BoghdadyJR/codesearch_python · Datasets at Hugging Face

text stringlengths 75 104k
def insert(self, loc, item, value, allow_duplicates=False): """ Insert item at selected position. Parameters ---------- loc : int item : hashable value : array_like allow_duplicates: bool If False, trying to insert non-unique item will raise ...
def reindex_axis(self, new_index, axis, method=None, limit=None, fill_value=None, copy=True): """ Conform block manager to new index. """ new_index = ensure_index(new_index) new_index, indexer = self.axes[axis].reindex(new_index, method=method, ...
def reindex_indexer(self, new_axis, indexer, axis, fill_value=None, allow_dups=False, copy=True): """ Parameters ---------- new_axis : Index indexer : ndarray of int64 or None axis : int fill_value : object allow_dups : bool ...
def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None): """ Slice/take blocks along axis=0. Overloaded for SingleBlock Returns ------- new_blocks : list of Block """ allow_fill = fill_tuple is not None sl_type, slobj, sllen = _pre...
def take(self, indexer, axis=1, verify=True, convert=True): """ Take items along any axis. """ self._consolidate_inplace() indexer = (np.arange(indexer.start, indexer.stop, indexer.step, dtype='int64') if isinstance(indexer, slice) ...
def unstack(self, unstacker_func, fill_value): """Return a blockmanager with all blocks unstacked. Parameters ---------- unstacker_func : callable A (partially-applied) ``pd.core.reshape._Unstacker`` class. fill_value : Any fill_value for newly introduced...
def delete(self, item): """ Delete single item from SingleBlockManager. Ensures that self.blocks doesn't become empty. """ loc = self.items.get_loc(item) self._block.delete(loc) self.axes[0] = self.axes[0].delete(loc)
def concat(self, to_concat, new_axis): """ Concatenate a list of SingleBlockManagers into a single SingleBlockManager. Used for pd.concat of Series objects with axis=0. Parameters ---------- to_concat : list of SingleBlockManagers new_axis : Index of the...
def from_array(cls, arr, index=None, name=None, copy=False, fill_value=None, fastpath=False): """Construct SparseSeries from array. .. deprecated:: 0.23.0 Use the pd.SparseSeries(..) constructor instead. """ warnings.warn("'from_array' is deprecated and wi...
def as_sparse_array(self, kind=None, fill_value=None, copy=False): """ return my self as a sparse array, do not copy by default """ if fill_value is None: fill_value = self.fill_value if kind is None: kind = self.kind return SparseArray(self.values, sparse_index=...
def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, kwds): """ perform a reduction operation """ return op(self.get_values(), skipna=skipna, kwds)
def _ixs(self, i, axis=0): """ Return the i-th value or values in the SparseSeries by location Parameters ---------- i : int, slice, or sequence of integers Returns ------- value : scalar (int) or Series (slice, sequence) """ label = self...
def abs(self): """ Return an object with absolute value taken. Only applicable to objects that are all numeric Returns ------- abs: same type as caller """ return self._constructor(np.abs(self.values), index=self.index).__...
def get(self, label, default=None): """ Returns value occupying requested label, default to specified missing value if not present. Analogous to dict.get Parameters ---------- label : object Label value looking for default : object, optional ...
def get_value(self, label, takeable=False): """ Retrieve single value at passed index label .. deprecated:: 0.21.0 Please use .at[] or .iat[] accessors. Parameters ---------- index : label takeable : interpret the index as indexers, default False ...
def set_value(self, label, value, takeable=False): """ Quickly set single value at passed label. If label is not contained, a new object is created with the label placed at the end of the result index .. deprecated:: 0.21.0 Please use .at[] or .iat[] accessors. ...
def to_dense(self): """ Convert SparseSeries to a Series. Returns ------- s : Series """ return Series(self.values.to_dense(), index=self.index, name=self.name)
def copy(self, deep=True): """ Make a copy of the SparseSeries. Only the actual sparse values need to be copied """ # TODO: https://github.com/pandas-dev/pandas/issues/22314 # We skip the block manager till that is resolved. new_data = self.values.copy(deep=deep) ...
def sparse_reindex(self, new_index): """ Conform sparse values to new SparseIndex Parameters ---------- new_index : {BlockIndex, IntIndex} Returns ------- reindexed : SparseSeries """ if not isinstance(new_index, splib.SparseIndex): ...
def cumsum(self, axis=0, args, *kwargs): """ Cumulative sum of non-NA/null values. When performing the cumulative summation, any non-NA/null values will be skipped. The resulting SparseSeries will preserve the locations of NaN values, but the fill value will be `np.nan` regard...
def dropna(self, axis=0, inplace=False, **kwargs): """ Analogous to Series.dropna. If fill_value=NaN, returns a dense Series """ # TODO: make more efficient # Validate axis self._get_axis_number(axis or 0) dense_valid = self.to_dense().dropna() if inplace:...
def combine_first(self, other): """ Combine Series values, choosing the calling Series's values first. Result index will be the union of the two indexes Parameters ---------- other : Series Returns ------- y : Series """ if isinst...
def _maybe_cache(arg, format, cache, convert_listlike): """ Create a cache of unique dates from an array of dates Parameters ---------- arg : integer, float, string, datetime, list, tuple, 1-d array, Series format : string Strftime format to parse time cache : boolean True a...
def _convert_and_box_cache(arg, cache_array, box, errors, name=None): """ Convert array of dates with a cache and box the result Parameters ---------- arg : integer, float, string, datetime, list, tuple, 1-d array, Series cache_array : Series Cache of converted, unique dates box : b...
def _return_parsed_timezone_results(result, timezones, box, tz, name): """ Return results from array_strptime if a %z or %Z directive was passed. Parameters ---------- result : ndarray int64 date representations of the dates timezones : ndarray pytz timezone objects box : bo...
