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Google Colab lets you write and run Python in a web browser without installing Python on your computer. Open Colab, create a notebook, enter Python in a code cell, and click the play button. The code runs on a Google-managed virtual machine connected to your browser—not inside the browser itself.
Colab is excellent for learning Python, experimenting with NumPy and pandas, analyzing data, and trying machine-learning libraries. The important limitation is that a saved notebook and the runtime that executes it are different things: your notebook can remain in Google Drive, while variables, installed packages, uploaded files, and other runtime data can disappear when the temporary session is disconnected or recycled.
What is Google Colab?
Google Colab is a hosted Jupyter Notebook service. It provides a browser-based interface for writing and executing Python, so you do not need to install Python, Jupyter, or most data-science dependencies locally.
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A Colab notebook combines several types of content:
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- Code cells contain executable Python and display results below the cell.
- Text or Markdown cells contain headings, explanations, links, formulas, and notes.
- Outputs can include printed text, tables, charts, images, and error messages.
- The runtime is the temporary cloud computer that executes your code.
Colab is based on the open-source Jupyter project, but Google manages the hosted runtime for you. The browser is the control panel; the Python process and its files run on the connected backend virtual machine.
What you need before starting
- A modern web browser and an internet connection.
- A Google account for normal Colab use. A school or company Google Workspace account may also be subject to administrator restrictions.
- No local Python installation.
Google says Colab is most thoroughly tested with current versions of Chrome, Firefox, and Safari, although it works with most major browsers. Browser privacy settings, extensions, blocked third-party cookies, pop-up restrictions, or an organization’s policies can interfere with sign-in and rich notebook output. If Colab behaves unexpectedly, try a current browser with extensions temporarily disabled or allow the relevant Google and googleusercontent.com sites.
Use the Google account requirement guidance and the Colab FAQ for account and browser details.
Create your first Google Colab notebook
- Open colab.research.google.com.
- Sign in if Colab asks you to choose a Google account.
- Choose the option to create a new notebook. If the full menu is visible, use File → New → New Python 3 notebook.
- Click the notebook title at the top and give the file a useful name, such as
python-practice.ipynb.
You can also create one from Google Drive. In Drive, choose New → More → Colaboratory. A notebook created through the Colab interface commonly appears in a Drive folder named Colab Notebooks; a notebook created inside a particular Drive folder normally remains there. Menu wording and the initial file-picker layout can change, so look for the stable action: create a new Python notebook.
Colab notebooks use the open Jupyter .ipynb format. You can also open notebooks from Google Drive or load notebooks hosted on GitHub.
For the current creation workflow, see Google’s Colab notebook guide.
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- In the first code cell, enter the following:
print('Hello, world!')- Run the cell by clicking the play button beside it.
- Alternatively, press Command/Ctrl + Enter. Shift + Enter is also a commonly supported Jupyter shortcut and runs the cell, then moves to the next cell.
Colab connects to a runtime the first time you run code. After a short setup delay, the output appears beneath the cell:
Hello, world!The current Colab welcome material documents the play button and Command/Ctrl + Enter. In the Jupyter execution model, running a cell sends its contents to a live Python kernel and displays returned output below it.
Printed output and the final expression
You do not always need print(). Colab displays the value of the final expression in a cell:
name = 'Colab'
nameExpected output:
'Colab'However, intermediate expressions are not automatically displayed. Use print() when you want to show several values or control the formatting.
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How code cells share variables
Code cells normally run in the same live Python session. That means a variable created in one cell is available to later cells:
message = 'Python is running'Run this in a second cell:
print(message)The second cell prints:
Python is runningExecution order matters. If you run the second cell first, Python raises a NameError because message does not yet exist in that runtime. Running cells out of order can also overwrite variables, append duplicate items to a list, or leave a notebook with outputs that no longer reflect a clean top-to-bottom run.
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A reliable beginner workflow is:
- Put imports and configuration near the top.
- Run setup cells before analysis cells.
- Execute cells from top to bottom when checking that a notebook works from a clean session.
- After a reconnect or runtime restart, run the setup and import cells again.
