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Cannot plot datetime64[ns] on hvplot axis ('pandas_datetime_types' is not defined' error)

I am simply trying to plot some values against datetime64[ns] timestamps with holoviews.
That is,
x-axis = nx1 datetime64[ns] values
y-axis = nx1 data.
Here is a screen shot of what I have:
Screenshot of my dataframe
<class 'pandas._libs.tslibs.timestamps.Timestamp'>
and my overall code:
import hvplot.pandas
import pandas as pd
##
Code ommitted at the start to extract data and create dictionary to convert to data frame
##
#create dictionary
temp_dict = dict(sampling_time=time_y_value_is_taken, y_axis_values = y_values)
df = pd.Dataframe.from_dict(temp_dict)
df.sampling_time=df.sampling_time.astype('datetime64[ns]')
df=df.set_index('sampling_time')
##The following code cannot run this line- it throws error
df.hvplot.line()
I keep getting the error code : 'pandas_datetime_types' is not defined. I have also tried importing datetime as datetime - but it does not work.
EDIT: Here is the traceback:
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
~\miniconda3\envs\mpess_visual\lib\site-packages\IPython\core\formatters.py in __call__(self, obj, include, exclude)
968
969 if method is not None:
--> 970 return method(include=include, exclude=exclude)
971 return None
972 else:
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\core\dimension.py in _repr_mimebundle_(self, include, exclude)
1315 combined and returned.
1316 """
-> 1317 return Store.render(self)
1318
1319
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\core\options.py in render(cls, obj)
1403 data, metadata = {}, {}
1404 for hook in hooks:
-> 1405 ret = hook(obj)
1406 if ret is None:
1407 continue
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\ipython\display_hooks.py in pprint_display(obj)
280 if not ip.display_formatter.formatters['text/plain'].pprint:
281 return None
--> 282 return display(obj, raw_output=True)
283
284
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\ipython\display_hooks.py in display(obj, raw_output, **kwargs)
250 elif isinstance(obj, (CompositeOverlay, ViewableElement)):
251 with option_state(obj):
--> 252 output = element_display(obj)
253 elif isinstance(obj, (Layout, NdLayout, AdjointLayout)):
254 with option_state(obj):
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\ipython\display_hooks.py in wrapped(element)
144 try:
145 max_frames = OutputSettings.options['max_frames']
--> 146 mimebundle = fn(element, max_frames=max_frames)
147 if mimebundle is None:
148 return {}, {}
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\ipython\display_hooks.py in element_display(element, max_frames)
190 return None
191
--> 192 return render(element)
193
194
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\ipython\display_hooks.py in render(obj, **kwargs)
66 renderer = renderer.instance(fig='png')
67
---> 68 return renderer.components(obj, **kwargs)
69
70
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\renderer.py in components(self, obj, fmt, comm, **kwargs)
408 doc = Document()
409 with config.set(embed=embed):
--> 410 model = plot.layout._render_model(doc, comm)
411 if embed:
412 return render_model(model, comm)
~\miniconda3\envs\mpess_visual\lib\site-packages\panel\viewable.py in _render_model(self, doc, comm)
453 if comm is None:
454 comm = state._comm_manager.get_server_comm()
--> 455 model = self.get_root(doc, comm)
456
457 if config.embed:
~\miniconda3\envs\mpess_visual\lib\site-packages\panel\viewable.py in get_root(self, doc, comm, preprocess)
510 """
511 doc = init_doc(doc)
--> 512 root = self._get_model(doc, comm=comm)
513 if preprocess:
514 self._preprocess(root)
~\miniconda3\envs\mpess_visual\lib\site-packages\panel\layout\base.py in _get_model(self, doc, root, parent, comm)
120 if root is None:
121 root = model
--> 122 objects = self._get_objects(model, [], doc, root, comm)
123 props = dict(self._init_params(), objects=objects)
124 model.update(**self._process_param_change(props))
~\miniconda3\envs\mpess_visual\lib\site-packages\panel\layout\base.py in _get_objects(self, model, old_objects, doc, root, comm)
110 else:
111 try:
--> 112 child = pane._get_model(doc, root, model, comm)
113 except RerenderError:
114 return self._get_objects(model, current_objects[:i], doc, root, comm)
~\miniconda3\envs\mpess_visual\lib\site-packages\panel\pane\holoviews.py in _get_model(self, doc, root, parent, comm)
237 plot = self.object
238 else:
--> 239 plot = self._render(doc, comm, root)
240
241 plot.pane = self
~\miniconda3\envs\mpess_visual\lib\site-packages\panel\pane\holoviews.py in _render(self, doc, comm, root)
304 kwargs['comm'] = comm
305
--> 306 return renderer.get_plot(self.object, **kwargs)
307
308 def _cleanup(self, root):
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\bokeh\renderer.py in get_plot(self_or_cls, obj, doc, renderer, **kwargs)
71 combining the bokeh model with another plot.
