kloodia/raw_github · Datasets at Hugging Face

Attempt to convert a path-like object to a string. Parameters ---------- filepath_or_buffer : object to be converted Returns ------- str_filepath_or_buffer : maybe a string version of the object Notes ----- Objects supporting the fspath protocol (python 3.6+) are coerced accor...

def _stringify_path(filepath_or_buffer): """Attempt to convert a path-like object to a string. Parameters ---------- filepath_or_buffer : object to be converted Returns ------- str_filepath_or_buffer : maybe a string version of the object Notes ----- Objects supporting the fsp...

If the filepath_or_buffer is a url, translate and return the buffer. Otherwise passthrough. Parameters ---------- filepath_or_buffer : a url, filepath (str, py.path.local or pathlib.Path), or buffer compression : {{'gzip', 'bz2', 'zip', 'xz', None}}, optional encoding :...

def get_filepath_or_buffer(filepath_or_buffer, encoding=None, compression=None, mode=None): """ If the filepath_or_buffer is a url, translate and return the buffer. Otherwise passthrough. Parameters ---------- filepath_or_buffer : a url, filepath (str, py.path.local o...

Get the compression method for filepath_or_buffer. If compression='infer', the inferred compression method is returned. Otherwise, the input compression method is returned unchanged, unless it's invalid, in which case an error is raised. Parameters ---------- filepath_or_buffer : a path...

def _infer_compression(filepath_or_buffer, compression): """ Get the compression method for filepath_or_buffer. If compression='infer', the inferred compression method is returned. Otherwise, the input compression method is returned unchanged, unless it's invalid, in which case an error is raised. ...

Wrap comparison operations to convert timedelta-like to timedelta64

def _td_array_cmp(cls, op): """ Wrap comparison operations to convert timedelta-like to timedelta64 """ opname = '__{name}__'.format(name=op.__name__) nat_result = opname == '__ne__' def wrapper(self, other): if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)): re...

Parameters ---------- array : list-like copy : bool, default False unit : str, default "ns" The timedelta unit to treat integers as multiples of. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle elements that cannot be converted to timedelta64[ns]. See ``...

def sequence_to_td64ns(data, copy=False, unit="ns", errors="raise"): """ Parameters ---------- array : list-like copy : bool, default False unit : str, default "ns" The timedelta unit to treat integers as multiples of. errors : {"raise", "coerce", "ignore"}, default "raise" H...

Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating the integers as multiples of the given timedelta unit. Parameters ---------- data : numpy.ndarray with integer-dtype unit : str, default "ns" The timedelta unit to treat integers as multiples of. Returns -----...

def ints_to_td64ns(data, unit="ns"): """ Convert an ndarray with integer-dtype to timedelta64[ns] dtype, treating the integers as multiples of the given timedelta unit. Parameters ---------- data : numpy.ndarray with integer-dtype unit : str, default "ns" The timedelta unit to treat...

Convert a object-dtyped or string-dtyped array into an timedelta64[ns]-dtyped array. Parameters ---------- data : ndarray or Index unit : str, default "ns" The timedelta unit to treat integers as multiples of. errors : {"raise", "coerce", "ignore"}, default "raise" How to handle...

def objects_to_td64ns(data, unit="ns", errors="raise"): """ Convert a object-dtyped or string-dtyped array into an timedelta64[ns]-dtyped array. Parameters ---------- data : ndarray or Index unit : str, default "ns" The timedelta unit to treat integers as multiples of. errors : ...

Add DatetimeArray/Index or ndarray[datetime64] to TimedeltaArray.

def _add_datetime_arraylike(self, other): """ Add DatetimeArray/Index or ndarray[datetime64] to TimedeltaArray. """ if isinstance(other, np.ndarray): # At this point we have already checked that dtype is datetime64 from pandas.core.arrays import DatetimeArray ...

Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas. Returns ------- a DataFrame

def components(self): """ Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas. Returns ------- a DataFrame """ from pandas import DataFrame columns = ['days', 'hours', 'm...

Add engine to the excel writer registry.io.excel. You must use this method to integrate with ``to_excel``. Parameters ---------- klass : ExcelWriter

def register_writer(klass): """ Add engine to the excel writer registry.io.excel. You must use this method to integrate with ``to_excel``. Parameters ---------- klass : ExcelWriter """ if not callable(klass): raise ValueError("Can only register callables as engines") engine...

Convert Excel column name like 'AB' to 0-based column index. Parameters ---------- x : str The Excel column name to convert to a 0-based column index. Returns ------- num : int The column index corresponding to the name. Raises ------ ValueError Part of the...

def _excel2num(x): """ Convert Excel column name like 'AB' to 0-based column index. Parameters ---------- x : str The Excel column name to convert to a 0-based column index. Returns ------- num : int The column index corresponding to the name. Raises ------ ...