- Do not assume that a displayed saved output proves the current runtime still contains the variables that produced it.
The Colab FAQ explains why notebook content and runtime state are separate.
Add explanations with Markdown cells
A notebook is more useful than a plain script when it records both the process and the result. Add a text cell using the + Text control or the corresponding insert-cell menu. Use it to document:
- What the notebook is intended to do.
- Which data files it expects.
- Which packages it installs.
- How to interpret a chart or result.
- Any assumptions, credentials, or configuration the reader needs.
Use headings to divide a notebook into sections such as Setup, Load data, Clean data, Analyze, and Results. This is especially valuable for coursework and shared notebooks because code, output, and narrative explanation stay together.
Inspect the Python environment
Runtime images change over time, so do not hard-code an assumed Python version when compatibility matters. Run this diagnostic cell instead:
import sys
import platform
print(sys.version)
print(platform.platform())To inspect the runtime’s current directory and files, use:
!pwd
!ls -laThe leading ! is an IPython shell escape. It asks the runtime’s operating-system shell to execute a command; it is not standard Python syntax. The IPython documentation explains the difference between Python and IPython commands.
For context, Google’s past-runtime list showed runtime version 2026.07 with Python 3.12.13, NumPy 2.0.2, PyTorch 2.11.0, JAX 0.7.2, and TensorFlow 2.20.0 in an August 9, 2026 snapshot. Those are not permanent defaults. Check sys.version and the installed package versions in the runtime you actually receive. Past runtime versions are retained for one year, and connecting to one can take two or three minutes rather than only a few seconds. See Google’s past runtime versions FAQ.
Install Python packages in Colab
Many common packages are already available, but a shared notebook should install the dependencies it requires rather than relying on an undocumented preinstalled environment. For a small example, run:
!pip install -q pyfigletThen import and use it:
import pyfiglet
print(pyfiglet.figlet_format('Colab'))Package installation affects the current runtime. If that runtime is deleted or replaced, you may need to install the package again. Put installation commands in a clearly marked setup cell near the top of a notebook:
# Install dependencies for this runtime
!pip install -q pyfigletWhen a project depends on a particular release, pin it explicitly:
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Google specifically recommends including cells that install and load custom libraries because people who open a shared notebook do not receive the author’s installed packages or original runtime.
Upload a local file
There are two practical ways to upload a file from your computer.
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Upload through the file browser
- Open the folder icon in Colab’s left sidebar.
- Choose the upload control.
- Select a file on your computer.
- Inspect the path shown in the file browser.
- Read that path from Python.
For example, if you upload example.csv to the usual runtime working directory:
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df = pd.read_csv('/content/example.csv')
df.head()Upload from a code cell
The official Colab I/O notebook demonstrates this browser file-picker method:
from google.colab import files
uploaded = files.upload()The returned object contains the uploaded file data. The upload is convenient for a quick experiment, but it is not permanent storage. Files placed in the runtime’s local filesystem, commonly under /content, can disappear when the runtime is reset or deleted. For files that must survive a session, store them in Google Drive or another persistent, trusted data source.
See the official Colab I/O notebook for upload and download examples.
Use Google Drive for persistent personal files
To access files in your Drive from a hosted Colab runtime, run:
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drive.mount('/content/drive')Colab opens an authorization flow. After you approve access, your files are commonly available below:
/content/drive/MyDrive/For example:
import pandas as pd
path = '/content/drive/MyDrive/data/example.csv'
df = pd.read_csv(path)Drive caveats
- Mounting Drive does not make the runtime permanent. The Drive files persist, but the virtual machine, variables, installed packages, and local files can still disappear.
- Drive I/O can be slow. Repeatedly reading and writing many small files through a mounted Drive can be much slower than copying data to the runtime’s local filesystem and processing it there.
- Large or crowded folders can cause mount problems. Google warns that approximately 10,000 items in the top-level My Drive directory can cause mounting issues. Organize files into smaller subfolders.
- Authorization and quotas matter. Browser cookies, Drive quota, bandwidth limits, or many simultaneous reads and writes can produce timeouts and input/output errors.
