text_prompt stringlengths 157 13.1k | code_prompt stringlengths 7 19.8k ⌀ |
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def as_matrix(self, columns=None):
""" Convert the frame to its Numpy-array representation. .. deprecated:: 0.23.0 Use :meth:`DataFrame.values` instead. Paramete... |
warnings.warn("Method .as_matrix will be removed in a future version. "
"Use .values instead.", FutureWarning, stacklevel=2)
self._consolidate_inplace()
return self._data.as_array(transpose=self._AXIS_REVERSED,
items=columns) |
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def values(self):
""" Return a Numpy representation of the DataFrame. .. warning:: We recommend using :meth:`DataFrame.to_numpy` instead. Only the values in the ... |
self._consolidate_inplace()
return self._data.as_array(transpose=self._AXIS_REVERSED) |
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def get_ftype_counts(self):
""" Return counts of unique ftypes in this object. .. deprecated:: 0.23.0 This is useful for SparseDataFrame or for DataFrames contai... |
warnings.warn("get_ftype_counts is deprecated and will "
"be removed in a future version",
FutureWarning, stacklevel=2)
from pandas import Series
return Series(self._data.get_ftype_counts()) |
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def dtypes(self):
""" Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's... |
from pandas import Series
return Series(self._data.get_dtypes(), index=self._info_axis,
dtype=np.object_) |
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def as_blocks(self, copy=True):
""" Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype. .. deprecated:: 0.21.0 NOTE: the... |
warnings.warn("as_blocks is deprecated and will "
"be removed in a future version",
FutureWarning, stacklevel=2)
return self._to_dict_of_blocks(copy=copy) |
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def _to_dict_of_blocks(self, copy=True):
""" Return a dict of dtype -> Constructor Types that each is a homogeneous dtype. Internal ONLY """ |
return {k: self._constructor(v).__finalize__(self)
for k, v, in self._data.to_dict(copy=copy).items()} |
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def astype(self, dtype, copy=True, errors='raise', **kwargs):
""" Cast a pandas object to a specified dtype ``dtype``. Parameters dtype : data type, or dict of c... |
if is_dict_like(dtype):
if self.ndim == 1: # i.e. Series
if len(dtype) > 1 or self.name not in dtype:
raise KeyError('Only the Series name can be used for '
'the key in Series dtype mappings.')
new_type = dtype[... |
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def copy(self, deep=True):
""" Make a copy of this object's indices and data. When ``deep=True`` (default), a new object will be created with a copy of the calli... |
data = self._data.copy(deep=deep)
return self._constructor(data).__finalize__(self) |
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def _convert(self, datetime=False, numeric=False, timedelta=False, coerce=False, copy=True):
""" Attempt to infer better dtype for object columns Parameters date... |
return self._constructor(
self._data.convert(datetime=datetime, numeric=numeric,
timedelta=timedelta, coerce=coerce,
copy=copy)).__finalize__(self) |
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def convert_objects(self, convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True):
""" Attempt to infer better dtype for object columns. .... |
msg = ("convert_objects is deprecated. To re-infer data dtypes for "
"object columns, use {klass}.infer_objects()\nFor all "
"other conversions use the data-type specific converters "
"pd.to_datetime, pd.to_timedelta and pd.to_numeric."