def _convert_listlike_datetimes(arg, box, format, name=None, tz=None, unit=None, errors=None, infer_datetime_format=None, dayfirst=None, yearfirst=None, exact=None): """ Helper function for to_datetime. Performs the ...
def _adjust_to_origin(arg, origin, unit): """ Helper function for to_datetime. Adjust input argument to the specified origin Parameters ---------- arg : list, tuple, ndarray, Series, Index date to be adjusted origin : 'julian' or Timestamp origin offset for the arg unit ...
def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix', cache=False): """ Convert argument to datetime. Parameters ---------- arg : integ...
def _assemble_from_unit_mappings(arg, errors, box, tz): """ assemble the unit specified fields from the arg (DataFrame) Return a Series for actual parsing Parameters ---------- arg : DataFrame errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsin...
def _attempt_YYYYMMDD(arg, errors): """ try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like, arg is a passed in as an object dtype, but could really be ints/strings with nan-like/or floats (e.g. with nan) Parameters ---------- arg : passed value errors : 'raise','ignore',...
def to_time(arg, format=None, infer_time_format=False, errors='raise'): """ Parse time strings to time objects using fixed strptime formats ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p") Use infer_time_format if all the strings are in the same format to speed...
def deprecate(name, alternative, version, alt_name=None, klass=None, stacklevel=2, msg=None): """ Return a new function that emits a deprecation warning on use. To use this method for a deprecated function, another function `alternative` with the same signature must exist. The deprecated ...
def deprecate_kwarg(old_arg_name, new_arg_name, mapping=None, stacklevel=2): """ Decorator to deprecate a keyword argument of a function. Parameters ---------- old_arg_name : str Name of argument in function to deprecate new_arg_name : str or None Name of preferred argument in f...
def make_signature(func): """ Returns a tuple containing the paramenter list with defaults and parameter list. Examples -------- >>> def f(a, b, c=2): >>> return a * b * c >>> print(make_signature(f)) (['a', 'b', 'c=2'], ['a', 'b', 'c']) """ spec = inspect.getfullargspe...
def period_range(start=None, end=None, periods=None, freq=None, name=None): """ Return a fixed frequency PeriodIndex, with day (calendar) as the default frequency Parameters ---------- start : string or period-like, default None Left bound for generating periods end : string or peri...
def from_range(cls, data, name=None, dtype=None, **kwargs): """ Create RangeIndex from a range object. """ if not isinstance(data, range): raise TypeError( '{0}(...) must be called with object coercible to a ' 'range, {1} was passed'.format(cls.__name__, repr(...
def _format_attrs(self): """ Return a list of tuples of the (attr, formatted_value) """ attrs = self._get_data_as_items() if self.name is not None: attrs.append(('name', ibase.default_pprint(self.name))) return attrs
def min(self, axis=None, skipna=True): """The minimum value of the RangeIndex""" nv.validate_minmax_axis(axis) return self._minmax('min')
def max(self, axis=None, skipna=True): """The maximum value of the RangeIndex""" nv.validate_minmax_axis(axis) return self._minmax('max')
def argsort(self, args, *kwargs): """ Returns the indices that would sort the index and its underlying data. Returns ------- argsorted : numpy array See Also -------- numpy.ndarray.argsort """ nv.validate_argsort(args, kwargs) ...
def equals(self, other): """ Determines if two Index objects contain the same elements. """ if isinstance(other, RangeIndex): ls = len(self) lo = len(other) return (ls == lo == 0 or ls == lo == 1 and self._start ...
def intersection(self, other, sort=False): """ Form the intersection of two Index objects. Parameters ---------- other : Index or array-like sort : False or None, default False Sort the resulting index if possible .. versionadded:: 0.24.0 ...
def _min_fitting_element(self, lower_limit): """Returns the smallest element greater than or equal to the limit""" no_steps = -(-(lower_limit - self._start) // abs(self._step)) return self._start + abs(self._step) * no_steps
def _max_fitting_element(self, upper_limit): """Returns the largest element smaller than or equal to the limit""" no_steps = (upper_limit - self._start) // abs(self._step) return self._start + abs(self._step) * no_steps
def _extended_gcd(self, a, b): """ Extended Euclidean algorithms to solve Bezout's identity: ax + by = gcd(x, y) Finds one particular solution for x, y: s, t Returns: gcd, s, t """ s, old_s = 0, 1 t, old_t = 1, 0 r, old_r = b, a while ...
def union(self, other, sort=None): """ Form the union of two Index objects and sorts if possible Parameters ---------- other : Index or array-like sort : False or None, default None Whether to sort resulting index. ``sort=None`` returns a mononot...
def _add_numeric_methods_binary(cls): """ add in numeric methods, specialized to RangeIndex """ def _make_evaluate_binop(op, step=False): """ Parameters ---------- op : callable that accepts 2 parms perform the binary op step :...
def to_numpy(self, dtype=None, copy=False): """ Convert the PandasArray to a :class:`numpy.ndarray`. By default, this requires no coercion or copying of data. Parameters ---------- dtype : numpy.dtype The NumPy dtype to pass to :func:`numpy.asarray`. ...
def adjoin(space, lists, *kwargs): """ Glues together two sets of strings using the amount of space requested. The idea is to prettify. ---------- space : int number of spaces for padding lists : str list of str which being joined strlen : callable function used to...
def justify(texts, max_len, mode='right'): """ Perform ljust, center, rjust against string or list-like """ if mode == 'left': return [x.ljust(max_len) for x in texts] elif mode == 'center': return [x.center(max_len) for x in texts] else: return [x.rjust(max_len) for x in...
