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| Title: '.argmin()' | ||
| Description: 'Finds the index of the minimum value in a NumPy array or along a specified axis.' | ||
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| Subjects: | ||
| - 'Computer Science' | ||
| - 'Data Science' | ||
| Tags: | ||
| - 'Data Structures' | ||
| - 'Arrays' | ||
| - 'Functions' | ||
| - 'NumPy' | ||
| CatalogContent: | ||
| - 'learn-python-3' | ||
| - 'paths/data-science' |
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| Title: '.argmin()' | |
| Description: 'Finds the index of the minimum value in a NumPy array or along a specified axis.' | |
| Subjects: | |
| - 'Computer Science' | |
| - 'Data Science' | |
| Tags: | |
| - 'Data Structures' | |
| - 'Arrays' | |
| - 'Functions' | |
| - 'NumPy' | |
| CatalogContent: | |
| - 'learn-python-3' | |
| - 'paths/data-science' | |
| Title: '.argmin()' | |
| Description: 'Finds the index of the minimum value in a NumPy array, either across the entire array or along a specified axis.' | |
| Subjects: | |
| - 'Computer Science' | |
| - 'Data Science' | |
| Tags: | |
| - 'Arrays' | |
| - 'Data Structures' | |
| - 'Functions' | |
| - 'NumPy' | |
| CatalogContent: | |
| - 'learn-python-3' | |
| - 'paths/data-science' |
| - 'paths/data-science' | ||
| --- | ||
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| The **`.argmin()`** function in NumPy identifies the index of the smallest value in an array. When working with multi-dimensional arrays, it can also return the index position along a specified axis. |
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| The **`.argmin()`** function in NumPy identifies the index of the smallest value in an array. When working with multi-dimensional arrays, it can also return the index position along a specified axis. | |
| The **`.argmin()`** function in NumPy is used to find the index of the smallest value in a NumPy array. It works on both 1D and multi-dimensional arrays, and when an axis is specified, it returns the index of the minimum value along that axis. | |
| This function is commonly used in data analysis and numerical computations to quickly locate the position of the lowest value in an array. |
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| - `a`: (Required) The input array containing values to evaluate. | ||
| - `axis`: (Optional) The axis along which to find the minimum. If not provided, the array is flattened before computation. | ||
| - `out`: (Optional) An alternative array to store the result. It must be the correct shape. |
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| - `a`: (Required) The input array containing values to evaluate. | |
| - `axis`: (Optional) The axis along which to find the minimum. If not provided, the array is flattened before computation. | |
| - `out`: (Optional) An alternative array to store the result. It must be the correct shape. | |
| - `a`: The input array containing values to evaluate. | |
| - `axis` (Optional): The axis along which to find the index of the minimum value. If not specified, the array is flattened and the index of the global minimum is returned. | |
| - `out` (Optional): An alternative array to store the result. It must have the appropriate shape and data type. |
| ## Syntax | ||
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| ```pseudo | ||
| numpy.argmin(a, axis=None, out=None) |
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| numpy.argmin(a, axis=None, out=None) | |
| numpy.argmin(a, axis=None, out=None, keepdims=<no value>) |
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| Below is an example of using `.argmin()` on a 2-dimensional array to find index positions of minimum values: | ||
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| ```python |
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| ```python | |
| ```py |
| print(np.argmin(arr, axis=1)) # Minimum indices along rows | ||
| ``` | ||
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| **Output:** |
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| **Output:** | |
| This code produces the output as follows: | |
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| ## Codebyte Example | ||
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| In this Codebyte, we generate a 2D array with random integers and use `.argmin()` in different contexts: |
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avoid using pronouns
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Sure will do the changes
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