LM Object Data Types

Object Data Types (Lists, Arrays, DataFrames)

In Python, object data types are used to store and manage collections of values.
They are essential when working with real-world data, which usually consists of many related values, not just single numbers or strings.

The most important object data types introduced so far are:

  • Lists
  • Arrays (NumPy)
  • DataFrames (pandas)

Each of these structures serves a different purpose and is optimized for specific tasks.

Lists

A list is an ordered and mutable collection of elements.

Key properties of lists:

  • Elements can have different data types
  • Order is preserved
  • Elements can be added, removed, or modified
  • Indexing starts at 0

Typical use cases:

  • Storing small to medium collections
  • Mixed data types
  • Flexible data manipulation

Example

fruits = [...

Arrays (NumPy)

An array is a data structure provided by the NumPy library. Unlike lists, NumPy arrays are designed for numerical computations.

Key properties of arrays:

  • All elements must have the same data type
  • Much faster than lists for mathematical operations
  • Support vectorized operations (no explicit loops needed)

Typical use cases:

  • Numerical data
  • Scientific computing
  • Mathematical operations on large datasets

###Example

import numpy as np
...

##DataFrames (pandas)

A DataFrame is a two-dimensional, table-like data structure provided by the pandas library.

Key properties of DataFrames:

  • Data organized in rows and columns
  • Each column has a name
  • Columns can have different data types
  • Ideal for reading, analyzing, and cleaning data

Typical use cases:

  • CSV / Excel data
  • Data analysis
  • Statistics and visualization

Example

```python import pandas as pd …

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