Convert comma separated list of column names and ranges to indices. Parameters ---------- areas : str A string containing a sequence of column ranges (or areas). Returns ------- cols : list A list of 0-based column indices. Examples -------- >>> _range2cols('A:E') ...

def _range2cols(areas): """ Convert comma separated list of column names and ranges to indices. Parameters ---------- areas : str A string containing a sequence of column ranges (or areas). Returns ------- cols : list A list of 0-based column indices. Examples ...

Convert `usecols` into a compatible format for parsing in `parsers.py`. Parameters ---------- usecols : object The use-columns object to potentially convert. Returns ------- converted : object The compatible format of `usecols`.

def _maybe_convert_usecols(usecols): """ Convert `usecols` into a compatible format for parsing in `parsers.py`. Parameters ---------- usecols : object The use-columns object to potentially convert. Returns ------- converted : object The compatible format of `usecols`. ...

Forward fill blank entries in row but only inside the same parent index. Used for creating headers in Multiindex. Parameters ---------- row : list List of items in a single row. control_row : list of bool Helps to determine if particular column is in same parent index as the ...

def _fill_mi_header(row, control_row): """Forward fill blank entries in row but only inside the same parent index. Used for creating headers in Multiindex. Parameters ---------- row : list List of items in a single row. control_row : list of bool Helps to determine if particular...

Pop the header name for MultiIndex parsing. Parameters ---------- row : list The data row to parse for the header name. index_col : int, list The index columns for our data. Assumed to be non-null. Returns ------- header_name : str The extracted header name. tri...

def _pop_header_name(row, index_col): """ Pop the header name for MultiIndex parsing. Parameters ---------- row : list The data row to parse for the header name. index_col : int, list The index columns for our data. Assumed to be non-null. Returns ------- header_nam...

Ensure that we are grabbing the correct scope.

def _ensure_scope(level, global_dict=None, local_dict=None, resolvers=(), target=None, **kwargs): """Ensure that we are grabbing the correct scope.""" return Scope(level + 1, global_dict=global_dict, local_dict=local_dict, resolvers=resolvers, target=target)

Replace a number with its hexadecimal representation. Used to tag temporary variables with their calling scope's id.

def _replacer(x): """Replace a number with its hexadecimal representation. Used to tag temporary variables with their calling scope's id. """ # get the hex repr of the binary char and remove 0x and pad by pad_size # zeros try: hexin = ord(x) except TypeError: # bytes literals...

Return the padded hexadecimal id of ``obj``.

def _raw_hex_id(obj): """Return the padded hexadecimal id of ``obj``.""" # interpret as a pointer since that's what really what id returns packed = struct.pack('@P', id(obj)) return ''.join(map(_replacer, packed))

Return a prettier version of obj Parameters ---------- obj : object Object to pretty print Returns ------- s : str Pretty print object repr

def _get_pretty_string(obj): """Return a prettier version of obj Parameters ---------- obj : object Object to pretty print Returns ------- s : str Pretty print object repr """ sio = StringIO() pprint.pprint(obj, stream=sio) return sio.getvalue()

Resolve a variable name in a possibly local context Parameters ---------- key : str A variable name is_local : bool Flag indicating whether the variable is local or not (prefixed with the '@' symbol) Returns ------- value : ob...

def resolve(self, key, is_local): """Resolve a variable name in a possibly local context Parameters ---------- key : str A variable name is_local : bool Flag indicating whether the variable is local or not (prefixed with the '@' symbol) ...

Replace a variable name, with a potentially new value. Parameters ---------- old_key : str Current variable name to replace new_key : str New variable name to replace `old_key` with new_value : object Value to be replaced along with the possib...

def swapkey(self, old_key, new_key, new_value=None): """Replace a variable name, with a potentially new value. Parameters ---------- old_key : str Current variable name to replace new_key : str New variable name to replace `old_key` with new_value...

Get specifically scoped variables from a list of stack frames. Parameters ---------- stack : list A list of stack frames as returned by ``inspect.stack()`` scopes : sequence of strings A sequence containing valid stack frame attribute names that evalu...

def _get_vars(self, stack, scopes): """Get specifically scoped variables from a list of stack frames. Parameters ---------- stack : list A list of stack frames as returned by ``inspect.stack()`` scopes : sequence of strings A sequence containing valid sta...

Update the current scope by going back `level` levels. Parameters ---------- level : int or None, optional, default None

def update(self, level): """Update the current scope by going back `level` levels. Parameters ---------- level : int or None, optional, default None """ sl = level + 1 # add sl frames to the scope starting with the # most distant and overwriting with mor...

