A Pandas Series is a one-dimensional labeled array, similar to a column in a spreadsheet or a single column in a database table. Each element has a label (index) that allows easy access.
Physical meaning: Internally, a Series is stored in the computer's RAM as a contiguous block of data along with an array of index labels. This layout allows for very fast computations and vectorized operations.
Memory and lifecycle: A Series exists only while the program is running. Once the program ends, the Series disappears unless explicitly saved to a file like CSV or Excel.
Features:
- One-dimensional labeled array.
- Can hold any data type: int, float, string, Python objects.
- Supports automatic or custom index labels.
- Supports vectorized operations for fast computation.
- Foundation for Pandas DataFrames for multi-dimensional data.
Importance: Series combine the speed of arrays with the flexibility of labeled data. They are fundamental in data analysis, preprocessing for machine learning, and any scenario where structured, labeled data is needed.
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