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train | OpenAIGPTConfig.from_dict | Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters. | pytorch_pretrained_bert/modeling_openai.py | def from_dict(cls, json_object):
"""Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters."""
config = OpenAIGPTConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
return config | def from_dict(cls, json_object):
"""Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters."""
config = OpenAIGPTConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
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train | OpenAIGPTModel.set_num_special_tokens | Update input embeddings with new embedding matrice if needed | pytorch_pretrained_bert/modeling_openai.py | def set_num_special_tokens(self, num_special_tokens):
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train | OpenAIGPTLMHeadModel.set_num_special_tokens | Update input and output embeddings with new embedding matrice
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train | OpenAIAdam.step | Performs a single optimization step.
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train | whitespace_tokenize | Runs basic whitespace cleaning and splitting on a piece of text. | pytorch_pretrained_bert/tokenization.py | def whitespace_tokenize(text):
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train | _is_punctuation | Checks whether `chars` is a punctuation character. | pytorch_pretrained_bert/tokenization.py | def _is_punctuation(char):
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# We treat all non-letter/number ASCII as punctuation.
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train | BertTokenizer.convert_tokens_to_ids | Converts a sequence of tokens into ids using the vocab. | pytorch_pretrained_bert/tokenization.py | def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
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train | BertTokenizer.convert_ids_to_tokens | Converts a sequence of ids in wordpiece tokens using the vocab. | pytorch_pretrained_bert/tokenization.py | def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab."""
tokens = []
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train | BertTokenizer.save_vocabulary | Save the tokenizer vocabulary to a directory or file. | pytorch_pretrained_bert/tokenization.py | def save_vocabulary(self, vocab_path):
"""Save the tokenizer vocabulary to a directory or file."""
index = 0
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
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"""Save the tokenizer vocabulary to a directory or file."""
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train | BertTokenizer.from_pretrained | Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed. | pytorch_pretrained_bert/tokenization.py | def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a PreTrainedBertModel from a pre-trained model file.
Download and cache the pre-trained model file if needed.
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train | BasicTokenizer.tokenize | Tokenizes a piece of text. | pytorch_pretrained_bert/tokenization.py | def tokenize(self, text):
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text = self._clean_text(text)
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train | BasicTokenizer._run_strip_accents | Strips accents from a piece of text. | pytorch_pretrained_bert/tokenization.py | def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
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train | BasicTokenizer._tokenize_chinese_chars | Adds whitespace around any CJK character. | pytorch_pretrained_bert/tokenization.py | def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
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train | BasicTokenizer._is_chinese_char | Checks whether CP is the codepoint of a CJK character. | pytorch_pretrained_bert/tokenization.py | def _is_chinese_char(self, cp):
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# This defines a "chinese character" as anything in the CJK Unicode block:
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#
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train | WordpieceTokenizer.tokenize | Tokenizes a piece of text into its word pieces.
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For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespa... | pytorch_pretrained_bert/tokenization.py | def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
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For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
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"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
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For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
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train | load_rocstories_dataset | Output a list of tuples(story, 1st continuation, 2nd continuation, label) | examples/run_openai_gpt.py | def load_rocstories_dataset(dataset_path):
""" Output a list of tuples(story, 1st continuation, 2nd continuation, label) """
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f = csv.reader(f)
output = []
next(f) # skip the first line
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""" Output a list of tuples(story, 1st continuation, 2nd continuation, label) """
with open(dataset_path, encoding='utf_8') as f:
f = csv.reader(f)
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next(f) # skip the first line
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train | pre_process_datasets | Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
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""" Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
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train | random_word | Masking some random tokens for Language Model task with probabilities as in the original BERT paper.
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:param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)
:return: (list of str, list of int), masked tokens and related labels for L... | examples/lm_finetuning/simple_lm_finetuning.py | def random_word(tokens, tokenizer):
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:param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)
:return: (list of str, list... | def random_word(tokens, tokenizer):
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train | convert_example_to_features | Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with
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train | BERTDataset.random_sent | Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences
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:param index: int, index of sample.
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Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences
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train | BERTDataset.get_corpus_line | Get one sample from corpus consisting of a pair of two subsequent lines from the same doc.
:param item: int, index of sample.
