Search is not available for this dataset
text stringlengths 75 104k |
|---|
def count_words(text, to_lower=True, delimiters=DEFAULT_DELIMITERS):
"""
If `text` is an SArray of strings or an SArray of lists of strings, the
occurances of word are counted for each row in the SArray.
If `text` is an SArray of dictionaries, the keys are tokenized and the
values are the counts. C... |
def count_ngrams(text, n=2, method="word", to_lower=True,
delimiters=DEFAULT_DELIMITERS,
ignore_punct=True, ignore_space=True):
"""
Return an SArray of ``dict`` type where each element contains the count
for each of the n-grams that appear in the corresponding input element... |
def tf_idf(text):
"""
Compute the TF-IDF scores for each word in each document. The collection
of documents must be in bag-of-words format.
.. math::
\mbox{TF-IDF}(w, d) = tf(w, d) * log(N / f(w))
where :math:`tf(w, d)` is the number of times word :math:`w` appeared in
document :math:`... |
def drop_words(text, threshold=2, to_lower=True, delimiters=DEFAULT_DELIMITERS,
stop_words=None):
'''
Remove words that occur below a certain number of times in an SArray.
This is a common method of cleaning text before it is used, and can increase the
quality and explainability of the mo... |
def tokenize(text, to_lower=False, delimiters=DEFAULT_DELIMITERS):
"""
Tokenize the input SArray of text strings and return the list of tokens.
Parameters
----------
text : SArray[str]
Input data of strings representing English text. This tokenizer is not
intended to process XML, HT... |
def bm25(dataset, query, k1=1.5, b=.75):
"""
For a given query and set of documents, compute the BM25 score for each
document. If we have a query with words q_1, ..., q_n the BM25 score for
a document is:
.. math:: \sum_{i=1}^N IDF(q_i)\\frac{f(q_i) * (k_1+1)}{f(q_i) + k_1 * (1-b+b*|D|/d_avg))}... |
def parse_sparse(filename, vocab_filename):
"""
Parse a file that's in libSVM format. In libSVM format each line of the text
file represents a document in bag of words format:
num_unique_words_in_doc word_id:count another_id:count
The word_ids have 0-based indexing, i.e. 0 corresponds to the first... |
def parse_docword(filename, vocab_filename):
"""
Parse a file that's in "docword" format. This consists of a 3-line header
comprised of the document count, the vocabulary count, and the number of
tokens, i.e. unique (doc_id, word_id) pairs. After the header, each line
contains a space-separated trip... |
def random_split(dataset, prob=.5):
"""
Utility for performing a random split for text data that is already in
bag-of-words format. For each (word, count) pair in a particular element,
the counts are uniformly partitioned in either a training set or a test
set.
Parameters
----------
dat... |
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
maximize=False, early_stopping_rounds=None, evals_result=None,
verbose_eval=True, learning_rates=None, xgb_model=None):
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
"""Tra... |
def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None):
"""
Make an n-fold list of CVPack from random indices.
"""
evals = list(evals)
np.random.seed(seed)
randidx = np.random.permutation(dall.num_row())
kstep = len(randidx) / nfold
idset = [randidx[(i * kstep): min(len(randidx),... |
def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True):
# pylint: disable=invalid-name
"""
Aggregate cross-validation results.
"""
cvmap = {}
idx = rlist[0].split()[0]
for line in rlist:
arr = line.split()
assert idx == arr[0]
for it in arr[1:]:
... |
def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
obj=None, feval=None, fpreproc=None, as_pandas=True,
show_progress=None, show_stdv=True, seed=0):
# pylint: disable = invalid-name
"""Cross-validation with given paramaters.
Parameters
----------
params : dict
Boo... |
def create(dataset, target, feature=None, model = 'resnet-50',
l2_penalty=0.01,
l1_penalty=0.0,
solver='auto', feature_rescaling=True,
convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
step_size = _DEFAULT_SOLVER_OPTIONS['step_size'],
lbfgs_memory_level = _DEFAULT_SOLVER... |
def _get_native_state(self):
"""
Save the model as a dictionary, which can be loaded with the
:py:func:`~turicreate.load_model` method.
"""
state = self.__proxy__.get_state()
state['classifier'] = state['classifier'].__proxy__
del state['feature_extractor']
... |
def _load_version(cls, state, version):
"""
A function to load a previously saved ImageClassifier
instance.