).format(klas... |
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def infer_objects(self):
""" Attempt to infer better dtypes for object columns. Attempts soft conversion of object-dtyped columns, leaving non-object and unconve... |
# numeric=False necessary to only soft convert;
# python objects will still be converted to
# native numpy numeric types
return self._constructor(
self._data.convert(datetime=True, numeric=False,
timedelta=True, coerce=False,
... |
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def clip_upper(self, threshold, axis=None, inplace=False):
""" Trim values above a given threshold. .. deprecated:: 0.24.0 Use clip(upper=threshold) instead. Ele... |
warnings.warn('clip_upper(threshold) is deprecated, '
'use clip(upper=threshold) instead',
FutureWarning, stacklevel=2)
return self._clip_with_one_bound(threshold, method=self.le,
axis=axis, inplace=inplace) |
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def clip_lower(self, threshold, axis=None, inplace=False):
""" Trim values below a given threshold. .. deprecated:: 0.24.0 Use clip(lower=threshold) instead. Ele... |
warnings.warn('clip_lower(threshold) is deprecated, '
'use clip(lower=threshold) instead',
FutureWarning, stacklevel=2)
return self._clip_with_one_bound(threshold, method=self.ge,
axis=axis, inplace=inplace) |
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def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs):
""" Group DataFrame or Series... |
from pandas.core.groupby.groupby import groupby
if level is None and by is None:
raise TypeError("You have to supply one of 'by' and 'level'")
axis = self._get_axis_number(axis)
return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
so... |
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def asfreq(self, freq, method=None, how=None, normalize=False, fill_value=None):
""" Convert TimeSeries to specified frequency. Optionally provide filling method... |
from pandas.core.resample import asfreq
return asfreq(self, freq, method=method, how=how, normalize=normalize,
fill_value=fill_value) |
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def resample(self, rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, le... |
from pandas.core.resample import (resample,
_maybe_process_deprecations)
axis = self._get_axis_number(axis)
r = resample(self, freq=rule, label=label, closed=closed,
axis=axis, kind=kind, loffset=loffset,
conve... |
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def first(self, offset):
""" Convenience method for subsetting initial periods of time series data based on a date offset. Parameters offset : string, DateOffset... |
if not isinstance(self.index, DatetimeIndex):
raise TypeError("'first' only supports a DatetimeIndex index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
end_date = end = self.index[0] + offset
# Tick-like, e.g. 3 weeks
if no... |
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def last(self, offset):
""" Convenience method for subsetting final periods of time series data based on a date offset. Parameters offset : string, DateOffset, d... |
if not isinstance(self.index, DatetimeIndex):
raise TypeError("'last' only supports a DatetimeIndex index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
start_date = self.index[-1] - offset
start = self.index.searchsorted(start_date,... |
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def slice_shift(self, periods=1, axis=0):
""" Equivalent to `shift` without copying data. The shifted data will not include the dropped periods and the shifted a... |
if periods == 0:
return self
if periods > 0:
vslicer = slice(None, -periods)
islicer = slice(periods, None)
else:
vslicer = slice(-periods, None)
islicer = slice(None, periods)
new_obj = self._slice(vslicer, axis=axis)
... |
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def tshift(self, periods=1, freq=None, axis=0):
""" Shift the time index, using the index's frequency if available. Parameters periods : int Number of periods to... |
index = self._get_axis(axis)
if freq is None:
freq = getattr(index, 'freq', None)
if freq is None:
freq = getattr(index, 'inferred_freq', None)
if freq is None:
msg = 'Freq was not given and was not set in the index'
raise ValueError(ms... |
