How To Install Package In Jupyter Notebook?

If we want to install packages from Jupyter Notebook itself, we can put an exclamation point (!) before the pip3 / conda install command. This command will then act as if it were executed in the terminal. Note: we cannot run pip install from the Python shell.
Install Java. Make sure Java is installed.

Why does Jupyter notebook have a boilerplate for installing packages?

That bit of extra boiler-plate makes certain that you are running the pipversion associated with the current Python kernel, so that the installed packages can be used in the current notebook. This is related to the fact that, even setting Jupyter notebooks aside, it’s better to install packages using

What programming languages does Jupyter notebook support?

Jupyter has support for over 40 different programming languages and Python is one of them. Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the Jupyter Notebook itself. Refer to the following articles for the installation of the Jupyter Notebook. How to install Jupyter Notebook in Linux?

How do I install a new package in Jupyter Notebook?

Quick Fix: How To Install Packages from the Jupyter Notebook

  1. If you installed Python using Anaconda or Miniconda, then use conda to install Python packages.
  2. If you installed Python any other way (from source, using pyenv, virtualenv, etc.), then use pip to install Python packages.

How do I install Jupyter hub packages?

Install conda, pip or apt packages

  1. If you already have a terminal open as an admin user, that should work too!
  2. Install a package! sudo -E pip install numpy. This installs the numpy library from PyPI and makes it available to all users. Note.

How do I package a Jupyter Notebook?

From Jupyter Notebook to Python package

  1. Install VS Code with Python extension, Git and Anaconda.
  2. Create a folder with an empty file called
  3. Open your Jupyter Notebook in VS Code and store your code file in the package folder.
  4. Set up and call your package in using the VS Code debugger.

How do I install packages in anaconda?

Installing packages from

  1. To find the package named bottleneck, type bottleneck in the top-left box named Search Packages.
  2. Find the package that you want and click it to go to the detail page.
  3. Now that you know the channel name, use the conda install command to install the package.

How do I install packages in anaconda environment?

Go to Environments tab just below the Home tab and from there we can check what all packages are installed and what is not. It is very easy to install any package through anaconda navigator, simply search the required package, select package and click on apply to install it.

What is pip install?

pip is a standard package manager used to install and maintain packages for Python. The Python standard library comes with a collection of built-in functions and built-in packages.

What is pip not found?

The pip: command not found error is raised if you do not have pip installed on your system, or if you’ve accidentally used the pip command instead of pip3. To solve this error, make sure you have installed both Python 3 and pip3 onto your system.

How to install Golang go with Jupyter Notebook?

  • Run jupyter notebook command to start Juyputer Notebook and select “Go (lgo)” from New Notebook menu.
  • To show documents of packages,functions and variables in your code,move the cursor to the identifier you want to inspect and press Shift-Tab.
  • Press Tab to complete code
  • Click Format Go button in the toolbar to format code.
  • lgo works with JupyterLab.
  • How can we install libraries in a Jupyter Notebook?

    – conda create -n virtual_environment_name python=x.x anaconda – source activate virtual_environment_name (if you are using linux) – activate virtual_environment_name (if you are using Windows) – conda install -c conda-forge matplotlib – jupyter notebook (run in Anaconda Prompt)

    How to install Matplotlib on Jupyter Notebook?

    – import matplotlib.pyplot as plt – import numpy as np – x = np.linspace (0, 100, 1000) – y = np.sin (x) – plt.plot (x,y) – ()

    Installing packages globally and locally

    1. In contrast to the Python shell, Jupyter Notebook is a more interactive and easier-to-use version of the Python language.
    2. If we wish to install packages from within Jupyter Notebook, we may precede the pip3/conda install command with an exclamation mark (!).
    3. This command will then behave as if it had been entered into the terminal itself.
    4. Please keep in mind that we cannot perform pip install from the Python shell.

    In contrast to a command line, the Python shell is used to enter Python code rather than instructions.Installing packages in a Jupyter notebook should be done with extreme caution.After spending hour after stressful hour attempting to import a certain package that I pip3/conda installed, I’ve discovered the proper method to go about installing packages: follow these steps.

    1. When installing packages, there are two primary possibilities to consider:

    Locally installing packages (when using virtual environments):

    1. Set up your virtual environment in the following manner.
    2. Activate the virtual environment once it has been configured.
    3. When using a terminal: cd source bin/activate cd source bin/activate Consequently, you have reached the directory containing your virtual environment and the virtualenv has been launched.
    4. In the terminal, type jupyter notebook to get started.

    Create a new notepad and paste sys into it.Adding the path of your virtual environment site packages to the system path.sys.path is a critical step in creating a virtual environment.append(‘./lib/python3.7/site-packages’) The./ is a relative path, and it is assumed that you are presently in your virtual environment directory when you type it.

    1. Exercising in a Jupyter notebook is a simple method to see if something is true!
    2. pwd.
    3. Following that, you will be able to import any package that you have previously installed with ease.

    Globally installing packages:

    1. For packages that are not intended for a specific project, but rather are intended to be used across many directories, the following instructions explain how to utilize the proper convention when installing a package.
    2. As a general rule, we do the following: P3 install (Pip3 Installer) DON’T MAKE THIS MISTAKE.
    3. Here’s how you should go about it in practice: system!
    4. -m pip install import system!

    Alternatively, conda:import sys!conda install can be used.Prefix -yes -no Taking the longer way rather to simply using plain Python guarantees that commands are executed in the Python installation that corresponds to the currently running notebook when the longer path is taken.As a result, pip installs the package in the Jupyter kernel that is presently executing.

    1. When this happens, the disconnectedness between Jupyter kernels and Jupyter’s Shell is overcome; for example, the installer links to a different Python version than the one that is now being used in the notebook, which is a known issue.
    2. More information on why such installation procedures must be followed is provided in this article, even if the so-called ″normal″ approach of installing works in many cases (it will not always function in this manner) may be found here.

