Programming

Why is Excel widely used?

It is installed on most computers, easy to start with, and has advanced features.

What are some advantages of using Excel?

Visual format is easy to inspect, automatically updates for sensitivity testing, and has convenient inbuilt tools.

What is a disadvantage of using Excel for visual display?

Visual display updating can lead to slow models.

Why might someone prefer other software over Excel for graphing?

Better graphing or tools may be available in other packages.

What is a common issue with Excel files?

They can have large file sizes.

How does Excel help in checking calculations?

It displays every calculation, which is useful for prototyping, checking, and interrogating.

Screen Elements

Components visible on an application window that allow user interaction.

Tabs

Sections in the Ribbon that categorize functions such as Home, Insert, and Data.

Formula Bar

An area where you can view and edit the contents of the active cell.

Title Bar

The bar at the top of the window displaying the name of the document and the application.

Ribbon

A toolbar at the top of the application that contains various functions organized into tabs.

Scroll Bars

Elements that allow users to move through the document or spreadsheet content.

Conditional Format as Cell

A feature that allows changing the appearance of a cell based on conditions.

Active Cell

The currently selected cell in the worksheet, where data can be entered or modified.

AutoSum

A function that quickly sums a range of selected cells.

Sheet Tabs

Tabs at the bottom of the Excel workbook for navigating between different sheets.

IF Statement

A conditional statement that executes a particular action based on whether a specified condition is TRUE or FALSE.

Logical Test

An expression that evaluates to either TRUE or FALSE in an IF statement.

IF Statement Syntax

The format used to write an IF statement: =if(logical_test, [value_if_true], [value_if_false]).

Nested IF Statement

An IF statement used within another IF statement to test multiple conditions.

Decision Tree

A visual representation of a decision-making process that outlines various outcomes based on different conditions.

Variable

A named location in memory where we can store a value.

Operators

Symbols that perform operations on variables and values, such as + for addition and - for subtraction.

Data type

A classification that specifies which type of value a variable can hold, such as int, str, or float.

Casting

The process of converting a variable from one data type to another, such as using int(), float(), or str().

Function

A block of code designed to perform a specific task, which can be reused throughout the program.

Parameters

Variables that are used in the function definition to receive input values.

Arguments

The actual values passed to the function when it is called.

Positional arguments

Arguments that need to be provided in the correct order when calling a function.

Keyword arguments

Arguments that are passed to a function by specifying the parameter name followed by an equal sign.

Default values

Predefined values for parameters that are used when no argument is provided.

Built-in functions

Functions that are provided by Python and can be used without the need for additional definition.

String operations

Operations that manipulate string data types, such as concatenation and indexing.

PEP 8

Python Enhancement Proposal 8 is the style guide for Python code, promoting consistency and readability.

Immutable types

Data types that cannot be altered once they are created, such as str, tuple, and frozenset.

Mutable types

Data types that can be changed after they are created, such as list, dict, and set.

Kernel

The core component of an operating system that manages system resources and interactions between hardware and software.

Memory

The part of a computer where data is stored and which can be accessed by the CPU.

CPU

Central Processing Unit, the primary component of a computer that executes instructions.

Class

A blueprint for creating objects in object-oriented programming, encapsulating data for the object.

Object

An instance of a class that contains data and methods to manipulate that data.

Tuple

An ordered collection of elements that is immutable.

List

An ordered, mutable collection of elements that can contain duplicates.

Dictionary

A collection of key-value pairs, where each key is unique.

Set

An unordered collection of unique elements.

Float

A data type in Python that represents floating-point numbers.

Integer

A data type in Python that represents whole numbers.

Complex number

A data type in Python that represents numbers with both real and imaginary parts.

System

A combination of hardware and software that functions together.

Loop

A programming construct that repeats a block of code multiple times.

Modulus operator

An operator (%) that returns the remainder of a division operation.

Floor division

An operation (//) that divides and returns the largest whole number.

String concatenation

The operation of joining two or more strings together.

Function call

The process of invoking a function to execute its code.

Return value

The value that a function outputs after execution.

Error handling

The process of responding to and managing exceptions that occur during program execution.

Debugging

The practice of identifying and fixing errors or bugs in a program.

Primitive data types

The smallest unit of data type, also known as atomic data types.

Continuous data

Data that takes on values that vary over a continuous range.

Categorical data

Data that takes on values that fall into distinct categories.

Composite data types

Structures built around primitive data types.

User-defined data types

Higher-level data structures defined by users specific to their applications.

Tabular data

A two-dimensional data structure where columns represent attributes and each row stores a separate data record.

Time series data

A structure of equal-length arrays reserved for timestamps and data.

Univariate time series

A time series with a one-dimensional data array.

Multivariate time series

A time series with a two or more dimensional data array.

Image data

Data where every pixel is localized by coordinates and can have multiple channels.

RGB image

An image where each pixel has values for Red, Green, and Blue channels.

Data cube

A representation of a multispectral or hyperspectral image with multiple channels.

Pandas

A Python library built on top of NumPy, used for data analysis, data science, and machine learning.

Series

A one-dimensional labeled array in pandas, similar to a column in a spreadsheet.

DataFrame

A two-dimensional labeled data structure in pandas, similar to an Excel spreadsheet.

Panel Data

The term from which the name 'pandas' is derived, referring to multidimensional structured data.

Dunder Version

A method in pandas to check the current version of the library, accessed via pd.__version__.

Index

A label that identifies rows in a pandas Series or DataFrame.

Key Value Pairs

The structure used in Python dictionaries, consisting of keys and their corresponding values.

Custom Labels

User-defined identifying labels for data points in a Series or DataFrame.

Loc Property

A pandas property used to access data by label in a Series or DataFrame.

ILoc

A pandas property used to access data by integer location.

Filtering by Value

The process of creating a new DataFrame containing only rows that meet a specified condition.

Dictionary

A collection of key-value pairs in Python used for constructing Series or DataFrames.

Concatenation

The process of combining multiple DataFrames into one.

CSV File

A file format that stores tabular data in plain text, where each line represents a data record.

JSON File

A file format that represents data in a structured, human-readable way using JavaScript object notation.

Read CSV Function

A pandas function used to import data from a CSV file into a DataFrame.

To String Function

A pandas function to display the entire content of a DataFrame without truncation.

Aggregate Functions

Functions used to summarize and analyze data by reducing it to a single summary value.

Drop Function

A method in pandas to remove specified columns or rows from a DataFrame.

NaN

Represents a missing value in pandas, standing for 'Not a Number'.

Fill NA Function

A pandas method to replace missing values with specified replacements.

Standardization

The process of ensuring consistency in data representation, such as changing text to lowercase.

Duplicate Values

Redundant entries in a DataFrame that can be removed for data integrity.

Group By Function

A pandas method used to group DataFrame rows based on certain criteria.

Selection Techniques

Methods to access and display specific data from a DataFrame.

Condition

A logical expression used to filter data in a DataFrame.

Numeric Columns

Columns in a DataFrame that contain numeric data types suitable for mathematical operations.

Subscript Operator

The square brackets [] used in pandas to access elements in Series or DataFrames.

Column Selection

Accessing one or more columns from a DataFrame for analysis.

Row Selection

Accessing one or more rows from a DataFrame based on labels or indexes.

Intelligent Indexing

Accessing specific data points using logical conditions or integer positions.