| { |
| "scope": "All attempts in the published trace Parquets of this folder.", |
| "created_at": "2026-10-17T19:09:03.027557+00:00", |
| "input_rows": 15648, |
| "duplicate_records_removed": 0, |
| "unique_attempts": 15648, |
| "unique_tasks": 489, |
| "missing_trajectories": 0, |
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| "tools": { |
| "bash_command": { |
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| }, |
| "command_distribution": { |
| "\n": 4.668248938696449e-05, |
| "\n\n def __init__(self, values, categories=None, ordered=False, dtype=None):\n # Minimal implementation\n from pandas.core.arrays.categorical import Categorical\n self._categories = categories\n self._ordered = ordered\n self._codes = None\n self._values = values\n\n @classmethod\n def is_dtype_equal(cls, left, right):\n return left._categories.equals(right._categories) and left._ordered == right._ordered\n\n def min(self):\n return self._categories.min()\n\n def max(self):\n return self._categories.max()\n\n @classmethod\n def factorize_from_iterables(cls, arrays, sort=False, size_hint=None):\n from pandas.core.arrays.categorical import factorize_from_iterables\n return factorize_from_iterables(arrays, sort=sort, size_hint=size_hint)\n\n @property\n def _reverse_indexer(self):\n return self._reverse_indexer\n\n @property\n def ordered(self):\n return self._ordered\n\n @property\n def codes(self):\n return self._codes\n\n def tolist(self):\n return self._values.tolist()": 1.9846562257871938e-07, |
| "\n ": 4.99265081799591e-07, |
| "\n if dtype == \"Int64\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \"boolean\" or (isinstance(dtype, BooleanDtype) and dtype.name == \"boolean\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\"bool\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 3.68333893200851e-07, |
| "\n if dtype == \\\"Int64\\\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \\\"boolean\\\" or (isinstance(dtype, BooleanDtype) and dtype.name == \\\"boolean\\\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\\\"bool\\\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 1.841669466004255e-07, |
| "\n # Ensure key is a tuple of integers, one per axis\n if len(key) != self.ndim:\n return False\n for k in key:\n if not is_integer(k):\n return False\n return True\n": 1.2126362518092532e-07, |
| "\n # Handle None and NaT\n if other is None or isna(other):\n return self._where(cond, other)\n\n # Convert other to array-like\n other = self._maybe_cast_other(other)\n\n # Check dtype compatibility\n if isinstance(other, Categorical):\n if not hasattr(other.categories, 'dtype'):\n raise TypeError(Where": 2.1231206136328122e-07, |
| "\n # If the Timestamps have a timezone that is not UTC,\n # convert them into their i8 representation while\n # keeping their timezone and not using UTC\n timestamps = self._local_timestamps()\n\n # Use the fields module to compute ISO calendar components\n from pandas.core.arrays import fields\n return fields.isocalendar(timestamps)'''\n # Find the end of the class definition or insert before the last method\n lines = content.splitlines()\n insert_pos = -1\n for i, line in enumerate(lines):\n if line.strip().startswith('class DatetimeArray'):\n continue\n if line.strip().startswith(' def ') and i > 0:\n # Insert before the next method\n insert_pos = i\n break\n if insert_pos == -1:\n insert_pos = len(lines)\n # Insert the method\n lines.insert(insert_pos, method)\n new_content = '\\n'.join(lines)\n path.write_text(new_content)\n print('isocalendar method added successfully')": 1.3983792225459004e-07, |
