Introduction

Testing your app’s file handling and data workflows shouldn’t slow you down. Imagine streaming fresh sample data into a live chart—no copying, no manual tinkering. With a Python data generator powered by FileFaker and Dash, you can do just that.

In this guide, you’ll learn:
– How to install FileFaker, Dash, and Plotly.
– How to write a Python data generator.
– How to plug live test data into a Dash callback.
– Tips for customizing your real-time plot.

Ready? Let’s dive in.

Why Use FileFaker as Your Python Data Generator

FileFaker is a native macOS app that quickly creates realistic test files—documents, images, video clips, archives. Here’s why it makes the perfect Python data generator:

Offline privacy: No cloud uploads. Your data never leaves your machine.
Wide file support: From PDFs to MP4s.
Speed: Generate large files in seconds.
Simple CLI: Automate file creation with one command.

Pair that with Dash Python, and you get a smooth, live data pipeline for testing or demos.

1. Setting Up Your Environment

First, let’s install what we need:

# 1. Install Dash and Plotly
pip install dash plotly

# 2. (Optional) Create a virtual environment
python3 -m venv venv
source venv/bin/activate

# 3. Download FileFaker for macOS
# Visit https://filefaker.com/?ref=producthunt and install the app.

Once FileFaker is installed, you’ll have the filefaker CLI. We’ll use it to generate data files in your Python code.

2. Generating Test Data with FileFaker CLI

Open Terminal and try creating a CSV:

filefaker generate csv --rows 50 --columns 3 --output test_data.csv

That command spins up a 50×3 CSV in your current folder. Now, let’s automate it in Python.

3. Building a Python Data Generator

A Python data generator is a function that yields new file paths or data frames on demand. Here’s a simple example:

import subprocess
import pandas as pd
import time
from pathlib import Path

def filefaker_generator(interval=2, max_files=10):
    output_dir = Path("data_files")
    output_dir.mkdir(exist_ok=True)
    count = 0

    while count < max_files:
        filename = output_dir / f"test_{count}.csv"
        # Call FileFaker CLI
        subprocess.run([
            "filefaker", "generate", "csv",
            "--rows", "30",
            "--columns", "4",
            "--output", str(filename)
        ])
        # Load into DataFrame
        df = pd.read_csv(filename)
        yield df
        count += 1
        time.sleep(interval)

This Python data generator does three things:
1. Calls FileFaker to make a CSV.
2. Reads it into a pandas DataFrame.
3. Yields the DataFrame every interval seconds.

Simple. Flexible. Powerful.

4. Integrating with Dash Callbacks

Now let’s plug that generator into Dash for live plotting.

import dash
from dash import dcc, html
import plotly.express as px

app = dash.Dash(__name__)

app.layout = html.Div([
    dcc.Graph(id="live-graph"),
    dcc.Interval(id="interval-component", interval=2000)  # 2 seconds
])

# Create the generator
data_gen = filefaker_generator(interval=2)

@app.callback(
    dash.Output("live-graph", "figure"),
    dash.Input("interval-component", "n_intervals")
)
def update_graph(n):
    try:
        df = next(data_gen)
    except StopIteration:
        # Restart or stop updates
        return dash.no_update

    # Build plotly figure
    fig = px.line(df, x=df.columns[0], y=df.columns[1:])
    fig.update_layout(title="Real-Time Test Data", template="plotly_dark")
    return fig

if __name__ == "__main__":
    app.run_server(debug=True)

What’s happening here?
– dcc.Interval fires every 2 seconds.
– The callback calls next(data_gen) on our Python data generator.
– We build a Plotly line chart and return it.
– Voilà: a real-time updating graph powered by freshly generated data.

5. Customizing Your Plot

Want dots instead of lines? Color-coded traces? It’s easy:

fig = px.scatter(
    df,
    x="column_0",
    y=["column_1", "column_2", "column_3"],
    color_discrete_sequence=["#00FF00", "#FF00FF", "#FFFF00"]
)
fig.update_traces(mode="markers+lines")

Feel free to tweak:
– Axis labels
– Templates (light, dark, ggplot2)
– Annotations and legends

Your Python data generator can also yield JSON or images. Just adjust the FileFaker CLI flags and parsing logic.

Conclusion

With FileFaker and Dash, you have a robust Python data generator setup for real-time plotting. You’ll save time. You’ll avoid sensitive data. You’ll impress stakeholders with live demos that truly reflect your app’s capabilities.

Ready to power up your testing toolkit?

Try FileFaker today and see how easy generating realistic files can be:
👉 https://filefaker.com/?ref=producthunt