    Install Python package using Jupyter Notebook

    • It is possible to create and share documents that contain data in a variety of formats, including live code, equations, visualizations, and text. Jupyter Notebook is an open-source web application that allows users to create and share documents that contain data in a variety of formats, including live code, equations, visualizations, and text. Data cleansing and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and a variety of other applications are possible. Python is one of the computer languages supported by Jupyter, which supports over 40 different programming languages in total. Python is required for the installation of the Jupyter Notebook (Python 3.3 or above, or Python 2.7) in order to use it. Installation instructions for the Jupyter Notebook may be found in the following articles. What is the best way to install Jupyter Notebook in Linux?
    • In this article, we will discuss how to install Jupyter Notebook in Windows.

    Everything in Jupyter is organized into cells. It provides the ability to alter the cell type to markup, text, Python console, and other choices. Python code may be run within the Python IPython console cell, thanks to the jupyter library.

    Installing Python Library in Jupyter

    Using! pip install

    1. Installing Python libraries is accomplished through the use of the pip command on the operating system’s command line prompt.
    2. The operating system has a collection of paths to executable applications in its so-called environment variables, which it uses to determine exactly what the pip command implies.
    3. This is the reason why the pip command may be executed straight from the console whenever possible.
    4. It is possible to execute console commands within a cell in Jupyter by placing an exclamation point (!) before the command within the cell.

    For example, if you write the following code in a Jupyter cell, it will be executed as a command in CMD.!echo GeeksforGeeks Output Additionally, we can install any package using jupyter in the same manner, and it will run it immediately in the operating system’s shell.Syntax:!

    1. installation of python As an example, let’s install NumPy with the help of Jupyter.
    2. However, due of the nature of the operating system, this strategy is not advised.
    3. This command was ran on the most recent version of the operating system that was found in the $PATH variable.
    4. As a result, in the situation of various Python versions, it is possible that the same package will not be installed in the jupyter’s Python version.
    5. It may work in the simplest of circumstances.

    Using sys library

    1. It is advised that you utilize the sys library in Python to resolve the difficulty indicated above.
    2. The sys library will yield the path to the current version’s pip on which the jupyter is operating.
    3. The path to the Python.exe of the version of Python on which the current Jupyter instance is running will be returned by sys.executable.
    4. Import sys!

    -m pip install is the syntax to use.The following code example will ensure that the package is installed in the same Python version as the jupyter notebook is currently operating on.

    How to Install ipython-sql package in Jupyter Notebook?

    1. Ipython-sql is a percent sql magic for the Python programming language.
    2. This is a magical addon that allows you to run SQL queries into code cells and read the results into pandas DataFrames in real time.
    3. We may connect to any database that is supported by SQLAlchemy using this technique.
    4. This is relevant to both traditional notebooks and the modern Jupyter Labs environment, as well.

    ipython-sql will be installed in the Jupyter notebook, and we will see how to do so in this post.

    Installing ipython-sql in Jupyter Notebook


    • Anaconda
    1. The following are the procedures to take in order to install the ipython-sql package in the Jupyter notebook: Create a new notebook in Jupyter Notebook by opening it in the Anaconda Navigator and following the onscreen instructions.
    2. Step 2: In the code cell, enter any one of the commands listed below.
    3. pip3 install the ipython-sql package sys!
    4. -m pip import sys!

    -m pip install the ipython-sql package This will install ipython-sql, as well as all of the dependencies that are required.Step 3: Type the magic command that follows into your computer.sql load ext percent load ext This will cause the SQL module to be loaded into the notebook.

    1. Now, with the help of percent sql magic, you can run SQL queries straight in Jupyter Notebook without having to leave the environment.
    2. Python SQL in a Jupyter Notebook is demonstrated.
    3. In the code above, I’ve created a table called STUDENT and filled it with data to represent students.
    4. We can quickly and simply construct tables, connect to existing databases, and write complex queries with the help of ipython-sql.

    Installing Packages in a Jupyter Notebook

    1. Here’s a recipe that I use when I’m developing scratch code since it’s really speedy.
    2. If you have a Jupyter notebook open and you wish to install or update a package for that environment, you may enter the following code in a cell to do this: import sys into your program!
    3. pip install $sys.executable -m $sys.executable If you were to run the following command in the present environment, seaborn would be upgraded to the most recent version: import sys into your program!
    4. $sys.executable -m pip install -upgrade seaborn $sys.executable -m pip install -upgrade seaborn What causes it to work?

    Pip may be invoked from the python executable by using the -m flag, as follows: python -m pip install python Furthermore, in a Python session, the sys.executable attribute is a string containing the full path to the executable for the current environment, for example: sys.executable=python.executable=python.executable=python.executable=python.executable=python.executable=python.executable=python.executable=py /Users/myname/anaconda3/envs/myenv/bin/python was expected to return a result like.In addition, we utilize the!character to escape shell commands in a notebook and the $ character to inject Python variables from the current session into a shell command in that notebook.

    1. Following the execution of the program, it is possible that you will need to restart the laptop in order for the new package to become accessible.
    2. Finally, a word of caution: this is a pattern that you should generally avoid using!
    3. It is preferable to have your Python code run in a repeatable environment wherever feasible.
    4. Consider using a tool like Poetry for this, or at the very least saving dependencies in requirements.txt.
    5. Notebooks, on the other hand, can be handy for scratch programming, especially where replication is not a big issue.
    6. It is important to get your environment up and running as fast as possible in these situations.

    Try out this method if you’re dealing with this type of scenario!

    How to Install Packages in Jupyter Notebook

    1. Given that Python is an Object-Oriented Programming Language, it has a large number of third-party libraries as well as inherent libraries that make writing more convenient for users.
    2. To carry out operations in Python, a variety of code editors are available, in addition to the default code editor that Python offers, which is known as the IDLE editor.
    3. To write our programs, we may use any form of code editor of our choosing.
    4. However, when it comes to completing Data Science-related activities, the Data Science community prefers to use some of the top editors available on the market.

    The Jupyter Notebook and the Google Colab are examples of such tools.Although they have the extension.ipynb, it is incorrect to refer to them as text editors because they are scientific Python notebooks that aid in accurate data visualization and manipulation within their terminal.They are also widely favored by data scientists due to the fact that they provide code blocks as a feature.