| "\n future = Future()\n # Ensure the iterator is not garbage-collected before the future resolves\n # by holding a reference in the future's closure.\n self._next_future = future\n def callback():\n try:\n result = next(self._iterator)\n future.set_result(result)\n except StopIteration:\n future.set_result(None)\n except Exception as e:\n future.set_exception(e)\n self._iterator.add_done_callback(callback)\n return future\n''')": 8.193068009018929e-07, |
| "\n if dtype == boolean or isinstance(dtype, BooleanDtype):\n data = (self._data != 0).astype(bool)\n mask = self._mask\n from pandas import array\n return array(data, dtype=boolean, mask=mask, copy=False)\n\n return super().astype(dtype, copy=copy)\n''')": 1.5511148172414476e-07, |
| "\n if inplace:\n # Modify in place\n if to_replace not in self.categories:\n # If to_replace is not a category, do nothing\n return None\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n # Update codes to reflect missing values\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n self._codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # Return new Categorical\n if to_replace not in self.categories:\n # If to_replace is not a category, return a copy\n return type(self)(self._codes, dtype=self.dtype)\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))": 3.1793995258879424e-07, |
| "\n if self._dtype.ordered:\n if inplace:\n return None\n return self\n\n if inplace:\n self._dtype = CategoricalDtype(self._categories, ordered=True)\n return None\n\n return Categorical(\n codes=self._codes,\n categories=self._categories,\n ordered=True,\n dtype=self._dtype\n )": 7.140327426854486e-08, |
| "\n return Categorical(self._values, categories=categories, ordered=self._ordered)\n": 8.63593655004698e-08, |
| "\n return False\n": 7.092778076620161e-07, |
| "\n self.filepath_or_buffer.seek(0, 2)\n total_records_length = self.filepath_or_buffer.tell() - self.record_start\n\n if total_records_length % 80 != 0:\n warnings.warn(xport": 3.043139546207031e-07, |
| "\n a, b = self.get_view_interval()\n if inverted:\n self.set_view_interval(max(a, b), min(a, b), ignore=True)\n self.figure.canvas.draw_idle()\n": 1.383245248275923e-07, |
| "\n if dilation_rate == (1, 1):\n return backend.conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format\n )\n else:\n # Use atrous_conv2d_transpose for dilation\n return backend.atrous_conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format,\n dilation_rate=dilation_rate\n )\n": 3.9206092313096717e-07, |
| "\n if format is None:\n buffer_error_msg = (\n If": 1.1251044096892192e-07, |
| "\n if isinstance(status, str):\n status = int(status)\n if HTTPStatus(status).phrase:\n return f'{status} {HTTPStatus(status).phrase}'\n return f'{status} Unknown Status'\n": 1.0650988411724609e-06, |
| "\n if item is None or item is pd.NaT or (isinstance(item, np.ndarray) and item.size == 0):\n return arr._hasna\n if isinstance(item, (np.datetime64, np.timedelta64, np.datetime64, np.timedelta64)) and item == np.datetime64('NaT'):\n return arr._dtype.kind == 'M' and arr._hasna\n if isinstance(item, (np.timedelta64, np.timedelta64)) and item == np.timedelta64('NaT'):\n return arr._dtype.kind == 'm' and arr._hasna\n return item in arr._values": 1.9846562257871938e-07, |
| "\n if not is_numeric_dtype(values.dtype):\n if is_timedelta64_dtype(values.dtype):\n raise TypeError(reduction": 8.624282114756767e-08, |
| "\n pass\n": 3.2113532899672185e-07, |
| "\nRead JSON\n": 1.0356903497595356e-06, |
| "\ncat": 4.311894649317963e-07, |
| "\ncurl": 1.7556574305040565e-07, |
| "\nprintf": 2.556237218813906e-07, |
| "\nsed": 2.556237218813906e-07, |
| "\nwget": 3.511314861008113e-07, |
| "\u001b:1656": 5.0939509795204645e-06, |