:return: (str, str), two subsequent sentences from corpus | examples/lm_finetuning/simple_lm_finetuning.py | def get_corpus_line(self, item):
"""
Get one sample from corpus consisting of a pair of two subsequent lines from the same doc.
:param item: int, index of sample.
:return: (str, str), two subsequent sentences from corpus
"""
t1 = ""
t2 = ""
assert item < s... | def get_corpus_line(self, item):
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Get one sample from corpus consisting of a pair of two subsequent lines from the same doc.
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train | BERTDataset.get_random_line | Get random line from another document for nextSentence task.
:return: str, content of one line | examples/lm_finetuning/simple_lm_finetuning.py | def get_random_line(self):
"""
Get random line from another document for nextSentence task.
:return: str, content of one line
"""
# Similar to original tf repo: This outer loop should rarely go for more than one iteration for large
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"""
Get random line from another document for nextSentence task.
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train | BERTDataset.get_next_line | Gets next line of random_file and starts over when reaching end of file | examples/lm_finetuning/simple_lm_finetuning.py | def get_next_line(self):
""" Gets next line of random_file and starts over when reaching end of file"""
try:
line = next(self.random_file).strip()
#keep track of which document we are currently looking at to later avoid having the same doc as t1
if line == "":
... | def get_next_line(self):
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try:
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#keep track of which document we are currently looking at to later avoid having the same doc as t1
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train | create_masked_lm_predictions | Creates the predictions for the masked LM objective. This is mostly copied from the Google BERT repo, but
with several refactors to clean it up and remove a lot of unnecessary variables. | examples/lm_finetuning/pregenerate_training_data.py | def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_list):
"""Creates the predictions for the masked LM objective. This is mostly copied from the Google BERT repo, but
with several refactors to clean it up and remove a lot of unnecessary variables."""
cand_indices = []
... | def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_list):
"""Creates the predictions for the masked LM objective. This is mostly copied from the Google BERT repo, but
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train | create_instances_from_document | This code is mostly a duplicate of the equivalent function from Google BERT's repo.
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... | examples/lm_finetuning/pregenerate_training_data.py | def create_instances_from_document(
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"""This code is mostly a duplicate of the equivalent function from Google BERT's repo.
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train | sample_logits | embedding: an nn.Embedding layer
bias: [n_vocab]
labels: [b1, b2]
inputs: [b1, b2, n_emb]
sampler: you may use a LogUniformSampler
Return
logits: [b1, b2, 1 + n_sample] | pytorch_pretrained_bert/modeling_transfo_xl_utilities.py | def sample_logits(embedding, bias, labels, inputs, sampler):
"""
embedding: an nn.Embedding layer
bias: [n_vocab]
labels: [b1, b2]
inputs: [b1, b2, n_emb]
sampler: you may use a LogUniformSampler
Return
logits: [b1, b2, 1 + n_sample]
"""
true_log_probs, sa... | def sample_logits(embedding, bias, labels, inputs, sampler):
"""
embedding: an nn.Embedding layer
bias: [n_vocab]
labels: [b1, b2]
inputs: [b1, b2, n_emb]
sampler: you may use a LogUniformSampler
Return
logits: [b1, b2, 1 + n_sample]
"""
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train | ProjectedAdaptiveLogSoftmax.forward | Params:
hidden :: [len*bsz x d_proj]
target :: [len*bsz]
Return:
if target is None:
out :: [len*bsz] Negative log likelihood
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out :: [len*bsz x n_tokens] log probabilities of tokens over the vocabula... | pytorch_pretrained_bert/modeling_transfo_xl_utilities.py | def forward(self, hidden, target=None, keep_order=False):
'''
Params:
hidden :: [len*bsz x d_proj]
target :: [len*bsz]
Return:
if target is None:
out :: [len*bsz] Negative log likelihood
else:
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'''
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hidden :: [len*bsz x d_proj]
target :: [len*bsz]
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if target is None:
out :: [len*bsz] Negative log likelihood
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train | ProjectedAdaptiveLogSoftmax.log_prob | r""" Computes log probabilities for all :math:`n\_classes`
From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.py
Args:
hidden (Tensor): a minibatch of examples
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r""" Computes log probabilities for all :math:`n\_classes`
From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.py
Args:
hidden (Tensor): a minibatch of examples
Returns:
log-probabilities of for each class :ma... | def log_prob(self, hidden):
r""" Computes log probabilities for all :math:`n\_classes`
From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.py
Args:
hidden (Tensor): a minibatch of examples
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train | LogUniformSampler.sample | labels: [b1, b2]
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samp_log_probs: [n_sample]
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"""
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true_log_probs: [b1, b2]
samp_log_probs: [n_sample]
neg_samples: [n_sample]
"""
# neg_samples = torch.empty(0).long()
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n_tries = 2 * n_sample
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labels: [b1, b2]
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samp_log_probs: [n_sample]
neg_samples: [n_sample]
"""
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train | build_tf_to_pytorch_map | A map of modules from TF to PyTorch.
