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jupyter | Jupyter metapackage for installation , docs and chat | Data Visualization library

 by   jupyter Python Version: Current License: BSD-3-Clause

 by   jupyter Python Version: Current License: BSD-3-Clause

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kandi X-RAY | jupyter Summary

jupyter is a Python library typically used in Analytics, Data Visualization, Jupyter applications. jupyter has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub.
Jupyter metapackage for installation and docs.
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  • jupyter has a highly active ecosystem.
  • It has 12379 star(s) with 3212 fork(s). There are 684 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 179 open issues and 173 have been closed. On average issues are closed in 42 days. There are 6 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of jupyter is current.
jupyter Support
Best in #Data Visualization
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jupyter Support
Best in #Data Visualization
Average in #Data Visualization

quality kandi Quality

  • jupyter has 0 bugs and 0 code smells.
jupyter Quality
Best in #Data Visualization
Average in #Data Visualization
jupyter Quality
Best in #Data Visualization
Average in #Data Visualization

securitySecurity

  • jupyter has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • jupyter code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
jupyter Security
Best in #Data Visualization
Average in #Data Visualization
jupyter Security
Best in #Data Visualization
Average in #Data Visualization

license License

  • jupyter is licensed under the BSD-3-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
jupyter License
Best in #Data Visualization
Average in #Data Visualization
jupyter License
Best in #Data Visualization
Average in #Data Visualization

buildReuse

  • jupyter releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • jupyter saves you 56 person hours of effort in developing the same functionality from scratch.
  • It has 213 lines of code, 3 functions and 5 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
jupyter Reuse
Best in #Data Visualization
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jupyter Reuse
Best in #Data Visualization
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Top functions reviewed by kandi - BETA

kandi has reviewed jupyter and discovered the below as its top functions. This is intended to give you an instant insight into jupyter implemented functionality, and help decide if they suit your requirements.

  • Run Sphinx docs .
  • Install Sphinx docs .
  • Register custom CSS files .

jupyter Key Features

Jupyter metapackage for installation, docs and chat

Running the docs locally

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conda env create -f environment.yml  

Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)

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class_weights = compute_class_weight(
                                        class_weight = "balanced",
                                        classes = np.unique(train_classes),
                                        y = train_classes                                                    
                                    )
class_weights = dict(zip(np.unique(train_classes), class_weights))
class_weights

How to Export Jupyter Notebook by VSCode in PDF format? (Windows 10)

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conda activate <NAME_OF_VENV>
pip install notebook
conda install nbconvert
conda install pandoc
conda deactivate
sudo apt install texlive-xetex
-----------------------
conda activate <NAME_OF_VENV>
pip install notebook
conda install nbconvert
conda install pandoc
conda deactivate
sudo apt install texlive-xetex

How to undo/redo changes inside the selected cell in Jupyter notebook?

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{
    "experimentalDisableDocumentWideUndoRedo": true
}
pip install "jupyterlab<3.1"
conda install -c conda-forge "jupyterlab<3.1"
-----------------------
{
    "experimentalDisableDocumentWideUndoRedo": true
}
pip install "jupyterlab<3.1"
conda install -c conda-forge "jupyterlab<3.1"
-----------------------
{
    "experimentalDisableDocumentWideUndoRedo": true
}
pip install "jupyterlab<3.1"
conda install -c conda-forge "jupyterlab<3.1"

&quot;Attempting to perform BLAS operation using StreamExecutor without BLAS support&quot; error occurs

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gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    tf.config.experimental.set_virtual_device_configuration(
        gpus[0],[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])
  except RuntimeError as e:
    print(e)

Running cells with Python 3.10 requires ipykernel installed

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UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:

Specifications:

  - ipykernel -> python[version='>=2.7,<2.8.0a0|>=3.6,<3.7.0a0|>=3.7,<3.8.0a0|>=3.8,<3.9.0a0|>=3.5,<3.6.0a0|>=3.9,<3.10.0a0']

Your python: python=3.10
# Create virtual environment
# Use a version of Python that is less than 3.10
conda create --name your_env_name python<3.10

# Activate new environment
conda activate your_env_name

# Install ipykernel
conda install -c anaconda ipykernel

# Add this new environment to your Jupyter Notebook kernel list
ipython kernel install --name your_env_name --user

# Windows only: When trying to launch `jupyter notebook`, you may receive a win32api error.
# The command below fixes that issue.
conda install -c anaconda pywin32
-----------------------
UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:

