Karteikarten: Python Data Handling and Analysis Fundamentals — 24 Karten

Data Types — definition?

Categories of data that determine stored values.

Variables — role?

Store data values during program execution.

Type Casting — mechanism?

Converts data from one type to another.

Operators — symbols?

Perform operations on variables and values.

Arithmetic Expressions — purpose?

Evaluate to a single numerical value.

Escape Sequences — use?

Insert special characters in strings.

Escape Sequence — example?

` ` for newline.

Variables — dynamic?

Yes, their data types can change.

Input Function — returns?

User input as a string.

Assignment Statement — syntax?

`variable = value`.

Operator Precedence — effect?

Determines evaluation order in expressions.

Type Casting — explicit?

Manual conversion using functions like `int()`, `float()`.

Comments — start with?

`#` in Python.

Running Scripts — methods?

IDLE, terminal, REPL.

Built-In Functions — examples?

`print()`, `input()`, `type()`.

User Modules — purpose?

Organize and reuse code.

NumPy Array — core type?

ndarray, multi-dimensional, efficient.

Array Creation — functions?

`np.array()`, `np.zeros()`, `np.ones()`.

DataFrame — structure?

Tabular data with labeled axes.

DataFrame — created from?

Dictionaries, lists, external files.

NumPy Arrays — advantage?

Fast, memory-efficient numerical computations.

Pandas DataFrames — key feature?

Labeled, flexible tabular data structure.

Escape sequences — include?

` `, ` `, `\`, `"`, `rd.

Variables — assign?

Using `=` operator.