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def truncate(self, before=None, after=None, axis=None, copy=True):
""" Truncate a Series or DataFrame before and after some index value. This is a useful shortha... |
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
# GH 17935
# Check that index is sorted
if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
raise ValueError("truncate ... |
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def tz_convert(self, tz, axis=0, level=None, copy=True):
""" Convert tz-aware axis to target time zone. Parameters tz : string or pytz.timezone object axis : the... |
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
def _tz_convert(ax, tz):
if not hasattr(ax, 'tz_convert'):
if len(ax) > 0:
ax_name = self._get_axis_name(axis)
raise TypeError('%s is not a valid DatetimeIndex or '
... |
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def tz_localize(self, tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise'):
""" Localize tz-naive index of a Series or DataFrame to target ... |
nonexistent_options = ('raise', 'NaT', 'shift_forward',
'shift_backward')
if nonexistent not in nonexistent_options and not isinstance(
nonexistent, timedelta):
raise ValueError("The nonexistent argument must be one of 'raise',"
... |
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def _add_series_or_dataframe_operations(cls):
""" Add the series or dataframe only operations to the cls; evaluate the doc strings again. """ |
from pandas.core import window as rwindow
@Appender(rwindow.rolling.__doc__)
def rolling(self, window, min_periods=None, center=False,
win_type=None, on=None, axis=0, closed=None):
axis = self._get_axis_number(axis)
return rwindow.rolling(self, wind... |
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def _find_valid_index(self, how):
""" Retrieves the index of the first valid value. Parameters how : {'first', 'last'} Use this parameter to change between the f... |
assert how in ['first', 'last']
if len(self) == 0: # early stop
return None
is_valid = ~self.isna()
if self.ndim == 2:
is_valid = is_valid.any(1) # reduce axis 1
if how == 'first':
idxpos = is_valid.values[::].argmax()
if how == ... |
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def _reset_cache(self, key=None):
""" Reset cached properties. If ``key`` is passed, only clears that key. """ |
if getattr(self, '_cache', None) is None:
return
if key is None:
self._cache.clear()
else:
self._cache.pop(key, None) |
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def _shallow_copy(self, obj=None, obj_type=None, **kwargs):
""" return a new object with the replacement attributes """ |
if obj is None:
obj = self._selected_obj.copy()
if obj_type is None:
obj_type = self._constructor
if isinstance(obj, obj_type):
obj = obj.obj
for attr in self._attributes:
if attr not in kwargs:
kwargs[attr] = getattr(self,... |
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def itemsize(self):
""" Return the size of the dtype of the item of the underlying data. .. deprecated:: 0.23.0 """ |
warnings.warn("{obj}.itemsize is deprecated and will be removed "
"in a future version".format(obj=type(self).__name__),
FutureWarning, stacklevel=2)
return self._ndarray_values.itemsize |
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def base(self):
""" Return the base object if the memory of the underlying data is shared. .. deprecated:: 0.23.0 """ |
warnings.warn("{obj}.base is deprecated and will be removed "
"in a future version".format(obj=type(self).__name__),
FutureWarning, stacklevel=2)
return self.values.base |
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def array(self) -> ExtensionArray: """ The ExtensionArray of the data backing this Series or Index. .. versionadded:: 0.24.0 Returns ------- ExtensionArray An Ext... |
result = self._values
if is_datetime64_ns_dtype(result.dtype):
from pandas.arrays import DatetimeArray
result = DatetimeArray(result)
elif is_timedelta64_ns_dtype(result.dtype):
from pandas.arrays import TimedeltaArray
result = TimedeltaArray(res... |
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def to_numpy(self, dtype=None, copy=False):
""" A NumPy ndarray representing the values in this Series or Index. .. versionadded:: 0.24.0 Parameters dtype : str ... |
if is_datetime64tz_dtype(self.dtype) and dtype is None:
# note: this is going to change very soon.
# I have a WIP PR making this unnecessary, but it's
# a bit out of scope for the DatetimeArray PR.
dtype = "object"
result = np.asarray(self._values, dtype... |
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def _ndarray_values(self) -> np.ndarray: """ The data as an ndarray, possibly losing information. The expectation is that this is cheap to compute, and is primari... |
if is_extension_array_dtype(self):
return self.array._ndarray_values
return self.values |
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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 ar... |
nv.validate_minmax_axis(axis)
return nanops.nanmax(self._values, skipna=skipna) |
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def argmax(self, axis=None, skipna=True):
""" Return an ndarray of the maximum argument indexer. Parameters axis : {None} Dummy argument for consistency with Ser... |
nv.validate_minmax_axis(axis)
return nanops.nanargmax(self._values, skipna=skipna) |
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def min(self, axis=None, skipna=True):
""" Return the minimum value of the Index. Parameters axis : {None} Dummy argument for consistency with Series skipna : bo... |
nv.validate_minmax_axis(axis)
return nanops.nanmin(self._values, skipna=skipna) |
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def argmin(self, axis=None, skipna=True):
""" Return a ndarray of the minimum argument indexer. Parameters axis : {None} Dummy argument for consistency with Seri... |
nv.validate_minmax_axis(axis)
return nanops.nanargmin(self._values, skipna=skipna) |
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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... |
if is_datetimelike(self._values):
return [com.maybe_box_datetimelike(x) for x in self._values]
elif is_extension_array_dtype(self._values):
return list(self._values)
else:
return self._values.tolist() |
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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(
klass=self.__class__.__name__, op=name))
return func(skipna=skipna, **kwds) |
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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 i... |
uniqs = self.unique()
n = len(uniqs)
if dropna and isna(uniqs).any():
n -= 1
return n |
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def memory_usage(self, deep=False):
""" Memory usage of the values Parameters deep : bool Introspect the data deeply, interrogate `object` dtypes for system-leve... |
if hasattr(self.array, 'memory_usage'):
return self.array.memory_usage(deep=deep)
v = self.array.nbytes
if deep and is_object_dtype(self) and not PYPY:
v += lib.memory_usage_of_objects(self.array)
return v |
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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 ---... |
try:
import pathlib
_PATHLIB_INSTALLED = True
except ImportError:
_PATHLIB_INSTALLED = False
try:
from py.path import local as LocalPath
_PY_PATH_INSTALLED = True
except ImportError:
_PY_PATH_INSTALLED = False
if hasattr(filepath_or_buffer, '__fspat... |
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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 buff... |
filepath_or_buffer = _stringify_path(filepath_or_buffer)
if _is_url(filepath_or_buffer):
req = urlopen(filepath_or_buffer)
content_encoding = req.headers.get('Content-Encoding', None)
if content_encoding == 'gzip':
# Override compression based on Content-Encoding header
... |
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def _infer_compression(filepath_or_buffer, compression):
""" Get the compression method for filepath_or_buffer. If compression='infer', the inferred compression ... |
# No compression has been explicitly specified
if compression is None:
return None
# Infer compression
if compression == 'infer':
# Convert all path types (e.g. pathlib.Path) to strings
filepath_or_buffer = _stringify_path(filepath_or_buffer)
if not isinstance(filepath... |
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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)):
return NotImplemented
if _is_convertible_to_td(other) or other is NaT:
try:
othe... |
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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 : Exc... |
if not callable(klass):
raise ValueError("Can only register callables as engines")
engine_name = klass.engine
_writers[engine_name] = klass |
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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... |
index = 0
for c in x.upper().strip():
cp = ord(c)
if cp < ord("A") or cp > ord("Z"):
raise ValueError("Invalid column name: {x}".format(x=x))
index = index * 26 + cp - ord("A") + 1
return index - 1 |
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def _range2cols(areas):
""" Convert comma separated list of column names and ranges to indices. Parameters areas : str A string containing a sequence of column r... |
cols = []
for rng in areas.split(","):
if ":" in rng:
rng = rng.split(":")
cols.extend(lrange(_excel2num(rng[0]), _excel2num(rng[1]) + 1))
else:
cols.append(_excel2num(rng))
return cols |
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def _maybe_convert_usecols(usecols):
""" Convert `usecols` into a compatible format for parsing in `parsers.py`. Parameters usecols : object The use-columns obje... |
if usecols is None:
return usecols
if is_integer(usecols):
warnings.warn(("Passing in an integer for `usecols` has been "
"deprecated. Please pass in a list of int from "
"0 to `usecols` inclusive instead."),
FutureWarning, st... |
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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. Param... |
last = row[0]
for i in range(1, len(row)):
if not control_row[i]:
last = row[i]
if row[i] == '' or row[i] is None:
row[i] = last
else:
control_row[i] = False
last = row[i]
return row, control_row |
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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 ... |