| "\u001b:q!": 3.550329470574869e-06, |
| "\u001b:wq": 1.2081907437618113e-05, |
| "\u001b:wq!": 7.938624903148776e-08, |
| " pass": 5.649143025003108e-07, |
| " raise KeyError(key)": 1.9404634758202729e-07, |
| " analyze_all_pipes(self.pipeline)#; }": 5.862929400949326e-07, |
| " if isinstance(x, (pd.Series, np.ndarray)) and x.dtype == bool:\\n x = x.astype(int)\\n elif isinstance(x, list):\\n x = np.array(x).astype(int)": 8.994501121794179e-08, |
| " rs = data.div(shifted.groupby(level=0).first()) - 1#": 4.079101944915807e-07, |
| " rs = data.div(shifted.groupby(level=0).first()) - 1|": 6.798503241526345e-08, |
| " % entity": 3.0724005033820985e-06, |
| " in the fragment.": 6.455144491954308e-07, |
| "!=:": 1.4861844295429685e-06, |
| "\"\\\\": 7.793406154920446e-07, |
| "%3Fjs_to_json%3F": 5.325494205862303e-06, |
| "%B %d %Y,": 2.004429553497989e-06, |
| "%Y,": 2.004429553497989e-06, |
| "%Y-%m,": 2.004429553497989e-06, |
| "%Y-%m-%d,": 2.004429553497989e-06, |
| "%Y-%m-%dT%H:%M:%S.%f": 6.726940049510278e-07, |
| "%b %d %Y,": 2.004429553497989e-06, |
| "%d %B %Y,": 2.004429553497989e-06, |
| "%s": 3.0724005033820985e-06, |
| "'": 4.676043692952267e-06, |
| "'quantile' cannot be performed against 'object' dtypes!": 1.0905448885724855e-07, |
| "(if": 3.227572245977154e-07, |
| ")\n": 4.99265081799591e-07, |
| ") f.write(": 5.809630042758877e-06, |
| ") f_out.write(": 1.7731125217206284e-05, |
| "))": 6.554454407215143e-07, |
| "), text)\\n": 2.4579204027056787e-06, |
| "); new_lines.append(": 9.668068149825666e-08, |
| ",\n ": 1.5976482617586914e-05, |
| ", ": 3.6631686159643183e-06, |
| ", lambda m: _htmlentity_transform(m.group(0)), s)": 3.570163713427243e-07, |
| ", u": 4.99265081799591e-07, |
| ", xml_str)": 3.550329470574869e-06, |
| ", xml_str)\n": 1.4524075106897192e-06, |
| ", xml_str)\\\\n": 4.99265081799591e-07, |
| ",w": 2.6643796885246106e-06, |
| "--conf": 2.467410442870566e-07, |
| "...": 2.181089777144971e-07, |
| "...updated docstring with example...": 1.3313735514655758e-06, |
| "._get_grouper": 2.1663027278083948e-07, |
| ".join(map(re.escape, COMPARISON_OPERATORS.keys())))": 7.938624903148775e-07, |
| ".join(query_parts)\\n\\n # Handle fragments\\n fragment = if not keep_fragments else fragment\\n\\n return urlunparse((scheme, netloc.lower(), path, params, query, fragment))": 1.1211566749183797e-06, |
| "0": 1.6314663197936767e-05, |
| "000": 3.759172380608685e-06, |
| "00000000:": 8.375613429927609e-08, |
| "00000020:": 8.375613429927609e-08, |
| "00000030:": 8.375613429927609e-08, |
| "00000040:": 8.375613429927609e-08, |
| "00000050:": 8.375613429927609e-08, |
| "00000060:": 8.375613429927609e-08, |
| "00000070:": 8.375613429927609e-08, |
| "00000080:": 8.375613429927609e-08, |
| "00000090:": 8.375613429927609e-08, |
| "000000a0:": 8.375613429927609e-08, |
| "000000b0:": 8.375613429927609e-08, |
| "000000c0:": 8.375613429927609e-08, |
| "000000f0:": 8.375613429927609e-08, |
| "00000110:": 8.375613429927609e-08, |
| "00000120:": 8.375613429927609e-08, |
| "00000130:": 8.375613429927609e-08, |
| "00000140:": 8.375613429927609e-08, |
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| "00000160:": 8.375613429927609e-08, |
| "00000170:": 8.375613429927609e-08, |
| "00000180:": 8.375613429927609e-08, |
| "00000190:": 8.375613429927609e-08, |
| "000001c0:": 8.375613429927609e-08, |
| "000001d0:": 8.375613429927609e-08, |
| "000001e0:": 8.375613429927609e-08, |
| "000001f0:": 8.375613429927609e-08, |
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| "00000250:": 8.375613429927609e-08, |
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| "00000270:": 8.375613429927609e-08, |
| "00000280:": 8.375613429927609e-08, |
