BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def max(self, axis=None, skipna=True): """ Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Returns ------- scalar ...
def argmax(self, axis=None, skipna=True): """ Return an ndarray of the maximum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True See Also -------- numpy.ndarra...
def min(self, axis=None, skipna=True): """ Return the minimum value of the Index. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- scalar Minimum va...
def argmin(self, axis=None, skipna=True): """ Return a ndarray of the minimum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- numpy.ndarray ...
def tolist(self): """ Return a list of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns ------- list See Also -------- n...
def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds): """ perform the reduction type operation if we can """ func = getattr(self, name, None) if func is None: raise TypeError("{klass} cannot perform the operation {op}".format( ...
def _map_values(self, mapper, na_action=None): """ An internal function that maps values using the input correspondence (which can be a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series The input correspondence object ...
def value_counts(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True): """ Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. ...
def nunique(self, dropna=True): """ Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : bool, default True Don't include NaN in the count. Returns ------- int See Als...
def memory_usage(self, deep=False): """ Memory usage of the values Parameters ---------- deep : bool Introspect the data deeply, interrogate `object` dtypes for system-level memory consumption Returns ------- bytes used S...
def _expand_user(filepath_or_buffer): """Return the argument with an initial component of ~ or ~user replaced by that user's home directory. Parameters ---------- filepath_or_buffer : object to be converted if possible Returns ------- expanded_filepath_or_buffer : an expanded filepa...
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...
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...
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. ...
def _get_handle(path_or_buf, mode, encoding=None, compression=None, memory_map=False, is_text=True): """ Get file handle for given path/buffer and mode. Parameters ---------- path_or_buf : a path (str) or buffer mode : str mode to open path_or_buf with encodi...
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...
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...
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...
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 : ...
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 ...
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...
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...
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 ------ ...
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 ...
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`. ...
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...
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...
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)
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...
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))
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()
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) ...
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...
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...
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...
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...
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 ...
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 :...
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__ = "__{...
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,...
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...
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, ...
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)'" ...
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...
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...
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...
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...
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...
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...
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...
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 ...
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...
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 ...
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...
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...
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...
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...
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...
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 ...
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...
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...
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...
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...
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...
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...
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....
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 ...
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...
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])) >>...
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...
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...
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...
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...
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 ...
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...
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 ------- ...
def map(self, arg, na_action=None): """ Map values of Series according to input correspondence. Used for substituting each value in a Series with another value, that may be derived from a function, a ``dict`` or a :class:`Series`. Parameters ---------- a...
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 ...
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...
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...
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...
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...
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 ...
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...
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....
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...
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...
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...
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 ...
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 ...
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...
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 ...
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_...
def _maybe_to_sparse(array): """ array must be SparseSeries or SparseArray """ if isinstance(array, ABCSparseSeries): array = array.values.copy() return array
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): ...
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 ...
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 ...
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...
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...
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 ...

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

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

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

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

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

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

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

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

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

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

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

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...

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...

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. ...

def _get_handle(path_or_buf, mode, encoding=None, compression=None, memory_map=False, is_text=True): """ Get file handle for given path/buffer and mode. Parameters ---------- path_or_buf : a path (str) or buffer mode : str mode to open path_or_buf with encodi...

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...

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...

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...

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 : ...

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 ...

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...

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...

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 ------ ...

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 ...

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`. ...

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...

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...

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)

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...

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))

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()

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) ...

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...

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...

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...

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...

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 ...

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 :...

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__ = "__{...

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,...

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...

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, ...

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)'" ...

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...

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...

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...

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...

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...

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...

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...

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 ...

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...

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 ...

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...

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...

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...

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...

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...

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 ...

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...

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...

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...

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...

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...

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...

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....

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 ...

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...

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])) >>...

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...

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...

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...

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...

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 ...

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...

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 ------- ...

def map(self, arg, na_action=None): """ Map values of Series according to input correspondence. Used for substituting each value in a Series with another value, that may be derived from a function, a ``dict`` or a :class:`Series`. Parameters ---------- a...

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 ...

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...

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...

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...

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...

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 ...

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...

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....

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...

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...

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...

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 ...

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 ...

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...

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 ...

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_...

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

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): ...

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 ...

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 ...

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...

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...

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 ...