How to Install Anaconda & Set Up Jupyter Notebook in Python (2026 Guide)
Setting Up Your Python Environment: Jupyter Notebook & Installing Anaconda
Last Updated: August 2026
If you are beginning your journey with Python, Data Science, Machine Learning, Artificial Intelligence, or scientific computing, one of the first things you need to do is set up a proper development environment.
Writing Python code in a basic text editor can work for very small programs, but data science and AI workflows usually require much more. You may need Python packages such as NumPy, pandas, Matplotlib, SciPy, scikit-learn, and many others. You may also want an interactive environment where you can write code, execute it immediately, view results, create charts, document your experiments, and organize your work.
This is where Anaconda and Jupyter Notebook become extremely useful.
Anaconda provides a convenient Python distribution and package-management environment designed especially for scientific computing, data science, and machine learning. Jupyter Notebook provides an interactive workspace where you can combine executable Python code with explanations, mathematical formulas, tables, visualizations, and output.
In this guide, you will learn how to set up a Python environment using Anaconda and Jupyter from the beginning. We will cover installation, environment creation, launching Jupyter Notebook, installing packages, troubleshooting common problems, and recommended practices for modern Python projects.
1. What Is a Python Environment?
Before installing anything, it is important to understand what a Python environment actually means.
A Python environment is an isolated workspace containing:
A specific Python version
Python packages
Package versions
Configuration settings
Project dependencies
For example, imagine you have two projects.
Project A
Your first project might require:
Python 3.11
NumPy 1.x
pandas
scikit-learn
Project B
Another project might require:
Python 3.12
A newer NumPy version
PyTorch
Transformers
Jupyter
If everything is installed into one global Python installation, different projects can sometimes require incompatible package versions.
Virtual environments solve this problem.
Instead of having one huge Python installation containing everything, you can create separate environments:
Environment A
├── Python
├── NumPy
├── pandas
└── scikit-learn
Environment B
├── Python
├── NumPy
├── PyTorch
└── Transformers
This separation makes projects easier to manage.
2. What Is Anaconda?
Anaconda is a Python distribution and environment-management platform commonly used for data science, scientific computing, machine learning, and related workflows.
Instead of manually installing Python and then individually installing many packages, Anaconda provides a convenient ecosystem for managing Python environments and packages.
Anaconda is particularly popular among beginners because it can simplify the initial setup process.
A typical Anaconda installation gives you access to tools such as:
Python
Conda
Jupyter
Package management
Environment management
Scientific Python libraries
Development tools
One of the most important components is Conda.
Conda can create isolated environments and install packages while handling many dependencies automatically.
For example:
conda create -n myproject python=3.12
This creates an environment called myproject using Python 3.12.
You can then activate it:
conda activate myproject
And install packages:
conda install numpy pandas matplotlib
3. Anaconda vs Python
Anaconda and Python are not the same thing.
Python is the programming language.
Anaconda is a distribution and environment-management ecosystem that includes Python and tools for managing packages and environments.
A simple way to think about it is:
Python
↓
Programming language
Anaconda
↓
Python distribution + tools + package/environment management
You can install Python directly without Anaconda.
However, beginners working with data science or scientific Python may find Anaconda convenient because many common tools can be installed and managed through its ecosystem.
4. What Is Conda?
Conda is one of the most important parts of the Anaconda ecosystem.
It is an environment and package manager.
You can use Conda to:
Create environments
Delete environments
Activate environments
Install packages
Update packages
Manage dependencies
Select Python versions
For example:
conda create -n data-science python=3.12
Activate it:
conda activate data-science
Install packages:
conda install numpy pandas matplotlib
List environments:
conda env list
Deactivate the current environment:
conda deactivate
This makes Conda extremely useful when working on multiple projects.
5. What Is Jupyter?
Jupyter is an interactive computing environment widely used for Python programming, data analysis, scientific computing, education, and machine learning.
The name Jupyter originally comes from the combination of three programming languages:
Julia
Python
R
Today, Jupyter supports many languages through different kernels.