| "00000290:": 8.375613429927609e-08, |
| "000002a0:": 8.375613429927609e-08, |
| "000002b0:": 8.375613429927609e-08, |
| "000002c0:": 8.375613429927609e-08, |
| "000002d0:": 8.375613429927609e-08, |
| "000002f0:": 8.375613429927609e-08, |
| "00000320:": 8.375613429927609e-08, |
| "00000330:": 8.375613429927609e-08, |
| "00000340:": 8.375613429927609e-08, |
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| "00000390:": 8.375613429927609e-08, |
| "000003a0:": 8.375613429927609e-08, |
| "033:q!": 1.5877249806297552e-07, |
| "033:wq": 2.1373220893092858e-07, |
| "0G": 2.148098503204963e-06, |
| "0a": 2.815239227768619e-07, |
| "0i": 5.087656600551948e-06, |
| "1": 5.5968661775757674e-06, |
| "1,1000d": 7.038098069421548e-08, |
| "1,100d": 1.1290800436457182e-07, |
| "1,287d": 1.2126362518092532e-07, |
| "10": 1.253057460202895e-06, |
| "10,20s": 5.9120023783140996e-06, |
| "100": 1.7850818567136216e-07, |
| "100,110s": 9.831681610822717e-07, |
| "1000": 3.25746337108438e-07, |
| "1000000d": 2.323852017103551e-07, |
| "1000G": 1.7508474101465107e-06, |
| "1000d": 5.809630042758877e-07, |
| "1000i": 5.809630042758877e-07, |
| "1001": 3.492127348106429e-07, |
| "100i": 4.468946186737598e-07, |
| "1019i": 2.0482670022547322e-07, |
| "1030d": 1.0704510966557395e-07, |
| "1030i": 2.140902193311479e-07, |
| "1038,1045d": 1.4657323502373315e-07, |
| "1038i": 1.4657323502373315e-07, |
| "104": 3.610504546347325e-07, |
| "1060": 6.660949221311526e-07, |
| "1068": 3.1636599242746363e-07, |
| "1087": 1.1834431568582898e-07, |
| "1088,1090d": 1.8604346570698006e-07, |
| "1089": 3.492127348106429e-07, |
| "1095,1097d": 1.8604346570698006e-07, |
| "1103": 1.1834431568582898e-07, |
| "1112i": 8.937892373475196e-08, |
| "1117": 1.1834431568582898e-07, |
| "1124": 6.660949221311526e-07, |
| "1133": 1.1834431568582898e-07, |
| "1134,1168d": 4.915840805411358e-07, |
| "1134i": 4.915840805411358e-07, |
| "114,130d": 2.4391576515399866e-07, |
| "1173i": 3.417429436916986e-07, |
| "1177": 2.50611492040579e-07, |
| "12,20d": 1.2704956355933926e-07, |
| "1202": 1.3425615645031018e-07, |
| "1218": 1.6428259761014818e-07, |
| "1234": 3.4745249946561694e-07, |
| "1234,1238d": 2.3494827378804282e-07, |
| "1234i": 1.1747413689402141e-07, |
| "129": 2.6084053253203126e-07, |
| "1292c": 2.0033207043996126e-07, |
| "12d": 3.896703077460223e-07, |
| "1317i": 4.422555741892571e-07, |
| "133": 3.2772272036075715e-07, |
| "134": 4.122963256151461e-06, |
| "1348": 3.492127348106429e-07, |
| "137": 6.79127847718891e-08, |
| "138": 3.4358027134595514e-07, |
| "1424": 2.0884291003381586e-07, |
| "1427i": 1.0924090678691905e-07, |
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| "\n\n def __init__(self, values, categories=None, ordered=False, dtype=None):\n # Minimal implementation\n from pandas.core.arrays.categorical import Categorical\n self._categories = categories\n self._ordered = ordered\n self._codes = None\n self._values = values\n\n @classmethod\n def is_dtype_equal(cls, left, right):\n return left._categories.equals(right._categories) and left._ordered == right._ordered\n\n def min(self):\n return self._categories.min()\n\n def max(self):\n return self._categories.max()\n\n @classmethod\n def factorize_from_iterables(cls, arrays, sort=False, size_hint=None):\n from pandas.core.arrays.categorical import factorize_from_iterables\n return factorize_from_iterables(arrays, sort=sort, size_hint=size_hint)\n\n @property\n def _reverse_indexer(self):\n return self._reverse_indexer\n\n @property\n def ordered(self):\n return self._ordered\n\n @property\n def codes(self):\n return self._codes\n\n def tolist(self):\n return self._values.tolist()": 3.9693124515743877e-07, |
