MicroPython, Data Visualization, and Python Essentials

Posted on Dec 10, 2024 in Computers

MicroPython vs. Python

MicroPython: A lean implementation of Python, specifically designed for microcontrollers and embedded systems. It offers a subset of Python’s syntax and standard library, optimized for resource-constrained devices.

Python: A general-purpose programming language, known for its readability and versatility. It’s widely used for various applications, from web development to data science.

MicroPython and Supported Devices

MicroPython is a popular choice for rapid prototyping and IoT projects. It allows you to write Python code directly on microcontrollers, making development faster and more accessible.

Some devices that support MicroPython include:

  • Microcontrollers: Raspberry Pi Pico, ESP32, STM32
  • MicroPython Boards: PyBoard, Adafruit CircuitPython boards

Data Visualization

Data visualization is the graphical representation of information and data. It helps in understanding complex data patterns, trends, and insights more easily.

Need/Purpose:

  • Understanding Data: Visualizing data can reveal hidden patterns and anomalies.
  • Communicating Insights: Visualizations can effectively convey information to a wider audience.
  • Making Data-Driven Decisions: Visualizations can help in identifying trends and making informed decisions.

Best Practices:

  • Clarity: Keep visualizations simple and easy to understand.
  • Relevance: Choose the right visualization type for the data and the message.
  • Consistency: Use a consistent color scheme and labeling.
  • Context: Provide context for the data, such as units, timeframes, and sources.

Dashboard: A visual display of the most important information needed to achieve one or more specific objectives. It consolidates key performance indicators (KPIs) and other metrics into a single, easy-to-understand view.

Legend and Chart Customizations

Legend: A key that explains the meaning of different symbols or colors used in a chart.

Chart Customizations:

  • Line Chart: Line color, line width, marker style, marker size, axis labels, grid lines.
  • Bar Chart: Bar color, bar width, bar orientation (horizontal or vertical), axis labels, grid lines.
  • Histogram: Bin width, bin edges, bar color, axis labels, grid lines.

Programming Charts

import matplotlib.pyplot as plt

import numpy as np

# Line Chart

x = np.linspace(0, 10, 100)

y = np.sin(x)

plt.plot(x, y)

plt.xlabel('x')

plt.ylabel('sin(x)')

plt.title('Sine Wave')

plt.show()

import matplotlib.pyplot as plt

# Bar Chart

x = ['A', 'B', 'C']

y = [10, 20, 30]

plt.bar(x, y)

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Bar Chart')

plt.show()

import matplotlib.pyplot as plt

# Pie Chart

labels = ['A', 'B', 'C']

sizes = [15, 30, 45]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')

plt.title('Pie Chart')

plt.show()

Chart Examples

  • Line Chart: To show trends over time, like stock prices or temperature changes.
  • Bar Chart: To compare categorical data, like sales figures for different products.
  • Histogram: To visualize the distribution of numerical data, like age or income.
  • Pie Chart: To show the proportion of different categories within a whole.
  • Scatter Plot: To explore the relationship between two numerical variables, like height and weight.

NumPy

NumPy is a powerful Python library for numerical computations. It provides efficient array operations, linear algebra functions, and random number generation. It’s widely used in data science, machine learning, and scientific computing.

Errors and Exceptions in Python

Errors and exceptions are events that occur during the execution of a program, disrupting its normal flow. Python has a robust error handling mechanism to handle these exceptions gracefully.

Types of Errors

  • Syntax Errors: Errors that occur due to incorrect syntax, like missing parentheses or typos.
  • Runtime Errors: Errors that occur during program execution, such as dividing by zero or accessing an index out of bounds.
  • Logical Errors: Errors in the logic of the program, leading to incorrect results.

Exceptions in Python

  • ZeroDivisionError: Raised when dividing by zero.
  • ValueError: Raised when an operation or function receives an argument of an incorrect type.
  • TypeError: Raised when an operation or function is applied to an object of an incorrect type.
  • IndexError: Raised when trying to access a sequence with an invalid index.
  • KeyError: Raised when trying to access a dictionary key that does not exist.

Exception Handling in Python

try:

    # Code that might raise an exception

    result = 10 / 0

except ZeroDivisionError:

    print("Error: Division by zero")

else:

    print("Result:", result)

finally:

    print("This block always executes")

The try block contains code that might raise an exception. If an exception occurs, the except block is executed. The else block is executed if no exception occurs. The finally block is always executed, regardless of whether an exception occurs.