Specifications:

  - ipykernel -> python[version='>=2.7,<2.8.0a0|>=3.6,<3.7.0a0|>=3.7,<3.8.0a0|>=3.8,<3.9.0a0|>=3.5,<3.6.0a0|>=3.9,<3.10.0a0']

Your python: python=3.10
# Create virtual environment
# Use a version of Python that is less than 3.10
conda create --name your_env_name python<3.10

# Activate new environment
conda activate your_env_name

# Install ipykernel
conda install -c anaconda ipykernel

# Add this new environment to your Jupyter Notebook kernel list
ipython kernel install --name your_env_name --user

# Windows only: When trying to launch `jupyter notebook`, you may receive a win32api error.
# The command below fixes that issue.
conda install -c anaconda pywin32
-----------------------
python3.10 -m pip install ipykernel
-----------------------
python3.10 -m pip install ipykernel
sudo apt-get install python3.10-distutils
  curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
 /bin/python3.10 ~/.vscode/extensions/ms-python.python-2022.0.1786462952/pythonFiles/shell_exec.py /bin/python3.10 -m pip install -U notebook /tmp/tmp-5290PWIe78U4HgLu.log
-----------------------
python3.10 -m pip install ipykernel
sudo apt-get install python3.10-distutils
  curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
 /bin/python3.10 ~/.vscode/extensions/ms-python.python-2022.0.1786462952/pythonFiles/shell_exec.py /bin/python3.10 -m pip install -U notebook /tmp/tmp-5290PWIe78U4HgLu.log
-----------------------
python3.10 -m pip install ipykernel
sudo apt-get install python3.10-distutils
  curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
 /bin/python3.10 ~/.vscode/extensions/ms-python.python-2022.0.1786462952/pythonFiles/shell_exec.py /bin/python3.10 -m pip install -U notebook /tmp/tmp-5290PWIe78U4HgLu.log
-----------------------
python3.10 -m pip install ipykernel
sudo apt-get install python3.10-distutils
  curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
 /bin/python3.10 ~/.vscode/extensions/ms-python.python-2022.0.1786462952/pythonFiles/shell_exec.py /bin/python3.10 -m pip install -U notebook /tmp/tmp-5290PWIe78U4HgLu.log

ipykernel (Jupyter notebook/labs) cannot import name ''filefind&quot; from traitlets.utils

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pip3 install traitlets==5.1.1

pip3 install pygments==2.4.1

lfortran in a jupyter notebook kills kernel

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print *, "Hello world!"
subroutine new
  print *, "Hello world!"
end subroutine new
call new
-----------------------
print *, "Hello world!"
subroutine new
  print *, "Hello world!"
end subroutine new
call new
-----------------------
print *, "Hello world!"
subroutine new
  print *, "Hello world!"
end subroutine new
call new

Pandas - How to Save A Styled Dataframe to Image

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import pandas as pd
import numpy as np
import dataframe_image as dfi

col_name = 'TestColumn'

temp_df = pd.DataFrame({'TestColumn':['A','B','A',np.nan]})

t1 = (temp_df[col_name].fillna("Unknown").value_counts()/len(temp_df)*100).to_frame().reset_index()
t1.rename(columns={'index':' '}, inplace=True)
t1[' '] = t1[' '].astype(str) 


style_test = t1.style.bar(subset=[col_name], color='#5e81f2', vmax=100, vmin=0).set_table_attributes('style="font-size: 17px"').set_properties(
    **{'color': 'black !important',
       'border': '1px black solid !important'}
).set_table_styles([{
    'selector': 'th',
    'props': [('border', '1px black solid !important')]
}]).set_properties( **{'width': '500px'}).hide_index().set_properties(subset=[" "], **{'text-align': 'left'})

dfi.export(style_test, 'successful_test.png')

embedding jupyter notebook/ google colab in Django app

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from django.shortcuts import redirect,HttpResponse
import subprocess
import time