72 """
---> 73 plot = super(BokehRenderer, self_or_cls).get_plot(obj, doc, renderer, **kwargs)
74 if plot.document is None:
75 plot.document = Document() if self_or_cls.notebook_context else curdoc()
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\renderer.py in get_plot(self_or_cls, obj, doc, renderer, comm, **kwargs)
241 init_key = tuple(v if d is None else d for v, d in
242 zip(plot.keys[0], defaults))
--> 243 plot.update(init_key)
244 else:
245 plot = obj
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\plot.py in update(self, key)
980 def update(self, key):
981 if len(self) == 1 and ((key == 0) or (key == self.keys[0])) and not self.drawn:
--> 982 return self.initialize_plot()
983 item = self.__getitem__(key)
984 self.traverse(lambda x: setattr(x, '_updated', True))
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\bokeh\element.py in initialize_plot(self, ranges, plot, plots, source)
1388 element = self.hmap.last
1389 key = util.wrap_tuple(self.hmap.last_key)
-> 1390 ranges = self.compute_ranges(self.hmap, key, ranges)
1391 self.current_ranges = ranges
1392 self.current_frame = element
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\plot.py in compute_ranges(self, obj, key, ranges)
636 if (not (axiswise and not isinstance(obj, HoloMap)) or
637 (not framewise and isinstance(obj, HoloMap))):
--> 638 self._compute_group_range(group, elements, ranges, framewise,
639 axiswise, robust, self.top_level,
640 prev_frame)
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\plot.py in _compute_group_range(cls, group, elements, ranges, framewise, axiswise, robust, top_level, prev_frame)
853 continue
854 matching &= (
--> 855 len({'date' if isinstance(v, util.datetime_types) else 'number'
856 for rng in rs for v in rng if util.isfinite(v)}) < 2
857 )
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\plotting\plot.py in <setcomp>(.0)
854 matching &= (
855 len({'date' if isinstance(v, util.datetime_types) else 'number'
--> 856 for rng in rs for v in rng if util.isfinite(v)}) < 2
857 )
858 if matching:
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\core\util.py in isfinite(val)
902 return finite
903 elif isinstance(val, datetime_types+timedelta_types):
--> 904 return not isnat(val)
905 elif isinstance(val, (basestring, bytes)):
906 return True
~\miniconda3\envs\mpess_visual\lib\site-packages\holoviews\core\util.py in isnat(val)
866 elif pd and val is pd.NaT:
867 return True
--> 868 elif pd and isinstance(val, pandas_datetime_types+pandas_timedelta_types):
869 return pd.isna(val)
870 else:
NameError: name 'pandas_datetime_types' is not defined
Any suggestions? Thank you

Although I couldn't find any official doc to support my statement, it's a compatibility issue (HoloViews 1.14.4 was released before Pandas 1.3.0).
Looking at [gitHub]: holoviz/holoviews - (v1.14.4) holoviews/holoviews/core/util.py (starting with line #83), there are some conditional imports. One of them is ABCIndexClass.
[GitHub]: pandas-dev/pandas - (v1.3.0) pandas/pandas/core/dtypes/dtypes.py on the other hand, does not provide it (as opposed from let's say its v1.2.5 counterpart) yielding (silent) exception, and the behavior you're experiencing.
Ways to go:
Upgrade HoloViews to v1.14.5 which no longer has this problem, (or at least, there's a Pandas 1.3.0 conditional as well - fixed by [GitHub]: holoviz/holoviews - Add support for pandas>=1.3)
You could also downgrade Pandas to (e.g.) v1.2.5, although this is not the way to go

NotImplementedError: Failed in nopython mode pipeline. Use of unknown opcode MAP_ADD at line 116 of <ipython-input-287-147d4798a88b>

I try to launch a code with Numba and I get errors.
