Matplotlib Basic Plots

Original Tutorial Note

This lesson is original documentation that covers the same general Python learning area as popular beginner tutorials. It does not copy third-party tutorial text or examples.

Learning Goal

Create simple line, bar, scatter, and histogram charts.

This topic belongs to 31. Python Data Science Intro. The goal is to understand the concept clearly, practice it with small examples, and know how it connects to practical Python work in automation, data, AI, and server scripts.

Concept Overview

Python favors readable code, explicit names, and small reusable pieces. When learning Matplotlib Basic Plots, focus on what values enter the program, what transformation happens, and what output or side effect is produced.

Good beginner Python habits:

• Use clear names instead of abbreviations.
• Keep examples small until the concept is understood.
• Print intermediate values while learning.
• Prefer simple control flow before clever one-liners.
• Validate input before trusting it.

Basic Example

def run_03_matplotlib_basic_plots_demo() -> None:
    topic = "Matplotlib Basic Plots"
    message = f"Practicing {topic}"
    print(message)


run_03_matplotlib_basic_plots_demo()

Practical Example

from pathlib import Path


def write_learning_note(directory: Path, topic: str) -> Path:
    directory.mkdir(parents=True, exist_ok=True)
    note = directory / "python-notes.txt"
    note.write_text(f"Today I practiced: {topic}\n", encoding="utf-8")
    return note


path = write_learning_note(Path("practice-output"), "Matplotlib Basic Plots")
print(f"wrote {path}")

Syntax Notes

Area	Guidance
Names	Use lowercase names with underscores for variables and functions
Blocks	Indentation defines code blocks
Values	Inspect types with type(value) while learning
Output	Use print() for learning and logging for real tools
Errors	Read tracebacks from bottom to top to find the direct failure

Common Mistakes

Mistake	Why It Happens	Better Habit
Mixing tabs and spaces	Editors may insert different whitespace	Configure the editor for 4 spaces
Reusing vague names	x and data hide meaning	Choose names that explain purpose
Ignoring errors	Tracebacks look noisy at first	Find the exception type and failing line
Copying without changing	The code runs but understanding stays shallow	Rewrite examples with your own values

Practice Tasks

1. Run the basic example and change the topic text.
2. Add one more variable and print it.
3. Wrap the logic in a function with a clear name.
4. Add one invalid input and observe the error.
5. Rewrite the example so it solves a small task from your own work.

Mini Quiz

• What input does the example receive?
• What output does it produce?
• Which line would you change first to adapt it?
• What error might happen if a path, value, or type is wrong?

Real-World Uses

• Automation scripts that process files or records
• Server checks that print clear status messages
• AI scripts that prepare prompts or validate model output
• Data tasks that transform values consistently
• CLI tools that accept arguments and report useful results

Next Step

Practice this concept in a new file, then combine it with the previous lesson. Small connected examples build stronger Python skill than isolated snippets.

Extended Examples

Example 1: Parameterized Validation

from dataclasses import dataclass
from typing import Optional


@dataclass(frozen=True)
class ValidationResult:
    valid: bool
    value: Optional[str] = None
    error: Optional[str] = None


def validate_and_normalize(
    value: str,
    min_length: int = 1,
    max_length: int = 100,
    strip_whitespace: bool = True
) -> ValidationResult:
    if strip_whitespace:
        value = value.strip()
    
    if not value:
        return ValidationResult(valid=False, error="empty value after processing")
    
    if len(value) < min_length:
        return ValidationResult(valid=False, error=f"too short: {len(value)} < {min_length}")
    
    if len(value) > max_length:
        return ValidationResult(valid=False, error=f"too long: {len(value)} > {max_length}")
    
    return ValidationResult(valid=True, value=value)


# Test different scenarios
test_cases = ["  hello  ", "a", "x" * 200, "", "validinput"]
for case in test_cases:
    result = validate_and_normalize(case)
    print(f"input={case!r} -> valid={result.valid}, error={result.error}")

Example 2: Structured Result with Context

from dataclasses import dataclass
from datetime import datetime
from enum import Enum


class Status(Enum):
    SUCCESS = "success"
    FAILURE = "failure"
    PARTIAL = "partial"


@dataclass(frozen=True)
class OperationResult:
    status: Status
    message: str
    timestamp: datetime
    details: dict
    
    def is_success(self) -> bool:
        return self.status == Status.SUCCESS
    
    def to_dict(self) -> dict:
        return {
            "status": self.status.value,
            "message": self.message,
            "timestamp": self.timestamp.isoformat(),
            "details": self.details,
        }


def run_operation(value: str) -> OperationResult:
    if not value or len(value) < 3:
        return OperationResult(
            status=Status.FAILURE,
            message="value too short",
            timestamp=datetime.now(),
            details={"value_length": len(value) if value else 0}
        )
    
    return OperationResult(
        status=Status.SUCCESS,
        message="operation completed",
        timestamp=datetime.now(),
        details={"processed_length": len(value)}
    )


result = run_operation("test")
print(result.to_dict())