The most common Jupyter environment for beginners is Jupyter Notebook.
A notebook is divided into cells.
For example, you might have one cell containing:
x = 10
y = 20
x + y
Running the cell produces:
30
You can then create another cell:
name = "Python"
print(name)
The result appears directly below the cell.
This interactive approach makes Jupyter especially useful for experimentation.
6. Why Use Jupyter Notebook?
Jupyter Notebook is popular because it combines code, explanations, and results in one document.
A notebook can contain:
Python code
Text
Images
Charts
Tables
Mathematical equations
Output
Notes
Documentation
For example, a data science notebook could contain:
# Customer Data Analysis
Explanation of the dataset
↓
Python code
↓
Table of results
↓
Visualization
↓
Conclusion
This makes notebooks useful for learning as well as professional experimentation.
7. Jupyter Notebook vs Traditional Python Files
A traditional Python program usually looks like this:
import pandas as pd
data = pd.read_csv("data.csv")
print(data.head())
The file might be called:
analysis.py
You run the entire script.
Jupyter works differently.
You can divide your program into multiple cells.
Cell 1
import pandas as pd
Cell 2
data = pd.read_csv("data.csv")
Cell 3
data.head()
You can execute the cells independently.
This is particularly helpful when exploring data because you do not always need to rerun the entire program.
8. Installing Anaconda
The first major step is installing Anaconda.
Before installation, make sure your computer has enough storage space and that you have permission to install applications.
Anaconda provides installers for major operating systems, including:
Windows
macOS
Linux
Download the installer from the official Anaconda website rather than from random third-party download websites.
Choose the installer appropriate for your operating system and processor architecture.
9. Installing Anaconda on Windows
Windows users can normally install Anaconda through its graphical installer.
After downloading the installer:
Open the installer.
Follow the installation instructions.
Review the license information.
Select the installation location.
Complete the installation.
You may see options related to PATH configuration during installation.
For beginners, it is generally better to follow Anaconda's current recommended installation settings rather than manually changing system configuration unless you understand the consequences.
After installation, you can open Anaconda Prompt from the Windows Start menu.
Anaconda Prompt provides a command-line environment where Conda commands are available.
10. Checking Whether Anaconda Was Installed
Open Anaconda Prompt and run:
conda --version
You should receive a version number similar to:
conda 25.x.x
The exact version may be different because Conda is regularly updated.
You can also check Python:
python --version
You should see a Python version.
For example:
Python 3.12.x
The exact version depends on the current Anaconda distribution and environment.
11. Installing Anaconda on macOS
On macOS, Anaconda provides installers appropriate for supported Mac hardware.
Modern Macs may use Apple Silicon processors, while older Macs use Intel processors.
Before downloading, determine which architecture your Mac uses.
You can then download the appropriate Anaconda installer.
After installation, open Terminal and run:
conda --version
If Conda is correctly installed, you should see its version.
You can then check Python:
python --version
12. Installing Anaconda on Linux
Linux users can install Anaconda through the provided installer.
After downloading the installer, open a terminal and navigate to the directory containing it.
The installer can generally be executed from the shell.
After installation, initialize Conda if requested by the installer.
Then restart your terminal.
Check:
conda --version
If everything is configured correctly, Conda should respond with its installed version.
13. Understanding the Base Environment
After installing Anaconda, you will normally have an environment called:
base
The base environment is the environment created by the Anaconda installation.
You may see something like:
(base) C:\Users\User>
The (base) indicates that the base Conda environment is currently active.
You can check environments with:
conda env list
You may see:
# conda environments:
#
base * C:\Users\User\anaconda3
The asterisk indicates the currently active environment.
14. Should You Work Directly in the Base Environment?
For simple learning experiments, using the base environment can be convenient.
However, for larger projects, it is generally better to create separate environments.
