Hi everyone, I am Somnath Chakraborty. I am a banker. Programming is my passion. I am new to JupyterLab. I want to know efficient programming technique in python using Jupyter Lab. I believe this is the right forum
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Hi
I’m William
I’m retake administration of a Slurm cluster for a research lab and we use JupyterHub to launch jobs through Singularity containers.
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Hey there, everyone!
I’m Jesse, and I am a Ph.D. student in computer science whose focus is on the evolving design of computational notebooks, specifically the idea of nonlinear computational notebooks.
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Hi, everyone!
I am happy to a part of this great community.
I am a data enthusiast from Nigeria.
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Hi!
I’m John, in Scotland.
I suspect that I might be at the ‘older’ end of the spectrum of new Python users (I was 54 earlier this year 
): I have used SAS since 1999 but have finally (some might say, ridiculously late) seen the way in which the wind is blowing and have become a Python enthusiast.
With the way my adult life has worked out, I didn’t get the opportunity to finish university at the same time as my peer group through a combination of family illness, work and sheer stubbornness on my part. I’ve worked through a number of online MOOCs and this has sufficiently engaged me to apply for admission to a university MSc course in data science and artificial intelligence, as a precursor to a personal-passion-project which I’m hoping to do a PhD in, which is an “unusual” application of Bayesian search.
Any questions, please, do, feel free to message me.
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Hello Dear Community Members,
I am Umer Alvi Learning Jupyter Notebook, I am at beginner level right now.
I am having issue while working with Pandas exercise
If any member can help me resolve this issue? I can share the Notebook as well if you want?
CustID_grouped.mean()
got error msg
TypeError Traceback (most recent call last)
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\groupby.py:1942, in GroupBy._agg_py_fallback(self, how, values, ndim, alt)
1941 try:
→ 1942 res_values = self._grouper.agg_series(ser, alt, preserve_dtype=True)
1943 except Exception as err:
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\ops.py:864, in BaseGrouper.agg_series(self, obj, func, preserve_dtype)
862 preserve_dtype = True
→ 864 result = self._aggregate_series_pure_python(obj, func)
866 npvalues = lib.maybe_convert_objects(result, try_float=False)
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\ops.py:885, in BaseGrouper._aggregate_series_pure_python(self, obj, func)
884 for i, group in enumerate(splitter):
→ 885 res = func(group)
886 res = extract_result(res)
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\groupby.py:2454, in GroupBy.mean..(x)
2451 else:
2452 result = self._cython_agg_general(
2453 “mean”,
→ 2454 alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only),
2455 numeric_only=numeric_only,
2456 )
2457 return result.finalize(self.obj, method=“groupby”)
File ~\anaconda3\Lib\site-packages\pandas\core\series.py:6540, in Series.mean(self, axis, skipna, numeric_only, **kwargs)
6532 @doc(make_doc(“mean”, ndim=1))
6533 def mean(
6534 self,
(…)
6538 **kwargs,
6539 ):
→ 6540 return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
File ~\anaconda3\Lib\site-packages\pandas\core\generic.py:12417, in NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
12410 def mean(
12411 self,
12412 axis: Axis | None = 0,
(…)
12415 **kwargs,
12416 ) → Series | float:
12417 return self._stat_function(
12418 “mean”, nanops.nanmean, axis, skipna, numeric_only, **kwargs
12419 )
File ~\anaconda3\Lib\site-packages\pandas\core\generic.py:12374, in NDFrame._stat_function(self, name, func, axis, skipna, numeric_only, **kwargs)
12372 validate_bool_kwarg(skipna, “skipna”, none_allowed=False)
12374 return self._reduce(
12375 func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
12376 )
File ~\anaconda3\Lib\site-packages\pandas\core\series.py:6448, in Series._reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
6444 raise TypeError(
6445 f"Series.{name} does not allow {kwd_name}={numeric_only} "
6446 “with non-numeric dtypes.”
6447 )
→ 6448 return op(delegate, skipna=skipna, **kwds)
File ~\anaconda3\Lib\site-packages\pandas\core\nanops.py:147, in bottleneck_switch.call..f(values, axis, skipna, **kwds)
146 else:
→ 147 result = alt(values, axis=axis, skipna=skipna, **kwds)
149 return result
File ~\anaconda3\Lib\site-packages\pandas\core\nanops.py:404, in _datetimelike_compat..new_func(values, axis, skipna, mask, **kwargs)
402 mask = isna(values)
→ 404 result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
406 if datetimelike:
File ~\anaconda3\Lib\site-packages\pandas\core\nanops.py:720, in nanmean(values, axis, skipna, mask)
719 the_sum = values.sum(axis, dtype=dtype_sum)
→ 720 the_sum = _ensure_numeric(the_sum)
722 if axis is not None and getattr(the_sum, “ndim”, False):
File ~\anaconda3\Lib\site-packages\pandas\core\nanops.py:1701, in _ensure_numeric(x)
1699 if isinstance(x, str):
1700 # GH#44008, GH#36703 avoid casting e.g. strings to numeric
→ 1701 raise TypeError(f"Could not convert string ‘{x}’ to numeric")
1702 try:
TypeError: Could not convert string ‘UIPatDatRob’ to numeric
The above exception was the direct cause of the following exception:
TypeError Traceback (most recent call last)
Cell In[97], line 1
----> 1 CustID_grouped.mean()
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\groupby.py:2452, in GroupBy.mean(self, numeric_only, engine, engine_kwargs)
2445 return self._numba_agg_general(
2446 grouped_mean,
2447 executor.float_dtype_mapping,
2448 engine_kwargs,
2449 min_periods=0,
2450 )
2451 else:
→ 2452 result = self._cython_agg_general(
2453 “mean”,
2454 alt=lambda x: Series(x, copy=False).mean(numeric_only=numeric_only),
2455 numeric_only=numeric_only,
2456 )
2457 return result.finalize(self.obj, method=“groupby”)
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\groupby.py:1998, in GroupBy._cython_agg_general(self, how, alt, numeric_only, min_count, **kwargs)
1995 result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
1996 return result
→ 1998 new_mgr = data.grouped_reduce(array_func)
1999 res = self._wrap_agged_manager(new_mgr)
2000 if how in [“idxmin”, “idxmax”]:
File ~\anaconda3\Lib\site-packages\pandas\core\internals\managers.py:1469, in BlockManager.grouped_reduce(self, func)
1465 if blk.is_object:
1466 # split on object-dtype blocks bc some columns may raise
1467 # while others do not.
