Python Anaconda Tutorial: Everything You Need to Know

Welcome to our detailed Python Anaconda guide. This article will explain what Anaconda is, why you should use it, how to install it in your system, and how you can start using it. Anaconda is a must-have for every data science professional because of its popularity and features. You’ll learn more about this fantastic package manager in the following Python Anaconda tutorial, so let’s get started!

What is Anaconda?

Anaconda is an open-source package manager for Python and R. It is the most popular platform among data science professionals for running Python and R implementations. There are over 300 libraries in data science, so having a robust distribution system for them is a must for any professional in this field. 

Anaconda simplifies package deployment and management. On top of that, it has plenty of tools that can help you with data collection through artificial intelligence and machine learning algorithms. 

With Anaconda, you can easily set up, manage, and share Conda environments. Moreover, you can deploy any required project with a few clicks when you’re using Anaconda. 

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Why Use Anaconda?

There are many advantages to using Anaconda and following are the most prominent ones among them:

  • Anaconda is free and open-source. This means you can use it without spending any money. 
  • In the data science sector, Anaconda is an industry staple. It is open-source too, which has made it widely popular. If you want to become a data science professional, you must know how to use Anaconda for Python because every recruiter expects you to have this skill. It is a must-have for data science. 
  • It has more than 1500 Python and R data science packages, so you don’t face any compatibility issues while collaborating with others. For example, suppose your colleague sends you a project which requires packages called A and B but you only have package A. Without having the package B, you wouldn’t be able to run the project. Anaconda mitigates the chances of such errors. You can easily collaborate on projects without worrying about any compatibility issues.
  • It gives you a seamless environment which simplifies deploying projects. You can deploy any project with just a few clicks and commands while managing the rest. 
  • Anaconda has a thriving community of data scientists and machine learning professionals who use it regularly. If you encounter an issue, chances are, the community has already answered the same. On the other hand, you can also ask people in the community about the issues you face there, it’s a very helpful community ready to help new learners. 
  • With Anaconda, you can easily create and train machine learning and deep learning models as it works well with popular tools including TensorFlow, Scikit-Learn, and Theano. 
  • You can create visualizations by using Bokeh, Holoviews, Matplotlib, and Datashader while using Anaconda.
  • Anaconda works with all major Python libraries including Dask, Pandas, NumPy and Numba that allow you to analyze data quickly and scalably. 

How to Install Anaconda?

Our Python Anaconda guide would be incomplete if we don’t talk about the installation. You can download Anaconda for your system from the official website of Anaconda.

At the time of writing our Python Anaconda tutorial, Anaconda offers the individual plan for Python 3.8. You should download the version that matches your device’s compatibility as Anaconda is available for both 64-bit and 32-bit machines. 

After the download is complete, open the download.exe setup and click the ‘Next’ button. The installer would ask you to read the agreement and you will have to click on ‘I Agree’ to proceed. 

In the next window, the installer asks you if you want to download the software for all users or just yourself. Note that if you want to install Anaconda for all users, you’ll need administrator privileges, which can make things complicated.

In the following section, the installer asks you for the destination of the software. Here, you can choose the place where you want Anaconda to be installed. 

Now, the installer allows you to add Anaconda to your machine’s PATH environment variable and register as the primary system Python 3.8. By adding it to PATH, you ensure that it gets found before another installer. Now, you can click the ‘Install’ button and start the installation process. 

After the installer has completed the extraction of Anaconda and its related files, you will have to click the Next button after which the installer informs you about PyCharm. 

After that window, your installation process has completed. You can click on the Finish button to end the task or learn more about Anaconda Cloud through the installer’s final window.

Steps After the Installation

After you have completed the installation, you can search your system for Anaconda, which would show you the following files:

  • The Anaconda Prompt
  • The Jupyter Notebook
  • Anaconda Powershell Prompt
  • Spyder IDE 
  • Anaconda Navigator

Now, go to the command prompt and type ‘Jupyter notebook’ so it would open the Jupyter dashboard. 

At the top right of the menu, you’ll find the option to create a new notebook. In your new notebook, you can execute one or multiple statements at once and start working. 

How to Use Anaconda for Python 

Now that we have discussed all the basics in our Python Anaconda tutorial, let’s discuss some fundamental commands you can use to start using this package manager. 

Listing All Environments

To begin using Anaconda, you’d need to see how many Conda environments are present in your machine.

conda env list 

It will list all the available Conda environments in your machine. 

Creating a New Environment

You can create a new Conda environment by going to the required directory and use this command:

conda create -n <your_environment_name>

You can replace <your_environment_name> with the name of your environment. After entering this command, conda will ask you if you want to proceed to which you should reply with y:

proceed ([y])/n)?