For example:
base
├── General tools
machine-learning
├── Python
├── NumPy
├── pandas
├── scikit-learn
└── Jupyter
deep-learning
├── Python
├── PyTorch
└── other dependencies
This keeps projects isolated.
If one project requires a package version that conflicts with another project, separate environments can prevent many problems.
15. Creating Your First Conda Environment
Let's create an environment for learning Python and data science.
Open Anaconda Prompt and run:
conda create -n data-science python=3.12
Conda will calculate the required dependencies.
You may be asked to confirm the installation.
Type:
y
and press Enter if prompted.
Once the process finishes, activate the environment:
conda activate data-science
Your command prompt should now show:
(data-science)
This means your new environment is active.
16. Why Specify a Python Version?
You can create an environment without explicitly specifying a Python version:
conda create -n myproject
However, specifying a version can make the environment more predictable.
For example:
conda create -n myproject python=3.12
This tells Conda that the environment should use Python 3.12.
Different projects may have different Python compatibility requirements.
Therefore, checking the documentation of the libraries you intend to use is important before choosing a Python version.
17. Installing Jupyter
Once your environment is active, you can install Jupyter.
For example:
conda install jupyter
Conda will resolve the required packages and install them.
After installation, verify Jupyter:
jupyter --version
You should see information about the installed Jupyter components.
You can then launch Notebook with:
jupyter notebook
18. Launching Jupyter Notebook
Run:
jupyter notebook
Jupyter normally starts a local server on your computer.
Your default web browser may open automatically.
You will see a Jupyter interface showing files and folders.
The address may look similar to a local URL:
http://localhost:8888/tree
The important point is that Jupyter is running locally on your computer.
It does not mean your notebook has automatically been published to the internet.
19. Creating Your First Notebook
In Jupyter Notebook, navigate to the folder where you want to store your work.
Select:
New → Python
or the equivalent Python notebook option shown by your installed Jupyter interface.
A new notebook will open.
You can rename it from something like:
Untitled
to:
python-basics.ipynb
The .ipynb extension identifies a Jupyter Notebook file.
20. Your First Jupyter Program
Enter this code into a notebook cell:
print("Hello, Jupyter!")
Run the cell.
The output should appear below it:
Hello, Jupyter!
Congratulations — you have executed your first Python program inside Jupyter.
21. Understanding Notebook Cells
Jupyter notebooks are based around cells.
A cell can contain different types of content.
The most common types are:
Code
Used for Python programs.
x = 5
print(x)
Markdown
Used for explanations and documentation.
# My Python Experiment
This notebook demonstrates basic Python operations.
Raw content
Used for specialized unprocessed notebook content.
For beginners, focus primarily on Code and Markdown cells.
22. Using Markdown in Jupyter
Markdown allows you to write formatted documentation inside your notebook.
For example:
# Machine Learning Experiment
## Dataset
This experiment uses a sample dataset.
## Results
The model achieved a high validation score.
When rendered, the Markdown becomes formatted text.
You can use:
# Heading 1
## Heading 2
### Heading 3
You can also create lists:
- Python
- NumPy
- pandas
- Jupyter
This makes notebooks easier to understand.
23. Running Cells
There are several ways to execute a Jupyter cell.
A common keyboard shortcut is:
Shift + Enter
This executes the current cell and moves to the next cell.
You can also use the Run button in the Jupyter interface.
For example:
x = 100
Run it.
Then create another cell:
x * 2
The result will be:
200
The Python state remains available between cells while the kernel is running.
24. What Is the Jupyter Kernel?
The kernel is the process that executes your code.
When you create a Python notebook, Jupyter connects the notebook to a Python kernel.
For example:
Jupyter Notebook
↓
Python Kernel
↓
Python Code
↓
Output
If the kernel is running, variables can remain available between cell executions.
For example:
name = "AI"
Then in another cell:
print(name)
The output is:
AI
25. Installing NumPy
NumPy is one of the most important Python libraries for numerical computing.
Inside your Conda environment, install it with:
conda install numpy
Then test it in Jupyter:
import numpy as np
numbers = np.array([1, 2, 3, 4, 5])
print(numbers)
You should see the array displayed.
