kloodia/raw_github · Datasets at Hugging Face

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 if `key` matches an index level. - (axis=1): Ret...

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...

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 key matches an index label then drop the row. Otherwise...

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...

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 -------- Series.dropna ...

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 ...

Not a real Jupyter special repr method, but we use the same naming convention.

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...

Convert the object to a JSON string. Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps. Parameters ---------- path_or_buf : string or file handle, optional File path or object. If not specified, the result is ret...

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 ...

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 hold a mix of related objects which can be accessed as a gr...

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...

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 File path, buffer-like, or None if None, return generated string ap...

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...

Pickle (serialize) object to file. Parameters ---------- path : str File path where the pickled object will be stored. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, \ default 'infer' A string representing the compression to use in the output ...

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...

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 - True, use the provided separator, writing in a csv format ...

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 ...

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. See Also -------- DataFra...

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. ...

r""" Render an object to a LaTeX tabular environment table. Render an object to a tabular environment table. You can splice this into a LaTeX document. Requires \usepackage{booktabs}. .. versionchanged:: 0.20.2 Added to Series Parameters ---------- b...

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...

r""" Write object to a comma-separated values (csv) file. .. versionchanged:: 0.24.0 The order of arguments for Series was changed. Parameters ---------- path_or_buf : str or file handle, default None File path or object, if None is provided the result i...

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...

Create an indexer like _name in the class.

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__))

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(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 ...

Return the cached item, item represents a label indexer.

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 ...

Set the _cacher attribute on the calling object with a weakref to cacher.

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))

Return the cached item, item represents a positional indexer.

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

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, default True provide is_copy checks

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...

Construct a slice of this container. kind parameter is maintained for compatibility with Series slicing.

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)) ...

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-dtype meaning that the cacher should be updated following sett...

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...

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 element in the object. This is the internal version of ``.ta...

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...

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 element in the object. Parameters ---------- ...

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...

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 Label contained in the index, or partially in a MultiIndex. axis : {0...

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...

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). Should return True or False axis : int ...

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...

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 in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False...

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 ...

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, default None For MultiIndex errors : {'ignore', 'raise'}, default 'rai...

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...

Replace self internals with result. Parameters ---------- verify_is_copy : boolean, default True provide is_copy checks

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 ...

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 ------- Series or DataFrame ...

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 ...

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 ------- Series or DataFrame ...

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 ...

Sort by the values along either axis. Parameters ----------%(optional_by)s axis : %(axes_single_arg)s, default 0 Axis to be sorted. ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders....

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. ...

Sort object by labels (along an axis). Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns. level : int or level name or list of ints or list of level ...

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...

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``. Parameters ---------- %(optional_labels)s ...

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...

Perform the reindex for all the axes.

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._...

Check if we do need a multi reindex.

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)

allow_dups indicates an internal call here

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...

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. Parameters ---------- items : list-like Keep labels from axi...

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...

add the string-like attributes from the info_axis. If info_axis is a MultiIndex, it's first level values are used.

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...

Return a random sample of items from an axis of object. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional Number of items from axis to return. Cannot be used with `frac`. Default = 1 if `frac` = None. frac : float, o...

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 ...

Consolidate _data -- if the blocks have changed, then clear the cache

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

Consolidate data in place and return None

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

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 object Returns ------- consolidated ...

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...

check whether we allow in-place setting with this type of value

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): ...

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, otherwise, returns specified columns. Returns ...

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...

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 ------- numpy.ndarray The values of t...

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...

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 columns with each type and sparsity (den...

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...

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.dtypes>` for more. Returns ...

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...

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 :ref:`the User Guide <basics.dtypes...

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 ...

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 ---------- copy : boolean, default True Ret...

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 --------...

Return a dict of dtype -> Constructor Types that each is a homogeneous dtype. Internal ONLY

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()}

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 the same type. Alternatively, use {col: dtype, ...}, where col is a ...

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 ...

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 original object (see notes below). When ...

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...

Attempt to infer better dtype for object columns Parameters ---------- datetime : boolean, default False If True, convert to date where possible. numeric : boolean, default False If True, attempt to convert to numbers (including strings), with unconve...

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. ...

Attempt to infer better dtype for object columns. .. deprecated:: 0.21.0 Parameters ---------- convert_dates : boolean, default True If True, convert to date where possible. If 'coerce', force conversion, with unconvertible values becoming NaT. convert_n...

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 ...

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. .. versionadded:: 0.21.0 Retur...

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. ...

Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or c...

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...

Interpolate values according to different methods.

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...

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 columns (if not `None`) .. versionadded:: 0.19....

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 ...

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 performed element-wise in the specified axis. Parameters ---------- lower : float or array_like...

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...

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 value or an array, in the latter case it performs the truncation elemen...