What I want to do is to compute the cosine similarity with a cosinus_sparse function. This class method I use in the search class method, then I call search in the results method. Although I added the #jit decorator before each method I have this implementation error that appears.
Here is my code:
import numpy as np
from numba import jit
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import pandas as pd
import math
class Search:
def __init__(self, corpus, method='XTERM', stop_words='english', max_df=1.0, min_df=1, max_features=None):
self.corpus = corpus
self.method = method
self.stop_words = stop_words
self.max_df = max_df
self.min_df = min_df
self.max_features = max_features
self.vectorization()
self.get_shape()
self.features_names = self.bag_of_word.get_feature_names()
def vectorization(self):
if self.method == 'XTERM':
self.bag_of_word = CountVectorizer(stop_words=self.stop_words,
max_df=self.max_df, min_df=self.min_df,
max_features=self.max_features)
self.corpus_vectorized = self.bag_of_word.fit_transform(self.corpus)
elif self.method == 'TFxIDF':
self.bag_of_word = TfidfVectorizer(stop_words=self.stop_words,
max_df=self.max_df, min_df=self.min_df,
max_features=self.max_features)
self.corpus_vectorized = self.bag_of_word.fit_transform(self.corpus)
else:
raise MethodError('Method provided is not valid')
def get_shape(self):
self.n_docs, self.n_terms = self.corpus_vectorized.shape
def get_query(self, query):
self.indexes = [self.features_names.index(q) for q in query if q in self.features_names]
self.query_vec = np.zeros(self.n_terms)
self.query_vec[self.indexes] = 1
#staticmethod
#jit(nopython=True)
def cosinus_sparse(i, j):
num = i.dot(j)
spars = i * i.transpose()
den = math.sqrt(spars[0, 0]) * math.sqrt(sum(j * j))
if (den > 0):
return int(num) / den
else:
return 0
#jit(nopython=True)
def search(self, q) -> dict:
cc = {i: self.cosinus_sparse(self.corpus_vectorized[i, :], q) for i in range(self.n_docs)}
cc = sorted(cc.items(), key=lambda x: x[1], reverse=True)
return cc
#jit
def get_result(self) -> list:
self.result = self.search(self.query_vec)
def result_announcer(self):
self.search_lenght = len([i for i in self.result if i[1] > 0])
print('{} documents linked to your query where found'.format(search_lenght))
def verif_query_vec(self, query):
if int(sum(self.query_vec)) != len(query):
raise QueryError('Error in query or query_vec')
def processing(self, query):
try:
self.get_query(query)
self.verif_query_vec(query)
self.get_result()
except NameError:
self.vectorisation()
self.get_shape()
self.get_feature_names()
self.get_query(query)
self.verif_query_vec(query)
self.get_result()
import ipywidgets as widgets
from IPython.display import display
text = widgets.Text(
value='',
placeholder='Type words',
description='String:',
disabled=False
)
method_radio = widgets.RadioButtons(
options=['XTERM', 'TFxIDF'],
# value='TF',
description='Method:',
disabled=False
)
submit = widgets.Button(description = 'Search')
display(widgets.VBox([text, radio, submit]))
def handle_submit(sender):
global query
query = text.value.lower().split(' ')
method = method_radio.value
# instentiation de l'objet de recherche
global search_obj
search_obj = Search(corpus=corpus, method=method, )
search_obj.processing(query)
submit.on_click(handle_submit)
Here is the error
NotImplementedError Traceback (most recent call last)
<ipython-input-288-025a488daa60> in handle_submit(sender)
27 global search_obj
28 search_obj = Search(corpus=corpus, method=method, )
---> 29 search_obj.processing(query)
30
31 submit.on_click(handle_submit)
<ipython-input-287-147d4798a88b> in processing(self, query)
167 self.get_query(query)
168 self.verif_query_vec(query)
--> 169 self.get_result()
170
171 except NameError:
~\Anaconda3\lib\site-packages\numba\dispatcher.py in _compile_for_args(self, *args, **kws)
365 e.patch_message(''.join(e.args) + help_msg)
366 # ignore the FULL_TRACEBACKS config, this needs reporting!