NumPy provides efficient numerical operations and forms the foundation of many scientific Python libraries.
26. Installing pandas
pandas is widely used for working with structured data.
Install it:
conda install pandas
Then test:
import pandas as pd
data = {
"Name": ["Ali", "Sara", "John"],
"Score": [85, 92, 78]
}
df = pd.DataFrame(data)
df
Jupyter can display the DataFrame in a formatted table.
This is one reason notebooks are excellent for data analysis.
27. Installing Matplotlib
Matplotlib is a popular Python visualization library.
Install it:
conda install matplotlib
Then try:
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y)
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Simple Graph")
plt.show()
Jupyter will display the graph directly inside the notebook.
28. Installing scikit-learn
If you are learning machine learning, scikit-learn is another important library.
Install it:
conda install scikit-learn
You can verify it:
import sklearn
print(sklearn.__version__)
Scikit-learn provides tools for:
Classification
Regression
Clustering
Preprocessing
Model evaluation
Feature selection
Dimensionality reduction
It is especially useful for traditional machine-learning projects.
29. Installing Packages With pip
Python also has another major package manager called pip.
For example:
pip install requests
You may encounter projects whose documentation specifically recommends pip.
Conda and pip can coexist, but you should avoid randomly mixing package managers without understanding the environment.
A common approach is:
Create the Conda environment.
Install packages available through Conda.
Use pip for packages that are not conveniently available through your selected Conda channels.
Always follow the package's official installation instructions when possible.
30. Checking Installed Packages
You can inspect packages installed in your Conda environment using:
conda list
This displays installed packages and their versions.
For example, you may see entries corresponding to:
numpy
pandas
matplotlib
jupyter
python
You can also use:
pip list
This displays packages recognized by pip.
31. Managing Conda Environments
Creating environments is only the beginning.
You should also know how to manage them.
List environments
conda env list
Activate environment
conda activate data-science
Deactivate environment
conda deactivate
Remove environment
conda remove -n data-science --all
Be careful with the last command because it permanently removes that environment and its installed packages.
32. Creating an Environment for a Specific Project
Suppose you are building an AI project called:
ai-project
You can create:
conda create -n ai-project python=3.12
Activate:
conda activate ai-project
Then install your required packages.
For example:
conda install numpy pandas matplotlib
You can install additional AI libraries according to the requirements of your project.
This gives your project its own isolated environment.
33. Saving Environment Dependencies
One of the most useful Conda features is the ability to export an environment.
Run:
conda env export > environment.yml
This creates a file named:
environment.yml
The file describes the environment and its dependencies.
You can use it later to recreate the environment.
For example:
conda env create -f environment.yml
This is extremely useful when:
Moving a project to another computer
Sharing a project
Backing up dependencies
Reproducing experiments
Collaborating with others
34. A Good Project Folder Structure
A clean project structure makes development easier.
For example:
my-project/
│
├── notebooks/
│ ├── experiment-01.ipynb
│ └── experiment-02.ipynb
│
├── data/
│ ├── raw/
│ └── processed/
│
├── src/
│ └── main.py
│
├── models/
│
├── outputs/
│
├── requirements.txt
└── README.md
This is only one possible structure.
The ideal structure depends on your project.
For small learning projects, you do not need a complicated directory hierarchy.
35. Jupyter Notebook Files
Jupyter Notebook files normally use:
.ipynb
For example:
machine-learning.ipynb
The notebook stores information such as:
Code cells
Markdown cells
Outputs
Metadata
Kernel information
You can open .ipynb files with Jupyter-compatible tools.
36. Notebook State and Execution Order
One important concept beginners should understand is that notebook cells do not necessarily execute in the order they appear on the screen.
For example, suppose you first execute:
x = 10
Then:
x = 50
Then:
print(x)
The output will be:
50
Even if the cells are visually arranged differently.
This can sometimes cause confusing results.
