Data Types and Data Structures
Understanding data types and structures is crucial in data science. Various forms of data can be classified as structured, semi-structured, or unstructured, each with its own characteristics. Python provides a range of data types, such as integers, floats, strings, and booleans, and offers essential data structures like lists, dictionaries, and data frames for effective data management.
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Sections
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1Section 1: Types Of Data
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1.11. Structured Data
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1.22. Semi-Structured Data
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1.33. Unstructured Data
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2Section 2: Common Data Types In Python
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3Section 3: Data Structures In Python
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3.11. List
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3.22. Tuple
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3.33. Dictionary
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3.44. Set
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4Section 4: Data Frames With Pandas
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5Section 5: Choosing The Right Data Type
What we have learnt
- Data can be structured, semi-structured, or unstructured.
- Python supports several data types including int, float, str, and bool.
- Lists, tuples, dictionaries, and sets are essential data structures in Python.
- Pandas DataFrames are crucial for handling structured data in real-world projects.
Key Concepts
- -- Structured Data
- Data organized in tabular format, readily stored in databases.
- -- SemiStructured Data
- Data that lacks a strict structure but possesses organizational properties.
- -- Unstructured Data
- Data with no predefined format, making it complex to analyze.
- -- List
- An ordered and mutable collection of items.
- -- Tuple
- An ordered and immutable collection of items.
- -- Dictionary
- An unordered collection that stores data in key-value pairs.
- -- Set
- An unordered collection of unique elements.
- -- DataFrame
- A two-dimensional table structure used in Pandas for handling data.
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