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- Jupyter Notebook different ways to display out - Stack Overflow
There seems to be 3 ways to display output in Jupyter: By using print By using display By just writing the variable name What is the exact difference, especially between number 2 and 3?
- python - Variable Explorer in Jupyter Notebook - Stack Overflow
If you use Jupyter Notebooks within Jupyter Lab there has been a lot of discussion about implementing a variable explorer inspector You can follow the issue here As of right now there is one Jupyter Lab extension in the works that implements a Spyder-like variable explorer It is based on the notebook extension that James mentioned in his answer
- Jupyter Notebook: interactive plot with widgets - Stack Overflow
Jupyter version info: The version of the notebook server is 4 3 1 and is running on Python 3 6 6, IPython
- How to fix jupyter is not recognized as an internal or external . . .
$ jupyter notebook [I 19:05:30 459 NotebookApp] Serving notebooks from local directory: [I 19:05:30 459 NotebookApp] 0 active kernels [I 19:05:30 459 NotebookApp] The Jupyter Notebook is running at: [I 19:05:30 459 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation)
- How to set env variable in Jupyter notebook - Stack Overflow
@Royi Not just on Windows, but in a Jupyter Notebook on Linux, this did not change the environment variable either, at least not well enough: it does change something as it does somehow claim the memory, but it does not seem to fully pass it to the compiler, it seems to be a rights issue of the user that you are in %set_env and os environ[] will both fail if code must run with settings from
- Display all dataframe columns in a Jupyter Python Notebook
I know this question is a little old but the following worked for me in a Jupyter Notebook running pandas 0 22 0 and Python 3: import pandas as pd pd set_option('display max_columns', <number of columns>) You can do the same for the rows too: pd set_option('display max_rows', <number of rows>)
- Conda environments not showing up in Jupyter Notebook
So, inside Jupyter, conda kernelspec list does list my two environments Their respective kernel json points to two different python paths However, if I do jupyter notebook inside a certain activated env, whatever kernel I am using, !which python always uses that python of the activated environment Major limitation for me
- How do I import scikit-learn in a jupyter notebook?
If that didn't work, check the system paths in jupyter notebook: import sys sys path and the system executable: sys executable These must correspond to the python in your current loaded environment For me, the issue was with the jupyter Notebook's kernel See the kernel specifications in kernel json file in the path
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