1468 for sb in blk._split():
→ 1469 applied = sb.apply(func)
1470 result_blocks = extend_blocks(applied, result_blocks)
1471 else:
File ~\anaconda3\Lib\site-packages\pandas\core\internals\blocks.py:393, in Block.apply(self, func, **kwargs)
387 @final
388 def apply(self, func, **kwargs) → list[Block]:
389 “”"
390 apply the function to my values; return a block if we are not
391 one
392 “”"
→ 393 result = func(self.values, **kwargs)
395 result = maybe_coerce_values(result)
396 return self._split_op_result(result)
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\groupby.py:1995, in GroupBy._cython_agg_general..array_func(values)
1992 return result
1994 assert alt is not None
→ 1995 result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
1996 return result
File ~\anaconda3\Lib\site-packages\pandas\core\groupby\groupby.py:1946, in GroupBy._agg_py_fallback(self, how, values, ndim, alt)
1944 msg = f"agg function failed [how->{how},dtype->{ser.dtype}]"
1945 # preserve the kind of exception that raised
→ 1946 raise type(err)(msg) from err
1948 if ser.dtype == object:
1949 res_values = res_values.astype(object, copy=False)
TypeError: agg function failed [how->mean,dtype->object]
Hiya!
I am Walid, a computational engineer at St. Jude. Been a long time user of all Jupyter products, and am very excited to join the discourse. Hoping to get into contributing at some point. 
Nice to meet folks!
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Hello. My name is Dawit. I feel happy joining this interesting community.
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Hi! My name is Jiayi. I am a senior CS undergrad at UC Berkeley. I have been using Jupyter Lab for years is would love to know how it works behind then scene and maybe also make some contribution to it.
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Hi there, I am Eduardo. I am moving from MATLAB to Python, and Jupyter Notebooks are helping me with the process.
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Hi everyone, I’m Alex. I work at Scanner.dev a lightning fast search engine and api for logs in cloud storage. We have a new beta project the allows security teams to use Jupyter Notebooks for Threat Hunting and Response.
You can learn more about the project here if you’re curious: Jupyter Notebooks | SCANNER
Excited to learn more from everyone!
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Hi everyone,
I am Clement and im studying computer science at a bachelor’s degree level (last year).
With an international friend we would like to make a small contribution within a week and a half for the university, and we’d love to have some recommendations for issues we could look into.
If you have any advice for us feel free to contact me !
Nice to meet you all !
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Hi everyone,
Actually have been part of this community for a year now, but I’ve never introduced myself. I’m Thibaut and I’ve used Jupyterlab extensively throughout my career. I now work on a visual ETL pipeline editor which is available as a Jupyterlab extension for those interested.
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Hi all,
Name’s Florian, I tend to go by Zersiax on the web. I guess I could describe myself as a fully blind (yes really 100% fully no vision blind) developer, accessibility expert and content creator.
I build stuff, I make sure stuff’s accessible and I teach others to essentially do both of those things and more, when I can. I try to primarily do that through Twitch and Youtube streams.
Given my next deep dive will be on Jupyter notebooks and friends, I decided to come say hi 
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Hi. I’m Andrés. I’m student of economy at UNFV (Perú) . My purpose with Jupyter is learn about How does data science work and how can it be automated? I hope to learn something of this and not die tryng.
Thank you so much to all the jupyter team!
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Hi, i’m a non-employed bit of an outcast, and just looking at the marvel i guess.
Wanted to sign on to be able to learn something new, yeah that’s right.
Now i’m unfamiliar to programming or computational stuff but, i’ll manage or not…
I guess i just tangle on than…
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Hi, I’m Sai Chand. Student at NAU. i am here for contribute to the Jupyter notebook an become a part of Jupyter notebook community.
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Hi
I’m Alexander Salikov, Head of external solutions at Nebius AI. My main goal is to make ML/DS tools as accessible as possible. For this reason, we published JupiterHub in our marketplace. We want every ML engineer to be able to launch it with a few clicks.
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Hi Everyone, I am a contend creator looking at how to improve our user experience by moving to Moodle and hopefully integrate some Juypter notebooks into our lessons. It has been some time since I have used Moodle, and never as an admin, so I expect to be asking lots of questions.
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Hi, Manoo here…excited to get going on GenAI
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