# Pop out header name and fill w/blank.
i = index_col if not is_list_like(index_col) else max(index_col)
header_name = row[i]
header_name = None if header_name == "" else header_name
return header_name, row[:i] + [''] + row[i + 1:] |
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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) |
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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 masquerade as ints when iterating in py3
hexin = x
return hex(hexin) |
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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)) |
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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() |
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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 whe... |
try:
# only look for locals in outer scope
if is_local:
return self.scope[key]
# not a local variable so check in resolvers if we have them
if self.has_resolvers:
return self.resolvers[key]
# if we're here that means ... |
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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 ... |
if self.has_resolvers:
maps = self.resolvers.maps + self.scope.maps
else:
maps = self.scope.maps
maps.append(self.temps)
for mapping in maps:
if old_key in mapping:
mapping[new_key] = new_value
return |
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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 ... |
variables = itertools.product(scopes, stack)
for scope, (frame, _, _, _, _, _) in variables:
try:
d = getattr(frame, 'f_' + scope)
self.scope = self.scope.new_child(d)
finally:
# won't remove it, but DECREF it
# in ... |
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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 more current
# makes sure that we can capture variable scope
stack = inspect.stack()
try:
self._get_vars(stack[:sl], scopes=['locals'])
finally:
... |
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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 = '{name}_{num}_{hex_id}'.format(name=type(value).__name__,
num=self.ntemps,
hex_id=_raw_hex_id(self))
# add to inner most scope
assert name not in self.temps
self.temps[name] = value
... |
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def full_scope(self):
"""Return the full scope for use with passing to engines transparently as a mapping. Returns ------- vars : DeepChainMap All variables in t... |
maps = [self.temps] + self.resolvers.maps + self.scope.maps
return DeepChainMap(*maps) |
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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 f... |
if format is None:
buffer_error_msg = ("If this is a buffer object rather "
"than a string name, you must specify "
"a format string")
filepath_or_buffer = _stringify_path(filepath_or_buffer)
if not isinstance(filepath_or_buffer, str):... |
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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__ = "__{name}__".format(name=converter.__name__)
return wrapper |
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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... |
# Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
# raises KeyError), so we iterate the entire dict, and align
if data:
keys, values = zip(*data.items())
values = list(values)
elif index is not None:
# fastpath for Series(data=None). ... |
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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(.... |
warnings.warn("'from_array' is deprecated and will be removed in a "
"future version. Please use the pd.Series(..) "
"constructor instead.", FutureWarning, stacklevel=2)
if isinstance(arr, ABCSparseArray):
from pandas.core.sparse.series import Spa... |
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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,
(DatetimeIndex, PeriodIndex, TimedeltaIndex)):
try:
labels = DatetimeIndex(lab... |
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def asobject(self):
""" Return object Series which contains boxed values. .. deprecated :: 0.23.0 Use ``astype(object)`` instead. *this is an internal non-public... |
warnings.warn("'asobject' is deprecated. Use 'astype(object)'"
" instead", FutureWarning, stacklevel=2)
return self.astype(object).values |
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def compress(self, condition, *args, **kwargs):
""" Return selected slices of an array along given axis as a Series. .. deprecated:: 0.24.0 See Also -------- num... |
msg = ("Series.compress(condition) is deprecated. "
"Use 'Series[condition]' or "
"'np.asarray(series).compress(condition)' instead.")
