Pandas Super Useful Tips: Getting Started with Data Cleaning, Easy for Beginners to Master

Data cleaning is crucial for data analysis, and pandas is an efficient tool for this task. This article teaches beginners how to perform core data cleaning using pandas: first, install and import data (via `pd.read_csv()` or creating a sample DataFrame), then use `head()` and `info()` for initial inspection. For missing values: identify with `isnull()`, remove with `dropna()`, or fill with `fillna()` (e.g., mean/median). Duplicates are detected via `duplicated()` and removed with `drop_duplicates()`. Outliers can be identified through `describe()` statistics or logical filtering (e.g., income ≤ 20000). Data type conversion is done using `astype()` or `to_datetime()`. The beginner workflow is: Import → Inspect → Handle missing values → Duplicates → Outliers → Type conversion. Emphasize hands-on practice to flexibly apply these tools to solve real-world data problems.