def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, **kwds): """ internal. pprinter for iterables. you should probably use pprint_thing() rather then calling this directly. bounds length of printed sequence, depending on options """ if isinstance(seq, set): fmt = "{{{body}}}" else...
def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, **kwds): """ internal. pprinter for iterables. you should probably use pprint_thing() rather then calling this directly. """ fmt = "{{{things}}}" pairs = [] pfmt = "{key}: {val}" if max_seq_items is False: nitems = len(seq)...
def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False, quote_strings=False, max_seq_items=None): """ This function is the sanctioned way of converting objects to a unicode representation. properly handles nested sequences containing unicode strings (unicode(...
def format_object_summary(obj, formatter, is_justify=True, name=None, indent_for_name=True): """ Return the formatted obj as a unicode string Parameters ---------- obj : object must be iterable and support __getitem__ formatter : callable string formatt...
def format_object_attrs(obj): """ Return a list of tuples of the (attr, formatted_value) for common attrs, including dtype, name, length Parameters ---------- obj : object must be iterable Returns ------- list """ attrs = [] if hasattr(obj, 'dtype'): at...
def read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, auth_local_webserver=False, dialect=None, location=None, configuration=None, credentials=None, use_bqstorage_api=None, private_key=None, verbose=None): """ Load data from Google BigQuery. ...
def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, diagonal='hist', marker='.', density_kwds=None, hist_kwds=None, range_padding=0.05, **kwds): """ Draw a matrix of scatter plots. Parameters ---------- frame : DataFrame alpha : float, o...
def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds): """ Plot a multidimensional dataset in 2D. Each Series in the DataFrame is represented as a evenly distributed slice on a circle. Each data point is rendered in the circle according to the value on each Series. Highly corr...
def andrews_curves(frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwds): """ Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data. Andrews curves have the functional form: f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t...
def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds): """ Bootstrap plot on mean, median and mid-range statistics. The bootstrap plot is used to estimate the uncertainty of a statistic by relaying on random sampling with replacement [1]_. This function will generate bootstrapping plot...
def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None, use_columns=False, xticks=None, colormap=None, axvlines=True, axvlines_kwds=None, sort_labels=False, **kwds): """Parallel coordinates plotting. Parameters ...
def lag_plot(series, lag=1, ax=None, **kwds): """Lag plot for time series. Parameters ---------- series : Time series lag : lag of the scatter plot, default 1 ax : Matplotlib axis object, optional kwds : Matplotlib scatter method keyword arguments, optional Returns ------- clas...
def autocorrelation_plot(series, ax=None, **kwds): """ Autocorrelation plot for time series. Parameters: ----------- series: Time series ax: Matplotlib axis object, optional kwds : keywords Options to pass to matplotlib plotting method Returns: ----------- class:`matplo...
def _any_pandas_objects(terms): """Check a sequence of terms for instances of PandasObject.""" return any(isinstance(term.value, pd.core.generic.PandasObject) for term in terms)
def _align(terms): """Align a set of terms""" try: # flatten the parse tree (a nested list, really) terms = list(com.flatten(terms)) except TypeError: # can't iterate so it must just be a constant or single variable if isinstance(terms.value, pd.core.generic.NDFrame): ...
def _reconstruct_object(typ, obj, axes, dtype): """Reconstruct an object given its type, raw value, and possibly empty (None) axes. Parameters ---------- typ : object A type obj : object The value to use in the type constructor axes : dict The axes to use to construc...
def tsplot(series, plotf, ax=None, **kwargs): import warnings """ Plots a Series on the given Matplotlib axes or the current axes Parameters ---------- axes : Axes series : Series Notes _____ Supports same kwargs as Axes.plot .. deprecated:: 0.23.0 Use Series.plot(...
def _decorate_axes(ax, freq, kwargs): """Initialize axes for time-series plotting""" if not hasattr(ax, '_plot_data'): ax._plot_data = [] ax.freq = freq xaxis = ax.get_xaxis() xaxis.freq = freq if not hasattr(ax, 'legendlabels'): ax.legendlabels = [kwargs.get('label', None)] ...
def _get_ax_freq(ax): """ Get the freq attribute of the ax object if set. Also checks shared axes (eg when using secondary yaxis, sharex=True or twinx) """ ax_freq = getattr(ax, 'freq', None) if ax_freq is None: # check for left/right ax in case of secondary yaxis if hasattr(...
def format_timedelta_ticks(x, pos, n_decimals): """ Convert seconds to 'D days HH:MM:SS.F' """ s, ns = divmod(x, 1e9) m, s = divmod(s, 60) h, m = divmod(m, 60) d, h = divmod(h, 24) decimals = int(ns * 10**(n_decimals - 9)) s = r'{:02d}:{:02d}:{:02d}'.format(int(h), int(m), int(s)) ...
def format_dateaxis(subplot, freq, index): """ Pretty-formats the date axis (x-axis). Major and minor ticks are automatically set for the frequency of the current underlying series. As the dynamic mode is activated by default, changing the limits of the x axis will intelligently change the pos...
def _is_homogeneous_type(self): """ Whether all the columns in a DataFrame have the same type. Returns ------- bool Examples -------- >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type True >>> DataFrame({"A": [1, 2], "B": [3....
def _repr_html_(self): """ Return a html representation for a particular DataFrame. Mainly for IPython notebook. """ if self._info_repr(): buf = StringIO("") self.info(buf=buf) # need to escape the <class>, should be the first line. ...
def to_string(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, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', ...