| "\n ": 9.98530163599182e-07, |
| "\n if dtype == \"Int64\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \"boolean\" or (isinstance(dtype, BooleanDtype) and dtype.name == \"boolean\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\"bool\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 7.36667786401702e-07, |
| "\n if dtype == \\\"Int64\\\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \\\"boolean\\\" or (isinstance(dtype, BooleanDtype) and dtype.name == \\\"boolean\\\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\\\"bool\\\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 3.68333893200851e-07, |
| "\n # Ensure key is a tuple of integers, one per axis\n if len(key) != self.ndim:\n return False\n for k in key:\n if not is_integer(k):\n return False\n return True\n": 2.4252725036185064e-07, |
| "\n # Handle None and NaT\n if other is None or isna(other):\n return self._where(cond, other)\n\n # Convert other to array-like\n other = self._maybe_cast_other(other)\n\n # Check dtype compatibility\n if isinstance(other, Categorical):\n if not hasattr(other.categories, 'dtype'):\n raise TypeError(Where": 4.2462412272656244e-07, |
| "\n # If the Timestamps have a timezone that is not UTC,\n # convert them into their i8 representation while\n # keeping their timezone and not using UTC\n timestamps = self._local_timestamps()\n\n # Use the fields module to compute ISO calendar components\n from pandas.core.arrays import fields\n return fields.isocalendar(timestamps)'''\n # Find the end of the class definition or insert before the last method\n lines = content.splitlines()\n insert_pos = -1\n for i, line in enumerate(lines):\n if line.strip().startswith('class DatetimeArray'):\n continue\n if line.strip().startswith(' def ') and i > 0:\n # Insert before the next method\n insert_pos = i\n break\n if insert_pos == -1:\n insert_pos = len(lines)\n # Insert the method\n lines.insert(insert_pos, method)\n new_content = '\\n'.join(lines)\n path.write_text(new_content)\n print('isocalendar method added successfully')": 2.796758445091801e-07, |
| "\n future = Future()\n # Ensure the iterator is not garbage-collected before the future resolves\n # by holding a reference in the future's closure.\n self._next_future = future\n def callback():\n try:\n result = next(self._iterator)\n future.set_result(result)\n except StopIteration:\n future.set_result(None)\n except Exception as e:\n future.set_exception(e)\n self._iterator.add_done_callback(callback)\n return future\n''')": 1.6386136018037857e-06, |
| "\n if dtype == boolean or isinstance(dtype, BooleanDtype):\n data = (self._data != 0).astype(bool)\n mask = self._mask\n from pandas import array\n return array(data, dtype=boolean, mask=mask, copy=False)\n\n return super().astype(dtype, copy=copy)\n''')": 3.102229634482895e-07, |
| "\n if inplace:\n # Modify in place\n if to_replace not in self.categories:\n # If to_replace is not a category, do nothing\n return None\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n # Update codes to reflect missing values\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n self._codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # Return new Categorical\n if to_replace not in self.categories:\n # If to_replace is not a category, return a copy\n return type(self)(self._codes, dtype=self.dtype)\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))": 6.358799051775885e-07, |
| "\n if self._dtype.ordered:\n if inplace:\n return None\n return self\n\n if inplace:\n self._dtype = CategoricalDtype(self._categories, ordered=True)\n return None\n\n return Categorical(\n codes=self._codes,\n categories=self._categories,\n ordered=True,\n dtype=self._dtype\n )": 1.428065485370897e-07, |
