Must - read for Beginners! Basic Operations in pandas: Creating, Viewing, and Modifying Data

This article introduces basic pandas operations, covering data creation, viewing, and modification. **Data Creation**: The core structures are Series (1D with index) and DataFrame (2D table). A Series can be created from a list (with default 0,1… indices) or custom indices (e.g., ['a','b']). A DataFrame can be created from a dictionary (keys = column names, values = column data) or a 2D list (with columns specified explicitly). **Data Viewing**: `head(n)`/`tail(n)` previews the first/last n rows (default 5 rows). `info()` shows data types and non-null values; `describe()` summarizes numerical columns (count, mean, etc.). `columns`/`index` display column names and row indices, respectively. **Data Modification**: Cell values are modified using `loc[label, column]` (label-based) or `iloc[position, column position]` (position-based). New columns are added via direct assignment (e.g., `df['Class'] = 'Class 1'`) or calculations based on existing columns. Columns are dropped with `drop(column name, axis=1, inplace=True)`. Indices can be modified by direct assignment to `index`/`columns` or renamed using `rename()`. The core is "locating data," requiring clear distinction between `loc` (label-based) and `iloc` (position-based) indexing.