Add a temporary variable to the scope. Parameters ---------- value : object An arbitrary object to be assigned to a temporary variable. Returns ------- name : basestring The name of the temporary variable created.

def add_tmp(self, value): """Add a temporary variable to the scope. Parameters ---------- value : object An arbitrary object to be assigned to a temporary variable. Returns ------- name : basestring The name of the temporary variable crea...

Return the full scope for use with passing to engines transparently as a mapping. Returns ------- vars : DeepChainMap All variables in this scope.

def full_scope(self): """Return the full scope for use with passing to engines transparently as a mapping. Returns ------- vars : DeepChainMap All variables in this scope. """ maps = [self.temps] + self.resolvers.maps + self.scope.maps return ...

Read SAS files stored as either XPORT or SAS7BDAT format files. Parameters ---------- filepath_or_buffer : string or file-like object Path to the SAS file. format : string {'xport', 'sas7bdat'} or None If None, file format is inferred from file extension. If 'xport' or 'sas7bdat...

def read_sas(filepath_or_buffer, format=None, index=None, encoding=None, chunksize=None, iterator=False): """ Read SAS files stored as either XPORT or SAS7BDAT format files. Parameters ---------- filepath_or_buffer : string or file-like object Path to the SAS file. format :...

Install the scalar coercion methods.

def _coerce_method(converter): """ Install the scalar coercion methods. """ def wrapper(self): if len(self) == 1: return converter(self.iloc[0]) raise TypeError("cannot convert the series to " "{0}".format(str(converter))) wrapper.__name__ = "__{...

Derive the "_data" and "index" attributes of a new Series from a dictionary input. Parameters ---------- data : dict or dict-like Data used to populate the new Series index : Index or index-like, default None index for the new Series: if None, use dict ke...

def _init_dict(self, data, index=None, dtype=None): """ Derive the "_data" and "index" attributes of a new Series from a dictionary input. Parameters ---------- data : dict or dict-like Data used to populate the new Series index : Index or index-like,...

Construct Series from array. .. deprecated :: 0.23.0 Use pd.Series(..) constructor instead.

def from_array(cls, arr, index=None, name=None, dtype=None, copy=False, fastpath=False): """ Construct Series from array. .. deprecated :: 0.23.0 Use pd.Series(..) constructor instead. """ warnings.warn("'from_array' is deprecated and will be remov...

Override generic, we want to set the _typ here.

def _set_axis(self, axis, labels, fastpath=False): """ Override generic, we want to set the _typ here. """ if not fastpath: labels = ensure_index(labels) is_all_dates = labels.is_all_dates if is_all_dates: if not isinstance(labels, ...

Return object Series which contains boxed values. .. deprecated :: 0.23.0 Use ``astype(object)`` instead. *this is an internal non-public method*

def asobject(self): """ Return object Series which contains boxed values. .. deprecated :: 0.23.0 Use ``astype(object)`` instead. *this is an internal non-public method* """ warnings.warn("'asobject' is deprecated. Use 'astype(object)'" ...

Return selected slices of an array along given axis as a Series. .. deprecated:: 0.24.0 See Also -------- numpy.ndarray.compress

def compress(self, condition, *args, **kwargs): """ Return selected slices of an array along given axis as a Series. .. deprecated:: 0.24.0 See Also -------- numpy.ndarray.compress """ msg = ("Series.compress(condition) is deprecated. " "U...

Return the *integer* indices of the elements that are non-zero. .. deprecated:: 0.24.0 Please use .to_numpy().nonzero() as a replacement. This method is equivalent to calling `numpy.nonzero` on the series data. For compatibility with NumPy, the return value is the same (a tu...

def nonzero(self): """ Return the *integer* indices of the elements that are non-zero. .. deprecated:: 0.24.0 Please use .to_numpy().nonzero() as a replacement. This method is equivalent to calling `numpy.nonzero` on the series data. For compatibility with NumPy, the...

Create a new view of the Series. This function will return a new Series with a view of the same underlying values in memory, optionally reinterpreted with a new data type. The new data type must preserve the same size in bytes as to not cause index misalignment. Parameters ...

def view(self, dtype=None): """ Create a new view of the Series. This function will return a new Series with a view of the same underlying values in memory, optionally reinterpreted with a new data type. The new data type must preserve the same size in bytes as to not ca...

Return the i-th value or values in the Series by location. Parameters ---------- i : int, slice, or sequence of integers Returns ------- scalar (int) or Series (slice, sequence)

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

Repeat elements of a Series. Returns a new Series where each element of the current Series is repeated consecutively a given number of times. Parameters ---------- repeats : int or array of ints The number of repetitions for each element. This should be a ...

def repeat(self, repeats, axis=None): """ Repeat elements of a Series. Returns a new Series where each element of the current Series is repeated consecutively a given number of times. Parameters ---------- repeats : int or array of ints The number of...

