Introduction to pandas DataFrame: 3-Step Quick Start for Data Selection and Filtering

This article introduces 3 core steps for data selection and filtering in pandas DataFrames, suitable for beginners to quickly master. Step 1: Column Selection. For a single column, use `df['column_name']` to return a Series; for multiple columns, use `df[['column_name1', 'column_name2']]` to return a DataFrame. Step 2: Row Selection. Two methods are provided: `iloc` (by position, integer indexing) and `loc` (by label, custom index). Examples: `df.iloc[row_range]` or `df.loc[row_label]`. Step 3: Conditional Filtering. For single conditions, use `df[condition]`. For multiple conditions, connect them with `&` (AND) / `|` (OR), and each condition must be enclosed in parentheses. Key Reminder: When filtering with multiple conditions, always use `&`/`|` instead of `and`/`or`, and enclose each condition in parentheses. Through these three steps, basic data extraction can be completed, laying the foundation for subsequent analysis.