A notebook may contain variables from previous executions that are no longer obvious from reading the document.
If your notebook behaves strangely, restarting the kernel and running cells again from the beginning can help.
37. Restarting the Kernel
If you want to reset the Python state, restart the kernel.
The exact menu wording depends on your Jupyter version, but you will normally find an option related to:
Kernel → Restart
After restarting, variables created earlier will no longer exist.
For example, if you previously had:
x = 100
then restart the kernel and run:
print(x)
Python will report that x has not been defined.
This is useful for checking whether your notebook can execute cleanly from the beginning.
38. Jupyter Notebook vs JupyterLab
You may also encounter JupyterLab.
JupyterLab is a more advanced interface for interactive computing.
It provides a workspace where you can work with:
Notebooks
Terminals
Text files
Data files
Multiple documents
Code consoles
Jupyter Notebook is simpler and can be easier for beginners.
JupyterLab is useful when your projects become larger and you want a more complete development workspace.
You can explore the Jupyter ecosystem through the official Jupyter website.
39. Using Jupyter for Data Science
A typical data science workflow might look like this:
Collect Data
↓
Load Data
↓
Explore Data
↓
Clean Data
↓
Visualize Data
↓
Build Model
↓
Evaluate Model
↓
Document Results
Jupyter fits naturally into this workflow because code, visualizations, and explanations can live together.
For example:
import pandas as pd
df = pd.read_csv("data.csv")
df.head()
You can immediately inspect the result.
Then write a Markdown explanation about what you observed.
40. Using Jupyter for Machine Learning
Jupyter is also frequently used for machine-learning experimentation.
A notebook might contain:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
Then:
data = pd.read_csv("dataset.csv")
Then:
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.2,
random_state=42
)
You can train the model, evaluate it, visualize results, and explain your findings in the same notebook.
41. Using Jupyter for Artificial Intelligence
Jupyter is also commonly used when experimenting with AI technologies.
For example, an AI experiment might include:
Install libraries
↓
Load dataset
↓
Preprocess data
↓
Create model
↓
Train model
↓
Evaluate model
↓
Visualize results
This interactive workflow is useful because machine-learning and AI development often involves repeated experimentation.
You can change one parameter, rerun a cell, inspect the output, and continue.
42. Common Installation Problems
Even when installation is straightforward, beginners can encounter errors.
One common issue is:
'conda' is not recognized
This generally means that the terminal you are using does not have Conda configured or available in its PATH.
If you installed Anaconda, try opening Anaconda Prompt instead of a regular Command Prompt.
Then run:
conda --version
If it works there, the installation itself may be fine.
43. Jupyter Command Not Found
Another possible problem is:
jupyter is not recognized
First check whether your intended Conda environment is active:
conda activate data-science
Then try:
jupyter --version
If Jupyter is not installed in that environment, install it:
conda install jupyter
Then launch:
jupyter notebook
44. Wrong Python Environment
One of the most common beginner mistakes is installing a package in one environment and running Python from another.
For example:
base
data-science
machine-learning
You might install pandas in data-science but accidentally launch Jupyter using base.
Then your notebook may report:
ModuleNotFoundError
even though you know you installed pandas.
The solution is to make sure the correct environment and kernel are being used.
45. ModuleNotFoundError
Suppose you run:
import pandas
and receive:
ModuleNotFoundError: No module named 'pandas'
First check your active environment.
In the terminal:
conda env list
Activate the intended environment:
conda activate data-science
Then install pandas if necessary:
conda install pandas
Restart or reconnect the Jupyter kernel afterward if required.
46. Keep Your Environment Organized
Do not install every Python library you find into one environment.
For example, avoid creating a single environment containing hundreds of unrelated packages.
Instead, create environments around projects or related workflows.
Examples:
python-learning
data-analysis
machine-learning
deep-learning
web-development
This makes dependency management easier.
47. Update Packages Carefully
Conda allows you to update packages.