warnings.warn(msg, FutureWarning, stacklevel=2)
nv.validate_compress(args, kwargs)
return self[condition] |
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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, opti... |
return self._constructor(self._values.view(dtype),
index=self.index).__finalize__(self) |
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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 ------- scal... |
try:
# dispatch to the values if we need
values = self._values
if isinstance(values, np.ndarray):
return libindex.get_value_at(values, i)
else:
return values[i]
except IndexError:
raise
except Exception... |
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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 ... |
nv.validate_repeat(tuple(), dict(axis=axis))
new_index = self.index.repeat(repeats)
new_values = self._values.repeat(repeats)
return self._constructor(new_values,
index=new_index).__finalize__(self) |
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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 ind... |
inplace = validate_bool_kwarg(inplace, 'inplace')
if drop:
new_index = ibase.default_index(len(self))
if level is not None:
if not isinstance(level, (tuple, list)):
level = [level]
level = [self.index._get_level_number(lev) for... |
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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 strin... |
formatter = fmt.SeriesFormatter(self, name=name, length=length,
header=header, index=index,
dtype=dtype, na_rep=na_rep,
float_format=float_format,
max... |
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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 ... |
if name is None:
df = self._constructor_expanddim(self)
else:
df = self._constructor_expanddim({name: self})
return df |
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def to_sparse(self, kind='block', fill_value=None):
""" Convert Series to SparseSeries. Parameters kind : {'block', 'integer'}, default 'block' fill_value : floa... |
# TODO: deprecate
from pandas.core.sparse.series import SparseSeries
values = SparseArray(self, kind=kind, fill_value=fill_value)
return SparseSeries(
values, index=self.index, name=self.name
).__finalize__(self) |
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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 inplace else self.copy()
ser.name = name
return ser |
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def drop_duplicates(self, keep='first', inplace=False):
""" Return Series with duplicate values removed. Parameters keep : {'first', 'last', ``False``}, default ... |
return super().drop_duplicates(keep=keep, inplace=inplace) |
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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 ... |
skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
i = nanops.nanargmin(com.values_from_object(self), skipna=skipna)
if i == -1:
return np.nan
return self.index[i] |
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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 ... |
skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
i = nanops.nanargmax(com.values_from_object(self), skipna=skipna)
if i == -1:
return np.nan
return self.index[i] |
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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 place... |
nv.validate_round(args, kwargs)
result = com.values_from_object(self).round(decimals)
result = self._constructor(result, index=self.index).__finalize__(self)
return result |
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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 ... |
self._check_percentile(q)
# We dispatch to DataFrame so that core.internals only has to worry
# about 2D cases.
df = self.to_frame()
result = df.quantile(q=q, interpolation=interpolation,
numeric_only=False)
if result.ndim == 2:
... |
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def corr(self, other, method='pearson', min_periods=None):
""" Compute correlation with `other` Series, excluding missing values. Parameters other : Series Serie... |
this, other = self.align(other, join='inner', copy=False)
if len(this) == 0:
return np.nan
if method in ['pearson', 'spearman', 'kendall'] or callable(method):
return nanops.nancorr(this.values, other.values, method=method,
min_periods=... |
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def cov(self, other, min_periods=None):
""" Compute covariance with Series, excluding missing values. Parameters other : Series Series with which to compute the ... |
this, other = self.align(other, join='inner', copy=False)
if len(this) == 0:
return np.nan
return nanops.nancov(this.values, other.values,
min_periods=min_periods) |
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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 anothe... |
from pandas.core.frame import DataFrame
if isinstance(other, (Series, DataFrame)):
common = self.index.union(other.index)
if (len(common) > len(self.index) or
len(common) > len(other.index)):
raise ValueError('matrices are not aligned')
... |
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def append(self, to_append, ignore_index=False, verify_integrity=False):
""" Concatenate two or more Series. Parameters to_append : Series or list/tuple of Serie... |
from pandas.core.reshape.concat import concat
if isinstance(to_append, (list, tuple)):
to_concat = [self] + to_append
else:
to_concat = [self, to_append]
