Python Data Science Fundamentals Lecture Notes

Comprehensive vocabulary flashcards covering Python distributions, data types, string operations, control flow, functions, error handling, and core data science libraries based on Unit 1 through Unit 6 lecture notes.

Anaconda distribution

One of the most popular Python distributions tailored specifically for data science and machine learning, which includes libraries such as NumPy, SciPy, and Pandas.

NumPy

A foundational library built for high-efficiency computation on large datasets and multi-dimensional arrays, often used for statistical analysis.

SciPy

A library used for scientific computing tasks such as linear algebra, interpolation, and signal or image processing.

Pandas

A library designed for manipulating data in numerical tables and time-series formats, identified by its core structure, the DataFrame.

Enthought

A library specialized in data visualization and manipulation, mentioned as an alternative to the Anaconda distribution.

Reserved word

An existing built-in command in Python, such as $\text{print}$, that cannot be used as a variable name because it has a predefined job.

Snake Case

The style convention for multi-word variables in Python where words are written in lowercase and separated using an underscore ($\_$).

Literal

A raw data value represented directly in source code that means exactly and explicitly what it says on the screen without calculation or transformation.

int Data Type

Represents any positive, negative, or zero whole number that does not contain a fraction or a decimal point.

float Data Type

Represents numerical values that contain a decimal point or fractional part, used for scientific calculations or measurements.

Scientific Notation ($e / E$)

A designation where $e$ or $E$ stands for exponent (base $10$); the number following it indicates how many times to multiply by $10$ (for positive exponents) or $10^{-1}$ (for negative exponents).

Complex Number

In Python, these use the $\text{complex}$ data type and are written in the format $a + bJ$, where $a$ is the real part and $b$ is the imaginary part representing $\sqrt{-1}$.

Hexadecimal

A Base $16$ number system using digits $0$-$9$ and letters $A$-$F$, identified in Python by the prefix $0x$.

Octal

A Base $8$ number system using digits $0$-$7$, identified in Python by the prefix $0o$.

Modulo

The arithmetic operator represented by the percent symbol ($\%$) that returns the remainder of the division of two numbers.

int() conversion

A function that truncates a floating-point number by chopping off everything after the decimal point to return an integer.

Escape Character

The backslash (\) which tells Python to treat the next character in a special way rather than as normal text.

Escape Sequence

The combination of a backslash and a specific character, such as \n for a new line or \t for a tab, used to insert special characters in strings.

len()

A string operation that calculates the total number of characters in a string, including spaces and punctuation.

.index()

A string method used to find the numerical position (index) of the first matching character, starting the count at $0$.

Substring Operation

The process of extracting a smaller piece of text from a larger string using square brackets ($[ ]$) and index parameters.

Frozenset

An immutable version of a set created with the $\text{frozenset()}$ keyword; its contents cannot be changed after creation.

List

An ordered collection data type created with square brackets ($[ ]$) that allows duplicate values and is optimized for looping and manipulation.

File Lock

A built-in safety mechanism that prevents multiple applications from editing or writing to the same file simultaneously to prevent data corruption.

Assignment Operators

Operators such as $=$ that assign specific values to variables, or shortcut operators like $+=$ and $*=$ that perform arithmetic and update the variable instantly.

PEMDAS

An acronym for the mathematical Order of Operations: Parentheses, Exponents, Multiplication, Division, Addition, and Subtraction.

Statement

An instruction or full line of code that the Python interpreter executes to take an action.

Expression

A specific section of code that the Python interpreter evaluates to produce a single value.

Comparison Operators

Symbols such as $==$, $!=$, and $>$ used to compare values, resulting in a Boolean value.

break

A loop control statement that completely and permanently terminates the loop.

continue

A loop control statement that skips only the active iteration and jumps back to the start of the loop for the next round.

Comprehensions

A shorthand syntax using square brackets ($[x \text{ for } x \text{ in data}]$) to create complete collections instantly in memory.

Iterators

Data structures that traverse streams sequentially and generate elements one-at-a-time (lazily) using $\text{iter()}$ and $\text{next()}$ functions.

Generator Expressions

A 'middle ground' between comprehensions and iterators that uses parentheses ($(x \text{ for } x \text{ in data})$) to execute lazily and save memory.

for Loop

A loop used to repeat code a specific, known number of times or to iterate through a sequence.

while Loop

A loop used to repeat a block of code continuously as long as a specific condition remains true.

Function Definition

The phase of creating a reusable block of code using the $\text{def}$ keyword, parentheses, and a colon.

Scope

The visibility and definition of variables or functions within code, determining where data can be accessed or modified.

Parameters

The placeholder variables listed inside the parentheses when defining a function.

Arguments

The actual values or data passed into a function when it is called.

return

A keyword used to pass calculated data back from a function to the program, which also immediately terminates the function's execution.

Syntax Error

A grammar mistake in the code (e.g., a missing colon) that prevents the program from starting because Python cannot understand the instructions.

Exception

A runtime crash occurs when perfectly written code attempts an impossible operation, such as dividing by $0$.

try and except

Code blocks used to handle exceptions gracefully, where the 'try' block contains risky code and the 'except' block provides backup code if an error occurs.

raise

A keyword used to manually trigger an exception when program logic is violated, even if the code is technically valid.

finally

A block attached to a try/except statement that always runs regardless of whether an error occurred, often used for cleanup tasks like closing files.

Logging Severity Levels

A hierarchy of recorded messages: DEBUG, INFO, WARNING, ERROR, and CRITICAL.

Module

A Python file ending in $.py$ that contains pre-written code—including functions and variables—that can be reused by other programs via the $\text{import}$ statement.

Docstring

A short description created using triple quotes ($"""..."""$) immediately inside a function to explain what the function does.

Matplotlib

A flexible data visualization library used to turn raw data into visual graphics like histograms, scatterplots, and line graphs.

Interpolation

The ability provided by SciPy to mathematically estimate the value of a point that falls between two known data points.

DataFrame

The core feature of Pandas; it is a two-dimensional table that organizes data into rows and columns, similar to a spreadsheet.

Scikit-Learn

Python’s primary library for machine learning, providing algorithms for classification, regression, and clustering.

Classification

A machine learning technique where an algorithm learns to assign new data into specific categories or groups.

Regression

A machine learning technique that analyzes inputs to predict a specific output or future numerical value.

Clustering

A machine learning technique where an algorithm automatically groups raw data based on shared attributes or hidden patterns.