> For the complete documentation index, see [llms.txt](https://disc4all-qupath.gitbook.io/qupath-project/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://disc4all-qupath.gitbook.io/qupath-project/non-expert-docs/python-installation-and-packages.md).
# Python Installation & Packages
### Step 1: Download and Install Python via website
1. **Visit the Python website:**
Go to the[ Python website ](< https://www.python.org/>)in your browser.
2. **Download Python:**
Click on the "Downloads" tab, and you'll see a button for the latest version of Python. Click on it to start the download.
{% hint style="warning" %}
Be careful of downloading the Python version that is compliant with your system (Windows, MacOS, or Linux)
{% endhint %}
3. **Run the Installer:**\
Once the download is complete, open the installer from you Downloads directory. Make sure to check the box that says "Add Python to PATH" during the installation. This makes it easier to run Python from the command line.
4. **Verify Installation:**
Open a command prompt (on Windows or on macOS using [Git Bash ](/qupath-project/non-expert-docs/git-bash-installation-1.md)or Linux) and type:
```bash
python --version
```
This should display the installed Python version, confirming a successful installation.
## Step 1A: Dowloading Python via Git Bash prompt
1. **Open a Git Bash Terminal or Command Prompt:**
You can do that opening the search bar and type in "**Git Bash**" and select the Git Bash application.
2. **Navigate to a Directory:**
In this case, navigate to the ProjectFolder using the path:
```
cd Path/to/ProjectFolder
```
3. **Download Python Installer:**
Use the ***`curl`*** command to download the Python installer. You can get the link for the latest version from the [Python website ](< https://www.python.org/>) page.
* Go to the **Dowloads** section, search for the newest version compatible with your computer system, click on it and on the *address ba*r of the webpage you can copy the **url** of the version using **Ctrl + C**. Replace ***``*** with the desired version using ***Shift + Insert*** in the Git Bash terminal when typing this command:
{% code overflow="wrap" %}
```
curl -O https://www.python.org/ftp/python//python--amd64.exe
```
{% endcode %}
For example, to downlad *Python 3.9.7:*
```
curl -O https://www.python.org/ftp/python/3.9.7/python-3.9.7-amd64.exe
```
4. **Run Python Installer:**
Execute the downloaded Python installer using the **`start`**command.
```
start python--amd64.exe
start python-3.9.7-amd64.exe #as in the previous example
```
5. **Python Installer Setup:**
* The Python installer will open. Make sure to check the box that says "Add Python to PATH" during installation.
* Click "Install Now" to proceed with the default settings.
6. **Wait for Installation and Verify it:**
* Open a new Git Bash terminal and check if Python is installed by typing:
```
python --version
```
7. Done! Now you can come back to the original Git Bash prompt to continue with the Data Science Packages.
### Step 2: Install Data Science Packages
Now that you have Python installed, you can use the package manager `pip` to install the data science packages you need.
1. **Open a Terminal or Command Prompt:**
Open a terminal or command prompt, depending on your system. Use [Git Bash](/qupath-project/non-expert-docs/git-bash-installation-1.md) if on Windows/MacOS directly from the Start Menu.
2. **Install pandas, numpy, seaborn, and matplotlib:**
Type the following command and press Enter:
```bash
pip install pandas numpy seaborn matplotlib scipy openpyxl
```
This command installs the required packages:
* pandas: Data manipulation and analysis library.
* numpy: Numerical computing library.
* seaborn: Statistical data visualization library based on matplotlib.
* matplotlib: Plotting library for creating static, animated, and interactive visualizations.
* scipy: Modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing
* openpyxl: Library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files
3. **Verify Installation:**
You can verify the installation by opening another Git Bash terminal and open there a Python shell (type ***python*** in your terminal) and trying to import each library:
```python
import pandas
import numpy
import seaborn
import matplotlib
```
If there are no errors, the packages are successfully installed.
{% hint style="success" %}
Congratulations! You've now installed Python and some essential data science libraries. You're ready to start the analysis and follow the tutorial[ here](/qupath-project/result-analysis-docs/processing-package-tutorial.md#step-by-step-python-tutorial).
{% endhint %}
> **License**:
>
> Van Rossum, G., & Drake, F. L. (2009). *Python 3 Reference Manual*. Scotts Valley, CA: CreateSpace.
>
> [Data structures for statistical computing in python](https://conference.scipy.org/proceedings/scipy2010/pdfs/mckinney.pdf), McKinney, Proceedings of the 9th Python in Science Conference, Volume 445, 2010.
>
> Harris, C.R., Millman, K.J., van der Walt, S.J. et al. *Array programming with NumPy*. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2).
>
> Waskom, M. L., (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021, .
>
> J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp
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