Pandas Multi-Column Processing: Essential Methods

Pandas offers various methods for handling multi-column data, here are some commonly used methods:

1. Column selection: You can select specified columns by column name, column index, regular expression, etc. For example, use a single column name to select a column: df[‘column_name’]; use multiple column names to select multiple columns: df[[‘column_name1’, ‘column_name2′]]; use regular expressions to select columns: df.filter(regex=’regex_pattern’).
2. Column addition and deletion: New columns can be added using the syntax df[‘new_column’] = value, and specific columns can be deleted using df.drop(columns=[‘column_name’]).
3. Column Renaming: You can use the df.rename(columns={‘old_column_name’: ‘new_column_name’}) method to rename columns.
4. Perform calculations: You can use arithmetic operators (+, -, *, /) to calculate multiple columns and store the result in a new column. For example, df[‘new_column’] = df[‘column1’] + df[‘column2’].
5. Sort rows: The data can be sorted by the values of a specified column using the method df.sort_values(by=’column_name’).
6. Convert column types: You can use the astype() method to change the data type of a column to another type. For example, df[‘column_name’] = df[‘column_name’].astype(int) will convert the column’s data type to integer.
7. Column statistics: You can use aggregate functions (such as mean, sum, max, min, etc.) to calculate statistics on columns. For example, df[‘column_name’].mean() calculates the average value of the column.
8. Splitting and merging columns: You can use the str.split() method to split a column containing multiple values into multiple columns, or use the str.cat() method to merge multiple columns into one column.

These methods are just a few common ways of processing data, Pandas also offers many more functionalities and methods to choose from based on specific needs.