    1. So how do we download Python packages into them, or more specifically, how do we get Python packages into the Jupyter environment?
    2. It is feasible, and the answer to this issue is really straightforward and is provided below:
    See also:  How To Send A Package To Colombia?

    Installing Python Packages using Jupyter Notebook

    1. Regardless of whether you are accessing the Jupyter notebook through Azure, Python, or Anaconda, you have the option to get Python packages from any of these platforms.
    2. The procedures required to download packages in Jupyter are the same as those required to get packages from the Command Prompt or Anaconda Prompt, which is done using pip or conda, as described above.
    3. To download a package, such as Numpy, into Jupyter, you must first download Jupyter using the command prompt, or access the same using Anaconda or Azure, and then open the Jupyter console from the command line.
    4. Wait for the kernel to become ready once you have opened the kernel or the console.

    Once the kernel is ready, just run the pip install or conda install instructions, followed by the name of the package that you wish to install; wait for a few minutes, and the item will be downloaded and installed.However, there is a little difference between utilizing Jupyter on our local system and accessing the same through a cloud service.The difference is that when we are obtaining packages on our local system using Jupyter, we can just type pip install, however when we are downloading the same packages in the cloud, we must include an exclamation mark before pip, which is!pip, and the rest of the commands will be same.

    1. Taking NumPy as an example, the command to install it on a local system is pip install numpy, while the command to install it on a cloud system is!
    2. numpy may be installed via pip.
    3. In this way, we may download any package from the Jupyter Notebook on the cloud or a local machine using this small subtlety.
    4. However, getting packages using the Command Prompt is often favored by Pythonists due to the fact that Jupyter takes a significant amount of time to download programs that would have been downloaded quickly if we had utilized the Command Prompt.
    5. Additionally, there are difficulties with the Jupyter Kernel, which either goes down due to a large amount of load or hangs a lot owing to the download of large packages.
    6. More information may be found at: How to install Jupyter notebooks on Windows 10/7.


    If you are using your local system rather than a cloud-based service, it is preferable to download packages from the Command Prompt rather than from the Jupyter installing packages. This is because the downloading speed is significantly faster when using CMD rather than the Jupyter installing packages.

    Install conda, pip or apt packages — The Littlest JupyterHub v0.1 documentation

    TLJH begins all users in the same conda environment, regardless of their roles. All users on the JupyterHub have access to the packages and libraries that have been installed in this environment. Users with administrative privileges can quickly install packages.

    Installing pip packages¶

    Installing packages in Python from the Python Packaging Index is best accomplished using the Pip package manager (PyPI). At the moment, PyPI has almost 145,000 packages, which means that a large portion of what you want will be available.

    1. Log into your Jupyter Notebook as the administrator account and start a Terminal window. In the event that you already have a terminal window open as an administrator user, that should also work!
    2. Install a package on your computer! numpy may be installed using sudo -E pip This script downloads and installs the numpy library from the PyPI repository, making it available to all users. Note If you see an error message such as sudo: pip: command not found, check to see if you have the -E argument after sudo after the command.

    Installing conda packages¶

    1. In addition to new languages (such as the latest versions of Python, Node.JS, R and others), Conda also allows you to install packages written in those languages.
    2. Installing conda is sometimes simpler and easier than installing pip for a large number of scientific software packages – especially when the program relates to C or Fortran code.
    3. We propose that you install packages from conda-forge, which is a community-maintained repository ofconda-based software.
    1. Log into your Jupyter Notebook as the administrator account and start a Terminal window. In the event that you already have a terminal window open as an administrator user, that should also work!
    2. Install a package on your computer! conda install -c sudo -E conda install conda-forge gdal is a kind of forge. Using this command, the gdal library from conda-forge is installed and made available to all users. gdal is significantly more difficult to install with pip. Note If you see an error message such as sudo: conda: command not found, check to see if you have the -E argument after sudo after the command.

    Installing apt packages¶

    1. Apt is the official package manager for the Ubuntu Linux distribution, and it is available for download here.
    2. You may install programs (such as vim, sl, htop, and so on), servers (such as postgres, mysql, nginx, and so on), and a far greater number of languages than are available in conda (haskell, prolog, INTERCAL).
    3. Some third-party software (such as RStudio) is supplied as.deb files, which are the same files that are used by apt to install software on a computer.
    4. It is possible to search for packages using the Ubuntu Package search tool – just be sure to search in the version of Ubuntu that you are currently running!
    1. Log into your Jupyter Notebook as the administrator account and start a Terminal window. In the event that you already have a terminal window open as an administrator user, that should also work!
    2. List of available packages has been updated. This ensures that you receive the most up-to-date versions of the packages available from the repositories.
    3. Install just the packages that you choose. mysql-server may be installed using sudo apt install mysql-server git This script installs (and runs) a MySQL database server as well as the git software.

    User environment location¶

    Conda environments are configured under /opt/tljh/user, and a Python3 kernel is used as the default for the user environment. It is viewable by all users, but it can only be written by users who have root access to the system. This makes it feasible for JupyterHub administrators (who have root access through the sudo command) to quickly install applications in the user environment.

    Accessing user environment outside JupyterHub¶

    1. Because we add /opt/tljh/user/bin to the $PATH environment variable for all JupyterHub users, they will be able to access anything installed in the user environment without having to do anything manually.
    2. If you want to connect to your server using ssh, you may acquire access to the same environment by typing the following commands: PATH=/opt/tljh/user/bin:$ is exported as PATH.
    3. Whenever you execute a command, the user environment will be looked for first, before the system environment is searched for.
    4. As a result, if you run python3, it will utilize the version of Python3 that is installed in the user environment (/opt/tljh/user/bin/python3) rather than the version of Python3 that is installed in the system environment (/usr/bin/python3).

    Usually, this is exactly what you want!At ensure that this change is permanent, you may add the following line to the end of the.bashrc file in your home directory.It is common practice to reset the PATH environment variable when using the sudo command for security concerns.

