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Challenge 5: Subarray Sorting | NumPy
Data Science Interview Challenge
course content

Зміст курсу

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

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.

Завдання

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Завдання

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Все було зрозуміло?

Секція 2. Розділ 5
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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.

Завдання

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Завдання

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Все було зрозуміло?

Секція 2. Розділ 5
toggle bottom row

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.

Завдання

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Завдання

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Все було зрозуміло?

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.

Завдання

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.

  1. Extract the central subarray.
  2. Flatten the extracted array.
  3. Sort the subarray.
  4. Integrate it back into the original array.

Секція 2. Розділ 5
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