Generate a new DataFrame or Series with the index reset. This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation. Parameters ---------- level : int, str, tuple, or list...

def reset_index(self, level=None, drop=False, name=None, inplace=False): """ Generate a new DataFrame or Series with the index reset. This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before anoth...

Render a string representation of the Series. Parameters ---------- buf : StringIO-like, optional Buffer to write to. na_rep : str, optional String representation of NaN to use, default 'NaN'. float_format : one-parameter function, optional Fo...

def to_string(self, buf=None, na_rep='NaN', float_format=None, header=True, index=True, length=False, dtype=False, name=False, max_rows=None): """ Render a string representation of the Series. Parameters ---------- buf : StringIO-like, optiona...

Convert Series to {label -> value} dict or dict-like object. Parameters ---------- into : class, default dict The collections.abc.Mapping subclass to use as the return object. Can be the actual class or an empty instance of the mapping type you want. If you ...

def to_dict(self, into=dict): """ Convert Series to {label -> value} dict or dict-like object. Parameters ---------- into : class, default dict The collections.abc.Mapping subclass to use as the return object. Can be the actual class or an empty ...

Convert Series to DataFrame. Parameters ---------- name : object, default None The passed name should substitute for the series name (if it has one). Returns ------- DataFrame DataFrame representation of Series. Examples ...

def to_frame(self, name=None): """ Convert Series to DataFrame. Parameters ---------- name : object, default None The passed name should substitute for the series name (if it has one). Returns ------- DataFrame DataFra...

Convert Series to SparseSeries. Parameters ---------- kind : {'block', 'integer'}, default 'block' fill_value : float, defaults to NaN (missing) Value to use for filling NaN values. Returns ------- SparseSeries Sparse representation of th...

def to_sparse(self, kind='block', fill_value=None): """ Convert Series to SparseSeries. Parameters ---------- kind : {'block', 'integer'}, default 'block' fill_value : float, defaults to NaN (missing) Value to use for filling NaN values. Returns ...

Set the Series name. Parameters ---------- name : str inplace : bool whether to modify `self` directly or return a copy

def _set_name(self, name, inplace=False): """ Set the Series name. Parameters ---------- name : str inplace : bool whether to modify `self` directly or return a copy """ inplace = validate_bool_kwarg(inplace, 'inplace') ser = self if i...

Return number of non-NA/null observations in the Series. Parameters ---------- level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series. Returns ------- i...

def count(self, level=None): """ Return number of non-NA/null observations in the Series. Parameters ---------- level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller S...

Return Series with duplicate values removed. Parameters ---------- keep : {'first', 'last', ``False``}, default 'first' - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - ``False`` : Drop ...

def drop_duplicates(self, keep='first', inplace=False): """ Return Series with duplicate values removed. Parameters ---------- keep : {'first', 'last', ``False``}, default 'first' - 'first' : Drop duplicates except for the first occurrence. - 'last' : Dro...

Return the row label of the minimum value. If multiple values equal the minimum, the first row label with that value is returned. Parameters ---------- skipna : bool, default True Exclude NA/null values. If the entire Series is NA, the result will be NA....

def idxmin(self, axis=0, skipna=True, *args, **kwargs): """ Return the row label of the minimum value. If multiple values equal the minimum, the first row label with that value is returned. Parameters ---------- skipna : bool, default True Exclude NA...

Return the row label of the maximum value. If multiple values equal the maximum, the first row label with that value is returned. Parameters ---------- skipna : bool, default True Exclude NA/null values. If the entire Series is NA, the result will be NA....

def idxmax(self, axis=0, skipna=True, *args, **kwargs): """ Return the row label of the maximum value. If multiple values equal the maximum, the first row label with that value is returned. Parameters ---------- skipna : bool, default True Exclude NA...

Round each value in a Series to the given number of decimals. Parameters ---------- decimals : int Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. Retu...

def round(self, decimals=0, *args, **kwargs): """ Round each value in a Series to the given number of decimals. Parameters ---------- decimals : int Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of ...

Return value at the given quantile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} .. versionadded:: 0.18.0 This ...

def quantile(self, q=0.5, interpolation='linear'): """ Return value at the given quantile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute. interpolation : {'linear', 'lower', 'higher', 'midpoi...