    1. As a result, error messages such as the following appear: Gdal is installed with sudo conda install -c conda-forge.
    2. sudo: conda: command could not be located When using ssh, the most popular and portable solution to remedy this is as follows: sudo PATH=$ conda install -c conda-forge gdal sudo PATH=$

    Upgrade to a newer Python version¶

    1. All new TLJH installations make use of miniconda 4.7.10, which provides the users with a Python 3.7 environment to work in. The earlier TLJH installations came with miniconda 4.5.4, which resulted in a Python 3.6 environment, according to the documentation. To upgrade the Python version of the user environment, one can perform one of the following: Start from scratch on a system that does not already have TLJH installed. For information on how to install TLJH, see the installation guide section.
    2. Manually upgrading Python is not recommended. In order to avoid breaking packages already installed under the previous Python version, updating your current TLJH installation will not automatically upgrade the Python version of the user environment
    3. however, you may do so manually if you like. Steps:
    1. If you’re connecting through ssh, you’ll need to activate the user environment. If the terminal was launched using JupyterHub, the following steps can be skipped: source /opt/tljh/user/bin/activate
    2. source /opt/tljh/user/bin/activate
    3. Get a list of the pip packages that are now installed (so that you may subsequently install them under the new Python): The command pip freeze is followed by the command pip pkgs.txt.
    4. Update all of the conda packages that have been installed in the environment: sudo PATH=$ conda update -all
    5. sudo PATH=$ conda update
    6. Update the Python version using the following command: sudo PATH=$ conda install python=3.7
    7. Use pip install -r pip pkgs.txt to install the pip packages that were previously saved:

    Managing packages — conda 4.12.0.post6+e7012690 documentation

    Note For the commands provided on this page, there are a plethora of alternatives to consider. For further information, check the Command Reference.

    Searching for packages

    1. For the next instructions, you may either use the terminal or an Anaconda Prompt.
    2. To check whether a certain package, such as SciPy, is available for installation, run the following command: For example, to check whether a certain package, such as SciPy, is available for installation from, run the following command: conda search -override-channels -channel defaults scipy The following command can be used to determine whether a given package, such as iminuit, is present in a specific channel, such as and is accessible for installation: conda search -channeliminuit -override-channels

    Installing packages

    1. For the next instructions, you may either use the terminal or an Anaconda Prompt.
    2. In order to install a specific package, such as SciPy, into an existing environment ″myenv,″ follow the steps below.
    3. scipy install -name myenv conda install scipy Without specifying the environment name, which in this case is accomplished by using the -name myenv option, the package is installed into the current environment as follows: Alternatively, you may install a specific version of a package, such as SciPy, by running the command conda install scipy=0.15.0 Installing multiple packages at the same time, such as SciPy and cURL, is accomplished as follows: Note It is preferable to install all of the packages at the same time in order to ensure that all of the dependencies are installed at the same time.
    4. Installing numerous packages at the same time while specifying the package’s version is accomplished by using the following command: curl=7.26.0 is installed by conda install scipy=0.15.0.

    Installing a package for a certain Python version is as follows: conda install scipy=0.15.0 curl=7.26.0 -n py34 env scipy=0.15.0 curl=7.26.0 If you wish to work with a certain Python version, it is ideal to do so in an environment that supports that version of the language.More information may be found at Troubleshooting.

    Installing similar packages

    1. Installing packages that have similar filenames and fulfill similar functions may result in unexpected effects if they are installed together.
    2. The outcome will most likely be determined by the item that was most recently installed, which may be unfavorable.
    3. If the names of the two packages change, or if you’re creating variations of packages and need to align other software in the stack, we propose that you use Mutex metapackages to connect them.

    Installing packages from

    A package management service for both public and private package repositories is provided at, which may be used to retrieve packages that are not yet available through conda install. is an Anaconda product, in the same way as Anaconda and Miniconda are products of Anaconda. Installing a package from is as follows:

    1. Navigate to the following address in a browser: Enter the package name bottleneck in the top-left box labeled ″Search Packages″ to locate the package called bottleneck.
    2. Find the package that you’re interested in and click on it to be taken to the detail page for it. The name of the channel is displayed on the detail page of the channel. In this case, the ″pandas″ channel is being discussed.
    3. Now that you have the name of the channel, you may install the package using the conda install command. Run the following commands in a terminal window or an Anaconda Prompt: conda install -c pandas bottleneck It instructs conda to get and install the bottleneck package from the pandas channel on
    4. To verify that the package has been installed, execute the following commands in your terminal window or Anaconda Prompt: A list of packages, including bottleneck, displays.

    Note See Managing channels for details on how to install packages from many sources at the same time.

    Installing non-conda packages

    1. If a package is not accessible from conda or, you may be able to locate and install the package using conda-forge or another package manager such as pip if the package is not available from conda or
    2. Pip packages do not contain all of the functionality available in conda packages, thus we recommend that you first try to install any program using conda before using pip.
    3. Try searching for and installing the package with the help of conda-forge if the package is not accessible through conda.
    4. If you are still unable to install the program, you may try installing it using the pip package manager.

    While there are some inevitable limitations to the compatibility of pip and conda packages due to the differences between the two packages, conda strives to be as compatible with pip as feasible.Note Both pip and conda are included in Anaconda and Miniconda, so you won’t have to worry about installing them individually.Virtual environments have been replaced with conda environments, hence there is no longer a requirement to activate a virtualenv before using pip.