"""
_tkutl._model_version_check(version, cls._PYTHON_IMAGE_CLASSIFIER_VERSION)
from turicreate.toolkits.classifier.logistic_classifier import LogisticClassifier
... |
def predict(self, dataset, output_type='class', batch_size=64):
"""
Return predictions for ``dataset``, using the trained logistic
regression model. Predictions can be generated as class labels,
probabilities that the target value is True, or margins (i.e. the
distance of the obs... |
def predict_topk(self, dataset, output_type="probability", k=3, batch_size=64):
"""
Return top-k predictions for the ``dataset``, using the trained model.
Predictions are returned as an SFrame with three columns: `id`,
`class`, and `probability`, `margin`, or `rank`, depending on the ``... |
def evaluate(self, dataset, metric='auto', verbose=True, batch_size=64):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset of new observations. Must include colum... |
def export_coreml(self, filename):
"""
Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('myModel.mlmodel')
"""
import coremltools
# First define three internal helper functions
... |
def make_input_layers(self):
"""
Extract the ordering of the input layers.
"""
self.input_layers = []
in_nodes = self.model._inbound_nodes if hasattr(
self.model,'_inbound_nodes') else self.model.inbound_nodes
if hasattr(self.model, 'input_layers'):
... |
def make_output_layers(self):
"""
Extract the ordering of output layers.
"""
self.output_layers = []
# import pytest; pytest.set_trace()
if hasattr(self.model, 'output_layers'):
# find corresponding output layers in CoreML model
# assume output lay... |
def _remove_layer_and_reconnect(self, layer):
""" Remove the layer, and reconnect each of its predecessor to each of
its successor
"""
successors = self.get_successors(layer)
predecessors = self.get_predecessors(layer)
# remove layer's edges
for succ in successors... |
def defuse_activation(self):
""" Defuse the fused activation layers in the network.
"""
idx, nb_layers = 0, len(self.layer_list)
while idx < nb_layers:
layer = self.layer_list[idx]
k_layer = self.keras_layer_map[layer]
if (isinstance(k_layer, _keras.la... |
def date_range(cls,start_time,end_time,freq):
'''
Returns a new SArray that represents a fixed frequency datetime index.
Parameters
----------
start_time : datetime.datetime
Left bound for generating dates.
end_time : datetime.datetime
Right bound fo... |
def from_const(cls, value, size, dtype=type(None)):
"""
Constructs an SArray of size with a const value.
Parameters
----------
value : [int | float | str | array.array | list | dict | datetime]
The value to fill the SArray
size : int
The size of the S... |
def from_sequence(cls, *args):
"""
from_sequence(start=0, stop)
Create an SArray from sequence
.. sourcecode:: python
Construct an SArray of integer values from 0 to 999
>>> tc.SArray.from_sequence(1000)
This is equivalent, but more efficient than... |
def read_json(cls, filename):
"""
Construct an SArray from a json file or glob of json files.
The json file must contain a list of dictionaries. The returned
SArray type will be of dict type
Parameters
----------
filename : str
The filename or glob to l... |
def where(cls, condition, istrue, isfalse, dtype=None):
"""
Selects elements from either istrue or isfalse depending on the value
of the condition SArray.
Parameters
----------
condition : SArray
An SArray of values such that for each value, if non-zero, yields a... |
def save(self, filename, format=None):
"""
Saves the SArray to file.
The saved SArray will be in a directory named with the `targetfile`
parameter.
Parameters
----------
filename : string
A local path or a remote URL. If format is 'text', it will be... |
def vector_slice(self, start, end=None):
"""
If this SArray contains vectors or lists, this returns a new SArray
containing each individual element sliced, between start and
end (exclusive).
Parameters
----------
start : int
The start position of the ... |
def element_slice(self, start=None, stop=None, step=None):
"""
This returns an SArray with each element sliced accordingly to the
slice specified. This is conceptually equivalent to:
>>> g.apply(lambda x: x[start:step:stop])
The SArray must be of type list, vector, or string.
... |
def _count_words(self, to_lower=True, delimiters=["\r", "\v", "\n", "\f", "\t", " "]):
"""
This returns an SArray with, for each input string, a dict from the unique,
delimited substrings to their number of occurrences within the original
string.