This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible. | pytorch_pretrained_bert/modeling_transfo_xl.py | def build_tf_to_pytorch_map(model, config):
""" A map of modules from TF to PyTorch.
This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
"""
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
# We are loading in a TransfoXLLMHeadModel... | def build_tf_to_pytorch_map(model, config):
""" A map of modules from TF to PyTorch.
This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.
"""
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
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train | load_tf_weights_in_transfo_xl | Load tf checkpoints in a pytorch model | pytorch_pretrained_bert/modeling_transfo_xl.py | def load_tf_weights_in_transfo_xl(model, config, tf_path):
""" Load tf checkpoints in a pytorch model
"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
... | def load_tf_weights_in_transfo_xl(model, config, tf_path):
""" Load tf checkpoints in a pytorch model
"""
try:
import numpy as np
import tensorflow as tf
except ImportError:
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
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train | TransfoXLPreTrainedModel.init_weights | Initialize the weights. | pytorch_pretrained_bert/modeling_transfo_xl.py | def init_weights(self, m):
""" Initialize the weights.
"""
classname = m.__class__.__name__
if classname.find('Linear') != -1:
if hasattr(m, 'weight') and m.weight is not None:
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"""
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train | TransfoXLPreTrainedModel.from_pretrained | Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
Params:
pretrained_model_name_or_path: either:
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"""
Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
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Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
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train | TransfoXLModel.forward | Params:
input_ids :: [bsz, len]
mems :: optional mems from previous forwar passes (or init_mems)
list (num layers) of mem states at the entry of each layer
shape :: [self.config.mem_len, bsz, self.config.d_model]
Note that t... | pytorch_pretrained_bert/modeling_transfo_xl.py | def forward(self, input_ids, mems=None):
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input_ids :: [bsz, len]
mems :: optional mems from previous forwar passes (or init_mems)
list (num layers) of mem states at the entry of each layer
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""" Params:
input_ids :: [bsz, len]
mems :: optional mems from previous forwar passes (or init_mems)
list (num layers) of mem states at the entry of each layer
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train | TransfoXLLMHeadModel.tie_weights | Run this to be sure output and input (adaptive) softmax weights are tied | pytorch_pretrained_bert/modeling_transfo_xl.py | def tie_weights(self):
""" Run this to be sure output and input (adaptive) softmax weights are tied """
# sampled softmax
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target :: [bsz, len]
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new_mems: list (num layers) of hidden states at the entry of each layer
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target :: [bsz, len]
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train | to_offset | Return DateOffset object from string or tuple representation
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Parameters
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freq : str, tuple, datetime.timedelta, DateOffset or None
Returns
-------
DateOffset
None if freq is None.
Raises
------
ValueError
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"""
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or datetime.timedelta object
Parameters
----------
freq : str, tuple, datetime.timedelta, DateOffset or None
Returns
-------
DateOffset
None if freq is None.