def iteritems(self): r""" Iterator over (column name, Series) pairs. Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. Yields ------ label : object The column names for the DataFrame being iterat...
def iterrows(self): """ Iterate over DataFrame rows as (index, Series) pairs. Yields ------ index : label or tuple of label The index of the row. A tuple for a `MultiIndex`. data : Series The data of the row as a Series. it : generator ...
def itertuples(self, index=True, name="Pandas"): """ Iterate over DataFrame rows as namedtuples. Parameters ---------- index : bool, default True If True, return the index as the first element of the tuple. name : str or None, default "Pandas" The...
def dot(self, other): """ Compute the matrix mutiplication between the DataFrame and other. This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array. It can also be called using ``self @ other`` in Python >= 3.5...
def from_dict(cls, data, orient='columns', dtype=None, columns=None): """ Construct DataFrame from dict of array-like or dicts. Creates DataFrame object from dictionary by columns or by index allowing dtype specification. Parameters ---------- data : dict ...
def to_numpy(self, dtype=None, copy=False): """ Convert the DataFrame to a NumPy array. .. versionadded:: 0.24.0 By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are ``float16`` and ``fl...
def to_dict(self, orient='dict', into=dict): """ Convert the DataFrame to a dictionary. The type of the key-value pairs can be customized with the parameters (see below). Parameters ---------- orient : str {'dict', 'list', 'series', 'split', 'records', 'index'} ...
def to_gbq(self, destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=False, table_schema=None, location=None, progress_bar=True, credentials=None, verbose=None, private_key=None): """ Write a DataFrame to a...
def from_records(cls, data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None): """ Convert structured or record ndarray to DataFrame. Parameters ---------- data : ndarray (structured dtype), list of tuples, dict, or DataFrame in...
def to_records(self, index=True, convert_datetime64=None, column_dtypes=None, index_dtypes=None): """ Convert DataFrame to a NumPy record array. Index will be included as the first field of the record array if requested. Parameters ---------- ...
def from_items(cls, items, columns=None, orient='columns'): """ Construct a DataFrame from a list of tuples. .. deprecated:: 0.23.0 `from_items` is deprecated and will be removed in a future version. Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>` ...
def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=None, infer_datetime_format=False): """ Read CSV file. .. deprecated:: 0.21.0 Use :func:`read_csv` instead. It is preferable to use the m...
def to_sparse(self, fill_value=None, kind='block'): """ Convert to SparseDataFrame. Implement the sparse version of the DataFrame meaning that any data matching a specific value it's omitted in the representation. The sparse DataFrame allows for a more efficient storage. ...
def to_stata(self, fname, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, data_label=None, variable_labels=None, version=114, convert_strl=None): """ Export DataFrame object to Stata dta format. Writes...
def to_feather(self, fname): """ Write out the binary feather-format for DataFrames. .. versionadded:: 0.20.0 Parameters ---------- fname : str string file path """ from pandas.io.feather_format import to_feather to_feather(self, fnam...
def to_parquet(self, fname, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs): """ Write a DataFrame to the binary parquet format. .. versionadded:: 0.21.0 This function writes the dataframe as a `parquet file <https://parquet.ap...
def to_html(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, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', bold_rows=...
def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None): """ Print a concise summary of a DataFrame. This method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage. P...
def memory_usage(self, index=True, deep=False): """ Return the memory usage of each column in bytes. The memory usage can optionally include the contribution of the index and elements of `object` dtype. This value is displayed in `DataFrame.info` by default. This can be ...
def transpose(self, args, *kwargs): """ Transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property :attr:`.T` is an accessor to the method :meth:`transpose`. Parameters ---------- c...
def get_value(self, index, col, takeable=False): """ Quickly retrieve single value at passed column and index. .. deprecated:: 0.21.0 Use .at[] or .iat[] accessors instead. Parameters ---------- index : row label col : column label takeable :...
def set_value(self, index, col, value, takeable=False): """ Put single value at passed column and index. .. deprecated:: 0.21.0 Use .at[] or .iat[] accessors instead. Parameters ---------- index : row label col : column label value : scalar ...
def _ixs(self, i, axis=0): """ Parameters ---------- i : int, slice, or sequence of integers axis : int Notes ----- If slice passed, the resulting data will be a view. """ # irow if axis == 0: if isinstance(i, slice): ...
def query(self, expr, inplace=False, **kwargs): """ Query the columns of a DataFrame with a boolean expression. Parameters ---------- expr : str The query string to evaluate. You can refer to variables in the environment by prefixing them with an '@' cha...
def eval(self, expr, inplace=False, **kwargs): """ Evaluate a string describing operations on DataFrame columns. Operates on columns only, not specific rows or elements. This allows `eval` to run arbitrary code, which can make you vulnerable to code injection if you pass user i...
def select_dtypes(self, include=None, exclude=None): """ Return a subset of the DataFrame's columns based on the column dtypes. Parameters ---------- include, exclude : scalar or list-like A selection of dtypes or strings to be included/excluded. At least ...