    1. It is possible to have pip installed both outside and inside of a conda environment, depending on your preferences.
    2. Installation of pip inside the currently active conda environment and subsequent installation of packages using that instance of pip are required in order to benefit from conda integration.
    3. The command conda list displays packages that have been installed in this manner, with a label indicating that they have been installed via pip.
    4. As explained in Using pip in an environment, you may install pip in the current conda environment by using the command conda install pip in the current conda environment.
    5. Even if there are instances of pip installed both inside and outside of the current conda environment, only the instance of pip installed inside the current conda environment will be utilized unless otherwise specified.
    6. To install a non-conda package, follow these steps:
    1. Activate the environment in which you wish to run the software by doing the following: Run the command activate myenv in your Anaconda Prompt on Windows.
    2. Run the command conda activate myenv in your terminal window on Mac OS X and Linux.
    1. To use pip to install a software such as See, open a terminal window or an Anaconda Prompt and type the following commands:
    2. Run the following commands in your terminal window or Anaconda Prompt to check that the package was successfully installed: If the package is not shown, install pip as stated in Using pip in an environment and try these commands again.
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    Installing commercial packages

    1. In the same way that you would install any other product, installing a commercial package such as IOPro is no different.
    2. Run the following commands in a terminal window or an Anaconda Prompt: conda install -name myenv iopro When you run this command, you will be presented with a free trial version of IOPro, one of Anaconda’s commercial tools that can help you speed up your Python processing.
    3. This free trial, with the exception of academic usage, expires after 30 days.

    Viewing a list of installed packages

    For the next instructions, you may either use the terminal or an Anaconda Prompt. To get a complete list of all of the packages currently installed in the active environment, type: To see a complete list of all of the packages in a deactivated environment, type the following into your browser:

    Listing package dependencies

    There isn’t a single conda command that can be used to discover which packages are dependent on a certain package inside your environment. The process consists of the following steps:

    1. List the dependencies that a certain package requires in order to be able to operate: search package name -info with conda
    2. Locate the package cache directory for your installation using the following command: conda info
    3. Identify package dependencies and resolve them. On macOS Catalina, Anaconda/Miniconda defaults to storing packages in the /anaconda/pkgs/ directory (or in the /opt/pkgs/ directory on Windows). Each package contains an index.json file, which contains a list of the packages that it depends on. This file may be found in the directory anaconda/pkgs/package name/info/index.json.
    4. With this information, you may determine which packages are dependent on a certain package. The following command will scan all index.json files in the directory: grep package name /anaconda/pkgs/info/index.json
    1. Everything that contains the will have its complete package path and version shown as a result of this query.
    2. As an illustration, grep numpy /anaconda3/pkgs/*/info/index.json is a command.
    3. The following is the output of the previous command: The following information may be found in the index.json file: numpy 1.11.3 py36 0, numpydoc, and numpydoc.json: numpydoc, numpydoc, and numpydoc.json: numpydoc, and numpydoc.json: numpydoc, and numpydoc.json: numpydoc, and numpydoc.json: numpydoc, and numpydoc.
    4. Python 3.6.0, version py36 0, located in /Users/testuser/anconda3/pkgs/anconda Information on numpy 1.11.3 py36 0 may be found at -4.3.0-np111py36 0/info/index.json.

    It is important to note that this also returned ″numpydoc″ since it contains the string ″numpy.″ You can add to your search to get a more targeted set of results.

    Updating packages

    • Use the conda update command to see whether there is a new version of conda available. If conda informs you that an update is available, you will have the option of whether or not to install the update. For the next instructions, you may either use the terminal or an Anaconda Prompt. To make changes to a given package, do the following:
    • To get Python up to date, do the following:
    • To update conda itself, do the following:
    1. Note Conda always updates to the most recent version available in its series, so Python 2.7 always updates to the most recent version available in the 2.x series and Python 3.6 always updates to the most recent version available in the 3.x series.
    2. To update the Anaconda metapackage, use the following command: conda update conda anaconda conda version update The conda command compares versions of any package you are upgrading and then displays what is available for installation.
    3. Alternatively, if no updates are available, conda will state that ″All requested packages have already been installed.″ You can update your package if a newer version of it is available and you want to do so by typing y to update:

    Preventing packages from updating (pinning)

    1. In an environment, pinning a package specification prohibits the updates of any packages specified in the pinned file from taking effect.
    2. Add a file entitled pinned to the conda-meta directory of the environment’s conda-meta directory, which contains a list of the packages that you do not want updated.
    3. EXAMPLE: If you use the file provided below, NumPy will be forced to stay on the 1.7 series, which is any version that begins with the letter 1.7.
    4. This also causes SciPy to remain at the exact same version as before: 0.14.2.

    Python 1.7.* scipy ==0.14.2 numpy ==0.14.2 In the presence of this pinned file, conda update numpy maintains NumPy at version 1.7.1, whereas conda install scipy=0.15.0 results in an error.The -no-pin flag can be used to overcome the update limitation on a package that has been installed.Run the following command in the terminal or an Anaconda Prompt: conda update numpy -no-pin Because the pinned specifications are supplied with each conda install, running successive conda update commands without the -no-pin option would restore NumPy back to the 1.7 series of versions.

    Adding default packages to new environments automatically

    To have default packages automatically added to each new environment that you build, follow these steps:

    1. Enter these commands in the Anaconda Prompt or Terminal: conda configuration -add create default packages PACKAGENAME1 PACKAGENAME2
    2. You may now establish new environments, and the default packages will be installed in each of them
    3. you can also delete existing environments.

    You may also add a list of packages to be created by default to the.condarc file, which can be edited. By using -no-default-packages at the command prompt, you may prevent this option from being used by default.

    Removing packages

    • For the next instructions, you may either use the terminal or an Anaconda Prompt. For example, to remove SciPy from an environment such as myenv, run the following command: conda remove -n myenv scipy
    • For example, to uninstall SciPy from the present environment, do the following:
    • To uninstall many packages at the same time, such as SciPy and cURL:
    • To validate that a package has been uninstalled, do the following:

    Add packages to Anaconda environment in Python

    1. In order to include new packages into our existing anaconda system, we must first determine which packages we want to include.
    2. Method 1 When adding packages to our anaconda environment, one frequent technique is to utilize the ″Anaconda Navigator.″ As soon as the ″Ananconda Navigator″ is launched, the home page will appear something like this: Go to the Environments tab, which is located immediately below the Home tab, and from there we can see what packages have been installed and which have been left out.
    3. It is quite simple to install any package using the anaconda navigator; simply search for the desired package, choose the package, and then click on apply to have it successfully installed.
    4. Imagine that your computer does not have the tensorflow packages.