Compute correlation with `other` Series, excluding missing values. Parameters ---------- other : Series Series with which to compute the correlation. method : {'pearson', 'kendall', 'spearman'} or callable * pearson : standard correlation coefficient ...

def corr(self, other, method='pearson', min_periods=None): """ Compute correlation with `other` Series, excluding missing values. Parameters ---------- other : Series Series with which to compute the correlation. method : {'pearson', 'kendall', 'spearman'} or...

Compute covariance with Series, excluding missing values. Parameters ---------- other : Series Series with which to compute the covariance. min_periods : int, optional Minimum number of observations needed to have a valid result. Returns ------- ...

def cov(self, other, min_periods=None): """ Compute covariance with Series, excluding missing values. Parameters ---------- other : Series Series with which to compute the covariance. min_periods : int, optional Minimum number of observations need...

First discrete difference of element. Calculates the difference of a Series element compared with another element in the Series (default is element in previous row). Parameters ---------- periods : int, default 1 Periods to shift for calculating difference, accepts ...

def diff(self, periods=1): """ First discrete difference of element. Calculates the difference of a Series element compared with another element in the Series (default is element in previous row). Parameters ---------- periods : int, default 1 Period...

Compute the dot product between the Series and the columns of other. This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array. It can also be called using `self @ other` in Python ...

def dot(self, other): """ Compute the dot product between the Series and the columns of other. This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array. It can also...

Concatenate two or more Series. Parameters ---------- to_append : Series or list/tuple of Series Series to append with self. ignore_index : bool, default False If True, do not use the index labels. .. versionadded:: 0.19.0 verify_integrity :...

def append(self, to_append, ignore_index=False, verify_integrity=False): """ Concatenate two or more Series. Parameters ---------- to_append : Series or list/tuple of Series Series to append with self. ignore_index : bool, default False If True, d...

Perform generic binary operation with optional fill value. Parameters ---------- other : Series func : binary operator fill_value : float or object Value to substitute for NA/null values. If both Series are NA in a location, the result will be NA regardle...

def _binop(self, other, func, level=None, fill_value=None): """ Perform generic binary operation with optional fill value. Parameters ---------- other : Series func : binary operator fill_value : float or object Value to substitute for NA/null values....

Combine the Series with a Series or scalar according to `func`. Combine the Series and `other` using `func` to perform elementwise selection for combined Series. `fill_value` is assumed when value is missing at some index from one of the two objects being combined. Parameters ...

def combine(self, other, func, fill_value=None): """ Combine the Series with a Series or scalar according to `func`. Combine the Series and `other` using `func` to perform elementwise selection for combined Series. `fill_value` is assumed when value is missing at some index ...

Combine Series values, choosing the calling Series's values first. Parameters ---------- other : Series The value(s) to be combined with the `Series`. Returns ------- Series The result of combining the Series with the other object. See A...

def combine_first(self, other): """ Combine Series values, choosing the calling Series's values first. Parameters ---------- other : Series The value(s) to be combined with the `Series`. Returns ------- Series The result of combin...

Modify Series in place using non-NA values from passed Series. Aligns on index. Parameters ---------- other : Series Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, 5, 6])) >>> s 0 4 1 5 2 ...

def update(self, other): """ Modify Series in place using non-NA values from passed Series. Aligns on index. Parameters ---------- other : Series Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s.update(pd.Series([4, 5, 6])) >>...

Sort by the values. Sort a Series in ascending or descending order by some criterion. Parameters ---------- axis : {0 or 'index'}, default 0 Axis to direct sorting. The value 'index' is accepted for compatibility with DataFrame.sort_values. ascen...

def sort_values(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last'): """ Sort by the values. Sort a Series in ascending or descending order by some criterion. Parameters ---------- axis : {0 or 'index'}, default...

Sort Series by index labels. Returns a new Series sorted by label if `inplace` argument is ``False``, otherwise updates the original series and returns None. Parameters ---------- axis : int, default 0 Axis to direct sorting. This can only be 0 for Series. l...

def sort_index(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True): """ Sort Series by index labels. Returns a new Series sorted by label if `inplace` argument is ``False``, otherwise updates the original...

Override ndarray.argsort. Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values. Parameters ---------- axis : int Has no effect but is accepted for compatibility with numpy. kind : {'mergesort', 'quicksort', 'he...

def argsort(self, axis=0, kind='quicksort', order=None): """ Override ndarray.argsort. Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values. Parameters ---------- axis : int Has no effect but is accepte...

Return the largest `n` elements. Parameters ---------- n : int, default 5 Return this many descending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that cannot all fit in a Series of `n` elements: ...

def nlargest(self, n=5, keep='first'): """ Return the largest `n` elements. Parameters ---------- n : int, default 5 Return this many descending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that c...

