Python

Python Language Reference

WebVB Studio runs Python via Pyodide—a full Python interpreter compiled to WebAssembly. Write real Python code with access to popular data science libraries.

🐍

Python 3.11

Full language support

📦

50+ Packages

Pre-installed libraries

⚡

WebAssembly

Near-native speed

Getting Started with Python

To use Python in WebVB Studio, select "Python" as your project language when creating a new project. Your event handlers and code will use Python syntax instead of VB6.

Your First Python Event Handler

# Event handlers are simple functions
def cmdHello_Click():
    lblMessage.Caption = "Hello from Python!"
    lblMessage.ForeColor = "#00ff00"

def Form_Load():
    # Runs when the form loads
    print("Form loaded!")
    txtName.Text = "Enter your name"

First Load Note

The first time you run Python code, Pyodide downloads (~50MB). This is cached for future sessions.

Event Handler Syntax

Event handlers in Python follow a simple naming convention: controlName_EventName()

Event	Python Syntax	Description
Click	`def btnSave_Click():`	Button or control clicked
Change	`def txtInput_Change():`	Text or value changed
Load	`def Form_Load():`	Form initialization
KeyPress	`def txtInput_KeyPress(key):`	Key pressed (receives key code)
Timer	`def Timer1_Timer():`	Timer interval elapsed
MouseMove	`def Canvas1_MouseMove(x, y):`	Mouse moved (receives coordinates)

Accessing Controls

Controls are available as global objects. Access their properties directly:

Control Properties

# Reading properties
name = txtName.Text
is_checked = chkAgree.Value
selected = cboCountry.ListIndex

# Setting properties
lblStatus.Caption = "Processing..."
lblStatus.ForeColor = "#ff6600"
txtPassword.PasswordChar = "*"
cmdSubmit.Enabled = False

# Working with lists
lstItems.AddItem("New Item")
lstItems.RemoveItem(0)
count = lstItems.ListCount
lstItems.Clear()

Available Libraries

WebVB Studio includes many popular Python libraries pre-installed via Pyodide:

🐼

pandas 2.0+

Data manipulation and analysis

🔢

numpy 1.24+

Numerical computing

📊

matplotlib 3.7+

Data visualization

🔬

scipy 1.10+

Scientific computing

🤖

scikit-learn 1.2+

Machine learning

🖼️

PIL/Pillow 9.5+

Image processing

🌐

requests 2.28+

HTTP client (simulated)

📄

json built-in

JSON parsing

📅

datetime built-in

Date/time operations

➕

math built-in

Mathematical functions

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re built-in

Regular expressions

🎲

random built-in

Random number generation

Installing Additional Packages

Use micropip.install("package_name") to install additional pure-Python packages from PyPI at runtime.

Data Science with Python

🐼 Pandas DataFrames

Working with DataGrid

import pandas as pd

def Form_Load():
    # Create a DataFrame
    data = {
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['NYC', 'LA', 'Chicago']
    }
    df = pd.DataFrame(data)
    
    # Display in DataGrid
    DataGrid1.DataFrame = df

def btnFilter_Click():
    # Filter data
    df = DataGrid1.DataFrame
    filtered = df[df['Age'] > 28]
    DataGrid1.DataFrame = filtered

def btnExport_Click():
    df = DataGrid1.DataFrame
    df.to_csv('export.csv', index=False)
    lblStatus.Caption = "Exported!"

📊 Matplotlib Charts

Creating Visualizations

import matplotlib.pyplot as plt
import numpy as np

def btnPlot_Click():
    # Generate data
    x = np.linspace(0, 10, 100)
    y = np.sin(x)
    
    # Create chart
    Chart1.plot(x, y, label='sin(x)')
    Chart1.title = "Sine Wave"
    Chart1.xlabel = "X"
    Chart1.ylabel = "Y"
    Chart1.legend()

def btnHistogram_Click():
    data = np.random.normal(0, 1, 1000)
    Chart1.histogram(data, bins=30)
    Chart1.title = "Normal Distribution"

🔢 NumPy Arrays

Numerical Computing

import numpy as np

def btnCalculate_Click():
    # Create arrays
    a = np.array([1, 2, 3, 4, 5])
    b = np.array([10, 20, 30, 40, 50])
    
