What methods are used for selecting and filtering data in pandas?

Pandas offers various methods for selecting and filtering data, here are some commonly used ones:

1. loc method: selecting data by labels, you can use both row labels and column labels for selection. For example, df.loc[row label, column label].
2. iloc method: Select data by position using row index and column index. For example, df.iloc[row index, column index].
3. Simplified selection: Data can be selected using square brackets [], with options like boolean conditions, slicing, and lists. For instance, df[condition], df[start:end], df[column_list], etc.
4. Query method: Select data using a string expression, with the ability to use SQL-like syntax for filtering. For example, df.query(‘condition’).
5. isin method: select data by checking if the values of a certain column are in a given list. For example, df[df[‘column name’].isin(list)].
6. The where method selects data based on a condition, replacing elements that do not meet the condition with NaN. For example, df.where(condition).

These methods can be used individually or in combination. Depending on the specific needs and different data structures, choose the appropriate method for data selection and filtering.