For example:
conda update numpy
You can also update Conda packages more broadly, but large updates can sometimes change dependency versions.
For important projects, avoid blindly updating everything immediately before an important experiment.
If an environment works correctly, consider exporting it:
conda env export > environment.yml
This provides a useful backup.
48. Security and Download Recommendations
When installing Anaconda or Jupyter-related software, download packages from trusted sources.
Avoid downloading installers from unknown websites.
Also be careful when running notebooks obtained from unknown sources.
A notebook can contain executable code.
Before executing an unfamiliar notebook, inspect its code and understand what it does.
This is especially important when notebooks contain commands that access files, execute shell commands, install packages, or connect to external services.
49. Jupyter and Internet Access
Jupyter itself does not require your notebook to be publicly accessible.
When you run:
jupyter notebook
the notebook server normally runs locally.
The browser communicates with a local server on your computer.
Conceptually:
Your Computer
│
├── Jupyter Server
│
└── Python Kernel
↑
│
Web Browser
This local architecture is one reason Jupyter is convenient for experimentation.
50. Basic Jupyter Workflow
A good beginner workflow is:
Step 1
Create a project environment:
conda create -n learning python=3.12
Step 2
Activate it:
conda activate learning
Step 3
Install Jupyter:
conda install jupyter
Step 4
Install common packages:
conda install numpy pandas matplotlib
Step 5
Launch Jupyter:
jupyter notebook
Step 6
Create a notebook.
Step 7
Test Python:
print("Python is working!")
This gives you a complete basic environment.
51. Recommended Beginner Environment
If your primary goal is learning Python and data science, you can start with:
Python
Jupyter
NumPy
pandas
Matplotlib
scikit-learn
You do not need dozens of libraries on your first day.
Start with the fundamentals.
Learn:
Python
↓
NumPy
↓
pandas
↓
Visualization
↓
Machine Learning
As your projects become more advanced, install additional libraries when they are actually needed.
52. Anaconda Environment Example
Here is a complete example.
Create an environment:
conda create -n ai-learning python=3.12
Activate it:
conda activate ai-learning
Install Jupyter and common libraries:
conda install jupyter numpy pandas matplotlib scikit-learn
Launch Jupyter:
jupyter notebook
Create:
AI-Learning.ipynb
Then test:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn
print("Environment is ready!")
If the imports execute successfully, your environment is ready for many beginner-level data science and machine-learning experiments.
53. Useful Conda Commands Cheat Sheet
Here are some commands worth remembering.
Check Conda
conda --version
Check Python
python --version
List environments
conda env list
Create environment
conda create -n myenv python=3.12
Activate
conda activate myenv
Deactivate
conda deactivate
Install package
conda install package-name
Remove package
conda remove package-name
List packages
conda list
Export environment
conda env export > environment.yml
Recreate environment
conda env create -f environment.yml
Remove environment
conda remove -n myenv --all
Start Jupyter
jupyter notebook
54. Common Mistakes Beginners Should Avoid
Mistake 1: Installing everything globally
Avoid putting every package into your main Python installation.
Use environments.
Mistake 2: Forgetting to activate the environment
Before installing project-specific packages, activate the intended environment:
conda activate myproject
Mistake 3: Mixing environments
Always check which environment is active.
Mistake 4: Ignoring package versions
Different projects may require different versions.
Mistake 5: Running random commands from the internet
Understand what a command does before executing it.
Mistake 6: Never documenting dependencies
Export important environments or maintain dependency files.
Mistake 7: Creating giant notebooks
As projects grow, move reusable code into Python modules instead of putting everything into one enormous notebook.
55. When Should You Move Beyond Jupyter?
Jupyter is excellent for:
Learning
Data exploration
Experiments
Visualization
Prototyping
Research
Machine-learning experiments
However, it may not be the ideal place for an entire production application.
For larger software projects, you may eventually use:
.pyfilesVS Code
PyCharm
Git
Automated tests
Package structures
CI/CD systems
A common workflow is:
Jupyter
↓
Experiment
↓
Validate idea
↓
Move reusable code
↓
Python modules
↓
Production application
This allows you to enjoy Jupyter's interactive features while keeping production code organized.