| "\n return Categorical(self._values, categories=categories, ordered=self._ordered)\n": 1.727187310009396e-07, |
| "\n self.filepath_or_buffer.seek(0, 2)\n total_records_length = self.filepath_or_buffer.tell() - self.record_start\n\n if total_records_length % 80 != 0:\n warnings.warn(xport": 6.086279092414062e-07, |
| "\n a, b = self.get_view_interval()\n if inverted:\n self.set_view_interval(max(a, b), min(a, b), ignore=True)\n self.figure.canvas.draw_idle()\n": 2.766490496551846e-07, |
| "\n if dilation_rate == (1, 1):\n return backend.conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format\n )\n else:\n # Use atrous_conv2d_transpose for dilation\n return backend.atrous_conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format,\n dilation_rate=dilation_rate\n )\n": 7.841218462619343e-07, |
| "\n if format is None:\n buffer_error_msg = (\n If": 2.2502088193784383e-07, |
| "\n if isinstance(status, str):\n status = int(status)\n if HTTPStatus(status).phrase:\n return f'{status} {HTTPStatus(status).phrase}'\n return f'{status} Unknown Status'\n": 2.1301976823449218e-06, |
| "\n if item is None or item is pd.NaT or (isinstance(item, np.ndarray) and item.size == 0):\n return arr._hasna\n if isinstance(item, (np.datetime64, np.timedelta64, np.datetime64, np.timedelta64)) and item == np.datetime64('NaT'):\n return arr._dtype.kind == 'M' and arr._hasna\n if isinstance(item, (np.timedelta64, np.timedelta64)) and item == np.timedelta64('NaT'):\n return arr._dtype.kind == 'm' and arr._hasna\n return item in arr._values": 3.9693124515743877e-07, |
| "\n if not is_numeric_dtype(values.dtype):\n if is_timedelta64_dtype(values.dtype):\n raise TypeError(reduction": 1.7248564229513534e-07, |
| "\n pass\n": 6.422706579934437e-07, |
| "\ncat": 8.623789298635926e-07, |
| "\ncurl": 3.511314861008113e-07, |
| "\nprintf": 5.112474437627812e-07, |
| "\nsed": 5.112474437627812e-07, |
| "\nwget": 7.022629722016226e-07, |
| " % entity": 6.144801006764197e-06, |
| " in the fragment.": 1.2910288983908617e-06, |
| "\"\\\\": 1.5586812309840891e-06, |
| "%B %d %Y,": 4.008859106995978e-06, |
| "%Y,": 4.008859106995978e-06, |
| "%Y-%m,": 4.008859106995978e-06, |
| "%Y-%m-%d,": 4.008859106995978e-06, |
| "%b %d %Y,": 4.008859106995978e-06, |
| "%d %B %Y,": 4.008859106995978e-06, |
| "%s": 6.144801006764197e-06, |
| "'": 9.352087385904534e-06, |
| "'quantile' cannot be performed against 'object' dtypes!": 2.181089777144971e-07, |
| "(if": 6.455144491954308e-07, |
| ") f.write(": 1.1619260085517754e-05, |
| ") f_out.write(": 3.546225043441257e-05, |
| "); new_lines.append(": 1.9336136299651332e-07, |
| ",\n ": 3.195296523517383e-05, |
| ", ": 4.049110028321065e-06, |
| ", lambda m: _htmlentity_transform(m.group(0)), s)": 7.140327426854486e-07, |
| ", xml_str)": 7.100658941149738e-06, |
| ",w": 5.328759377049221e-06, |
| "...": 4.362179554289942e-07, |
| ".join(map(re.escape, COMPARISON_OPERATORS.keys())))": 1.587724980629755e-06, |
| ".join(query_parts)\\n\\n # Handle fragments\\n fragment = if not keep_fragments else fragment\\n\\n return urlunparse((scheme, netloc.lower(), path, params, query, fragment))": 2.2423133498367594e-06, |
| "0": 2.4806749990391624e-06, |
| "00000000:": 1.6751226859855217e-07, |
| "00000020:": 1.6751226859855217e-07, |
| "00000030:": 1.6751226859855217e-07, |
| "00000040:": 1.6751226859855217e-07, |
| "00000050:": 1.6751226859855217e-07, |
| "00000060:": 1.6751226859855217e-07, |
| "00000070:": 1.6751226859855217e-07, |
| "00000080:": 1.6751226859855217e-07, |
| "00000090:": 1.6751226859855217e-07, |
