Python-for-Data-Science

PYTHON - 0

EX00 - Mutable vs Immutable Objects

Key Concepts:

• Mutable objects (List, Set, Dictionary): Can be modified in place after creation
• Immutable objects (Tuple): Cannot be modified; must create new object to change values

Python Data Structures:

1. List [] - Ordered, mutable collection
2. Tuple () - Ordered, immutable collection
3. Set {} - Unordered, mutable collection of unique elements
4. Dictionary {} - Unordered collection of key-value pairs

Lesson:

Understanding mutability is crucial for data manipulation, memory efficiency, and data integrity in Python programming and data science.

EX01 - Date and Time Formatting

Key Concepts:

• Unix Epoch: January 1, 1970 - the reference point for Unix timestamps
• Timestamp: Number of seconds elapsed since the Unix epoch
• Date Formatting: Using strftime() to format dates in specific patterns

Python Libraries:

• datetime module for date and time operations
• strftime() for formatting dates (e.g., "%b %d %Y" → "Oct 21 2022")
• timestamp() to get seconds since Unix epoch

F-String Formatting:

• :,.4f - Comma separator + 4 decimal places (e.g., 1,234,567.1235)
• :.2e - Scientific notation with 2 decimals (e.g., 1.23e+06)

Code	Name	Example	Output
f	Fixed-point	f"{123.456:.2f}"	123.46
e	Scientific	f"{1234:.2e}"	1.23e+03
%	Percentage	f"{0.1234:.2%}"	12.34%
d	Decimal	f"{1234:d}"	1234

Breakdown

: - starts the format specification , - adds thousand separators (commas) .4 - specifies 4 decimal places f - fixed-point notation (decimal format)

Lesson:

Understanding date/time manipulation is essential for data science tasks like time series analysis, data logging, and temporal data processing.

EX02 - Type Checking and Function Returns

Key Concepts:

• Type introspection: Using type() to identify object types at runtime
• Type names: Accessing type names with type().__name__
• Function returns: Functions can return values while performing side effects (printing)

Python Techniques:

• type(object) - Returns the type of an object
• type().__name__ - Gets the string name of the type
• Conditional logic based on type checking
• Return values in functions

Lesson:

Type checking is fundamental for data validation, error handling, and creating flexible functions that handle different data types appropriately.

EX03 - Detecting "Null-like" Values

Key Concepts:

• Falsy values: Different representations of "null" or "empty" in Python
• Type checking order: Checking bool before int (since False == 0 is True)
• NaN detection: Using math.isnan() to identify Not-a-Number floats
• Return codes: Using 0 for success, 1 for error

Python "Null" Values:

• None - The true null value
• float("NaN") - Not a Number (mathematical undefined)
• 0 - Zero integer
• "" - Empty string
• False - Boolean false

Important:

• False == 0 returns True in Python!
• Always check bool type before int when both could be 0
• Use is None not == None for identity check

Lesson:

Understanding Python's truthiness and falsy values is crucial for data validation, especially when cleaning datasets with missing or null values in data science.