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 ...

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 value or an array, in the latter case it performs the truncation elemen...

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 ...

Group DataFrame or Series using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups. Par...

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...

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 frequency. ``resample`` is more appropriate if an operation, such as summarization, is necessary to represe...

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 ...

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 ------- Series or DataFrame Raises ------ ...

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 --...

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* between the two times. Parameters ---------- start_time : datetime.time or str end_time : datetime.time ...

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...

Resample time-series data. Convenience method for frequency conversion and resampling of time series. Object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`, or `TimedeltaIndex`), or pass datetime-like values to the `on` or `level` keyword. Parameters --...

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...

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 Raises ------ TypeError ...

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 ...

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 Raises ------ TypeError ...

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 ...

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 ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 index to direct ranking method : {'average', 'min', 'max',...

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 -------...

Equivalent to public method `where`, except that `other` is not applied as a function even if callable. Used in __setitem__.

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_...

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 periods to move, can be positive or negative Returns ...

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...

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, default None Increment to use from the tseries module or ...

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...

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 ---------- before : date, string, int Truncate all rows before this index value. ...

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 ---------- ...

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, convert a specific level. Otherwise must be None copy : boolean...

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...

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 :meth:`Series.dt.tz_localize`. Parameters ---------- tz : string or pytz.timezone object axis : the axis to l...

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...

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 ``DataFrame`` column sets of mixed data types. The output will vary depending on w...

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...

Validate percentiles (used by describe and quantile).

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: ...

Add the operations to the cls; evaluate the doc strings again

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...

Add the series only operations to the cls; evaluate the doc strings again.

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) ...

Add the series or dataframe only operations to the cls; evaluate the doc strings again.

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,...

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 index

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...

Reset cached properties. If ``key`` is passed, only clears that key.

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)

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

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...

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 ------- tuple of result, how Notes ----- ...

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 ------- ...

return a new object with the replacement attributes

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): ...

Return the size of the dtype of the item of the underlying data. .. deprecated:: 0.23.0

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__), ...

Return the base object if the memory of the underlying data is shared. .. deprecated:: 0.23.0

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__), ...

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 array. For NumPy native types, this is a thin ...

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...

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 : bool, default False Whether to ensure that the returned...

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...

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

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...

Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Returns ------- scalar Maximum value. See Also ------...

def max(self, axis=None, skipna=True): """ Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Returns ------- scalar ...

Return an ndarray of the maximum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True See Also -------- numpy.ndarray.argmax

def argmax(self, axis=None, skipna=True): """ Return an ndarray of the maximum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True See Also -------- numpy.ndarra...

Return the minimum value of the Index. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- scalar Minimum value. See Also -------- Index.max :...

def min(self, axis=None, skipna=True): """ Return the minimum value of the Index. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- scalar Minimum va...

Return a ndarray of the minimum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- numpy.ndarray See Also -------- numpy.ndarray.argmin

def argmin(self, axis=None, skipna=True): """ Return a ndarray of the minimum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- numpy.ndarray ...

Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns ------- list See Also -------- numpy.ndarray.tolist

def tolist(self): """ Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns ------- list See Also -------- n...

perform the reduction type operation if we can

def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): """ perform the reduction type operation if we can """ func = getattr(self, name, None) if func is None: raise TypeError("{klass} cannot perform the operation {op}".format( ...

An internal function that maps values using the input correspondence (which can be a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series The input correspondence object na_action : {None, 'ignore'} If 'ignore', propagate N...

def _map_values(self, mapper, na_action=None): """ An internal function that maps values using the input correspondence (which can be a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series The input correspondence object ...

Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters ---------- normalize : boolean, default False I...

def value_counts(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True): """ Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. ...

Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : bool, default True Don't include NaN in the count. Returns ------- int See Also -------- DataFrame.nunique: Method...

def nunique(self, dropna=True): """ Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : bool, default True Don't include NaN in the count. Returns ------- int See Als...

Memory usage of the values Parameters ---------- deep : bool Introspect the data deeply, interrogate `object` dtypes for system-level memory consumption Returns ------- bytes used See Also -------- numpy.ndarray.nbytes ...

def memory_usage(self, deep=False): """ Memory usage of the values Parameters ---------- deep : bool Introspect the data deeply, interrogate `object` dtypes for system-level memory consumption Returns ------- bytes used S...

Return the argument with an initial component of ~ or ~user replaced by that user's home directory. Parameters ---------- filepath_or_buffer : object to be converted if possible Returns ------- expanded_filepath_or_buffer : an expanded filepath or the i...

def _expand_user(filepath_or_buffer): """Return the argument with an initial component of ~ or ~user replaced by that user's home directory. Parameters ---------- filepath_or_buffer : object to be converted if possible Returns ------- expanded_filepath_or_buffer : an expanded filepa...