, axis=0, level=None, copy=True): """ Convert tz-aware axis to target time zone. Parameters ---------- tz : string or pytz.timezone object axis : the axis to convert level : int, str, default None If axis ia a MultiIndex, conver...

def tz_localize(self, tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise'): """ Localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. To localize the values in a timezone-naive Series, use :met...

def describe(self, percentiles=None, include=None, exclude=None): """ Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding ``NaN`` values. Analyzes both numeric and object series, as well as ``Da...

def _check_percentile(self, q): """ Validate percentiles (used by describe and quantile). """ msg = ("percentiles should all be in the interval [0, 1]. " "Try {0} instead.") q = np.asarray(q) if q.ndim == 0: if not 0 <= q <= 1: ...

def _add_numeric_operations(cls): """ Add the operations to the cls; evaluate the doc strings again """ axis_descr, name, name2 = _doc_parms(cls) cls.any = _make_logical_function( cls, 'any', name, name2, axis_descr, _any_desc, nanops.nanany, _any_see_al...

def _add_series_only_operations(cls): """ Add the series only operations to the cls; evaluate the doc strings again. """ axis_descr, name, name2 = _doc_parms(cls) def nanptp(values, axis=0, skipna=True): nmax = nanops.nanmax(values, axis, skipna) ...

def _add_series_or_dataframe_operations(cls): """ Add the series or dataframe only operations to the cls; evaluate the doc strings again. """ from pandas.core import window as rwindow @Appender(rwindow.rolling.__doc__) def rolling(self, window, min_periods=None,...

def _find_valid_index(self, how): """ Retrieves the index of the first valid value. Parameters ---------- how : {'first', 'last'} Use this parameter to change between the first or last valid index. Returns ------- idx_first_valid : type of in...

def _reset_cache(self, key=None): """ Reset cached properties. If ``key`` is passed, only clears that key. """ if getattr(self, '_cache', None) is None: return if key is None: self._cache.clear() else: self._cache.pop(key, None)

def _try_aggregate_string_function(self, arg, *args, **kwargs): """ if arg is a string, then try to operate on it: - try to find a function (or attribute) on ourselves - try to find a numpy function - raise """ assert isinstance(arg, str) f = getattr(sel...

def _aggregate(self, arg, *args, **kwargs): """ provide an implementation for the aggregators Parameters ---------- arg : string, dict, function *args : args to pass on to the function **kwargs : kwargs to pass on to the function Returns ------- ...

def _shallow_copy(self, obj=None, obj_type=None, **kwargs): """ return a new object with the replacement attributes """ if obj is None: obj = self._selected_obj.copy() if obj_type is None: obj_type = self._constructor if isinstance(obj, obj_type): ...

def itemsize(self): """ Return the size of the dtype of the item of the underlying data. .. deprecated:: 0.23.0 """ warnings.warn("{obj}.itemsize is deprecated and will be removed " "in a future version".format(obj=type(self).__name__), ...

def base(self): """ Return the base object if the memory of the underlying data is shared. .. deprecated:: 0.23.0 """ warnings.warn("{obj}.base is deprecated and will be removed " "in a future version".format(obj=type(self).__name__), ...

def array(self) -> ExtensionArray: """ The ExtensionArray of the data backing this Series or Index. .. versionadded:: 0.24.0 Returns ------- ExtensionArray An ExtensionArray of the values stored within. For extension types, this is the actual arr...

def to_numpy(self, dtype=None, copy=False): """ A NumPy ndarray representing the values in this Series or Index. .. versionadded:: 0.24.0 Parameters ---------- dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray` copy : boo...

def _ndarray_values(self) -> np.ndarray: """ The data as an ndarray, possibly losing information. The expectation is that this is cheap to compute, and is primarily used for interacting with our indexers. - categorical -> codes """ if is_extension_array_dtype(se...