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Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including GPUs and TPUs, regardless of the power of your machine. All you need is a browser.
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With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just a few lines of code. Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including GPUs and TPUs, regardless of the power of your machine.
Colab provides automatic completions to explore attributes of Python objects, as well as to quickly view documentation strings. As an example, first run the following cell to import the numpy module.
Colab is a Python development environment that runs in the browser using Google Cloud. For example, to print "Hello World", just hover the mouse over [ ] and press the play button to the upper left. Or press shift-enter to execute.
access Jupyter Notebooks online, specifically using Google Colab. run python code in Jupyter Notebooks; 1. Introduction to Jupyter Notebooks. Jupyter Notebooks are a fantastic tool for writing and running Python code. We'll use Google Colab as an online Jupyter Notebook environment. 2. Basic Layout of Jupyter Notebooks 2.1 Markdown Cells:
print(nums[2:4]) # Get a slice from index 2 to 4 (exclusive); prints "[2, 3]" print(nums[2:]) # Get a slice from index 2 to the end; prints "[2, 3, 4]" print(nums[:2]) # Get a slice from the start to index 2 (exclusive); prints "[0, 1]" print(nums[:]) # Get a slice of the whole list; prints ["0, 1, 2, 3, 4]"
Colab and the Jupyter notebook editor in Anaconda are two of the many pieces of software you may use to write and run a Jupyter notebook. For this course we recommend using the online Google Colab tool, but you can use Anaconda to run the notebooks on your own machine within an internet connection.
The main data structures in Python are strings, lists, tuples, dictionaries, and sets. We saw some examples of lists, when we discussed for loops. And we saw briefly tuples when we introduced argument packing and unpacking.
Gain an introduction to the DataFrame and Series data structures of the pandas library. Access and manipulate data within a DataFrame and Series. Import CSV data into a pandas DataFrame. Reindex a DataFrame to shuffle data. pandas is a column-oriented data analysis API.
