Challenge 5: Subarray Sorting
As you dive deeper into data science, you often encounter the need for advanced data manipulations. NumPy caters to this with:
- Sophisticated Reshaping Tools: Beyond the basics, you can flatten, tile, and even stack arrays in intricate ways.
- Seamless Integration: You can easily mix and match reshaping tools with other NumPy functions for fluid data processing.
- Maintained Data Integrity: Despite complex operations, NumPy ensures the underlying data remains consistent.
For intricate data transformation tasks, NumPy's advanced reshaping tools prove invaluable.
Swipe to start coding
Given a 5x5 NumPy array filled with random integers between 1 and 100, extract the central 3x3 subarray. Flatten this subarray, sort it, and then put it back into the original 5x5 array while maintaining its original structure.
- Extract the central subarray.
- Flatten the extracted array.
- Sort the subarray.
- Integrate it back into the original array.
Oplossing
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As you dive deeper into data science, you often encounter the need for advanced data manipulations. NumPy caters to this with:
- Sophisticated Reshaping Tools: Beyond the basics, you can flatten, tile, and even stack arrays in intricate ways.
- Seamless Integration: You can easily mix and match reshaping tools with other NumPy functions for fluid data processing.
- Maintained Data Integrity: Despite complex operations, NumPy ensures the underlying data remains consistent.
For intricate data transformation tasks, NumPy's advanced reshaping tools prove invaluable.
Swipe to start coding
Given a 5x5 NumPy array filled with random integers between 1 and 100, extract the central 3x3 subarray. Flatten this subarray, sort it, and then put it back into the original 5x5 array while maintaining its original structure.
- Extract the central subarray.
- Flatten the extracted array.
- Sort the subarray.
- Integrate it back into the original array.
Oplossing
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