What methods are used for data preprocessing in Jupyter?

The methods for data preprocessing in Jupyter can include the following steps:

1. Data import: Utilize code blocks in Jupyter Notebook to read data files, such as those in CSV, Excel, JSON, and other formats.
2. Data cleaning involves cleaning and processing data, such as handling missing values, handling outliers, removing duplicates, and dealing with data type mismatches.
3. Data transformation: This involves transforming data, including data normalization, data discretization, data encoding, etc.
4. Feature selection: Choose suitable features based on specific problems, including methods such as correlation analysis and feature importance evaluation.
5. Feature engineering involves constructing and transforming data features through methods such as statistics, mathematics, and machine learning.
6. Dataset splitting: dividing the data into training, validation, and testing sets for model training and evaluation purposes.
7. Standardization of data involves processing data using methods such as Z-score standardization or MinMax standardization.
8. Data visualization: Utilize visualization tools in Jupyter Notebook, such as Matplotlib, Seaborn, and other libraries, to analyze data visually for better understanding.

These methods can be selected and applied based on the specific data preprocessing task and requirements.