The SArray must be of type strin... |
def _count_ngrams(self, n=2, method="word", to_lower=True, ignore_space=True):
"""
For documentation, see turicreate.text_analytics.count_ngrams().
..WARNING:: This function is deprecated, and will be removed in future
versions of Turi Create. Please use the `text_analytics.count_words`... |
def dict_trim_by_keys(self, keys, exclude=True):
"""
Filter an SArray of dictionary type by the given keys. By default, all
keys that are in the provided list in ``keys`` are *excluded* from the
returned SArray.
Parameters
----------
keys : list
A col... |
def dict_trim_by_values(self, lower=None, upper=None):
"""
Filter dictionary values to a given range (inclusive). Trimming is only
performed on values which can be compared to the bound values. Fails on
SArrays whose data type is not ``dict``.
Parameters
----------
... |
def dict_has_any_keys(self, keys):
"""
Create a boolean SArray by checking the keys of an SArray of
dictionaries. An element of the output SArray is True if the
corresponding input element's dictionary has any of the given keys.
Fails on SArrays whose data type is not ``dict``.
... |
def dict_has_all_keys(self, keys):
"""
Create a boolean SArray by checking the keys of an SArray of
dictionaries. An element of the output SArray is True if the
corresponding input element's dictionary has all of the given keys.
Fails on SArrays whose data type is not ``dict``.
... |
def apply(self, fn, dtype=None, skip_na=True, seed=None):
"""
apply(fn, dtype=None, skip_na=True, seed=None)
Transform each element of the SArray by a given function. The result
SArray is of type ``dtype``. ``fn`` should be a function that returns
exactly one value which can be ... |
def filter(self, fn, skip_na=True, seed=None):
"""
Filter this SArray by a function.
Returns a new SArray filtered by this SArray. If `fn` evaluates an
element to true, this element is copied to the new SArray. If not, it
isn't. Throws an exception if the return type of `fn` is... |
def sample(self, fraction, seed=None, exact=False):
"""
Create an SArray which contains a subsample of the current SArray.
Parameters
----------
fraction : float
Fraction of the rows to fetch. Must be between 0 and 1.
if exact is False (default), the numb... |
def hash(self, seed=0):
"""
Returns an SArray with a hash of each element. seed can be used
to change the hash function to allow this method to be used for
random number generation.
Parameters
----------
seed : int
Defaults to 0. Can be changed to dif... |
def random_integers(cls, size, seed=None):
"""
Returns an SArray with random integer values.
"""
if seed is None:
seed = abs(hash("%0.20f" % time.time())) % (2 ** 31)
return cls.from_sequence(size).hash(seed) |
def argmin(self):
"""
Get the index of the minimum numeric value in SArray.
Returns None on an empty SArray. Raises an exception if called on an
SArray with non-numeric type.
Returns
-------
out : int
index of the minimum value of SArray
See... |
def mean(self):
"""
Mean of all the values in the SArray, or mean image.
Returns None on an empty SArray. Raises an exception if called on an
SArray with non-numeric type or non-Image type.
Returns
-------
out : float | turicreate.Image
Mean of all v... |
def datetime_to_str(self,format="%Y-%m-%dT%H:%M:%S%ZP"):
"""
Create a new SArray with all the values cast to str. The string format is
specified by the 'format' parameter.
Parameters
----------
format : str
The format to output the string. Default format is "... |
def str_to_datetime(self,format="%Y-%m-%dT%H:%M:%S%ZP"):
"""
Create a new SArray with all the values cast to datetime. The string format is
specified by the 'format' parameter.
Parameters
----------
format : str
The string format of the input SArray. Default ... |
def pixel_array_to_image(self, width, height, channels, undefined_on_failure=True, allow_rounding=False):
"""
Create a new SArray with all the values cast to :py:class:`turicreate.image.Image`
of uniform size.
Parameters
----------
width: int
The width of the... |
def astype(self, dtype, undefined_on_failure=False):
"""
Create a new SArray with all values cast to the given type. Throws an
exception if the types are not castable to the given type.
Parameters
----------
dtype : {int, float, str, list, array.array, dict, datetime.dat... |
def clip(self, lower=float('nan'), upper=float('nan')):
"""
Create a new SArray with each value clipped to be within the given
bounds.
