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Sandhya Govindaraju - Introduction to Numerical Computing With NumPy | SciPy 2023
Introduction to Numerical Computing with NumPy, covering vectorization, indexing, slicing, boolean indexing, array operations, reshaping, broadcasting, error handling, and more, with a focus on practical applications and avoiding common pitfalls.
- Pay attention to the details: Sandhya Govindaraju emphasizes the importance of carefully checking the details when working with NumPy, as simple mistakes can lead to incorrect results.
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Use NumPy’s vectorization: Vectorization is the process of applying an operation to each element of an array. NumPy provides several functions that can be used to perform vectorized operations, such as
np.array,np.arange, andnp.random.rand. - Indexing: NumPy uses 0-based indexing, which means that the first element of an array is at index 0.
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Slicing: NumPy allows for slicing of arrays using the
[]notation, which can be used to extract a subset of rows and columns. - Boolean indexing: Boolean indexing is a way to select specific rows and columns based on a condition.
- Array operations: NumPy provides several array operations, including addition, subtraction, multiplication, and division.
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Reshaping: NumPy’s
reshapefunction can be used to change the shape of an array. -
Broadcasting: NumPy’s broadcasting is a way to combine arrays of different shapes using the
+operator. - getID and compression: NumPy provides functions for reading and writing ID and compression formats such as .txt and .npy.
- Error handling: NumPy provides several error handling mechanisms, such as trying to load the array in reads from the file and writing the array to a file.
- Practice and familiarity: Sandhya Govindaraju emphasizes the importance of practicing with NumPy and becoming familiar with its functions and operations.