- Do not mount Drive in an untrusted notebook. Once mounted, notebook code can access the authorized Drive files. Inspect code before running it.
For large archives or intensive processing, copy only the required data to the runtime and reduce repeated reads and writes to mounted Drive. Google documents these performance and folder-size limitations in the Colab FAQ.
Save, share, and download your work
What saving a notebook preserves
The saved .ipynb file can contain:
- Python code.
- Markdown explanations.
- Cell outputs such as text, tables, and charts.
- Comments and notebook metadata.
Saving does not preserve the live runtime. It does not automatically preserve variables in memory, packages installed with pip, or files uploaded to /content. Save those items separately by adding setup cells, storing important files in Drive, or documenting how to recreate the environment.
Share a notebook
Use the notebook’s sharing control to set the appropriate Google Drive permission, such as viewer, commenter, or editor. A shared notebook gives another person the document and its saved content; it does not give them your original virtual machine, installed packages, variables, or private runtime files.
A notebook intended for other people should include:
- Installation commands and, where useful, package versions.
- Imports and configuration.
- Instructions for obtaining or uploading data.
- Expected file paths.
- Authentication steps that do not expose your credentials.
- A small test cell that confirms the setup works.
To avoid sharing stale, sensitive, or unnecessarily large output, use Edit → Notebook settings → Omit code cell output when saving this notebook. You can also clear outputs before sharing when appropriate.
Download a copy
Use the File menu or Google Drive’s download option to export the notebook as an .ipynb file. Because the format is open Jupyter Notebook format, it can also be opened in local Jupyter or supported editors such as VS Code, provided the required local environment is installed.
What happens when Colab disconnects?
A Colab runtime is temporary. Google says runtime machines are deleted after an idle period and have a maximum lifetime. A disconnect, manual reset, resource limit, or runtime recycling can therefore remove:
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- Variables and imported modules in memory.
- Packages installed during the session.
- Files uploaded to the runtime’s local filesystem.
- Processes and computations still running there.
The notebook saved in Drive can remain available, but reopening it creates or connects to a different runtime. Treat the notebook as a reproducible recipe, not as a permanent server.
Free resources are not guaranteed or unlimited. Google’s FAQ says free notebooks can run for at most 12 hours depending on availability and usage patterns. Google also describes Pro+ continuous execution of up to 24 hours when sufficient compute units are available. Plan and policy conditions can change, so do not design a critical, unattended production job around these limits.
After reconnecting, run the notebook’s setup cells from the top. If the runtime is stuck, try interrupting the current cell, reconnecting, or choosing Runtime → Disconnect and delete runtime, then reconnecting and running the setup again.
Use a GPU or TPU when appropriate
Colab may offer access to GPUs and TPUs, including in free usage, but the hardware type, availability, usage limits, and session duration are dynamic. A GPU is not guaranteed, and choosing one does not automatically make ordinary Python code faster.
- Open Runtime → Change runtime type.
- Choose an available hardware accelerator.
- Reconnect or restart the runtime if Colab prompts you.
- Verify that the library and operations in your program support the selected accelerator.
For a PyTorch environment, you can check whether CUDA is visible:
import torch
print(torch.cuda.is_available())A result of True means that PyTorch can see a CUDA-capable device; it does not prove that your model or data has been moved to it. Your code must use accelerator-aware operations. If you are practicing basic Python, processing a small CSV, or running ordinary string code, a GPU usually adds no value. Switch back to a standard runtime when acceleration is unnecessary because accelerated resources are subject to usage limits.
See Google’s current resource and accelerator guidance before planning a long machine-learning run.
A complete beginner starter notebook
The following cells demonstrate the basic workflow. Run them from top to bottom in a new notebook:
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# 1. Confirm that Python is available
import sys
print(sys.version)
# 2. Run ordinary Python
numbers = [1, 2, 3, 4, 5]
print(sum(numbers))
# 3. Install a package for this runtime
!pip install -q pyfiglet
# 4. Import and use it
import pyfiglet
print(pyfiglet.figlet_format('Hello'))
# 5. Inspect the runtime filesystem
!pwd
!ls -laFor a real shared notebook, add a Markdown cell before these commands explaining what the setup does. If the runtime is later replaced, rerun the installation and import cells.