Raises
------
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"""
Return DateOffset object from string or tuple representation
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Parameters
----------
freq : str, tuple, datetime.timedelta, DateOffset or None
Returns
-------
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train | get_offset | Return DateOffset object associated with rule name
Examples
--------
get_offset('EOM') --> BMonthEnd(1) | pandas/tseries/frequencies.py | def get_offset(name):
"""
Return DateOffset object associated with rule name
Examples
--------
get_offset('EOM') --> BMonthEnd(1)
"""
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
name = libfreqs._lite_r... | def get_offset(name):
"""
Return DateOffset object associated with rule name
Examples
--------
get_offset('EOM') --> BMonthEnd(1)
"""
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
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train | infer_freq | Infer the most likely frequency given the input index. If the frequency is
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index : DatetimeIndex or TimedeltaIndex
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train | _FrequencyInferer.get_freq | Find the appropriate frequency string to describe the inferred
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train | load | load a pickle, with a provided encoding
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fake the old class hierarchy
if it works, then return the new type objects
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fh : a filelike object
encoding : an optional encoding
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"""load a pickle, with a provided encoding
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fh : a filelike object
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train | ensure_index_from_sequences | Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
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sequences : sequence of sequences
names : sequence of str
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index : Index or MultiIndex
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"""
Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
MultiIndex.
Parameters
----------
sequences : sequence of sequences
names : sequence of str
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"""
Construct an index from sequences of data.
A single sequence returns an Index. Many sequences returns a
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Parameters
----------
sequences : sequence of sequences
names : sequence of str
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train | ensure_index | Ensure that we have an index from some index-like object.
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index : sequence
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copy : bool
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index : Index or MultiIndex
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>>> ensure_index(['a', 'b'])
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index : sequence
An Index or other sequence
copy : bool
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index : Index or MultiIndex
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Ensure that we have an index from some index-like object.
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train | _trim_front | Trims zeros and decimal points. | pandas/core/indexes/base.py | def _trim_front(strings):
"""
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train | Index._simple_new | We require that we have a dtype compat for the values. If we are passed
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other : object
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train | Index._assert_take_fillable | Internal method to handle NA filling of take. | pandas/core/indexes/base.py | def _assert_take_fillable(self, values, indices, allow_fill=True,
fill_value=None, na_value=np.nan):
"""
Internal method to handle NA filling of take.
"""
indices = ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
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train | Index._format_data | Return the formatted data as a unicode string. | pandas/core/indexes/base.py | def _format_data(self, name=None):
"""
Return the formatted data as a unicode string.
"""
# do we want to justify (only do so for non-objects)
is_justify = not (self.inferred_type in ('string', 'unicode') or
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Return the formatted data as a unicode string.
"""
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is_justify = not (self.inferred_type in ('string', 'unicode') or
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train | Index.format | Render a string representation of the Index. | pandas/core/indexes/base.py | def format(self, name=False, formatter=None, **kwargs):
"""
Render a string representation of the Index.
"""
header = []
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slicer : int, array-like
An indexer into `self` that specifies which values
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train | Index._format_native_types | Actually format specific types of the index. | pandas/core/indexes/base.py | def _format_native_types(self, na_rep='', quoting=None, **kwargs):
"""
Actually format specific types of the index.
"""
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
else:
values = np.array(self, d... | def _format_native_types(self, na_rep='', quoting=None, **kwargs):
"""
Actually format specific types of the index.
"""
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
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train | Index._summary | Return a summarized representation.
Parameters
----------
name : str
name to use in the summary representation
Returns
-------
String with a summarized representation of the index | pandas/core/indexes/base.py | def _summary(self, name=None):
"""
Return a summarized representation.
Parameters
----------
name : str
name to use in the summary representation
Returns
-------
String with a summarized representation of the index
"""
if len(... | def _summary(self, name=None):
"""
Return a summarized representation.
Parameters
----------
name : str
name to use in the summary representation
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String with a summarized representation of the index
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train | Index.summary | Return a summarized representation.
.. deprecated:: 0.23.0 | pandas/core/indexes/base.py | def summary(self, name=None):
"""
Return a summarized representation.
.. deprecated:: 0.23.0
"""
warnings.warn("'summary' is deprecated and will be removed in a "
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return self._summary(name) | def summary(self, name=None):
"""
Return a summarized representation.
.. deprecated:: 0.23.0
"""
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train | Index.to_series | Create a Series with both index and values equal to the index keys
useful with map for returning an indexer based on an index.
Parameters
----------
index : Index, optional
index of resulting Series. If None, defaults to original index
name : string, optional
... | pandas/core/indexes/base.py | def to_series(self, index=None, name=None):
"""
Create a Series with both index and values equal to the index keys
useful with map for returning an indexer based on an index.
