Data Cleaning Methods in Python

Python中常用的数据清洗方法包括：

1. Handling missing values: Use dropna() to remove rows or columns containing missing values, and use fillna() to fill in missing values.
2. Duplicate Value Handling: Use the duplicate() method to find duplicate values and use the drop_duplicates() method to remove duplicate values.
3. Convert the data format: Use astype() to change the data type to a specified format, and use str.strip() to remove spaces from text data.
4. Outlier handling: Detect outliers using methods like describe() and boxplot(), and use conditional filtering or replacement methods to address them.
5. Text data processing involves using regular expressions or string manipulation methods to clean, extract, replace, and perform other operations on text data.
6. Standardization of data: Normalize the data using methods such as MinMaxScaler or StandardScaler.
7. Data normalization: Normalize the data using normalization methods such as MinMaxScaler.
8. Removing duplicate data: The drop_duplicates() method can be used to eliminate duplicated data within a dataset.

These are some commonly used data cleaning methods, you can choose the appropriate method for data cleaning based on the actual situation.