In this case, "clipped" means that values below the lower bound will be
set to the lower bound value. Values above the upper bound will be set
... |
def clip_lower(self, threshold):
"""
Create new SArray with all values clipped to the given lower bound. This
function can operate on numeric arrays, as well as vector arrays, in
which case each individual element in each vector is clipped. Throws an
exception if the SArray is em... |
def tail(self, n=10):
"""
Get an SArray that contains the last n elements in the SArray.
Parameters
----------
n : int
The number of elements to fetch
Returns
-------
out : SArray
A new SArray which contains the last n rows of the... |
def fillna(self, value):
"""
Create new SArray with all missing values (None or NaN) filled in
with the given value.
The size of the new SArray will be the same as the original SArray. If
the given value is not the same type as the values in the SArray,
`fillna` will att... |
def is_topk(self, topk=10, reverse=False):
"""
Create an SArray indicating which elements are in the top k.
Entries are '1' if the corresponding element in the current SArray is a
part of the top k elements, and '0' if that corresponding element is
not. Order is descending by de... |
def summary(self, background=False, sub_sketch_keys=None):
"""
Summary statistics that can be calculated with one pass over the SArray.
Returns a turicreate.Sketch object which can be further queried for many
descriptive statistics over this SArray. Many of the statistics are
ap... |
def value_counts(self):
"""
Return an SFrame containing counts of unique values. The resulting
SFrame will be sorted in descending frequency.
Returns
-------
out : SFrame
An SFrame containing 2 columns : 'value', and 'count'. The SFrame will
be so... |
def append(self, other):
"""
Append an SArray to the current SArray. Creates a new SArray with the
rows from both SArrays. Both SArrays must be of the same type.
Parameters
----------
other : SArray
Another SArray whose rows are appended to current SArray.
... |
def unique(self):
"""
Get all unique values in the current SArray.
Raises a TypeError if the SArray is of dictionary type. Will not
necessarily preserve the order of the given SArray in the new SArray.
Returns
-------
out : SArray
A new SArray that ... |
def show(self, title=LABEL_DEFAULT, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT):
"""
Visualize the SArray.
Notes
-----
- The plot will render either inline in a Jupyter Notebook, or in a
native GUI window, depending on the value provided in
`turicreate.visual... |
def plot(self, title=LABEL_DEFAULT, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT):
"""
Create a Plot object representing the SArray.
Notes
-----
- The plot will render either inline in a Jupyter Notebook, or in a
native GUI window, depending on the value provided in
... |
def item_length(self):
"""
Length of each element in the current SArray.
Only works on SArrays of dict, array, or list type. If a given element
is a missing value, then the output elements is also a missing value.
This function is equivalent to the following but more performant:... |
def random_split(self, fraction, seed=None):
"""
Randomly split the rows of an SArray into two SArrays. The first SArray
contains *M* rows, sampled uniformly (without replacement) from the
original SArray. *M* is approximately the fraction times the original
number of rows. The s... |
def split_datetime(self, column_name_prefix = "X", limit=None, timezone=False):
"""
Splits an SArray of datetime type to multiple columns, return a
new SFrame that contains expanded columns. A SArray of datetime will be
split by default into an SFrame of 6 columns, one for each
y... |
def stack(self, new_column_name=None, drop_na=False, new_column_type=None):
"""
Convert a "wide" SArray to one or two "tall" columns in an SFrame by
stacking all values.
The stack works only for columns of dict, list, or array type. If the
column is dict type, two new columns a... |
def unpack(self, column_name_prefix = "X", column_types=None, na_value=None, limit=None):
"""
Convert an SArray of list, array, or dict type to an SFrame with
multiple columns.
`unpack` expands an SArray using the values of each list/array/dict as
elements in a new SFrame of mul... |
def sort(self, ascending=True):
"""
Sort all values in this SArray.
Sort only works for sarray of type str, int and float, otherwise TypeError
will be raised. Creates a new, sorted SArray.
Parameters
----------
ascending: boolean, optional
If true, th... |
def rolling_sum(self, window_start, window_end, min_observations=None):
"""
Calculate a new SArray of the sum of different subsets over this
SArray.
Also known as a "moving sum" or "running sum". The subset that
the sum is calculated over is defined as an inclusive range relativ... |
def rolling_max(self, window_start, window_end, min_observations=None):
"""
Calculate a new SArray of the maximum value of different subsets over
this SArray.