def insert(self, loc, item, value, allow_duplicates=False): """ Insert item at selected position. Parameters ---------- loc : int item : hashable value : array_like allow_duplicates: bool If False, trying to insert non-unique item will raise ...

def reindex_axis(self, new_index, axis, method=None, limit=None, fill_value=None, copy=True): """ Conform block manager to new index. """ new_index = ensure_index(new_index) new_index, indexer = self.axes[axis].reindex(new_index, method=method, ...

def reindex_indexer(self, new_axis, indexer, axis, fill_value=None, allow_dups=False, copy=True): """ Parameters ---------- new_axis : Index indexer : ndarray of int64 or None axis : int fill_value : object allow_dups : bool ...

def _slice_take_blocks_ax0(self, slice_or_indexer, fill_tuple=None): """ Slice/take blocks along axis=0. Overloaded for SingleBlock Returns ------- new_blocks : list of Block """ allow_fill = fill_tuple is not None sl_type, slobj, sllen = _pre...

def take(self, indexer, axis=1, verify=True, convert=True): """ Take items along any axis. """ self._consolidate_inplace() indexer = (np.arange(indexer.start, indexer.stop, indexer.step, dtype='int64') if isinstance(indexer, slice) ...

def unstack(self, unstacker_func, fill_value): """Return a blockmanager with all blocks unstacked. Parameters ---------- unstacker_func : callable A (partially-applied) ``pd.core.reshape._Unstacker`` class. fill_value : Any fill_value for newly introduced...

def delete(self, item): """ Delete single item from SingleBlockManager. Ensures that self.blocks doesn't become empty. """ loc = self.items.get_loc(item) self._block.delete(loc) self.axes[0] = self.axes[0].delete(loc)

def concat(self, to_concat, new_axis): """ Concatenate a list of SingleBlockManagers into a single SingleBlockManager. Used for pd.concat of Series objects with axis=0. Parameters ---------- to_concat : list of SingleBlockManagers new_axis : Index of the...

def from_array(cls, arr, index=None, name=None, copy=False, fill_value=None, fastpath=False): """Construct SparseSeries from array. .. deprecated:: 0.23.0 Use the pd.SparseSeries(..) constructor instead. """ warnings.warn("'from_array' is deprecated and wi...

def as_sparse_array(self, kind=None, fill_value=None, copy=False): """ return my self as a sparse array, do not copy by default """ if fill_value is None: fill_value = self.fill_value if kind is None: kind = self.kind return SparseArray(self.values, sparse_index=...

def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): """ perform a reduction operation """ return op(self.get_values(), skipna=skipna, **kwds)

def _ixs(self, i, axis=0): """ Return the i-th value or values in the SparseSeries by location Parameters ---------- i : int, slice, or sequence of integers Returns ------- value : scalar (int) or Series (slice, sequence) """ label = self...

def abs(self): """ Return an object with absolute value taken. Only applicable to objects that are all numeric Returns ------- abs: same type as caller """ return self._constructor(np.abs(self.values), index=self.index).__...

def get(self, label, default=None): """ Returns value occupying requested label, default to specified missing value if not present. Analogous to dict.get Parameters ---------- label : object Label value looking for default : object, optional ...

def get_value(self, label, takeable=False): """ Retrieve single value at passed index label .. deprecated:: 0.21.0 Please use .at[] or .iat[] accessors. Parameters ---------- index : label takeable : interpret the index as indexers, default False ...

def set_value(self, label, value, takeable=False): """ Quickly set single value at passed label. If label is not contained, a new object is created with the label placed at the end of the result index .. deprecated:: 0.21.0 Please use .at[] or .iat[] accessors. ...

def to_dense(self): """ Convert SparseSeries to a Series. Returns ------- s : Series """ return Series(self.values.to_dense(), index=self.index, name=self.name)

def copy(self, deep=True): """ Make a copy of the SparseSeries. Only the actual sparse values need to be copied """ # TODO: https://github.com/pandas-dev/pandas/issues/22314 # We skip the block manager till that is resolved. new_data = self.values.copy(deep=deep) ...

def sparse_reindex(self, new_index): """ Conform sparse values to new SparseIndex Parameters ---------- new_index : {BlockIndex, IntIndex} Returns ------- reindexed : SparseSeries """ if not isinstance(new_index, splib.SparseIndex): ...

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

def dropna(self, axis=0, inplace=False, **kwargs): """ Analogous to Series.dropna. If fill_value=NaN, returns a dense Series """ # TODO: make more efficient # Validate axis self._get_axis_number(axis or 0) dense_valid = self.to_dense().dropna() if inplace:...

def combine_first(self, other): """ Combine Series values, choosing the calling Series's values first. Result index will be the union of the two indexes Parameters ---------- other : Series Returns ------- y : Series """ if isinst...

def _maybe_cache(arg, format, cache, convert_listlike): """ Create a cache of unique dates from an array of dates Parameters ---------- arg : integer, float, string, datetime, list, tuple, 1-d array, Series format : string Strftime format to parse time cache : boolean True a...

def _convert_and_box_cache(arg, cache_array, box, errors, name=None): """ Convert array of dates with a cache and box the result Parameters ---------- arg : integer, float, string, datetime, list, tuple, 1-d array, Series cache_array : Series Cache of converted, unique dates box : b...