--> 367 raise e
368
369 def inspect_llvm(self, signature=None):
~\Anaconda3\lib\site-packages\numba\dispatcher.py in _compile_for_args(self, *args, **kws)
322 argtypes.append(self.typeof_pyval(a))
323 try:
--> 324 return self.compile(tuple(argtypes))
325 except errors.TypingError as e:
326 # Intercept typing error that may be due to an argument
~\Anaconda3\lib\site-packages\numba\compiler_lock.py in _acquire_compile_lock(*args, **kwargs)
30 def _acquire_compile_lock(*args, **kwargs):
31 with self:
---> 32 return func(*args, **kwargs)
33 return _acquire_compile_lock
34
~\Anaconda3\lib\site-packages\numba\dispatcher.py in compile(self, sig)
653
654 self._cache_misses[sig] += 1
--> 655 cres = self._compiler.compile(args, return_type)
656 self.add_overload(cres)
657 self._cache.save_overload(sig, cres)
~\Anaconda3\lib\site-packages\numba\dispatcher.py in compile(self, args, return_type)
80 args=args, return_type=return_type,
81 flags=flags, locals=self.locals,
---> 82 pipeline_class=self.pipeline_class)
83 # Check typing error if object mode is used
84 if cres.typing_error is not None and not flags.enable_pyobject:
~\Anaconda3\lib\site-packages\numba\compiler.py in compile_extra(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)
924 pipeline = pipeline_class(typingctx, targetctx, library,
925 args, return_type, flags, locals)
--> 926 return pipeline.compile_extra(func)
927
928
~\Anaconda3\lib\site-packages\numba\compiler.py in compile_extra(self, func)
372 self.lifted = ()
373 self.lifted_from = None
--> 374 return self._compile_bytecode()
375
376 def compile_ir(self, func_ir, lifted=(), lifted_from=None):
~\Anaconda3\lib\site-packages\numba\compiler.py in _compile_bytecode(self)
855 """
856 assert self.func_ir is None
--> 857 return self._compile_core()
858
859 def _compile_ir(self):
~\Anaconda3\lib\site-packages\numba\compiler.py in _compile_core(self)
842 self.define_pipelines(pm)
843 pm.finalize()
--> 844 res = pm.run(self.status)
845 if res is not None:
846 # Early pipeline completion
~\Anaconda3\lib\site-packages\numba\compiler_lock.py in _acquire_compile_lock(*args, **kwargs)
30 def _acquire_compile_lock(*args, **kwargs):
31 with self:
---> 32 return func(*args, **kwargs)
33 return _acquire_compile_lock
34
~\Anaconda3\lib\site-packages\numba\compiler.py in run(self, status)
253 # No more fallback pipelines?
254 if is_final_pipeline:
--> 255 raise patched_exception
256 # Go to next fallback pipeline
257 else:
~\Anaconda3\lib\site-packages\numba\compiler.py in run(self, status)
244 try:
245 event(stage_name)
--> 246 stage()
247 except _EarlyPipelineCompletion as e:
248 return e.result
~\Anaconda3\lib\site-packages\numba\compiler.py in stage_inline_pass(self)
582 self.flags.auto_parallel,
583 self.parfor_diagnostics.replaced_fns)
--> 584 inline_pass.run()
585 # Remove all Dels, and re-run postproc
586 post_proc = postproc.PostProcessor(self.func_ir)
~\Anaconda3\lib\site-packages\numba\inline_closurecall.py in run(self)
75
76 if guard(self._inline_closure,
---> 77 work_list, block, i, func_def):
78 modified = True
79 break # because block structure changed
~\Anaconda3\lib\site-packages\numba\ir_utils.py in guard(func, *args, **kwargs)
1358 """
1359 try:
-> 1360 return func(*args, **kwargs)
1361 except GuardException:
1362 return None
~\Anaconda3\lib\site-packages\numba\inline_closurecall.py in _inline_closure(self, work_list, block, i, func_def)
212 inline_closure_call(self.func_ir,
213 self.func_ir.func_id.func.__globals__,
--> 214 block, i, func_def, work_list=work_list)
215 return True
216
~\Anaconda3\lib\site-packages\numba\inline_closurecall.py in inline_closure_call(func_ir, glbls, block, i, callee, typingctx, arg_typs, typemap, calltypes, work_list)
253 callee_closure = callee.closure if hasattr(callee, 'closure') else callee.__closure__
254 # first, get the IR of the callee
--> 255 callee_ir = get_ir_of_code(glbls, callee_code)
256 callee_blocks = callee_ir.blocks
257
~\Anaconda3\lib\site-packages\numba\ir_utils.py in get_ir_of_code(glbls, fcode)
1572 f.__name__ = fcode.co_name
1573 from numba import compiler
-> 1574 ir = compiler.run_frontend(f)
1575 # we need to run the before inference rewrite pass to normalize the IR
1576 # XXX: check rewrite pass flag?