    I may simply search for the appropriate package (such as tensorflow), pick it, and click on apply to have it installed on your machine.Installing packages using the terminal or an Anaconda Prompt is another method of accomplishing this.opencv is installed using the conda command.

    1. Installation of the OpenCV package into your present environment will be accomplished with the command above.
    2. For example, to install a specific version of the opencv package, use the command conda install opencv-3.4.2 Multiple packages, such as OpenCV and Tensorflow, can be installed at the same time.
    3. install opencv and tensorflow with conda Note: It is advised that you install all of the essential packages at the same time in order to ensure that all of the dependencies are installed at the same time.
    4. To install a specific package, such as opencv, into your current environment ″myenv,″ use the command ″myenv install″ (in case you have a virtual environment to install project specific packages).
    5. opencv –name myenv conda install –name myenv The third method is to discover and install the package using a different package manager, such as pip, if the package is not accessible in our conda environment or using the anaconda navigator (method 2).
    6. The command conda install pip’ will enable us to add pip to our existing conda environment without having to change anything.

    It will display something along the lines of ″Now, if you want to install any particular package, using pip in the conda environment, we can do it along the lines of ″Above, we have installed opencv package through pip in the conda environment.″

    Viewing a list of installed packages

    • For example, we can use the conda command to list all of the packages installed in the current environment: conda list. Published on the 19th of February, 2019 at 11:02:10. Questions and Answers on a related topic
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    Installing a pip package from within a Jupyter Notebook not working

    1. I was asked this question 5 years and 8 months ago.
    2. The page has been viewed 202k times.
    3. When I run!pip install geocoder in Jupyter Notebook, I get the same output as when I run!pip install geocoder at the terminal, but when I try to import the geocoder package, it says it is unavailable.
    4. Puppet 8.1.2 and Anaconda 4.0.0 are the tools I’m working with on Ubuntu 14.04.

    Installing the geocoder is as follows:!pip install geocoder In addition, the cache has been deactivated since the directory ‘/home/ubuntu/.cache/pip/ or its parent directory does not belong to the current user.Please double-check the directory’s permissions and who owns the directory.If you’re running pip with sudo, you might want to use the -H switch.

    1. It is not possible to cache wheels since the directory ‘/home/ubuntu/.cache/pip’ or its parent directory is not owned by the current user, and therefore caching wheels have been disabled.
    2. Examine the permissions and the owner of the directory in question.
    3. If you’re running pip with sudo, you might want to use the -H switch.
    4. Geocoding data is being gathered.
    5. Geocoder-1.15.1-py2.py3-none-any.whl is being downloaded (195kB) one hundred percent ||
    6. 204 kilobyte 3.2MB/s requests in /usr/local/lib/python2.7/dist-packages have been met (use -upgrade to upgrade): this need has already been satisfied (from geocoder) ratelim in /usr/local/lib/python2.7/dist-packages has already been satisfied (use -upgrade to upgrade) and is no longer required (from geocoder) Six packages in /usr/local/lib/python2.7/dist-packages have already been installed to satisfy the need (use -upgrade to upgrade) (from geocoder) Requirement already met (use -upgrade to upgrade): go to /usr/local/lib/python2.7/dist-packages by double-clicking it (from geocoder) Requirement already met (use -upgrade to upgrade): decorator in /usr/local/lib/python2.7/dist-packages/decorator-4.0.10-py2.7.egg (from ratelim->geocoder) is already present (use -upgrade to upgrade).

    Installing the packages that have been collected: geocoder Geocoder-1.15.1 has been successfully installed.Then attempt to import it as follows: import geocoder – ImportErrorTraceback (most recent call last) in () -> 1 import geocoder – ImportErrorTraceback (most recent call last) ImportError: There is no module called geocoder available.I also tried closing down and restarting the laptop, but this didn’t work either time.The geocoder package is installed in /usr/local/lib/python2.7/site-packages when using the terminal, but it is installed in /usr/local/lib/python2.7/dist-packages while using a notebook, which is not in the path specified in sys.path.This solution resolves the issue for the current session by appending the string /usr/local/lib/python2.7/dist-packages to the end of the function.Is there a way to adjust the path in a permanent manner or tell pip where to install geocoder?

    1. asked on July 14, 2016, 7:42 a.m.
    2. Mikhail Janowski is a Russian actor and director.
    3. Mikhail Janowski is a Russian actor and director.
    4. 3,4255 gold badges have been awarded.
    5. a total of 23 silver badges 35 bronze medals were awarded.
    1. 5!
    2. pip install -user (pip install -user The exclamation point instructs the notebook to treat the cell as a shell command.
    3. 13.5k gold badges, 95 silver badges, 182 bronze badges, and nbro13.5k total badges replied At 17:54 on July 21, 2017, Ajinkya Ajinkya has 9856 silver badges, 3 bronze badges, and 2 gold badges.
    4. In IPython (jupyter) 7.3 and later, there is a magic percent pip and percent conda command that will install into the current kernel without the need to restart the computer (rather than into the instance of Python that launched the notebook).
    • pip install geocoder (percentage) In older versions, you had to use the sys command to resolve the issue, as demonstrated in the response by FlyingZebra1.
    • import sys into your program!
    • -m pip install geocoder was successfully completed.
    • At 16:41 on May 17, 2019, EponymousEponymous4,9413 gold badges have been awarded to you.
    • There are 40 silver badges and 42 bronze badges.
    • 1 Fedex Fedex = package name in 2019, according to percent pip install Fedex In earlier versions of Conda, the import sys command was used.

    Note that you will need to import sys answered in order to use the -m pip install fedex option.22nd of May, 2018 at 17:18 FlyingZebra1FlyingZebra11,09715 silver badges23 bronze badgesFlyingZebra11,09715 silver badgesFlyingZebra11,09715 bronze badges 1 The following line of code works in a jupyter notebook running Python 3.6:!source activate py36;pip install was successfully completed.