def _return_parsed_timezone_results(result, timezones, box, tz, name): """ Return results from array_strptime if a %z or %Z directive was passed. Parameters ---------- result : ndarray int64 date representations of the dates timezones : ndarray pytz timezone objects box : bo...

def _convert_listlike_datetimes(arg, box, format, name=None, tz=None, unit=None, errors=None, infer_datetime_format=None, dayfirst=None, yearfirst=None, exact=None): """ Helper function for to_datetime. Performs the ...

def _adjust_to_origin(arg, origin, unit): """ Helper function for to_datetime. Adjust input argument to the specified origin Parameters ---------- arg : list, tuple, ndarray, Series, Index date to be adjusted origin : 'julian' or Timestamp origin offset for the arg unit ...

def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix', cache=False): """ Convert argument to datetime. Parameters ---------- arg : integ...

def _assemble_from_unit_mappings(arg, errors, box, tz): """ assemble the unit specified fields from the arg (DataFrame) Return a Series for actual parsing Parameters ---------- arg : DataFrame errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsin...

def _attempt_YYYYMMDD(arg, errors): """ try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like, arg is a passed in as an object dtype, but could really be ints/strings with nan-like/or floats (e.g. with nan) Parameters ---------- arg : passed value errors : 'raise','ignore',...

def to_time(arg, format=None, infer_time_format=False, errors='raise'): """ Parse time strings to time objects using fixed strptime formats ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p") Use infer_time_format if all the strings are in the same format to speed...

def deprecate(name, alternative, version, alt_name=None, klass=None, stacklevel=2, msg=None): """ Return a new function that emits a deprecation warning on use. To use this method for a deprecated function, another function `alternative` with the same signature must exist. The deprecated ...

def deprecate_kwarg(old_arg_name, new_arg_name, mapping=None, stacklevel=2): """ Decorator to deprecate a keyword argument of a function. Parameters ---------- old_arg_name : str Name of argument in function to deprecate new_arg_name : str or None Name of preferred argument in f...

def make_signature(func): """ Returns a tuple containing the paramenter list with defaults and parameter list. Examples -------- >>> def f(a, b, c=2): >>> return a * b * c >>> print(make_signature(f)) (['a', 'b', 'c=2'], ['a', 'b', 'c']) """ spec = inspect.getfullargspe...

def period_range(start=None, end=None, periods=None, freq=None, name=None): """ Return a fixed frequency PeriodIndex, with day (calendar) as the default frequency Parameters ---------- start : string or period-like, default None Left bound for generating periods end : string or peri...

def from_range(cls, data, name=None, dtype=None, **kwargs): """ Create RangeIndex from a range object. """ if not isinstance(data, range): raise TypeError( '{0}(...) must be called with object coercible to a ' 'range, {1} was passed'.format(cls.__name__, repr(...

def _format_attrs(self): """ Return a list of tuples of the (attr, formatted_value) """ attrs = self._get_data_as_items() if self.name is not None: attrs.append(('name', ibase.default_pprint(self.name))) return attrs

def min(self, axis=None, skipna=True): """The minimum value of the RangeIndex""" nv.validate_minmax_axis(axis) return self._minmax('min')

def max(self, axis=None, skipna=True): """The maximum value of the RangeIndex""" nv.validate_minmax_axis(axis) return self._minmax('max')

def argsort(self, *args, **kwargs): """ Returns the indices that would sort the index and its underlying data. Returns ------- argsorted : numpy array See Also -------- numpy.ndarray.argsort """ nv.validate_argsort(args, kwargs) ...

def equals(self, other): """ Determines if two Index objects contain the same elements. """ if isinstance(other, RangeIndex): ls = len(self) lo = len(other) return (ls == lo == 0 or ls == lo == 1 and self._start ...

def intersection(self, other, sort=False): """ Form the intersection of two Index objects. Parameters ---------- other : Index or array-like sort : False or None, default False Sort the resulting index if possible .. versionadded:: 0.24.0 ...

def _min_fitting_element(self, lower_limit): """Returns the smallest element greater than or equal to the limit""" no_steps = -(-(lower_limit - self._start) // abs(self._step)) return self._start + abs(self._step) * no_steps

def _max_fitting_element(self, upper_limit): """Returns the largest element smaller than or equal to the limit""" no_steps = (upper_limit - self._start) // abs(self._step) return self._start + abs(self._step) * no_steps

def _extended_gcd(self, a, b): """ Extended Euclidean algorithms to solve Bezout's identity: a*x + b*y = gcd(x, y) Finds one particular solution for x, y: s, t Returns: gcd, s, t """ s, old_s = 0, 1 t, old_t = 1, 0 r, old_r = b, a while ...

def union(self, other, sort=None): """ Form the union of two Index objects and sorts if possible Parameters ---------- other : Index or array-like sort : False or None, default None Whether to sort resulting index. ``sort=None`` returns a mononot...

def _add_numeric_methods_binary(cls): """ add in numeric methods, specialized to RangeIndex """ def _make_evaluate_binop(op, step=False): """ Parameters ---------- op : callable that accepts 2 parms perform the binary op step :...

def to_numpy(self, dtype=None, copy=False): """ Convert the PandasArray to a :class:`numpy.ndarray`. By default, this requires no coercion or copying of data. Parameters ---------- dtype : numpy.dtype The NumPy dtype to pass to :func:`numpy.asarray`. ...

def adjoin(space, *lists, **kwargs): """ Glues together two sets of strings using the amount of space requested. The idea is to prettify. ---------- space : int number of spaces for padding lists : str list of str which being joined strlen : callable function used to...