On the other hand, if you want to create an environment with a particular version of Python, you should use the following command:

conda create -n <your_environment_name> python=3.6

Similarly, if you want to create an environment with a particular package, you can use the following command:

conda create -n <your_environment_name> pack_name

Here, you can replace pack_name with the name of the package you want to use. 

If you have a .yml file, you can use the following command to create a new Conda environment based on that file:

conda env create -n <your_environment_name> -f <file_name>.yml

We have also discussed how you can export an existing Conda environment to a .yml file later in this article.

Activating an Environment

You can activate a Conda environment by using the following command:

conda activate <environment_name>

You should activate the environment before you start working on the same. Also, replace the term <environment_name> with the environment name you want to activate. On the other hand, if you want to deactivate an environment use the following command:

conda deactivate 

Installing Packages in an Environment

Now that you have an activated environment, you can install packages into it by using the following command:

conda install <pack_name>

Replace the term <pack_name> with the name of the package you want to install in your Conda environment while using this command. 

Updating Packages in an Environment

If you want to update the packages present in a particular Conda environment, you should use the following command:

conda update

The above command will update all the packages present in the environment. However, if you want to update a package to a certain version, you will need to use the following command:

conda install <package_name>=<version> 

Exporting an Environment Configuration

Suppose you want to share your project with someone else (colleague, friend, etc.). While you can share the directory on Github, it would have many Python packages, making the transfer process very challenging. Instead of that, you can create an environment configuration .yml file and share it with that person. Now, they can create an environment like your one by using the .yml file.

For exporting the environment to the .yml file, you’ll first have to activate the same and run the following command:

conda env export > <file_name>.yml

The person you want to share the environment with only has to use the exported file by using the ‘Creating a New Environment’ command we shared before. 

Removing a Package from an Environment

If you want to uninstall a package from a specific Conda environment, use the following command:

conda remove -n <env_name> <package_name>

On the other hand, if you want to uninstall a package from an activated environment, you’d have to use the following command:

conda remove <package_name>

Deleting an Environment

Sometimes, you don’t need to add a new environment but remove one. In such cases, you must know how to delete a Conda environment, which you can do so by using the following command:

conda env remove –name <env_name>

The above command would delete the Conda environment right away. 

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Final Thoughts

There you have it, our detailed Python Anaconda guide. Getting started with Anaconda is quite simple. However, you should be familiar with the basics of Python and programming, when you start using this tool.

If you’re interested in learning more about data science, here are some additional resources for your reading: 

On the other hand, if you want a more individualized learning experience, we recommend taking a data science course. You’ll study from industry experts through assignments, live lectures, and videos while taking a data science course.

What are the applications of Anaconda Distribution?

The following are the applications that are provided by Anaconda Distribution:
1. Jupyter Notebook
Jupyter Notebook is a web-based interactive environment that works as an IDE for many programming languages including Python and is considered to be the best platform for data science beginners.
2. JupyterLab
Jupyter Lab is another development environment that is based on the Jupyter Notebook architecture.
3. Visual Studio Code
Visual Studio Code or simply VS Code is a code editor by Microsoft that supports almost every programming language and provides various extensions to support them.
4. Spyder
Spyder is a Python IDE that comes with advanced features such as interactive testing, debugging, task running, advanced editing, and introspective features.

Give us some of the salient features of Python Anaconda?

Anaconda is a package manager for Python and R and is considered to be one of the most popular platforms for data science aspirants. The following are some of the reasons that get Anaconda way ahead of its competitors.
1. Its robust distribution system helps in managing languages like Python which has over 300 libraries.
2. It is a free and open-source platform. Its open-source community has many eligible developers that keep helping the newbies constantly.
3. It has some AI and ML-based tools that can extract the data from different sources easily.
4. Anaconda has over 1500 Python and R data science packages and is considered the industry standard for testing and training models.

How good is Python for data analysis?

The following reasons make Python an essential language that every Data Scientist should know:
1. Python is considered to be the most suitable language for all fields of Data Science. Data analysis becomes efficient when Python is used in combination with R.
2. It has a rich library of data-oriented packages. You can visualize your data using different plots and charts.
3. Complex data sets can be handled using the powerful tools provided along with data frames.
4. The power-packed packages of Python such as Numpy, Pandas, Sci-kit provide features to produce accurate results.
5. It provides scalable and flexible solutions for the applications.
6. Matplotlib provides various plotting tools for accurate graphics and visualizations of your data.

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