    • Oct.
    • 11, 2017, 2:32 p.m.
    • Import sys worked for me in Jupyter Notebook on the Mac Platform using Python 3: import sys!
    See also:  What Is Included In A Branding Package?

    -m pip install -r requirements.txt was successfully completed.August 17, 2018, 7:53 a.m.There is an issue with pyarrow since it is stored by pip into dist-packages (in your instance, this is /etc/python2.7/dist-packages/pyarrow).Because this path is bypassed by Jupyter, pip will not be of use.As a workaround, I recommend include the following code in the first block: import sys sys.path.append(‘/usr/local/lib/python2.7/dist-packages’) or whatever the path or Python version is in the first block.

    1. Specifically, import sys.path.append(″/usr/local/lib/python3/dist-packages″) in Python 3.5 is the appropriate syntax.
    2. answered @ 4:12 p.m.
    3. on May 10, 2018 Dawid Laszuk is a professional photographer.
    4. Dawid Laszuk has 1,35217 silver badges and 34 bronze badges to his credit.

    Make use of some shell magic to your advantage: percent percent sh percent percent sh percent percent sh geocoder is installed via pip.Please let me know if it is successful.answered 3rd of August, 2016 at 18:14 Alternative option: you can also build a bash cell in jupyter using the bash kernel and then pip install geocoder to complete the process.That should be effective.answered @ 2:33 p.m.on September 8, 2017 The same thing happened to me.

    1. I discovered these guidelines, which were successful for me.
    2. Installing handcalcs straight from a notebook is demonstrated here!
    3. install -upgrade-strategy with the flag only-if-needed handcalculators (ref: When pip and conda are used simultaneously, there may be complications.

    When integrating conda with pip, it is recommended to utilize a conda environment that has been isolated.If there are any remaining packages after conda has been used to install as many as feasible, pip should be used to install any remaining applications.If any changes are required to the environment, it is preferable to establish a new environment rather than executing conda after pip, since this will save time.Text files should be used to hold conda and pip needs where they are appropriate.We recommend that you do the following: Pip should only be used after conda.

    Conda should be used to install as many prerequisites as feasible before using pip.Pip should only be run with the -upgrade-strategy option if it is required (the default).Do not use pip with the -user parameter, and do not install on behalf of all users.answered At 1:11 a.m.on February 21, 2021, conda python=2.7 ipykernel source active py27 pip create -n py27 python=2.7 Geocoder should be installed.Nissa4,5888 gold badges have been earned.

    a total of 27 silver badges 37 bronze medals were awarded.answered 12th of March, 2017 at 13:26 Using pip2 was successful for me:!Installing the geocoder with pip2 and importing the geocoder Mountain View, California’); g =‘Mountain View, CA’) g.latlng responded on November 19, 2016, at 21:58.

    8 bronze badges, 14 silver badges, and 3314 elwarrenelwarren3314 silver badges

    Install python packages on Jupyter Notebook

    1. You should record the following information in your Jupyter notebook using Anaconda: Ensure that the current Jupyter kernel import sys has the conda package!
    2. conda install -yes -prefixpackagename is equivalent to If you have a standard Python installation, you should type the following in your Jupyter notebook: Ensure that a pip package is installed in the current Jupyter kernel import system.
    3. install packagename with -m pip By substituting packagename with the name of your package, such as numpy, for example.
    4. Cheers answered At 18:45 on November 20, 2019, The Jupyter notebook server on which you are working does not have access to the internet.

    Because this is a business laptop, your operating system may include a firewall or may restrict internet access to third-party programs.Regardless, installing components with pip is a simple procedure.If you are unable to connect to the internet from within the laptop, try opening a Command Prompt as administrator and typing pip install iapws.

    1. @ 9:40 a.m.
    2. on February 21, 2019 darkskydarksky1,86916 1 silver badge, 26 bronze badges, and 1 gold badge I followed the identical procedure on my own computer and was successful in completing the installation.
    3. Thank you for your assistance with the troubleshooting.
    4. 22nd of February, 2019 at 0:56

    How do I install Python packages in Jupyter notebook running from WSL2+ubuntu20.04

    1. Ubuntu20.04 with WSL2 and jupyter notebook is operating on my computer.
    2. The jupyter notebook image, on the other hand, does not include many packages.
    3. For example, I would want to install pandas and attach the appropriate sys path to the end of the installation.
    4. (UPDATE) In accordance with Prayson’s advice, I carried out the following actions from the Jupytr notebook terminal: pip install -upgrade -user pip python3 -m pip install -upgrade pandas -user python3 -m pip install -user python3 Both stages were completed successfully.

    After that, I was able to correctly call the following code snipper for the FIRST TIME ONLY: I used the following code snippet to test it: the import system import numpy as np the import pandas as pd import matplotlib.pyplotlib.pyplotlib as plt the import sys However, after I stopped down and restarted my computer, I was unable to import pandas again and received the following error message: ModuleNotFoundErrorTraceback (most recent call last) in 7 8 import numpy as np -> 9 import pandas as pd 10 import matplotlib.pyplot as plt 11 import matplotlib.pyplot as plt 12 import matplotlib.pyplot as plt ModuleNotFoundError: There is no module named ‘pandas’, which I may have overlooked while adding the route.However, I am unsure on how to go about doing it right.Any advice would be much appreciated.

    How do I install python packages for use in Jupyter notebooks?

    1. When working with python environments that you’ve generated on the command line, you’ll need to construct what is known as a ‘kernel’ for the environment in order to utilize it in a Jupyter notebook environment.
    2. In this way, Jupyter is able to recognize and make use of the matching environment.
    3. As of right now, the most practical method to accomplish this at MSI is to first construct an environment using conda, and then to generate a kernel for that environment using ipykernel.
    4. The following is an example of a technique for accomplishing this goal: we construct an environment for utilizing the tensorflow package from the conda-forge channel.

    Let’s start by laying the groundwork for the environment into which we’ll be installing.The only packages I’m installing here are python3 and tensorflow, as well as ipykernel, which will be used to construct our Jupyter kernel later on.You may add any additional packages you require to the end of the ″conda create″ command.