return concat(to_concat, ignore_index=ignore_index,
verify_integrity=verify_integrity) |
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def _binop(self, other, func, level=None, fill_value=None):
""" Perform generic binary operation with optional fill value. Parameters other : Series func : binar... |
if not isinstance(other, Series):
raise AssertionError('Other operand must be Series')
new_index = self.index
this = self
if not self.index.equals(other.index):
this, other = self.align(other, level=level, join='outer',
cop... |
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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` ... |
if fill_value is None:
fill_value = na_value_for_dtype(self.dtype, compat=False)
if isinstance(other, Series):
# If other is a Series, result is based on union of Series,
# so do this element by element
new_index = self.index.union(other.index)
... |
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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... |
new_index = self.index.union(other.index)
this = self.reindex(new_index, copy=False)
other = other.reindex(new_index, copy=False)
if is_datetimelike(this) and not is_datetimelike(other):
other = to_datetime(other)
return this.where(notna(this), other) |
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def update(self, other):
""" Modify Series in place using non-NA values from passed Series. Aligns on index. Parameters other : Series Examples -------- 0 4 1 5 ... |
other = other.reindex_like(self)
mask = notna(other)
self._data = self._data.putmask(mask=mask, new=other, inplace=True)
self._maybe_update_cacher() |
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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 descend... |
inplace = validate_bool_kwarg(inplace, 'inplace')
# Validate the axis parameter
self._get_axis_number(axis)
# GH 5856/5853
if inplace and self._is_cached:
raise ValueError("This Series is a view of some other array, to "
"sort in-place y... |
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def sort_index(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True):
""" Sort Series by index labe... |
# TODO: this can be combined with DataFrame.sort_index impl as
# almost identical
inplace = validate_bool_kwarg(inplace, 'inplace')
# Validate the axis parameter
self._get_axis_number(axis)
index = self.index
if level is not None:
new_index, indexer ... |
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def nlargest(self, n=5, keep='first'):
""" Return the largest `n` elements. Parameters n : int, default 5 Return this many descending sorted values. keep : {'fir... |
return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest() |
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def nsmallest(self, n=5, keep='first'):
""" Return the smallest `n` elements. Parameters n : int, default 5 Return this many ascending sorted values. keep : {'fi... |
return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest() |
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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 ... |
new_index = self.index.swaplevel(i, j)
return self._constructor(self._values, index=new_index,
copy=copy).__finalize__(self) |
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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, ... |
new_values = super()._map_values(
arg, na_action=na_action)
return self._constructor(new_values,
index=self.index).__finalize__(self) |
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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 Se... |
if len(self) == 0:
return self._constructor(dtype=self.dtype,
index=self.index).__finalize__(self)
# dispatch to agg
if isinstance(func, (list, dict)):
return self.aggregate(func, *args, **kwds)
# if we are a string, try to ... |
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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 valu... |
delegate = self._values
if axis is not None:
self._get_axis_number(axis)
if isinstance(delegate, Categorical):
# TODO deprecate numeric_only argument for Categorical and use
# skipna as well, see GH25303
return delegate._reduce(name, numeric_onl... |
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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 / S... |
kwargs['inplace'] = validate_bool_kwarg(kwargs.get('inplace', False),
'inplace')
non_mapping = is_scalar(index) or (is_list_like(index) and
not is_dict_like(index))
if non_mapping:
return sel... |
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def reindex_axis(self, labels, axis=0, **kwargs):
""" Conform Series to new index with optional filling logic. .. deprecated:: 0.21.0 Use ``Series.reindex`` inst... |
# for compatibility with higher dims
if axis != 0:
raise ValueError("cannot reindex series on non-zero axis!")
msg = ("'.reindex_axis' is deprecated and will be removed in a future "
"version. Use '.reindex' instead.")
warnings.warn(msg, FutureWarning, stackle... |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
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 ... |
v = super().memory_usage(deep=deep)
if index:
v += self.index.memory_usage(deep=deep)
return v |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
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... |
result = algorithms.isin(self, values)
return self._constructor(result, index=self.index).__finalize__(self) |
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