Parameters
----------
index : Index, optional
index of resulting Series. If None, de... | def to_series(self, index=None, name=None):
"""
Create a Series with both index and values equal to the index keys
useful with map for returning an indexer based on an index.
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----------
index : Index, optional
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train | Index.to_frame | Create a DataFrame with a column containing the Index.
.. versionadded:: 0.24.0
Parameters
----------
index : boolean, default True
Set the index of the returned DataFrame as the original Index.
name : object, default None
The passed name should substit... | pandas/core/indexes/base.py | def to_frame(self, index=True, name=None):
"""
Create a DataFrame with a column containing the Index.
.. versionadded:: 0.24.0
Parameters
----------
index : boolean, default True
Set the index of the returned DataFrame as the original Index.
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"""
Create a DataFrame with a column containing the Index.
.. versionadded:: 0.24.0
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----------
index : boolean, default True
Set the index of the returned DataFrame as the original Index.
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train | Index._validate_names | Handles the quirks of having a singular 'name' parameter for general
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"""
Handles the quirks of having a singular 'name' parameter for general
Index and plural 'names' parameter for MultiIndex.
"""
from copy import deepcopy
if names is not None and name is not None:
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train | Index._set_names | Set new names on index. Each name has to be a hashable type.
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----------
values : str or sequence
name(s) to set
level : int, level name, or sequence of int/level names (default None)
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"""
Set new names on index. Each name has to be a hashable type.
Parameters
----------
values : str or sequence
name(s) to set
level : int, level name, or sequence of int/level names (default None)
If the ... | def _set_names(self, values, level=None):
"""
Set new names on index. Each name has to be a hashable type.
Parameters
----------
values : str or sequence
name(s) to set
level : int, level name, or sequence of int/level names (default None)
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train | Index.set_names | Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label
Name(s) to set.
level : int, label or list of int or label, optional
If the index is a MultiIndex, level(s) to set (None for ... | pandas/core/indexes/base.py | def set_names(self, names, level=None, inplace=False):
"""
Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label
Name(s) to set.
level : int, label or list of int or label, op... | def set_names(self, names, level=None, inplace=False):
"""
Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label
Name(s) to set.
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train | Index.rename | Alter Index or MultiIndex name.
Able to set new names without level. Defaults to returning new index.
Length of names must match number of levels in MultiIndex.
Parameters
----------
name : label or list of labels
Name(s) to set.
inplace : boolean, default F... | pandas/core/indexes/base.py | def rename(self, name, inplace=False):
"""
Alter Index or MultiIndex name.
Able to set new names without level. Defaults to returning new index.
Length of names must match number of levels in MultiIndex.
Parameters
----------
name : label or list of labels
... | def rename(self, name, inplace=False):
"""
Alter Index or MultiIndex name.
Able to set new names without level. Defaults to returning new index.
Length of names must match number of levels in MultiIndex.
Parameters
----------
name : label or list of labels
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train | Index._validate_index_level | Validate index level.
For single-level Index getting level number is a no-op, but some
verification must be done like in MultiIndex. | pandas/core/indexes/base.py | def _validate_index_level(self, level):
"""
Validate index level.
For single-level Index getting level number is a no-op, but some
verification must be done like in MultiIndex.
"""
if isinstance(level, int):
if level < 0 and level != -1:
rais... | def _validate_index_level(self, level):
"""
Validate index level.
For single-level Index getting level number is a no-op, but some
verification must be done like in MultiIndex.
"""
if isinstance(level, int):
if level < 0 and level != -1:
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train | Index.sortlevel | For internal compatibility with with the Index API.
Sort the Index. This is for compat with MultiIndex
Parameters
----------
ascending : boolean, default True
False to sort in descending order
level, sort_remaining are compat parameters
Returns
---... | pandas/core/indexes/base.py | def sortlevel(self, level=None, ascending=True, sort_remaining=None):
"""
For internal compatibility with with the Index API.
Sort the Index. This is for compat with MultiIndex
Parameters
----------
ascending : boolean, default True
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"""
For internal compatibility with with the Index API.
Sort the Index. This is for compat with MultiIndex
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----------
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train | Index.droplevel | Return index with requested level(s) removed.
If resulting index has only 1 level left, the result will be
of Index type, not MultiIndex.