def justify(texts, max_len, mode='right'): """ Perform ljust, center, rjust against string or list-like """ if mode == 'left': return [x.ljust(max_len) for x in texts] elif mode == 'center': return [x.center(max_len) for x in texts] else: return [x.rjust(max_len) for x in...

def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, **kwds): """ internal. pprinter for iterables. you should probably use pprint_thing() rather then calling this directly. bounds length of printed sequence, depending on options """ if isinstance(seq, set): fmt = "{{{body}}}" else...

def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, **kwds): """ internal. pprinter for iterables. you should probably use pprint_thing() rather then calling this directly. """ fmt = "{{{things}}}" pairs = [] pfmt = "{key}: {val}" if max_seq_items is False: nitems = len(seq)...

def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False, quote_strings=False, max_seq_items=None): """ This function is the sanctioned way of converting objects to a unicode representation. properly handles nested sequences containing unicode strings (unicode(...

def format_object_summary(obj, formatter, is_justify=True, name=None, indent_for_name=True): """ Return the formatted obj as a unicode string Parameters ---------- obj : object must be iterable and support __getitem__ formatter : callable string formatt...

def format_object_attrs(obj): """ Return a list of tuples of the (attr, formatted_value) for common attrs, including dtype, name, length Parameters ---------- obj : object must be iterable Returns ------- list """ attrs = [] if hasattr(obj, 'dtype'): at...

def read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, auth_local_webserver=False, dialect=None, location=None, configuration=None, credentials=None, use_bqstorage_api=None, private_key=None, verbose=None): """ Load data from Google BigQuery. ...

def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False, diagonal='hist', marker='.', density_kwds=None, hist_kwds=None, range_padding=0.05, **kwds): """ Draw a matrix of scatter plots. Parameters ---------- frame : DataFrame alpha : float, o...

def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds): """ Plot a multidimensional dataset in 2D. Each Series in the DataFrame is represented as a evenly distributed slice on a circle. Each data point is rendered in the circle according to the value on each Series. Highly corr...

def andrews_curves(frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwds): """ Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data. Andrews curves have the functional form: f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t...

def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds): """ Bootstrap plot on mean, median and mid-range statistics. The bootstrap plot is used to estimate the uncertainty of a statistic by relaying on random sampling with replacement [1]_. This function will generate bootstrapping plot...

def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None, use_columns=False, xticks=None, colormap=None, axvlines=True, axvlines_kwds=None, sort_labels=False, **kwds): """Parallel coordinates plotting. Parameters ...

def lag_plot(series, lag=1, ax=None, **kwds): """Lag plot for time series. Parameters ---------- series : Time series lag : lag of the scatter plot, default 1 ax : Matplotlib axis object, optional kwds : Matplotlib scatter method keyword arguments, optional Returns ------- clas...

def autocorrelation_plot(series, ax=None, **kwds): """ Autocorrelation plot for time series. Parameters: ----------- series: Time series ax: Matplotlib axis object, optional kwds : keywords Options to pass to matplotlib plotting method Returns: ----------- class:`matplo...

def _any_pandas_objects(terms): """Check a sequence of terms for instances of PandasObject.""" return any(isinstance(term.value, pd.core.generic.PandasObject) for term in terms)

def _align(terms): """Align a set of terms""" try: # flatten the parse tree (a nested list, really) terms = list(com.flatten(terms)) except TypeError: # can't iterate so it must just be a constant or single variable if isinstance(terms.value, pd.core.generic.NDFrame): ...

def _reconstruct_object(typ, obj, axes, dtype): """Reconstruct an object given its type, raw value, and possibly empty (None) axes. Parameters ---------- typ : object A type obj : object The value to use in the type constructor axes : dict The axes to use to construc...

def tsplot(series, plotf, ax=None, **kwargs): import warnings """ Plots a Series on the given Matplotlib axes or the current axes Parameters ---------- axes : Axes series : Series Notes _____ Supports same kwargs as Axes.plot .. deprecated:: 0.23.0 Use Series.plot(...

def _decorate_axes(ax, freq, kwargs): """Initialize axes for time-series plotting""" if not hasattr(ax, '_plot_data'): ax._plot_data = [] ax.freq = freq xaxis = ax.get_xaxis() xaxis.freq = freq if not hasattr(ax, 'legendlabels'): ax.legendlabels = [kwargs.get('label', None)] ...

def _get_ax_freq(ax): """ Get the freq attribute of the ax object if set. Also checks shared axes (eg when using secondary yaxis, sharex=True or twinx) """ ax_freq = getattr(ax, 'freq', None) if ax_freq is None: # check for left/right ax in case of secondary yaxis if hasattr(...

def format_timedelta_ticks(x, pos, n_decimals): """ Convert seconds to 'D days HH:MM:SS.F' """ s, ns = divmod(x, 1e9) m, s = divmod(s, 60) h, m = divmod(m, 60) d, h = divmod(h, 24) decimals = int(ns * 10**(n_decimals - 9)) s = r'{:02d}:{:02d}:{:02d}'.format(int(h), int(m), int(s)) ...

def format_dateaxis(subplot, freq, index): """ Pretty-formats the date axis (x-axis). Major and minor ticks are automatically set for the frequency of the current underlying series. As the dynamic mode is activated by default, changing the limits of the x axis will intelligently change the pos...

