What are the common methods for detecting outliers in Python?

Common methods for detecting outliers in Python include:

1. Standard deviation and mean method: Determine if it is an outlier based on the mean and standard deviation of the data.
2. Box plot method: Determine whether it is an outlier based on the quartiles and box plot of the data.
3. Outlier detection algorithms: including statistical methods (such as Z-score and Tukey’s method), distance-based methods (such as LOF algorithm and DBSCAN algorithm), and density-based methods (such as Isolation Forest algorithm).
4. Confidence interval method: determine if a value is an outlier based on the confidence interval of the data.
5. Outlier detection method: Determine if a data point is an outlier based on its anomaly index, such as Mahalanobis distance.
6. Time series analysis methods: Using the trends of data over time to determine if they are outliers, such as the ARIMA model and seasonal decomposition method.
7. Machine learning algorithms: Employing machine learning algorithms to train models, which are then used to determine if there is an anomaly based on the predicted results, such as support vector machines and random forests.
8. Deep learning algorithms, such as neural networks and autoencoders, are used to train models and predict anomalies based on the model’s results. It is important to choose the appropriate anomaly detection method based on the specific data characteristics and problems.