~\Anaconda3\lib\site-packages\numba\compiler.py in run_frontend(func)
168 interp = interpreter.Interpreter(func_id)
169 bc = bytecode.ByteCode(func_id=func_id)
--> 170 func_ir = interp.interpret(bc)
171 post_proc = postproc.PostProcessor(func_ir)
172 post_proc.run()
~\Anaconda3\lib\site-packages\numba\interpreter.py in interpret(self, bytecode)
101 # Data flow analysis
102 self.dfa = dataflow.DataFlowAnalysis(self.cfa)
--> 103 self.dfa.run()
104
105 # Temp states during interpretation
~\Anaconda3\lib\site-packages\numba\dataflow.py in run(self)
26 def run(self):
27 for blk in self.cfa.iterliveblocks():
---> 28 self.infos[blk.offset] = self.run_on_block(blk)
29
30 def run_on_block(self, blk):
~\Anaconda3\lib\site-packages\numba\dataflow.py in run_on_block(self, blk)
76 for offset in blk:
77 inst = self.bytecode[offset]
---> 78 self.dispatch(info, inst)
79 return info
80
~\Anaconda3\lib\site-packages\numba\dataflow.py in dispatch(self, info, inst)
86 fname = "op_%s" % inst.opname.replace('+', '_')
87 fn = getattr(self, fname, self.handle_unknown_opcode)
---> 88 fn(info, inst)
89
90 def handle_unknown_opcode(self, info, inst):
~\Anaconda3\lib\site-packages\numba\dataflow.py in handle_unknown_opcode(self, info, inst)
91 msg = "Use of unknown opcode {} at line {} of {}"
92 raise NotImplementedError(msg.format(inst.opname, inst.lineno,
---> 93 self.bytecode.func_id.filename))
94
95 def dup_topx(self, info, inst, count):
NotImplementedError: Failed in nopython mode pipeline (step: inline calls to locally defined closures)
Use of unknown opcode MAP_ADD at line 116 of <ipython-input-287-147d4798a88b>
How do I fix this error?
Thanks a lot for your help.

pandas_profiling TypeError when using HTML format

I follow the pandas_profiling document script, but this problem always arises.
My dataset is the boston from sklearn.
I have the report, but without the features of an html version:
profile2 = ProfileReport(data, title="Relatório DATASET -data-", html={'style': {'full_width': True}}, sort="None")
The image below refers to this code:
from pandas_profiling import ProfileReport
profile = ProfileReport(data, title='Pandas Profiling Report', explorative=True)
[![enter image description here][1]][1]
My version of pandas_profiling
[![enter image description here][2]][2]
I don't have the problem related above if I use this code:
profile = ProfileReport (data)
UPDATE:
Uninstalled the previous version and got the new one (2.9.0), but this problems happens:
Summarize dataset: 75%
21/28 [00:07<00:02, 2.84it/s, Get scatter matrix]
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in _repr_html_(self)
407 def _repr_html_(self):
408 """The ipython notebook widgets user interface gets called by the jupyter notebook."""