.. versionadded:: 0.23.1 (support for non-MultiIndex)
Parameters
----------
level : int, str, or list-like, default 0
I... | pandas/core/indexes/base.py | def droplevel(self, level=0):
"""
Return index with requested level(s) removed.
If resulting index has only 1 level left, the result will be
of Index type, not MultiIndex.
.. versionadded:: 0.23.1 (support for non-MultiIndex)
Parameters
----------
level... | def droplevel(self, level=0):
"""
Return index with requested level(s) removed.
If resulting index has only 1 level left, the result will be
of Index type, not MultiIndex.
.. versionadded:: 0.23.1 (support for non-MultiIndex)
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----------
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train | Index._isnan | Return if each value is NaN. | pandas/core/indexes/base.py | def _isnan(self):
"""
Return if each value is NaN.
"""
if self._can_hold_na:
return isna(self)
else:
# shouldn't reach to this condition by checking hasnans beforehand
values = np.empty(len(self), dtype=np.bool_)
values.fill(False)
... | def _isnan(self):
"""
Return if each value is NaN.
"""
if self._can_hold_na:
return isna(self)
else:
# shouldn't reach to this condition by checking hasnans beforehand
values = np.empty(len(self), dtype=np.bool_)
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train | Index.get_duplicates | Extract duplicated index elements.
.. deprecated:: 0.23.0
Use idx[idx.duplicated()].unique() instead
Returns a sorted list of index elements which appear more than once in
the index.
Returns
-------
array-like
List of duplicated indexes.
... | pandas/core/indexes/base.py | def get_duplicates(self):
"""
Extract duplicated index elements.
.. deprecated:: 0.23.0
Use idx[idx.duplicated()].unique() instead
Returns a sorted list of index elements which appear more than once in
the index.
Returns
-------
array-like
... | def get_duplicates(self):
"""
Extract duplicated index elements.
.. deprecated:: 0.23.0
Use idx[idx.duplicated()].unique() instead
Returns a sorted list of index elements which appear more than once in
the index.
Returns
-------
array-like
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train | Index._get_unique_index | Returns an index containing unique values.
Parameters
----------
dropna : bool
If True, NaN values are dropped.
Returns
-------
uniques : index | pandas/core/indexes/base.py | def _get_unique_index(self, dropna=False):
"""
Returns an index containing unique values.
Parameters
----------
dropna : bool
If True, NaN values are dropped.
Returns
-------
uniques : index
"""
if self.is_unique and not dropn... | def _get_unique_index(self, dropna=False):
"""
Returns an index containing unique values.
Parameters
----------
dropna : bool
If True, NaN values are dropped.
Returns
-------
uniques : index
"""
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train | Index._get_reconciled_name_object | If the result of a set operation will be self,
return self, unless the name changes, in which
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"""
If the result of a set operation will be self,
return self, unless the name changes, in which
case make a shallow copy of self.
"""
name = get_op_result_name(self, other)
if self.name != name:
return se... | def _get_reconciled_name_object(self, other):
"""
If the result of a set operation will be self,
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"""
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Whether to sort the resulting Index.
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Form the union of two Index objects.
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train | Index.intersection | Form the intersection of two Index objects.
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Whether to sort the resulting index.
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Form the intersection of two Index objects.
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train | Index.difference | Return a new Index with elements from the index that are not in
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other : Index or array-like
sort : False or None, default None
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Return a new Index with elements from the index that are not in
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train | Index.symmetric_difference | Compute the symmetric difference of two Index objects.
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sort : False or None, default None
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train | Index._get_fill_indexer_searchsorted | Fallback pad/backfill get_indexer that works for monotonic decreasing
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"""
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Fallback pad/backfill get_indexer that works for monotonic decreasing
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train | Index._get_nearest_indexer | Get the indexer for the nearest index labels; requires an index with
values that can be subtracted from each other (e.g., not strings or
tuples). | pandas/core/indexes/base.py | def _get_nearest_indexer(self, target, limit, tolerance):
"""
Get the indexer for the nearest index labels; requires an index with
values that can be subtracted from each other (e.g., not strings or
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"""
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Get the indexer for the nearest index labels; requires an index with
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"""
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train | Index._convert_listlike_indexer | Parameters
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keyarr : list-like
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Returns
-------
indexer : numpy.ndarray or None
Return an ndarray or None if cannot convert.
keyarr : numpy.ndarray
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"""
Parameters
----------
keyarr : list-like
Indexer to convert.