    # Operations
    result = a + b
    mean = np.mean(a)
    std = np.std(a)
    
    lblResult.Caption = f"Sum: {np.sum(a)}, Mean: {mean:.2f}"

def btnMatrix_Click():
    # Matrix operations
    matrix = np.array([[1, 2], [3, 4]])
    inverse = np.linalg.inv(matrix)
    determinant = np.linalg.det(matrix)
    
    lblDet.Caption = f"Determinant: {determinant}"

Machine Learning

scikit-learn Example

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np

model = None

def btnTrain_Click():
    global model
    
    # Get training data from DataGrid
    df = DataGrid1.DataFrame
    X = df[['feature1', 'feature2']].values
    y = df['target'].values
    
    # Split and train
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    
    model = LinearRegression()
    model.fit(X_train, y_train)
    
    score = model.score(X_test, y_test)
    lblScore.Caption = f"R² Score: {score:.4f}"

def btnPredict_Click():
    if model is None:
        lblResult.Caption = "Train first!"
        return
    
    x1 = float(txtFeature1.Text)
    x2 = float(txtFeature2.Text)
    prediction = model.predict([[x1, x2]])[0]
    
    lblResult.Caption = f"Prediction: {prediction:.2f}"

Image Processing

PIL/Pillow Example

from PIL import Image, ImageFilter, ImageEnhance

current_image = None

def btnOpen_Click():
    global current_image
    filename = FileDialog1.FileName
    current_image = Image.open(filename)
    Canvas1.draw_image(current_image, 0, 0)

def btnBlur_Click():
    if current_image:
        blurred = current_image.filter(ImageFilter.BLUR)
        Canvas1.draw_image(blurred, 0, 0)

def btnGrayscale_Click():
    if current_image:
        gray = current_image.convert('L')
        Canvas1.draw_image(gray, 0, 0)

def btnBrightness_Click():
    if current_image:
        enhancer = ImageEnhance.Brightness(current_image)
        bright = enhancer.enhance(1.5)  # 50% brighter
        Canvas1.draw_image(bright, 0, 0)

def btnResize_Click():
    if current_image:
        new_size = (400, 300)
        resized = current_image.resize(new_size)
        Canvas1.draw_image(resized, 0, 0)

Working with APIs

Use the Inet control or Python's requests-like interface for HTTP calls:

API Integration

import json

def btnFetchData_Click():
    # Using Inet control
    Inet1.URL = "https://api.example.com/data"
    Inet1.Method = "GET"
    Inet1.Execute()

def Inet1_Complete():
    data = Inet1.ResponseJSON
    
    # Process the response
    for item in data:
        lstItems.AddItem(item['name'])
    
    lblCount.Caption = f"Loaded {len(data)} items"

def btnPostData_Click():
    payload = {
        'name': txtName.Text,
        'email': txtEmail.Text
    }
    
    Inet1.URL = "https://api.example.com/users"
    Inet1.Method = "POST"
    Inet1.RequestBody = json.dumps(payload)
    Inet1.SetHeader("Content-Type", "application/json")
    Inet1.Execute()

Python vs VB6 Syntax

Quick reference comparing Python and VB6 syntax in WebVB Studio:

Task	Python	VB6
Variables	x = 10	Dim x As Integer: x = 10
Strings	name = "Hello"	name = "Hello"
Concatenation	f"Hi {name}"	"Hi " & name
If statement	if x > 5:	If x > 5 Then
For loop	for i in range(10):	For i = 0 To 9
While loop	while x > 0:	Do While x > 0
Functions	def add(a, b):	Function Add(a, b)
Comments	# comment	' comment
Lists/Arrays	items = [1, 2, 3]	Dim items(2): items(0) = 1
Dictionary	d = {"key": "value"}	Set d = CreateObject("Scripting.Dictionary")

Best Practices

✓

Use Type Hints

Add type hints for better code clarity and IDE support.

def calculate(x: float, y: float) -> float:

✓

Handle Exceptions

Wrap risky operations in try-except blocks.

try:
 result = int(txtInput.Text)
except ValueError:
 lblError.Caption = "Invalid number"

✓

Use Global Variables Sparingly

Declare globals only when needed for state across events.

global my_data # Only when modifying in a function

✓

Lazy Load Heavy Libraries

Import large libraries in Form_Load for better startup time.

def Form_Load():
 global pd, np
 import pandas as pd
 import numpy as np

VB6 Reference Database Guide