Python Data Denoising Methods

There are several commonly used data denoising methods in Python.

1. Moving Average Method: This technique involves using a sliding window to calculate the average of data within the window as the estimated value for the current data point, thus smoothing the data and reducing the impact of noise.
2. Median filtering: This method uses the median value within a window as the estimated value for the current data point, thus removing outliers within a specific range.
3. Weighted average method is an improvement on the moving average method, where different data points are assigned different weights based on their positions. This allows newer data points to have a greater impact on the estimated value, making it more adaptable to rapidly changing data.
4. Adaptive filtering adjusts filter parameters based on changes in data points to better adapt to various noise environments.
5. Wavelet denoising method: This technique uses wavelet transforms to decompose a signal into multiple frequency components, and then selectively retains or removes different frequency components based on the energy distribution of the signal to achieve the goal of noise reduction.

The above are common methods for data denoising. Depending on the specific application and requirements, choose the appropriate method to denoise the data.