--> 409 self.to_notebook_iframe()
410
411 def __repr__(self):
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in to_notebook_iframe(self)
387 with warnings.catch_warnings():
388 warnings.simplefilter("ignore")
--> 389 display(get_notebook_iframe(self))
390
391 def to_widgets(self):
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\report\presentation\flavours\widget\notebook.py in get_notebook_iframe(profile)
63 output = get_notebook_iframe_src(profile)
64 elif attribute == "srcdoc":
---> 65 output = get_notebook_iframe_srcdoc(profile)
66 else:
67 raise ValueError(
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\report\presentation\flavours\widget\notebook.py in get_notebook_iframe_srcdoc(profile)
21 width = config["notebook"]["iframe"]["width"].get(str)
22 height = config["notebook"]["iframe"]["height"].get(str)
---> 23 src = html.escape(profile.to_html())
24
25 iframe = f'<iframe width="{width}" height="{height}" srcdoc="{src}" frameborder="0" allowfullscreen></iframe>'
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in to_html(self)
357
358 """
--> 359 return self.html
360
361 def to_json(self) -> str:
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in html(self)
177 def html(self):
178 if self._html is None:
--> 179 self._html = self._render_html()
180 return self._html
181
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in _render_html(self)
284 from pandas_profiling.report.presentation.flavours import HTMLReport
285
--> 286 report = self.report
287
288 disable_progress_bar = not config["progress_bar"].get(bool)
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in report(self)
171 def report(self):
172 if self._report is None:
--> 173 self._report = get_report_structure(self.description_set)
174 return self._report
175
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\profile_report.py in description_set(self)
152 def description_set(self):
153 if self._description_set is None:
--> 154 self._description_set = describe_df(self.title, self.df, self._sample)
155 return self._description_set
156
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\model\describe.py in describe(title, df, sample)
100 # Scatter matrix
101 pbar.set_postfix_str("Get scatter matrix")
--> 102 scatter_matrix = get_scatter_matrix(df, variables)
103 pbar.update()
104
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\model\summary.py in get_scatter_matrix(df, variables)
696 for y in continuous_variables:
697 if x in continuous_variables:
--> 698 scatter_matrix[x][y] = scatter_pairwise(df[x], df[y], x, y)
699 else:
700 scatter_matrix = {}
C:\ProgramData\Anaconda3\lib\contextlib.py in inner(*args, **kwds)
71 #wraps(func)
72 def inner(*args, **kwds):
---> 73 with self._recreate_cm():
74 return func(*args, **kwds)
75 return inner
C:\ProgramData\Anaconda3\lib\contextlib.py in __enter__(self)
110 del self.args, self.kwds, self.func
111 try:
--> 112 return next(self.gen)
113 except StopIteration:
114 raise RuntimeError("generator didn't yield") from None
C:\ProgramData\Anaconda3\lib\site-packages\pandas_profiling\visualisation\context.py in manage_matplotlib_context()
77 register_matplotlib_converters()
78 matplotlib.rcParams.update(customRcParams)
---> 79 sns.set_style(style="white")
80 yield
81 finally:
AttributeError: module 'seaborn' has no attribute 'set_style'

The solution was unistall / reinstall the Anaconda and the pandas-profiling. Probably some version issue as suggested by Paul H on the comments.

Folium Choropleth + GeoJSON raises AttributeError: 'NoneType' object has no attribute 'get'

Found someone met same problem in another post (Folium Choropleth + GeoJSON raises AttributeError: 'NoneType'). Followed the suggestions there but my problem still exists.
I have double checked that:
a) access to parameters is correct
b) all the available keys in the geoJSON are contained in the Pandas DataFrame for Choropleth.
Here's related code block:
import json
# load geojson file into dic
with open('chicago.geojson') as f:
data = json.load(f)
f.close()
found=0
notfound=0
# check if the features.properties.community data matches the dataframe data
for feature in data['features']:
if feature['properties']['community'] in df_cname['COMMUNITY_AREA_NAME'].tolist():
found=found+1
else:
notfound=notfound+1
# print out the check result (and the output shows that all features.properties.community in geojson are found in the dataframe to be used for choropleth drawing.)
print(found,' records found. ', notfound, ' records not found.')
chicago_geo='chicago.geojson'
latitude = 41.88425
longitude = -87.63245
map_chicago = folium.Map(location=[latitude, longitude], zoom_start=11)
map_chicago.choropleth(
geo_data=chicago_geo,
data=df_cname,
columns=['COMMUNITY_AREA_NAME', 'COUNT'],
key_on='features.properties.community',
fill_color='YlOrRd',
fill_opacity=0.7,
line_opacity=0.2,
legend_name='Crime Data in Chicago'
)
map_chicago
and the output / error message:
77 records found. 0 records not found.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/IPython/core/formatters.py in __call__(self, obj)
334 method = get_real_method(obj, self.print_method)
335 if method is not None:
--> 336 return method()
337 return None
338 else:
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/map.py in _repr_html_(self, **kwargs)
249 self._parent = None
250 else:
--> 251 out = self._parent._repr_html_(**kwargs)
252 return out
253
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/branca/element.py in _repr_html_(self, **kwargs)
326
327 """
--> 328 html = self.render(**kwargs)
329 html = "data:text/html;charset=utf-8;base64," + base64.b64encode(html.encode('utf8')).decode('utf8') # noqa
330
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/branca/element.py in render(self, **kwargs)
319 """Renders the HTML representation of the element."""