Return the smallest `n` elements. Parameters ---------- n : int, default 5 Return this many ascending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that cannot all fit in a Series of `n` elements: ...

def nsmallest(self, n=5, keep='first'): """ Return the smallest `n` elements. Parameters ---------- n : int, default 5 Return this many ascending sorted values. keep : {'first', 'last', 'all'}, default 'first' When there are duplicate values that ...

Swap levels i and j in a MultiIndex. Parameters ---------- i, j : int, str (can be mixed) Level of index to be swapped. Can pass level name as string. Returns ------- Series Series with levels swapped in MultiIndex. .. versionchanged:: 0...

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

Rearrange index levels using input order. May not drop or duplicate levels. Parameters ---------- order : list of int representing new level order (reference level by number or key) Returns ------- type of caller (new object)

def reorder_levels(self, order): """ Rearrange index levels using input order. May not drop or duplicate levels. Parameters ---------- order : list of int representing new level order (reference level by number or key) Returns ------- ...

Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. Parameters ---------- func : function Python function or NumPy ufunc to apply. convert_dtype : bool,...

def apply(self, func, convert_dtype=True, args=(), **kwds): """ Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. Parameters ---------- func : function ...

Perform a reduction operation. If we have an ndarray as a value, then simply perform the operation, otherwise delegate to the object.

def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): """ Perform a reduction operation. If we have an ndarray as a value, then simply perform the operation, otherwise delegate to the object. """ delegate = self._v...

Conform Series to new index with optional filling logic. .. deprecated:: 0.21.0 Use ``Series.reindex`` instead.

def reindex_axis(self, labels, axis=0, **kwargs): """ Conform Series to new index with optional filling logic. .. deprecated:: 0.21.0 Use ``Series.reindex`` instead. """ # for compatibility with higher dims if axis != 0: raise ValueError("cannot r...

Alter Series index labels or name. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` with a scalar value. See the :ref:`user guide <basics....

def rename(self, index=None, **kwargs): """ Alter Series index labels or name. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` wit...

Return the memory usage of the Series. The memory usage can optionally include the contribution of the index and of elements of `object` dtype. Parameters ---------- index : bool, default True Specifies whether to include the memory usage of the Series index. ...

def memory_usage(self, index=True, deep=False): """ Return the memory usage of the Series. The memory usage can optionally include the contribution of the index and of elements of `object` dtype. Parameters ---------- index : bool, default True Speci...

Check whether `values` are contained in Series. Return a boolean Series showing whether each element in the Series matches an element in the passed sequence of `values` exactly. Parameters ---------- values : set or list-like The sequence of values to test. Passing ...

def isin(self, values): """ Check whether `values` are contained in Series. Return a boolean Series showing whether each element in the Series matches an element in the passed sequence of `values` exactly. Parameters ---------- values : set or list-like ...

Return boolean Series equivalent to left <= series <= right. This function returns a boolean vector containing `True` wherever the corresponding Series element is between the boundary values `left` and `right`. NA values are treated as `False`. Parameters ---------- lef...

def between(self, left, right, inclusive=True): """ Return boolean Series equivalent to left <= series <= right. This function returns a boolean vector containing `True` wherever the corresponding Series element is between the boundary values `left` and `right`. NA values are tr...

Read CSV file. .. deprecated:: 0.21.0 Use :func:`pandas.read_csv` instead. It is preferable to use the more powerful :func:`pandas.read_csv` for most general purposes, but ``from_csv`` makes for an easy roundtrip to and from a file (the exact counterpart of ``to_csv...

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

Return a new Series with missing values removed. See the :ref:`User Guide <missing_data>` for more on which values are considered missing, and how to work with missing data. Parameters ---------- axis : {0 or 'index'}, default 0 There is only one axis to drop values...

def dropna(self, axis=0, inplace=False, **kwargs): """ Return a new Series with missing values removed. See the :ref:`User Guide <missing_data>` for more on which values are considered missing, and how to work with missing data. Parameters ---------- axis : {0 o...

Return Series without null values. .. deprecated:: 0.23.0 Use :meth:`Series.dropna` instead.

def valid(self, inplace=False, **kwargs): """ Return Series without null values. .. deprecated:: 0.23.0 Use :meth:`Series.dropna` instead. """ warnings.warn("Method .valid will be removed in a future version. " "Use .dropna instead.", FutureWarn...

Cast to DatetimeIndex of Timestamps, at *beginning* of period. Parameters ---------- freq : str, default frequency of PeriodIndex Desired frequency. how : {'s', 'e', 'start', 'end'} Convention for converting period to timestamp; start of period vs. en...

def to_timestamp(self, freq=None, how='start', copy=True): """ Cast to DatetimeIndex of Timestamps, at *beginning* of period. Parameters ---------- freq : str, default frequency of PeriodIndex Desired frequency. how : {'s', 'e', 'start', 'end'} Co...

Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). Parameters ---------- freq : str, default None Frequency associated with the PeriodIndex. copy : bool, default True Whether or not to return a cop...

def to_period(self, freq=None, copy=True): """ Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). Parameters ---------- freq : str, default None Frequency associated with the PeriodIndex. copy ...

Convert argument to a numeric type. The default return dtype is `float64` or `int64` depending on the data supplied. Use the `downcast` parameter to obtain other dtypes. Please note that precision loss may occur if really large numbers are passed in. Due to the internal limitations of `ndarray`, i...

def to_numeric(arg, errors='raise', downcast=None): """ Convert argument to a numeric type. The default return dtype is `float64` or `int64` depending on the data supplied. Use the `downcast` parameter to obtain other dtypes. Please note that precision loss may occur if really large numbers ...

Create a 0-dim ndarray containing the fill value Parameters ---------- arr : SparseArray Returns ------- fill_value : ndarray 0-dim ndarray with just the fill value. Notes ----- coerce fill_value to arr dtype if possible int64 SparseArray can have NaN as fill_value if ...

def _get_fill(arr: ABCSparseArray) -> np.ndarray: """ Create a 0-dim ndarray containing the fill value Parameters ---------- arr : SparseArray Returns ------- fill_value : ndarray 0-dim ndarray with just the fill value. Notes ----- coerce fill_value to arr dtype if...

Perform a binary operation between two arrays. Parameters ---------- left : Union[SparseArray, ndarray] right : Union[SparseArray, ndarray] op : Callable The binary operation to perform name str Name of the callable. Returns ------- SparseArray

def _sparse_array_op( left: ABCSparseArray, right: ABCSparseArray, op: Callable, name: str ) -> Any: """ Perform a binary operation between two arrays. Parameters ---------- left : Union[SparseArray, ndarray] right : Union[SparseArray, ndarray] op : Callable ...

wrap op result to have correct dtype

def _wrap_result(name, data, sparse_index, fill_value, dtype=None): """ wrap op result to have correct dtype """ if name.startswith('__'): # e.g. __eq__ --> eq name = name[2:-2] if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'): dtype = np.bool fill_value = lib.item_from_...

array must be SparseSeries or SparseArray

def _maybe_to_sparse(array): """ array must be SparseSeries or SparseArray """ if isinstance(array, ABCSparseSeries): array = array.values.copy() return array

return an ndarray for our input, in a platform independent manner

def _sanitize_values(arr): """ return an ndarray for our input, in a platform independent manner """ if hasattr(arr, 'values'): arr = arr.values else: # scalar if is_scalar(arr): arr = [arr] # ndarray if isinstance(arr, np.ndarray): ...

Convert ndarray to sparse format Parameters ---------- arr : ndarray kind : {'block', 'integer'} fill_value : NaN or another value dtype : np.dtype, optional copy : bool, default False Returns ------- (sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar)

def make_sparse(arr, kind='block', fill_value=None, dtype=None, copy=False): """ Convert ndarray to sparse format Parameters ---------- arr : ndarray kind : {'block', 'integer'} fill_value : NaN or another value dtype : np.dtype, optional copy : bool, default False Returns ...

The percent of non- ``fill_value`` points, as decimal. Examples -------- >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0) >>> s.density 0.6

def density(self): """ The percent of non- ``fill_value`` points, as decimal. Examples -------- >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0) >>> s.density 0.6 """ r = float(self.sp_index.npoints) / float(self.sp_index.length) return ...

Fill missing values with `value`. Parameters ---------- value : scalar, optional method : str, optional .. warning:: Using 'method' will result in high memory use, as all `fill_value` methods will be converted to an in-memory nd...

def fillna(self, value=None, method=None, limit=None): """ Fill missing values with `value`. Parameters ---------- value : scalar, optional method : str, optional .. warning:: Using 'method' will result in high memory use, as a...

Get the location of the first missing value. Returns ------- int

def _first_fill_value_loc(self): """ Get the location of the first missing value. Returns ------- int """ if len(self) == 0 or self.sp_index.npoints == len(self): return -1 indices = self.sp_index.to_int_index().indices if not len(ind...

Returns a Series containing counts of unique values. Parameters ---------- dropna : boolean, default True Don't include counts of NaN, even if NaN is in sp_values. Returns ------- counts : Series

def value_counts(self, dropna=True): """ Returns a Series containing counts of unique values. Parameters ---------- dropna : boolean, default True Don't include counts of NaN, even if NaN is in sp_values. Returns ------- counts : Series ...