The subset that the maximum is calculated over is defined as an
inclusive range relative to the position to each value i... |
def rolling_count(self, window_start, window_end):
"""
Count the number of non-NULL values of different subsets over this
SArray.
The subset that the count is executed on is defined as an inclusive
range relative to the position to each value in the SArray, using
`window... |
def cumulative_sum(self):
"""
Return the cumulative sum of the elements in the SArray.
Returns an SArray where each element in the output corresponds to the
sum of all the elements preceding and including it. The SArray is
expected to be of numeric type (int, float), or a numeri... |
def cumulative_mean(self):
"""
Return the cumulative mean of the elements in the SArray.
Returns an SArray where each element in the output corresponds to the
mean value of all the elements preceding and including it. The SArray
is expected to be of numeric type (int, float), or... |
def cumulative_min(self):
"""
Return the cumulative minimum value of the elements in the SArray.
Returns an SArray where each element in the output corresponds to the
minimum value of all the elements preceding and including it. The
SArray is expected to be of numeric type (int,... |
def cumulative_max(self):
"""
Return the cumulative maximum value of the elements in the SArray.
Returns an SArray where each element in the output corresponds to the
maximum value of all the elements preceding and including it. The
SArray is expected to be of numeric type (int,... |
def cumulative_std(self):
"""
Return the cumulative standard deviation of the elements in the SArray.
Returns an SArray where each element in the output corresponds to the
standard deviation of all the elements preceding and including it. The
SArray is expected to be of numeric ... |
def cumulative_var(self):
"""
Return the cumulative variance of the elements in the SArray.
Returns an SArray where each element in the output corresponds to the
variance of all the elements preceding and including it. The SArray is
expected to be of numeric type, or a numeric v... |
def filter_by(self, values, exclude=False):
"""
Filter an SArray by values inside an iterable object. The result is an SArray that
only includes (or excludes) the values in the given ``values`` :class:`~turicreate.SArray`.
If ``values`` is not an SArray, we attempt to convert it... |
def run_build_lib(folder):
"""Run the doxygen make command in the designated folder."""
try:
retcode = subprocess.call("cd %s; make" % folder, shell=True)
retcode = subprocess.call("rm -rf _build/html/doxygen", shell=True)
retcode = subprocess.call("mkdir _build", shell=True)
ret... |
def generate_doxygen_xml(app):
"""Run the doxygen make commands if we're on the ReadTheDocs server"""
read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True'
if read_the_docs_build:
run_doxygen('..')
sys.stderr.write('Check if shared lib exists\n')
run_build_lib('..')
... |
def MessageToJson(message,
including_default_value_fields=False,
preserving_proto_field_name=False):
"""Converts protobuf message to JSON format.
Args:
message: The protocol buffers message instance to serialize.
including_default_value_fields: If True, singular primitiv... |
def MessageToDict(message,
including_default_value_fields=False,
preserving_proto_field_name=False):
"""Converts protobuf message to a JSON dictionary.
Args:
message: The protocol buffers message instance to serialize.
including_default_value_fields: If True, singular pr... |
def ParseDict(js_dict, message, ignore_unknown_fields=False):
"""Parses a JSON dictionary representation into a message.
Args:
js_dict: Dict representation of a JSON message.
message: A protocol buffer message to merge into.
ignore_unknown_fields: If True, do not raise errors for unknown fields.
Ret... |
def _ConvertScalarFieldValue(value, field, require_str=False):
"""Convert a single scalar field value.
Args:
value: A scalar value to convert the scalar field value.
field: The descriptor of the field to convert.
require_str: If True, the field value must be a str.
Returns:
The converted scalar ... |
def _ConvertInteger(value):
"""Convert an integer.
Args:
value: A scalar value to convert.
Returns:
The integer value.
Raises:
ParseError: If an integer couldn't be consumed.
"""
if isinstance(value, float) and not value.is_integer():
raise ParseError('Couldn\'t parse integer: {0}.'.forma... |
def _ConvertFloat(value):
"""Convert an floating point number."""
if value == 'nan':
raise ParseError('Couldn\'t parse float "nan", use "NaN" instead.')
try:
# Assume Python compatible syntax.
return float(value)
except ValueError:
# Check alternative spellings.
if value == _NEG_INFINITY:
... |
def _ConvertBool(value, require_str):
"""Convert a boolean value.