320 for name, child in self._children.items():
--> 321 child.render(**kwargs)
322 return self._template.render(this=self, kwargs=kwargs)
323
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/map.py in render(self, **kwargs)
336 '</style>'), name='map_style')
337
--> 338 super(LegacyMap, self).render(**kwargs)
339
340
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/branca/element.py in render(self, **kwargs)
631
632 for name, element in self._children.items():
--> 633 element.render(**kwargs)
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/branca/element.py in render(self, **kwargs)
627 script = self._template.module.__dict__.get('script', None)
628 if script is not None:
--> 629 figure.script.add_child(Element(script(self, kwargs)),
630 name=self.get_name())
631
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/jinja2/runtime.py in __call__(self, *args, **kwargs)
547 (self.name, len(self.arguments)))
548
--> 549 return self._invoke(arguments, autoescape)
550
551 def _invoke(self, arguments, autoescape):
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/jinja2/runtime.py in _invoke(self, arguments, autoescape)
551 def _invoke(self, arguments, autoescape):
552 """This method is being swapped out by the async implementation."""
--> 553 rv = self._func(*arguments)
554 if autoescape:
555 rv = Markup(rv)
<template> in macro(l_1_this, l_1_kwargs)
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/jinja2/runtime.py in call(_Context__self, _Context__obj, *args, **kwargs)
258 args = (__self.environment,) + args
259 try:
--> 260 return __obj(*args, **kwargs)
261 except StopIteration:
262 return __self.environment.undefined('value was undefined because '
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/features.py in style_data(self)
563
564 for feature in self.data['features']:
--> 565 feature.setdefault('properties', {}).setdefault('style', {}).update(self.style_function(feature)) # noqa
566 feature.setdefault('properties', {}).setdefault('highlight', {}).update(self.highlight_function(feature)) # noqa
567 return json.dumps(self.data, sort_keys=True)
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/folium.py in style_function(x)
303 'color': line_color,
304 'fillOpacity': fill_opacity,
--> 305 'fillColor': color_scale_fun(x)
306 }
307
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/folium.py in color_scale_fun(x)
290 def color_scale_fun(x):
291 return color_range[len(
--> 292 [u for u in color_domain if
293 get_by_key(x, key_on) in color_data and
294 u <= color_data[get_by_key(x, key_on)]])]
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/folium.py in <listcomp>(.0)
291 return color_range[len(
292 [u for u in color_domain if
--> 293 get_by_key(x, key_on) in color_data and
294 u <= color_data[get_by_key(x, key_on)]])]
295 else:
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/folium.py in get_by_key(obj, key)
286 return (obj.get(key, None) if len(key.split('.')) <= 1 else
287 get_by_key(obj.get(key.split('.')[0], None),
--> 288 '.'.join(key.split('.')[1:])))
289
290 def color_scale_fun(x):
/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/folium/folium.py in get_by_key(obj, key)
285 def get_by_key(obj, key):
286 return (obj.get(key, None) if len(key.split('.')) <= 1 else
--> 287 get_by_key(obj.get(key.split('.')[0], None),
288 '.'.join(key.split('.')[1:])))
289
AttributeError: 'NoneType' object has no attribute 'get'
Your suggestions are welcome!

KeyError in `plotnine` (ggplot wrapper for python)

I am trying to use plotnine to build graphs and I keep coming across the same KeyError problem when I want to plot just the x-axis. See the traceback error below.
A sample of my data is:
WORD TAG TOPIC Value
0 hey aa 1 234
1 working bb 1 123
2 lullaby cc 2 32
3 Doggy cc 2 63
4 document aa 3 84
sample of my code:
from plotnine import *
import pandas as pd
inFile = 'infile.csv'
df = pd.read_csv(inFile, names = ['WORD', 'TAG','TOPIC','VALUE'], header=0,sep='\t')
df.sort_values('value',ascending=False)
sortedDf = df[:5]
plot1 = ggplot(sortedDf) + aes(x='TOPIC') + geom_histogram(binwidth=3)
where the final goal is to plot the count of each topic in a histogram.
I am not sure what data is missing that is raising the following key error, as there is no need for a weight as I am only interested in plotting the count of that one particular variable, ie. topic 1 = 2, topic 2= 2, topic 3 = 1.