Integration Patterns

Combining with argparse

import argparse
from dataclasses import dataclass


@dataclass(frozen=True)
class Config:
    input_value: str
    verbose: bool
    output_file: str


def parse_args() -> Config:
    parser = argparse.ArgumentParser(description="Process values with validation")
    parser.add_argument("input_value", help="Value to process")
    parser.add_argument("-v", "--verbose", action="store_true", help="Enable verbose output")
    parser.add_argument("-o", "--output", default="output.txt", help="Output file")
    args = parser.parse_args()
    return Config(
        input_value=args.input_value,
        verbose=args.verbose,
        output_file=args.output
    )


def main() -> int:
    config = parse_args()
    if config.verbose:
        print(f"Processing: {config.input_value}")
        print(f"Output to: {config.output_file}")
    print(f"Result: {config.input_value.upper()}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())

Combining with logging

import logging
from dataclasses import dataclass


logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s %(levelname)s %(message)s"
)
LOG = logging.getLogger(__name__)


@dataclass(frozen=True)
class LoggedResult:
    topic: str
    success: bool
    detail: str


def process_with_logging(value: str) -> LoggedResult:
    LOG.debug(f"Processing value: {value}")
    
    if not value:
        LOG.warning("Empty value received")
        return LoggedResult(topic="process", success=False, detail="empty")
    
    result = value.strip().upper()
    LOG.info(f"Processed to: {result}")
    return LoggedResult(topic="process", success=True, detail=result)


result = process_with_logging("hello")
LOG.debug(f"Final result: {result}")

Operational Considerations

When using this pattern in production environments:

1. Environment Variables: Read configuration from environment variables with sensible defaults
2. Exit Codes: Return non-zero exit codes when operations fail
3. Error Messages: Write useful error messages to stderr, not stdout
4. Dry Runs: Support --dry-run flag for state-changing operations
5. Logging: Use structured logging with appropriate log levels

Production Snippet

import os
import sys
import logging
from pathlib import Path


LOGGING_LEVEL = os.environ.get("LOG_LEVEL", "INFO").upper()
logging.basicConfig(
    level=getattr(logging, LOGGING_LEVEL, logging.INFO),
    format="%(asctime)s %(levelname)s %(name)s: %(message)s"
)
LOG = logging.getLogger(__name__)


def get_working_directory() -> Path:
    """Get working directory with validation."""
    cwd = os.environ.get("WORK_DIR")
    if cwd:
        path = Path(cwd)
        if path.is_dir():
            return path
        LOG.warning(f"WORK_DIR not a directory: {cwd}")
    return Path.cwd()


def main() -> int:
    LOG.info("Starting application")
    work_dir = get_working_directory()
    LOG.info(f"Working directory: {work_dir}")
    
    try:
        # Main logic here
        pass
    except Exception as e:
        LOG.error(f"Failed: {e}")
        return 1
    
    LOG.info("Completed successfully")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())

Extended Troubleshooting

Problem	Likely Cause	Solution
Function not found	Import error or typo in name	Check imports and function definition
Type error on result	Wrong return type or None handling	Add type hints and handle None
File not found	Wrong path or permissions	Use absolute paths and check permissions
Tests fail	Edge cases not handled	Add more test cases and validation
Works locally, fails on server	Environment differences	Check Python version, paths, and dependencies

Debugging Steps

1. Run with -v or --verbose flag
2. Print the input values and types
3. Check the Python version: python3 --version
4. Verify dependencies: python3 -c "import module_name"
5. Run with dry-run mode if available
6. Check logs for error messages

Additional Practice

1. Modify the core pattern to accept multiple values
2. Add a retry mechanism for failed operations
3. Implement a config file loading pattern
4. Add a simple progress indicator for long operations
5. Create a CLI with subcommands using argparse

Field Notes

This pattern forms the foundation of reliable Python automation. The key principles:

• Explicit is better than implicit - Name variables and functions clearly
• Errors should never pass silently - Handle failures explicitly
• Readability counts - Code is read more than written
• Flat is better than nested - Avoid deep nesting
• Small is better - Many small functions are easier to test

Build confidence by applying this pattern to real automation tasks, then expand to handle more complex scenarios.

Summary

• Validate early, validate often
• Return structured results, not just values
• Log usefully without overwhelming
• Handle errors explicitly
• Test the failure paths, not just success