Returns
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indexer : numpy.ndarray or None
Return an ndarray or None if cannot convert.
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train | Index._invalid_indexer | Consistent invalid indexer message. | pandas/core/indexes/base.py | def _invalid_indexer(self, form, key):
"""
Consistent invalid indexer message.
"""
raise TypeError("cannot do {form} indexing on {klass} with these "
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Consistent invalid indexer message.
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train | Index.reindex | Create index with target's values (move/add/delete values
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Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in ori... | pandas/core/indexes/base.py | def reindex(self, target, method=None, level=None, limit=None,
tolerance=None):
"""
Create index with target's values (move/add/delete values
as necessary).
Parameters
----------
target : an iterable
Returns
-------
new_index : pd... | def reindex(self, target, method=None, level=None, limit=None,
tolerance=None):
"""
Create index with target's values (move/add/delete values
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Parameters
----------
target : an iterable
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-------
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----------
target : an iterable
Returns
-------
new_index : pd.Index
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Create a new index with target's values (move/add/delete values as
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target : an iterable
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target : an iterable
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labels of the level in the MultiIndex.
If ```keep_order == True```, the order of the data indexed by the
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"""
The join method *only* affects the level of the resulting
MultiIndex. Otherwise it just exactly aligns the Index data to the
labels of the level in the MultiIndex.
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"""
The join method *only* affects the level of the resulting
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train | Index._try_convert_to_int_index | Attempt to convert an array of data into an integer index.
Parameters
----------
data : The data to convert.
copy : Whether to copy the data or not.
name : The name of the index returned.
Returns
-------
int_index : data converted to either an Int64Index... | pandas/core/indexes/base.py | def _try_convert_to_int_index(cls, data, copy, name, dtype):
"""
Attempt to convert an array of data into an integer index.
Parameters
----------
data : The data to convert.
copy : Whether to copy the data or not.
name : The name of the index returned.
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Attempt to convert an array of data into an integer index.
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----------
data : The data to convert.
copy : Whether to copy the data or not.
name : The name of the index returned.
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train | Index._coerce_to_ndarray | Coerces data to ndarray.
Converts other iterables to list first and then to array.
Does not touch ndarrays.
Raises
------
TypeError
When the data passed in is a scalar. | pandas/core/indexes/base.py | def _coerce_to_ndarray(cls, data):
"""
Coerces data to ndarray.
Converts other iterables to list first and then to array.
Does not touch ndarrays.
Raises
------
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"""
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Coerces data to ndarray.
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------
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When the data passed in is a scalar.
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train | Index._coerce_scalar_to_index | We need to coerce a scalar to a compat for our index type.
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----------
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"""
We need to coerce a scalar to a compat for our index type.
Parameters
----------
item : scalar item to coerce
"""
dtype = self.dtype
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We need to coerce a scalar to a compat for our index type.
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train | Index._assert_can_do_op | Check value is valid for scalar op. | pandas/core/indexes/base.py | def _assert_can_do_op(self, value):
"""
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train | Index._can_hold_identifiers_and_holds_name | Faster check for ``name in self`` when we know `name` is a Python
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train | Index.append | Append a collection of Index options together.
Parameters
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other : Index or list/tuple of indices
Returns
-------
appended : Index | pandas/core/indexes/base.py | def append(self, other):
"""
Append a collection of Index options together.
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
to_concat = [self]
if isinstance(other, (list, tuple))... | def append(self, other):
"""
Append a collection of Index options together.
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
to_concat = [self]
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train | Index.putmask | Return a new Index of the values set with the mask.
See Also
--------
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"""
Return a new Index of the values set with the mask.
See Also
--------
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"""
values = self.values.copy()
try:
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train | Index.equals | Determine if two Index objects contain the same elements. | pandas/core/indexes/base.py | def equals(self, other):
"""
Determine if two Index objects contain the same elements.