def _is_homogeneous_type(self): """ Whether all the columns in a DataFrame have the same type. Returns ------- bool Examples -------- >>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type True >>> DataFrame({"A": [1, 2], "B": [3....

def _repr_html_(self): """ Return a html representation for a particular DataFrame. Mainly for IPython notebook. """ if self._info_repr(): buf = StringIO("") self.info(buf=buf) # need to escape the <class>, should be the first line. ...

def to_string(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, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', ...

def iteritems(self): r""" Iterator over (column name, Series) pairs. Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. Yields ------ label : object The column names for the DataFrame being iterat...

def iterrows(self): """ Iterate over DataFrame rows as (index, Series) pairs. Yields ------ index : label or tuple of label The index of the row. A tuple for a `MultiIndex`. data : Series The data of the row as a Series. it : generator ...

def itertuples(self, index=True, name="Pandas"): """ Iterate over DataFrame rows as namedtuples. Parameters ---------- index : bool, default True If True, return the index as the first element of the tuple. name : str or None, default "Pandas" The...

def dot(self, other): """ Compute the matrix mutiplication between the DataFrame and other. This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array. It can also be called using ``self @ other`` in Python >= 3.5...

def from_dict(cls, data, orient='columns', dtype=None, columns=None): """ Construct DataFrame from dict of array-like or dicts. Creates DataFrame object from dictionary by columns or by index allowing dtype specification. Parameters ---------- data : dict ...

def to_numpy(self, dtype=None, copy=False): """ Convert the DataFrame to a NumPy array. .. versionadded:: 0.24.0 By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are ``float16`` and ``fl...

def to_dict(self, orient='dict', into=dict): """ Convert the DataFrame to a dictionary. The type of the key-value pairs can be customized with the parameters (see below). Parameters ---------- orient : str {'dict', 'list', 'series', 'split', 'records', 'index'} ...

def to_gbq(self, destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=False, table_schema=None, location=None, progress_bar=True, credentials=None, verbose=None, private_key=None): """ Write a DataFrame to a...

def from_records(cls, data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None): """ Convert structured or record ndarray to DataFrame. Parameters ---------- data : ndarray (structured dtype), list of tuples, dict, or DataFrame in...

def to_records(self, index=True, convert_datetime64=None, column_dtypes=None, index_dtypes=None): """ Convert DataFrame to a NumPy record array. Index will be included as the first field of the record array if requested. Parameters ---------- ...

def from_items(cls, items, columns=None, orient='columns'): """ Construct a DataFrame from a list of tuples. .. deprecated:: 0.23.0 `from_items` is deprecated and will be removed in a future version. Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>` ...

def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True, encoding=None, tupleize_cols=None, infer_datetime_format=False): """ Read CSV file. .. deprecated:: 0.21.0 Use :func:`read_csv` instead. It is preferable to use the m...

def to_sparse(self, fill_value=None, kind='block'): """ Convert to SparseDataFrame. Implement the sparse version of the DataFrame meaning that any data matching a specific value it's omitted in the representation. The sparse DataFrame allows for a more efficient storage. ...

def to_stata(self, fname, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, data_label=None, variable_labels=None, version=114, convert_strl=None): """ Export DataFrame object to Stata dta format. Writes...

def to_feather(self, fname): """ Write out the binary feather-format for DataFrames. .. versionadded:: 0.20.0 Parameters ---------- fname : str string file path """ from pandas.io.feather_format import to_feather to_feather(self, fnam...

def to_parquet(self, fname, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs): """ Write a DataFrame to the binary parquet format. .. versionadded:: 0.21.0 This function writes the dataframe as a `parquet file <https://parquet.ap...

def to_html(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, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', bold_rows=...

def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None): """ Print a concise summary of a DataFrame. This method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage. P...

def memory_usage(self, index=True, deep=False): """ Return the memory usage of each column in bytes. The memory usage can optionally include the contribution of the index and elements of `object` dtype. This value is displayed in `DataFrame.info` by default. This can be ...

def transpose(self, *args, **kwargs): """ Transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property :attr:`.T` is an accessor to the method :meth:`transpose`. Parameters ---------- c...

def get_value(self, index, col, takeable=False): """ Quickly retrieve single value at passed column and index. .. deprecated:: 0.21.0 Use .at[] or .iat[] accessors instead. Parameters ---------- index : row label col : column label takeable :...

def set_value(self, index, col, value, takeable=False): """ Put single value at passed column and index. .. deprecated:: 0.21.0 Use .at[] or .iat[] accessors instead. Parameters ---------- index : row label col : column label value : scalar ...

def _ixs(self, i, axis=0): """ Parameters ---------- i : int, slice, or sequence of integers axis : int Notes ----- If slice passed, the resulting data will be a view. """ # irow if axis == 0: if isinstance(i, slice): ...

def query(self, expr, inplace=False, **kwargs): """ Query the columns of a DataFrame with a boolean expression. Parameters ---------- expr : str The query string to evaluate. You can refer to variables in the environment by prefixing them with an '@' cha...

def eval(self, expr, inplace=False, **kwargs): """ Evaluate a string describing operations on DataFrame columns. Operates on columns only, not specific rows or elements. This allows `eval` to run arbitrary code, which can make you vulnerable to code injection if you pass user i...

def select_dtypes(self, include=None, exclude=None): """ Return a subset of the DataFrame's columns based on the column dtypes. Parameters ---------- include, exclude : scalar or list-like A selection of dtypes or strings to be included/excluded. At least ...