Change the dtype of a SparseArray. The output will always be a SparseArray. To convert to a dense ndarray with a certain dtype, use :meth:`numpy.asarray`. Parameters ---------- dtype : np.dtype or ExtensionDtype For SparseDtype, this changes the dtype of ...

def astype(self, dtype=None, copy=True): """ Change the dtype of a SparseArray. The output will always be a SparseArray. To convert to a dense ndarray with a certain dtype, use :meth:`numpy.asarray`. Parameters ---------- dtype : np.dtype or ExtensionDtype ...

Map categories using input correspondence (dict, Series, or function). Parameters ---------- mapper : dict, Series, callable The correspondence from old values to new. Returns ------- SparseArray The output array will have the same density as the...

def map(self, mapper): """ Map categories using input correspondence (dict, Series, or function). Parameters ---------- mapper : dict, Series, callable The correspondence from old values to new. Returns ------- SparseArray The out...

Tests whether all elements evaluate True Returns ------- all : bool See Also -------- numpy.all

def all(self, axis=None, *args, **kwargs): """ Tests whether all elements evaluate True Returns ------- all : bool See Also -------- numpy.all """ nv.validate_all(args, kwargs) values = self.sp_values if len(values) != l...

Tests whether at least one of elements evaluate True Returns ------- any : bool See Also -------- numpy.any

def any(self, axis=0, *args, **kwargs): """ Tests whether at least one of elements evaluate True Returns ------- any : bool See Also -------- numpy.any """ nv.validate_any(args, kwargs) values = self.sp_values if len(val...

Sum of non-NA/null values Returns ------- sum : float

def sum(self, axis=0, *args, **kwargs): """ Sum of non-NA/null values Returns ------- sum : float """ nv.validate_sum(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() if self._null_fill_value: return s...

Cumulative sum of non-NA/null values. When performing the cumulative summation, any non-NA/null values will be skipped. The resulting SparseArray will preserve the locations of NaN values, but the fill value will be `np.nan` regardless. Parameters ---------- axis : int ...

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 SparseArray will preserve the locations of NaN values, but the fill value will be `np.nan` regardl...

Mean of non-NA/null values Returns ------- mean : float

def mean(self, axis=0, *args, **kwargs): """ Mean of non-NA/null values Returns ------- mean : float """ nv.validate_mean(args, kwargs) valid_vals = self._valid_sp_values sp_sum = valid_vals.sum() ct = len(valid_vals) if self._nul...

Tokenize a Python source code string. Parameters ---------- source : str A Python source code string

def tokenize_string(source): """Tokenize a Python source code string. Parameters ---------- source : str A Python source code string """ line_reader = StringIO(source).readline token_generator = tokenize.generate_tokens(line_reader) # Loop over all tokens till a backtick (`) is...

Replace ``&`` with ``and`` and ``|`` with ``or`` so that bitwise precedence is changed to boolean precedence. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple of int, str Either the inpu...

def _replace_booleans(tok): """Replace ``&`` with ``and`` and ``|`` with ``or`` so that bitwise precedence is changed to boolean precedence. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple ...

Replace local variables with a syntactically valid name. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple of int, str Either the input or token or the replacement values Notes -----...

def _replace_locals(tok): """Replace local variables with a syntactically valid name. Parameters ---------- tok : tuple of int, str ints correspond to the all caps constants in the tokenize module Returns ------- t : tuple of int, str Either the input or token or the replac...

Clean up a column name if surrounded by backticks. Backtick quoted string are indicated by a certain tokval value. If a string is a backtick quoted token it will processed by :func:`_remove_spaces_column_name` so that the parser can find this string when the query is executed. See also :meth:`NDFra...

def _clean_spaces_backtick_quoted_names(tok): """Clean up a column name if surrounded by backticks. Backtick quoted string are indicated by a certain tokval value. If a string is a backtick quoted token it will processed by :func:`_remove_spaces_column_name` so that the parser can find this string ...

Compose a collection of tokenization functions Parameters ---------- source : str A Python source code string f : callable This takes a tuple of (toknum, tokval) as its argument and returns a tuple with the same structure but possibly different elements. Defaults to the ...

def _preparse(source, f=_compose(_replace_locals, _replace_booleans, _rewrite_assign, _clean_spaces_backtick_quoted_names)): """Compose a collection of tokenization functions Parameters ---------- source : str A Python source cod...

Filter out AST nodes that are subclasses of ``superclass``.

def _filter_nodes(superclass, all_nodes=_all_nodes): """Filter out AST nodes that are subclasses of ``superclass``.""" node_names = (node.__name__ for node in all_nodes if issubclass(node, superclass)) return frozenset(node_names)