Vectorizing Loop-based Time-Series Algorithm
$30-250 USD
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### Task Overview:
I need a **Pandas DataFrame expert** to **vectorize a loop-based algorithm** for time-series data. The task involves complex calculations to eliminate the need for Python loops, ensuring the solution is efficient and fast.
---
### DataFrame Details:
The DataFrame contains the following columns:
- `used_res` (Resource Used)
- `res_cap` (Resource Capacity)
- `qty` (Quantity)
---
### Algorithm Requirements:
1. **Step 1: Calculate Desired Quantity**
- Compute the percentage:
`percent_used = used_res / res_cap`
- If `percent_used > target_value` (a static threshold variable), calculate `desired_qty` using the formula:
```python
desired_qty = ceil(qty * (percent_used / target_value))
```
- Store the result in a new column: `desired_qty`.
2. **Step 2: Update Quantities Based on Conditions**
- Create another column: `updated_qty`.
- Logic for updating:
- If `desired_qty[index] > desired_qty[index - 1]`, then:
```python
updated_qty[index + 10] = desired_qty[index]
```
- If `desired_qty[index] <= desired_qty[index - 1]`, then:
```python
updated_qty[index + 5] = desired_qty[index]
```
- Ensure this is handled without using loops, `iterrows`, or `itertuples`. The entire process must be vectorized for performance.
---
### Deliverables:
1. A Python function/script implementing the vectorized solution using Pandas.
2. Verify correctness by ensuring the output matches the provided "before" and "after" CSV files.
3. Provide clean, well-documented, and maintainable code.
---
### Notes:
- The solution **must not use loops** or row-based iteration functions like `iterrows` or `itertuples`. Efficiency is critical.
- Use Pandas vectorization techniques and NumPy functions where appropriate.
项目ID： #38949947
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Hello, I have a lot of experience in heavily optimizing Python codes with Pandas' lambdas, it's indeed a very effective way to avoid the Python very slow loops.... and I have done such with a variety of algorithms. Eve 更多
Hello, I see that you want to remove the looping part in the second part and I can remove it using the vectorization concept of numpy/dataframe/series which is more efficient. Can you share the 2nd step exact logic wit 更多
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