def open_jupiter_notbook(request):
    b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
    if "9999" not in b:
        a=subprocess.Popen("jupyter-lab  --no-browser --port 9999".split())
    start_time = time.time()
    unreachable_time = 10
    while "9999" not in b:
        timer = time.time()
        elapsed_time = timer-start_time
        b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
        if "9999" in b:
            break
        if elapsed_time > unreachable_time:
            return HttpResponse("Unreachable")
    path = b.split('\n')[1].split('::',1)[0]
    #You can here add data to your path if you want to open file or anything
    return redirect(path)
<iframe src="{% url 'open_jupiter_notbook' %}" width= 600px height=200px></iframe>
-----------------------
from django.shortcuts import redirect,HttpResponse
import subprocess
import time

def open_jupiter_notbook(request):
    b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
    if "9999" not in b:
        a=subprocess.Popen("jupyter-lab  --no-browser --port 9999".split())
    start_time = time.time()
    unreachable_time = 10
    while "9999" not in b:
        timer = time.time()
        elapsed_time = timer-start_time
        b= subprocess.check_output("jupyter-lab list".split()).decode('utf-8')
        if "9999" in b:
            break
        if elapsed_time > unreachable_time:
            return HttpResponse("Unreachable")
    path = b.split('\n')[1].split('::',1)[0]
    #You can here add data to your path if you want to open file or anything
    return redirect(path)
<iframe src="{% url 'open_jupiter_notbook' %}" width= 600px height=200px></iframe>
-----------------------
from google.colab import files
src = list(files.upload().values())[0]
open('mylib.py','wb').write(src)
import mylib

JupyterLab Notebook cells going missing

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# (or conda-forge equivalent if you use conda/mamba)
pip install -U "jupyterlab>=3.1.10"
{
    "renderCellOnIdle": false,
    "numberCellsToRenderDirectly": 10000000000000
}
-----------------------
# (or conda-forge equivalent if you use conda/mamba)
pip install -U "jupyterlab>=3.1.10"
{
    "renderCellOnIdle": false,
    "numberCellsToRenderDirectly": 10000000000000
}
-----------------------
{
    "markdownCellConfig": {
        "lineNumbers": true
    },
    "codeCellConfig": {
         "lineNumbers": true
    },
    "renderCellOnIdle": false,
    "numberCellsToRenderDirectly": 10000000000000
}

Community Discussions

Trending Discussions on jupyter
  • Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)
  • How to Export Jupyter Notebook by VSCode in PDF format? (Windows 10)
  • How to undo/redo changes inside the selected cell in Jupyter notebook?
  • iPyKernel throwing &quot;TypeError: object NoneType can't be used in 'await' expression&quot;
  • &quot;Attempting to perform BLAS operation using StreamExecutor without BLAS support&quot; error occurs
  • Running cells with Python 3.10 requires ipykernel installed
  • ipykernel (Jupyter notebook/labs) cannot import name ''filefind&quot; from traitlets.utils
  • lfortran in a jupyter notebook kills kernel
  • Pandas - How to Save A Styled Dataframe to Image
  • embedding jupyter notebook/ google colab in Django app
Trending Discussions on jupyter

QUESTION

Compute class weight function issue in 'sklearn' library when used in 'Keras' classification (Python 3.8, only in VS code)

Asked 2022-Mar-27 at 23:14

The classifier script I wrote is working fine and recently added weight balancing to the fitting. Since I added the weight estimate function using 'sklearn' library I get the following error :

compute_class_weight() takes 1 positional argument but 3 were given

This error does not make sense per documentation. The script should have three inputs but not sure why it says expecting only one variable. Full error and code information is shown below. Apparently, this is failing only in VS code. I tested in the Jupyter notebook and working fine. So it seems an issue with VS code compiler. Any one notice? ( I am using Python 3.8 with other latest other libraries)

from sklearn.utils import compute_class_weight

train_classes = train_generator.classes

class_weights = compute_class_weight(
                                        "balanced",
                                        np.unique(train_classes),
                                        train_classes                                                    
                                    )
class_weights = dict(zip(np.unique(train_classes), class_weights)),
class_weights

In Jupyter Notebook,

enter image description here

enter image description here

ANSWER

Answered 2022-Mar-27 at 23:14

After spending a lot of time, this is how I fixed it. I still don't know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue.

class_weights = compute_class_weight(
                                        class_weight = "balanced",
                                        classes = np.unique(train_classes),
                                        y = train_classes                                                    
                                    )
class_weights = dict(zip(np.unique(train_classes), class_weights))
class_weights

Source https://stackoverflow.com/questions/69783897

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install jupyter

You can download it from GitHub.
You can use jupyter like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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