| "000000a0:": 1.6751226859855217e-07, |
| "000000b0:": 1.6751226859855217e-07, |
| "000000c0:": 1.6751226859855217e-07, |
| "000000f0:": 1.6751226859855217e-07, |
| "00000110:": 1.6751226859855217e-07, |
| "00000120:": 1.6751226859855217e-07, |
| "00000130:": 1.6751226859855217e-07, |
| "00000140:": 1.6751226859855217e-07, |
| "00000150:": 1.6751226859855217e-07, |
| "00000160:": 1.6751226859855217e-07, |
| "00000170:": 1.6751226859855217e-07, |
| "00000180:": 1.6751226859855217e-07, |
| "00000190:": 1.6751226859855217e-07, |
| "000001c0:": 1.6751226859855217e-07, |
| "000001d0:": 1.6751226859855217e-07, |
| "000001e0:": 1.6751226859855217e-07, |
| "000001f0:": 1.6751226859855217e-07, |
| "00000200:": 1.6751226859855217e-07, |
| "00000230:": 1.6751226859855217e-07, |
| "00000240:": 1.6751226859855217e-07, |
| "00000250:": 1.6751226859855217e-07, |
| "00000260:": 1.6751226859855217e-07, |
| "00000270:": 1.6751226859855217e-07, |
| "00000280:": 1.6751226859855217e-07, |
| "00000290:": 1.6751226859855217e-07, |
| "000002a0:": 1.6751226859855217e-07, |
| "000002b0:": 1.6751226859855217e-07, |
| "000002c0:": 1.6751226859855217e-07, |
| "000002d0:": 1.6751226859855217e-07, |
| "000002f0:": 1.6751226859855217e-07, |
| "00000320:": 1.6751226859855217e-07, |
| "00000330:": 1.6751226859855217e-07, |
| "00000340:": 1.6751226859855217e-07, |
| "00000350:": 1.6751226859855217e-07, |
| "00000360:": 1.6751226859855217e-07, |
| "00000370:": 1.6751226859855217e-07, |
| "00000390:": 1.6751226859855217e-07, |
| "000003a0:": 1.6751226859855217e-07, |
| "1": 2.3791212557932387e-06, |
| "1,1000d": 1.4076196138843096e-07, |
| "1,100d": 2.2581600872914363e-07, |
| "1,287d": 2.4252725036185064e-07, |
| "100": 3.570163713427243e-07, |
| "100,110s": 1.9663363221645433e-06, |
| "1000": 6.51492674216876e-07, |
| "1000000d": 4.647704034207102e-07, |
| "1000d": 1.1619260085517754e-06, |
| "1000i": 1.1619260085517754e-06, |
| "1001": 6.984254696212858e-07, |
| "100i": 8.937892373475196e-07, |
| "1019i": 4.0965340045094644e-07, |
| "1030d": 2.140902193311479e-07, |
| "1030i": 4.281804386622958e-07, |
| "1038,1045d": 2.931464700474663e-07, |
| "1038i": 2.931464700474663e-07, |
| "104": 7.22100909269465e-07, |
| "1060": 1.3321898442623053e-06, |
| "1068": 6.327319848549273e-07, |
| "1087": 2.3668863137165796e-07, |
| "1088,1090d": 3.720869314139601e-07, |
| "1089": 6.984254696212858e-07, |
| "1095,1097d": 3.720869314139601e-07, |
| "1103": 2.3668863137165796e-07, |
| "1112i": 1.7875784746950392e-07, |
| "1117": 2.3668863137165796e-07, |
| "1124": 1.3321898442623053e-06, |
| "1133": 2.3668863137165796e-07, |
| "1134,1168d": 9.831681610822717e-07, |
| "1134i": 9.831681610822717e-07, |
| "114,130d": 4.878315303079973e-07, |
| "1173i": 6.834858873833972e-07, |
| "1177": 5.01222984081158e-07, |
| "12,20d": 2.540991271186785e-07, |
| "1202": 2.6851231290062036e-07, |
| "1218": 3.2856519522029635e-07, |
| "1234": 6.949049989312339e-07, |
| "1234,1238d": 4.6989654757608565e-07, |
| "1234i": 2.3494827378804282e-07, |
| "129": 5.216810650640625e-07, |
| "1292c": 4.006641408799225e-07, |
| "12d": 7.793406154920446e-07, |
| "1317i": 8.845111483785142e-07, |
| "133": 6.554454407215143e-07, |
| "1348": 6.984254696212858e-07, |
| "137": 1.358255695437782e-07, |
| "138": 6.871605426919103e-07, |
| "1424": 4.176858200676317e-07, |
| "1427i": 2.184818135738381e-07, |
| "1441": 7.33270661896509e-07, |
| "1490,1500d": 3.932672644329086e-07, |
| "14i": 3.26050665665039e-07, |
| "1500": 6.622376214543798e-07, |
| "155": 8.577977244341966e-07, |
| "156": 8.577977244341966e-07, |
| "160,161d": 1.4879145627554748e-07, |
| "160,180d": 1.1584156580727672e-06, |