Common Colab problems and fixes
| Symptom | Likely cause | What to try |
|---|---|---|
| The cell will not run or Colab will not connect | The notebook has no active runtime, the browser blocked a required cookie or pop-up, or the session became unresponsive. | Check the connection indicator, reconnect, try a current browser, review cookie and extension settings, or choose Runtime → Disconnect and delete runtime before reconnecting. |
NameError |
A prerequisite cell was skipped, cells ran out of order, or the runtime restarted. | Run imports and setup cells from the top, then execute dependent cells in order. |
ModuleNotFoundError |
The package is not installed in this runtime, or it was installed only in a previous session. | Run !pip install package-name, then import it again. Put the command in a setup cell for shared notebooks. |
| A package installs but later fails | The package may be incompatible with the current Python version, require an unavailable system library, or conflict with another dependency. | Check sys.version, pin a compatible package release, review the installation error, and test in a clean runtime. |
| A Drive mount times out or reports an input/output error | Too many files in a top-level or heavily used folder, excessive Drive I/O, quota limits, or an authorization problem. | Move files into smaller subfolders, reduce repeated reads and writes, check Drive storage and browser authorization, and copy data locally for intensive processing. |
| An uploaded file is missing | The file was placed in the temporary runtime filesystem and the runtime was reset or replaced. | Upload it again or store it in Drive, then use the correct path. Do not treat /content as permanent storage. |
| The GPU is available but the program is not faster | The library or operation is still running on the CPU, or the workload is too small to benefit. | Use a library-specific accelerator check, move supported data and models to the GPU, and compare the complete workload rather than assuming hardware selection is enough. |
| Rich output or sign-in features fail | Third-party cookies or cross-origin content may be blocked by the browser or organization. | Review browser privacy settings and consider allowing the required Google or googleusercontent.com site exception. |
| A shared notebook works for the author but not for the recipient | The recipient received the document, not the author’s runtime, installed packages, variables, or private files. | Add setup, dependency, data, authentication, and file-path instructions; test the notebook in a fresh runtime before sharing. |
The official Colab FAQ is the best place to check limits and platform-specific changes because runtime behavior and interface labels evolve.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Security and privacy: treat notebooks as executable code
A notebook is not merely a document. Python cells can read files, download content, make network requests, install packages, and execute shell commands such as !ls or !pip. A public notebook can contain code that deletes files, sends data elsewhere, or attempts to access credentials.
Follow these rules:
- Do not run a notebook you do not understand simply because it came from a search result, social post, or AI tool.
- Review every code cell, especially cells beginning with
!, usingsubprocess, downloading files, or authenticating services. - Never paste passwords, API keys, private tokens, or confidential business and personal data into a notebook.
- Mount Google Drive only when you trust the notebook and its author. Mounted Drive access gives notebook code access to the authorized files.
- Remove credentials and sensitive outputs before sharing a notebook.
- Review and test AI-generated code for errors, vulnerabilities, unexpected file access, and licensing obligations.
Google’s Colab terms place responsibility on users to review and test AI-generated code. The managed runtime is not a guarantee that arbitrary notebook code is safe.
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Colab now includes an AI-oriented experience in addition to its ordinary notebook workflow. Depending on account age, supported locale, plan, and other eligibility conditions, features may include conversational code generation and explanation, error-fixing suggestions, code transformation, a Data Science Agent that proposes and executes multi-step analyses, and AI-assisted analysis of uploaded CSV, JSON, and Excel files.
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These features are optional; you can learn Python by writing and running ordinary cells without using them. Google says prompts, generated output, related usage information, and feedback may be collected and reviewed for product improvement. Do not submit sensitive information to Colab’s AI features, and verify every generated change before running it. See the Google Developers Blog announcement and the current FAQ for availability and data-handling qualifications.