56. Jupyter for Learning Python
Jupyter is particularly useful for beginners because you can experiment immediately.
For example:
numbers = [10, 20, 30, 40]
sum(numbers)
Then:
average = sum(numbers) / len(numbers)
average
The result appears immediately.
You can modify the code and rerun it.
This feedback loop makes learning programming concepts more interactive.
57. Jupyter for Documentation
One of the biggest advantages of notebooks is that code and documentation can coexist.
For example:
## Step 1: Load the Dataset
Then:
import pandas as pd
df = pd.read_csv("data.csv")
Then:
## Step 2: Inspect the Data
Then:
df.head()
This creates a readable experiment rather than just a collection of source-code files.
58. A Practical First Project
After installing everything, create a small data-analysis project.
Start with:
import pandas as pd
import matplotlib.pyplot as plt
Create sample data:
data = {
"Day": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"],
"Visitors": [100, 140, 120, 180, 200]
}
df = pd.DataFrame(data)
df
Then visualize it:
plt.plot(df["Day"], df["Visitors"])
plt.xlabel("Day")
plt.ylabel("Visitors")
plt.title("Website Visitors")
plt.show()
This tiny project demonstrates the core Jupyter workflow:
Code
↓
Data
↓
Analysis
↓
Visualization
↓
Conclusion
59. Why Environment Management Matters
As your Python skills grow, environment management becomes increasingly important.
Imagine a project requiring:
Python 3.11
Package A version 1
Package B version 2
Another project might require:
Python 3.12
Package A version 2
Package B version 3
Installing everything globally could create conflicts.
With Conda, you can separate them:
project-one
├── Python 3.11
├── Package A v1
└── Package B v2
project-two
├── Python 3.12
├── Package A v2
└── Package B v3
This isolation is one of the most important concepts to understand early in your Python journey.
60. Final Setup Checklist
Before starting your Python or AI learning journey, verify the following.
Anaconda
conda --version
works.
Python
python --version
works.
Environment
conda env list
shows your environment.
Jupyter
jupyter --version
works.
NumPy
import numpy
works.
pandas
import pandas
works.
Matplotlib
import matplotlib
works.
scikit-learn
import sklearn
works.
If all of these work, you have a solid starting environment for Python data science and machine-learning development.
To fully understand this topic, we recommend reading the previous lesson first. It explains the core concepts that this article builds upon.
Read the previous article here:
https://khayyamshah2007.blogspot.com/2026/08/jupyter-notebook-tutorial-how-to.html
Conclusion
Setting up your Python environment correctly is one of the most important first steps in learning Python, data science, machine learning, and artificial intelligence.
Anaconda makes environment and package management easier, while Jupyter Notebook provides an interactive workspace for writing Python code, analyzing data, creating visualizations, documenting experiments, and learning new concepts.
The basic workflow is simple:
Install Anaconda
↓
Create Conda Environment
↓
Activate Environment
↓
Install Jupyter
↓
Install Required Packages
↓
Launch Jupyter Notebook
↓
Create Notebook
↓
Write Python Code
↓
Analyze Results
A good starting command sequence is:
conda create -n data-science python=3.12
conda activate data-science
conda install jupyter numpy pandas matplotlib scikit-learn
jupyter notebook
From there, you can begin experimenting with Python.
As you progress, you can create separate environments for different projects, learn package management, export environments for reproducibility, and eventually move successful experiments from notebooks into organized Python applications.
The goal is not simply to install Python and Jupyter. The real goal is to build an environment that lets you learn, experiment, analyze, and develop efficiently.
Once your environment is ready, the next logical steps are learning Python fundamentals, NumPy, pandas, data visualization, and eventually machine learning.
A properly configured environment removes much of the unnecessary setup friction and lets you focus on what matters most: writing code, solving problems, and building useful projects.
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