| "160i": 5.349300089664697e-07, |
| "160r": 1.1584156580727672e-06, |
| "1646d": 1.3244752429087595e-07, |
| "1655": 8.814611099358296e-07, |
| "1711": 4.878315303079973e-07, |
| "1714": 8.796411627026517e-08, |
| "1714,1718c": 8.796411627026517e-08, |
| "1714,1718d": 1.1435335115134473e-06, |
| "1741a": 6.984254696212858e-07, |
| "1787i": 4.5728751678245185e-07, |
| "181,182d": 1.4879145627554748e-07, |
| "181,195d": 6.144801006764197e-07, |
| "181i": 6.144801006764197e-07, |
| "182,192d": 4.2803704266810214e-07, |
| "182i": 4.2803704266810214e-07, |
| "1845": 1.3321898442623053e-06, |
| "1848,1880d": 2.181089777144971e-07, |
| "1848i": 2.181089777144971e-07, |
| "1897,1910d": 1.7365741975637947e-07, |
| "190d": 4.031919903491965e-07, |
| "190i": 4.031919903491965e-07, |
| "1940,1961d": 2.358152415879987e-07, |
| "1953": 2.311245224967365e-07, |
| "1i": 2.6514202807988677e-06, |
| "2": 2.182745253256656e-06, |
| "2037": 7.100658941149739e-07, |
| "2066i": 1.841669466004255e-07, |
| "2108": 4.407305549679148e-07, |
| "2127": 6.471486629908623e-07, |
| "2131": 2.358152415879987e-07, |
| "214a": 6.455144491954308e-07, |
| "2154": 6.471486629908623e-07, |
| "215a": 6.455144491954308e-07, |
| "215d": 6.455144491954308e-07, |
| "218a": 3.501694820293022e-07, |
| "218d": 3.501694820293022e-07, |
| "219": 3.007337904486948e-07, |
| "2410": 4.407305549679148e-07, |
| "2500": 2.77249156053569e-07, |
| "2517": 6.086279092414062e-07, |
| "260": 6.622376214543798e-07, |
| "2660": 3.195296523517382e-07, |
| "2666a": 3.195296523517382e-07, |
| "276": 2.1773741216472795e-07, |
| "2773": 3.525844439743318e-07, |
| "2773,2781d": 1.762922219871659e-07, |
| "2773i": 1.762922219871659e-07, |
| "283": 4.4064194181347187e-07, |
| "284i": 1.1598172499155653e-07, |
| "2855,2873d": 5.445239045994119e-06, |
| "2855i": 3.025132803330066e-07, |
| "2855r": 2.5713628828305563e-06, |
| "2856r": 2.5713628828305563e-06, |
| "286d": 5.705886649138183e-07, |
| "2897d": 2.1480985032049632e-07, |
| "2897i": 2.1480985032049632e-07, |
| "3,27d": 6.774480261874309e-07, |
| "300G": 1.1514582066729305e-06, |
| "3134": 5.392905524923851e-07, |
| "322": 5.438802593221076e-07, |
| "327,335d": 1.9569280144029904e-06, |
| "327i": 1.223080009001869e-07, |
| "328": 2.2170314126746798e-07, |
| "332,357d": 7.409383242938858e-07, |
| "337,355d": 3.1249843750781247e-07, |
| "337i": 3.1249843750781247e-07, |
| "3569": 6.52101331330078e-07, |
| "357i": 7.51834476121737e-07, |
| "368i": 5.370246258012407e-07, |
| "370,380d": 2.184818135738381e-07, |
| "378d": 2.0648119699627674e-07, |
| "39": 2.448503083155082e-07, |
| "396,406d": 2.6794939400565054e-07, |
| "396i": 2.6794939400565054e-07, |
| "398,410d": 1.8795861903043426e-06, |
| "3G": 7.607848865517577e-07, |
| "40": 4.716304831759974e-07, |
| "400,442d": 6.656867757327879e-07, |
| "404r": 2.009620455042379e-07, |
| "414": 3.656991729347505e-07, |
| "414G": 3.148075392627963e-07, |
| "415,449d": 3.0613619386992886e-06, |
| "416d": 7.51834476121737e-07, |
| "428,470d": 6.726940049510279e-06, |
| "441i": 1.0391208206560595e-06, |
| "453i": 2.319634499831131e-06, |
| "454,480d": 1.7801094838536949e-07, |
| "454i": 1.7801094838536949e-07, |
| "473,485d": 1.0831513639041974e-06, |
| "477,481d": 4.6989654757608565e-07, |
| "489i": 1.5996478215356108e-07, |
| "490i": 1.5996478215356108e-07, |
| "4i": 2.3631889991874985e-06, |
| "518i": 1.9336136299651332e-07, |
| "531": 2.891671062006681e-07, |
| "532,556d": 2.8214538839005583e-07, |
| "543,553d": 2.540991271186785e-07, |
| "55i": 4.14973574482777e-07, |
| "60,62d": 2.0872922309313331e-07, |
| "621,634d": 6.40660957096217e-07, |
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