When Colab is a good choice—and when it is not
Colab is a good choice for
- Learning Python without configuring a local environment.
- Short exercises and classroom demonstrations.
- Interactive data analysis with pandas, NumPy, and charts.
- Machine-learning experiments that can benefit from an available accelerator.
- Sharing a self-contained notebook with a teacher, student, colleague, or reviewer.
- Opening and experimenting with a
.ipynbfile without installing Jupyter locally.
Choose a local environment instead when you need
- Offline work or dependable access to files on your own computer.
- A permanently configured environment with no managed-session lifetime.
- Long-running background jobs, a production web server, or a service that must stay online.
- Fine-grained control over operating-system packages, drivers, and hardware.
- Reliable access to a large local filesystem.
- A conventional multi-file application with tests, packaging, source control, and deployment.
- Strict control over sensitive data that should not be uploaded to a third-party cloud service.
Colab’s managed service also prohibits activities including cryptocurrency mining, torrenting, password cracking, denial-of-service attacks, remote-desktop use, and attempts to bypass platform restrictions. Consult the Colab terms before using the service for unusual workloads.
Alternatives to hosted Colab
Local Python with VS Code
VS Code’s Jupyter support lets you open .ipynb files, run individual cells, and run all cells. It is better suited to multi-file projects, source control, testing, debugging, and production-oriented development, but you must install Python and maintain the environment unless you connect VS Code to a remote service.
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Local Jupyter
Local Jupyter provides the same notebook style with control over your own files, packages, and hardware. It is a strong choice for offline or private work, but you are responsible for installing Python, Jupyter, dependencies, and updates.
Colab local runtime
A Colab local runtime keeps the Colab browser interface while executing code on your own computer. This can help when you need local hardware, software, or files that the hosted runtime does not provide. It also raises the security stakes: notebook code can read, modify, or delete local files. Google’s usual hosted-runtime Drive mounting workflow is not supported in the same way for local or custom runtimes.
Colab in VS Code
Google also offers a Colab extension for VS Code. It is useful if you want a desktop editor while continuing to use Colab runtimes, but it is a separate workflow from the simplest browser-only setup.
Further reading
- Google Colab FAQ — runtime limits, storage, sharing, accelerators, browser support, and current features.
- Official Colab I/O notebook — upload and download examples.
- IPython documentation — shell escapes and other notebook-specific syntax.
- Past runtime versions FAQ — runtime image history and version selection.
Frequently Asked Questions
Is Google Colab completely free?
Colab provides free access to hosted computing resources, but free resources are dynamic, limited, and not guaranteed. GPU or TPU availability, runtime duration, hardware type, and usage limits can change. Google’s FAQ says free notebooks may run for up to 12 hours depending on availability and usage patterns; paid plans have different conditions.
Do I need to install Python to use Google Colab?
No. For a hosted Colab runtime, you only need a supported browser, internet access, and a Google account. Python and the notebook runtime run on Google’s managed virtual machine. You would need local Python only if you choose a local Jupyter, VS Code, or Colab local-runtime workflow.
Why did my variables and installed packages disappear?
The temporary runtime was disconnected, reset, or recycled. The notebook saved in Drive is separate from the runtime. Reconnect, rerun the installation and import cells, and upload or remount any required files.
Can I use Google Colab for machine learning?
Yes. Colab is widely suited to interactive machine-learning experiments and may provide access to GPUs or TPUs. Availability is not guaranteed, and selecting an accelerator does not automatically move your code onto it. For long-running production jobs, permanent services, or predictable hardware, use an environment designed for those requirements.
Is it safe to open and run any Colab notebook?
No. A notebook can execute arbitrary Python and operating-system shell commands. Review code before running it, avoid entering credentials or confidential data, and mount Google Drive only in notebooks you trust.
Crashes, No Sound, or Screen Glitches?
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Bottom line: Colab is one of the fastest ways to start writing Python: create a notebook, run a cell, install dependencies, and save the .ipynb file in Drive. Just remember that the notebook is persistent but the runtime is temporary. Make setup reproducible, keep important files in trusted persistent storage, and treat every notebook as executable code.
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