    1. conda create -name my tensorflow env python=3 module load python3 conda create python=3 tensorflow ipykernel -channel tensorflow ipykernel -channel conda-forge Now that the new environment has been activated, let’s develop the kernel that will run on it.
    2. You have the option of changing the display name to something more appropriate for you.
    3. source my tensorflow env python -m my tensorflow env ipykernel install -user -name my tensorflow env -display-name ″Python 3 (my tensorflow env)″ ipykernel install -user -name my tensorflow env Using the custom kernel as a python notebook type in Jupyter, you should be able to pick the tensorflow package (along with any other packages you may have added to this environment) for usage in notebooks of that kind after completing the approach described above.
    4. Alternatively, you may pick the kernel from within a laptop by selecting the kernel option at the top right of the screen, which looks like this: You may then choose the new kernel from the pop-up menu, which looks like this: The following tiles are available when establishing a new notebook: When creating a new notebook, pick the tile that corresponds to your surroundings from the notebook creation page: At the following sites, you can get more detailed documentation on kernel development and conda environment management:

    PySpark + Anaconda + Jupyter (Windows)

    1. It feels like I have to install PySpark at least once every six months, and the experience is never the same twice as a result.
    2. It’s important to note that this isn’t always the fault of Spark.
    3. A combination of the numerous distinct conditions under which Spark may be deployed, a lack of official documentation for each and every one of those situations, and my failure to document the procedures I did to properly install it is responsible for the problem instead.
    4. Consequently, I decided to document the procedures required to install the most recent version of PySpark under the conditions under which I now require it: within an Anaconda environment on Windows 10 (instead of the default environment).

    Please keep in mind that the website that was most helpful in generating the following answer may be found here (Medium article).I then discovered a second website with instructions that are quite similar to the ones above, which may be found here (Towards Data Science article).

    Steps to Installing PySpark for use with Jupyter

    This solution implies that Anaconda has previously been installed, that an environment named ‘test’ has already been established, and that Jupyter has already been put into that environment.

    1. Install Java

    1. Make certain that Java is installed.
    2. It may be essential to modify the ‘JAVA HOME’ environment variable and to include the appropriate location in the ‘PATH’ variable.
    3. If you are unable to access the system menu to make changes to these parameters, they can be temporarily modified from within Jupyter: import os, sys os.environ = ″c:Program FilesJavajre1.8.0 202″ sys.path.insert(0, os.environ + ″bin″) import os, sys os.environ = ″c:Program FilesJavajre1.8.0 202″ sys.path.insert(0, os.environ + ″bin Make any required changes to the version name and number (e.g., jdk1.8.0.201, etc.).

    2. Install Spark

    1. Installing pyspark from the conda-forge channel is the choice we choose.
    2. Consider the following scenario: I want to include it in my ‘test’ environment.
    3. Then, on the terminal, I’d type in the following commands: Tests are run using the command ‘conda activate test’ and the installation command ‘conda install -c conda-forge pyspark’.
    4. Set the variable ‘SPARK HOME’.

    If, as in Step 1, you are unable to access the system menu in order to add this variable, it can be temporarily set from within Jupyter as follows: import os os.environ = ″c:usersAnaconda3envsLibsite-packagespyspark″ import os os.environ = ″c:usersAnaconda3envsLibsite-packagespyspark″

    3. Setup winutils.exe

    1. While I was able to get Spark to operate without the need of winutils, I needed to download and set up an environment variable for it in order to export my Spark dataframe to a parquet file for later use.
    2. Download a version of winutils from here (Github repo); I used hadoop-3.0.0 for this example.
    3. My notebook’s environment variable was set to ″c:hadoopbin″ as a result of dropping this into ″c:hadoopbin″ and setting the os.environ variable to ″c:hadoop″ in my notebook.

    4. Use Pyspark with Jupyter

    1. It may be sufficient to start a spark session with the following parameters: import pyspark from the pyspark package import Import SparkContext and SparkConf from the pyspark.sql file.
    2. pyspark is the configuration for the SparkSession.
    3. SparkConf() conf.setAppName(‘mySparkApp’) conf.setMaster(‘local’) sc = pyspark.SparkContext(conf=conf) where conf is the context to be used.
    4. sparkSession is an abbreviation for spark (sc) By performing the following cell, you can verify that spark is up and running: nums = sc.parallelize() nums.count nums = sc.parallelize() nums.count () Depending on whether or not the installation was successful, we may need to install and execute the ″findspark″ module again.

    Run the following commands from the command line to configure your environment: ‘conda install -c conda-forge findspark’ is an example of a command line installation.Afterwards, within the notebook, before to the import of pyspark and after the establishment of the ‘SPARK HOME’ variable, execute the following commands: findpark is imported findspark.init() findspark.find() findspark.find() ()


    1. It’s possible that at the conclusion of the day, we performed the following commands in the terminal: ‘conda activate test’ is an abbreviation.
    2. ‘conda install -c conda-forge pyspark’ is the command to run.
    3. ‘conda install -c conda-forge findspark’ is an example of a command line installation.
    4. The ability to test Spark straight from the terminal is not stated above, but it is an optional step in this process.

    After that, we would download ‘winutils.exe’ and save it to the ‘c:hadoopbin’ directory.Opening Jupyter, we may find something similar to the following in our Jupyter notebook when we first start it: import os, sys os.environ = ″c:hadoop″ os.environ = ″c:hadoop″ os.environ = ″c:hadoop″ When using os.environ, the value is ″c:/Program Files/Java/jre1.8.0 202″ sys.path.insert(0, os.environ + ″bin″) is a function that inserts a path into the system.os.environ = ″c:UsersAnaconda3envsLibsite-packagespyspark″ os.environ = ″c:UsersAnaconda3envsLibsite-packagespyspark″ os.environ = ″c:UsersAnaconda3envsLib″ os.environ = ″c:UsersAnaconda3 findpark is imported pyspark is imported from pyspark.find() and findspark.init(), respectively.

    1. impo

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