Does anyone have any link to more detailled documentation of plotline or any experience with the library to help me understand more in detail what I am missing.
Traceback Error:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-112-71707b4cf21a> in <module>()
1 plot2 = ggplot(sortedDf) + aes(x='TOPIC') + geom_histogram(binwidth=3)
----> 2 print plot2
/Users/anaconda/lib/python2.7/site-packages/plotnine/ggplot.pyc in __repr__(self)
82 Print/show the plot
83 """
---> 84 self.draw()
85 plt.show()
86 return '<ggplot: (%d)>' % self.__hash__()
/Users/anaconda/lib/python2.7/site-packages/plotnine/ggplot.pyc in draw(self)
139 # assign a default theme
140 self = deepcopy(self)
--> 141 self._build()
142
143 # If no theme we use the default
/Users/anaconda/lib/python2.7/site-packages/plotnine/ggplot.pyc in _build(self)
235
236 # Apply and map statistics
--> 237 layers.compute_statistic(layout)
238 layers.map_statistic(self)
239
/Users/anaconda/lib/python2.7/site-packages/plotnine/layer.pyc in compute_statistic(self, layout)
92 def compute_statistic(self, layout):
93 for l in self:
---> 94 l.compute_statistic(layout)
95
96 def map_statistic(self, plot):
/Users/anaconda/lib/python2.7/site-packages/plotnine/layer.pyc in compute_statistic(self, layout)
369 data = self.stat.use_defaults(data)
370 data = self.stat.setup_data(data)
--> 371 data = self.stat.compute_layer(data, params, layout)
372 self.data = data
373
/Users/anaconda/lib/python2.7/site-packages/plotnine/stats/stat.pyc in compute_layer(cls, data, params, layout)
194 return cls.compute_panel(pdata, pscales, **params)
195
--> 196 return groupby_apply(data, 'PANEL', fn)
197
198 #classmethod
/Users/anaconda/lib/python2.7/site-packages/plotnine/utils.pyc in groupby_apply(df, cols, func, *args, **kwargs)
615 # do not mark d as a slice of df i.e no SettingWithCopyWarning
616 d.is_copy = None
--> 617 lst.append(func(d, *args, **kwargs))
618 return pd.concat(lst, axis=axis, ignore_index=True)
619
/Users/anaconda/lib/python2.7/site-packages/plotnine/stats/stat.pyc in fn(pdata)
192 return pdata
193 pscales = layout.get_scales(pdata['PANEL'].iat[0])
--> 194 return cls.compute_panel(pdata, pscales, **params)
195
196 return groupby_apply(data, 'PANEL', fn)
/Users/anaconda/lib/python2.7/site-packages/plotnine/stats/stat.pyc in compute_panel(cls, data, scales, **params)
221 for _, old in data.groupby('group'):
222 old.is_copy = None
--> 223 new = cls.compute_group(old, scales, **params)
224 unique = uniquecols(old)
225 missing = unique.columns.difference(new.columns)
/Users/anaconda/lib/python2.7/site-packages/plotnine/stats/stat_bin.pyc in compute_group(cls, data, scales, **params)
107 new_data = assign_bins(
108 data['x'], breaks, data.get('weight'),
--> 109 params['pad'], params['closed'])
110 return new_data
/Users/anaconda/lib/python2.7/site-packages/plotnine/stats/binning.pyc in assign_bins(x, breaks, weight, pad, closed)
163 df = pd.DataFrame({'bin_idx': bin_idx, 'weight': weight})
164 wftable = df.pivot_table(
--> 165 'weight', index=['bin_idx'], aggfunc=np.sum)['weight']
166
167 # Empty bins get no value in the computed frequency table.
/Users/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
601 result = self.index.get_value(self, key)
602
--> 603 if not is_scalar(result):
604 if is_list_like(result) and not isinstance(result, Series):
605
/Users/anaconda/lib/python2.7/site-packages/pandas/indexes/base.pyc in get_value(self, series, key)
pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:3557)()
pandas/index.pyx in pandas.index.IndexEngine.get_value (pandas/index.c:3240)()
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4363)()
KeyError: 'weight'

Nesting aes in ggplot like it is done in R may solve your issue:
plot1 = ggplot(sortedDf, aes(x='TOPIC')) + geom_histogram(binwidth=3)