Args:
value: A scalar value to convert.
require_str: If True, value must be a str.
Returns:
The bool parsed.
Raises:
ParseError: If a boolean value couldn't be consumed.
"""
if require_str:
if value == 'true':
ret... |
def _MessageToJsonObject(self, message):
"""Converts message to an object according to Proto3 JSON Specification."""
message_descriptor = message.DESCRIPTOR
full_name = message_descriptor.full_name
if _IsWrapperMessage(message_descriptor):
return self._WrapperMessageToJsonObject(message)
if fu... |
def _RegularMessageToJsonObject(self, message, js):
"""Converts normal message according to Proto3 JSON Specification."""
fields = message.ListFields()
try:
for field, value in fields:
if self.preserving_proto_field_name:
name = field.name
else:
name = field.json_n... |
def _FieldToJsonObject(self, field, value):
"""Converts field value according to Proto3 JSON Specification."""
if field.cpp_type == descriptor.FieldDescriptor.CPPTYPE_MESSAGE:
return self._MessageToJsonObject(value)
elif field.cpp_type == descriptor.FieldDescriptor.CPPTYPE_ENUM:
enum_value = fie... |
def _AnyMessageToJsonObject(self, message):
"""Converts Any message according to Proto3 JSON Specification."""
if not message.ListFields():
return {}
# Must print @type first, use OrderedDict instead of {}
js = OrderedDict()
type_url = message.type_url
js['@type'] = type_url
sub_messag... |
def _ValueMessageToJsonObject(self, message):
"""Converts Value message according to Proto3 JSON Specification."""
which = message.WhichOneof('kind')
# If the Value message is not set treat as null_value when serialize
# to JSON. The parse back result will be different from original message.
if whic... |
def _StructMessageToJsonObject(self, message):
"""Converts Struct message according to Proto3 JSON Specification."""
fields = message.fields
ret = {}
for key in fields:
ret[key] = self._ValueMessageToJsonObject(fields[key])
return ret |
def ConvertMessage(self, value, message):
"""Convert a JSON object into a message.
Args:
value: A JSON object.
message: A WKT or regular protocol message to record the data.
Raises:
ParseError: In case of convert problems.
"""
message_descriptor = message.DESCRIPTOR
full_name... |
def _ConvertFieldValuePair(self, js, message):
"""Convert field value pairs into regular message.
Args:
js: A JSON object to convert the field value pairs.
message: A regular protocol message to record the data.
Raises:
ParseError: In case of problems converting.
"""
names = []
... |
def _ConvertAnyMessage(self, value, message):
"""Convert a JSON representation into Any message."""
if isinstance(value, dict) and not value:
return
try:
type_url = value['@type']
except KeyError:
raise ParseError('@type is missing when parsing any message.')
sub_message = _Create... |
def _ConvertWrapperMessage(self, value, message):
"""Convert a JSON representation into Wrapper message."""
field = message.DESCRIPTOR.fields_by_name['value']
setattr(message, 'value', _ConvertScalarFieldValue(value, field)) |
def _ConvertMapFieldValue(self, value, message, field):
"""Convert map field value for a message map field.
Args:
value: A JSON object to convert the map field value.
message: A protocol message to record the converted data.
field: The descriptor of the map field to be converted.
Raises:... |
def plot(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots the data in `x` on the X axis and the data in `y` on the Y axis
in a 2d visualization, and shows the resulting visualization.
Uses the following heuristic to choose the visualization:
* If `x` and `y` are bot... |
def show(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots the data in `x` on the X axis and the data in `y` on the Y axis
in a 2d visualization, and shows the resulting visualization.
Uses the following heuristic to choose the visualization:
* If `x` and `y` are bot... |
def scatter(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots the data in `x` on the X axis and the data in `y` on the Y axis
in a 2d scatter plot, and returns the resulting Plot object.
The function supports SArrays of dtypes: int, float.
Parameters
-------... |
def categorical_heatmap(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots the data in `x` on the X axis and the data in `y` on the Y axis
in a 2d categorical heatmap, and returns the resulting Plot object.
The function supports SArrays of dtypes str.
Parameters
... |
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