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sure! numpy is a powerful library in python used for numerical computing. one of the fundamental aspects of numpy is its ability to handle arrays, which are essentially rows and columns of data. in this tutorial, we'll cover the basics of creating and manipulating 1d and 2d arrays (or matrices) using numpy, along with some examples.

getting started with numpy

first, you need to install numpy if you haven't already. you can do this using pip:



importing numpy

once you have numpy installed, you can import it into your python script or jupyter notebook:



creating arrays

1d arrays (vectors)

a 1d array can be created using the `np.array()` function:



2d arrays (matrices)

a 2d array can also be created using `np.array()`, but you provide a list of lists:



accessing elements

you can access elements in a numpy array using indexing. remember that indexing starts at 0.

accessing elements in 1d array



accessing elements in 2d array



slicing arrays

you can slice arrays to get subarrays.

slicing 1d array



slicing 2d array



basic operations

numpy provides a variety of operations that can be performed on arrays, including addition, subtraction, multiplication, and more.

element-wise operations



aggregation functions

numpy also provides functions to perform calculations like sum, mean, and max.



reshaping arrays

you can change the shape of an array using the `reshape` method.



conclusion

in this tutorial, we've covered the basics of working with rows and columns using numpy. we've learned how to create 1d and 2d arrays, access elements, slice arrays, perform element-wise operations, and use aggregation functions.

numpy is a vast library with many more functionalities, so continue exploring its documentation and practicing with arrays. happy coding!

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