"""
if self.is_(other):
return True
if not isinstance(other, Index):
return False
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train | Index.identical | Similar to equals, but check that other comparable attributes are
also equal. | pandas/core/indexes/base.py | def identical(self, other):
"""
Similar to equals, but check that other comparable attributes are
also equal.
"""
return (self.equals(other) and
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train | Index.asof | Return the label from the index, or, if not present, the previous one.
Assuming that the index is sorted, return the passed index label if it
is in the index, or return the previous index label if the passed one
is not in the index.
Parameters
----------
label : object
... | pandas/core/indexes/base.py | def asof(self, label):
"""
Return the label from the index, or, if not present, the previous one.
Assuming that the index is sorted, return the passed index label if it
is in the index, or return the previous index label if the passed one
is not in the index.
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"""
Return the label from the index, or, if not present, the previous one.
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is in the index, or return the previous index label if the passed one
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train | Index.sort_values | Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
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Parameters
----------
return_indexer : bool, default False
Should the indices that would sort the index be returned.
ascendi... | pandas/core/indexes/base.py | def sort_values(self, return_indexer=False, ascending=True):
"""
Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
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Parameters
----------
return_indexer : bool, default False
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Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
that sorted the index itself.
Parameters
----------
return_indexer : bool, default False
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train | Index.argsort | Return the integer indices that would sort the index.
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Passed to `numpy.ndarray.argsort`.
**kwargs
Passed to `numpy.ndarray.argsort`.
Returns
-------
numpy.ndarray
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"""
Return the integer indices that would sort the index.
Parameters
----------
*args
Passed to `numpy.ndarray.argsort`.
**kwargs
Passed to `numpy.ndarray.argsort`.
Returns
-------
numpy... | def argsort(self, *args, **kwargs):
"""
Return the integer indices that would sort the index.
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Passed to `numpy.ndarray.argsort`.
**kwargs
Passed to `numpy.ndarray.argsort`.
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train | Index.get_value | Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing. | pandas/core/indexes/base.py | def get_value(self, series, key):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing.
"""
# if we have something that is Index-like, then
# use this, e.g. DatetimeIndex
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Fast lookup of value from 1-dimensional ndarray. Only use this if you
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train | Index.set_value | Fast lookup of value from 1-dimensional ndarray.
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-----
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"""
Fast lookup of value from 1-dimensional ndarray.
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-----
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"""
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train | Index.get_indexer_for | Guaranteed return of an indexer even when non-unique.
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"""
Guaranteed return of an indexer even when non-unique.
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as appropriate.
"""
if self.is_unique:
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indexer, _ ... | def get_indexer_for(self, target, **kwargs):
"""
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"""
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train | Index.groupby | Group the index labels by a given array of values.
Parameters
----------
values : array
Values used to determine the groups.
Returns
-------
groups : dict
{group name -> group labels} | pandas/core/indexes/base.py | def groupby(self, values):
"""
Group the index labels by a given array of values.
Parameters
----------
values : array
Values used to determine the groups.
Returns
-------
groups : dict
{group name -> group labels}
"""
... | def groupby(self, values):
"""
Group the index labels by a given array of values.
Parameters
----------
values : array
Values used to determine the groups.
Returns
-------
groups : dict
{group name -> group labels}
"""
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train | Index.map | Map values using input correspondence (a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping co... | pandas/core/indexes/base.py | def map(self, mapper, na_action=None):
"""
Map values using input correspondence (a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
na_action : {None, 'ignore'}
If 'ignore', propagate NA... | def map(self, mapper, na_action=None):
"""
Map values using input correspondence (a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
na_action : {None, 'ignore'}
If 'ignore', propagate NA... | [
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train | Index.isin | Return a boolean array where the index values are in `values`.
Compute boolean array of whether each index value is found in the
passed set of values. The length of the returned boolean array matches
the length of the index.
Parameters
----------
values : set or list-li... | pandas/core/indexes/base.py | def isin(self, values, level=None):
"""
Return a boolean array where the index values are in `values`.
Compute boolean array of whether each index value is found in the
passed set of values. The length of the returned boolean array matches
the length of the index.
Param... | def isin(self, values, level=None):
"""
Return a boolean array where the index values are in `values`.
Compute boolean array of whether each index value is found in the
passed set of values. The length of the returned boolean array matches
the length of the index.
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