This Python code does the following: 1. Imports Required Libraries: ...

July 5, 2025 at 07:36 PM

import matplotlib.pyplot as plt
import pandas as pd

# Data for the visual table
data = {
    "Component": [
        "Learning Management System (e.g., Moodle)",
        "Communication Platform (e.g., Slack/Google Chat)",
        "Digital Recognition System",
        "Training Content Development",
        "Wall-Mounted Monitor for Breakroom",
        "Upgraded Wi-Fi Router",
        "Onboarding Sessions/Staff Training",
        "Ongoing Monitoring & Adjustments",
        "Feedback Tools (Google Forms or similar)"
    ],
    "Type": [
        "Software", "Software", "Software", "Labor", "Hardware",
        "Hardware", "Labor", "Maintenance", "Software"
    ],
    "Direct Costs": [
        "$0–$150 annually", "$0 (free tier)", "$0", "$0 (in-house)", "~$250 one-time",
        "~$200 one-time", "$0", "$0", "$0"
    ],
    "Indirect Costs": [
        "Staff time for content upload", 
        "Time for onboarding/moderation",
        "Time to update/manage recognition", 
        "Significant BCBA/Lead RBT time", 
        "1–2 hrs installation time",
        "Installation and testing time", 
        "1–2 hrs per RBT for training", 
        "Monthly check-ins/updates", 
        "Monthly data review"
    ]
}

# Create DataFrame
df = pd.DataFrame(data)

# Set figure and axis
fig, ax = plt.subplots(figsize=(12, 6))
ax.axis('off')
table = ax.table(cellText=df.values, colLabels=df.columns, cellLoc='left', loc='center')

# Style table
table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1, 2)

# Save as image
image_path = "/mnt/data/Connect_and_Grow_Budget_Table.png"
plt.savefig(image_path, bbox_inches='tight', dpi=300)

image_path

This Python code does the following:

Imports Required Libraries:
- matplotlib.pyplot is imported to create and customize a plot (or table in this case).
- pandas is imported for data manipulation and DataFrame creation.
Sets Up Data:
- A dictionary called data is created, containing four columns: Component, Type, Direct Costs, and Indirect Costs. These represent a table of information about components, their types, costs, and additional factors.
Creates a DataFrame:
- The data dictionary is converted into a pandas DataFrame called df.
Configures the Figure and Table:
- A figure (fig) and an axis (ax) are created with specific dimensions (figsize=(12, 6)).
- The axis is turned off (ax.axis('off')) to remove axis lines and ticks.
- A table is added to the plot from the DataFrame using ax.table, displaying the data as cellText (content of the cells) and column labels (colLabels). The table is centered with loc='center'.
Styles the Table:
- The font size of the table is manually set using auto_set_font_size(False) and specified as 10 via set_fontsize(10).
- The table is scaled vertically (scale(1, 2)) to make it taller.
Saves the Table as an Image:
- The table is saved as a PNG image (Connect_and_Grow_Budget_Table.png) to the specified path (/mnt/data/). The bbox_inches='tight' option ensures no extra white space in the image, and dpi=300 guarantees high resolution.
Outputs the File Path:
- At the end of the code, the variable image_path, containing the file path to the saved image, is returned.

Purpose: This code is used to visualize tabular data in an aesthetically-pleasing table format using Matplotlib, save it as a high-quality image file, and provide the file path to the saved image. It is useful for generating visual representations of data for reports, presentations, or documentation.

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This Python code does the following: 1. **Imports Required Libraries**: ...

This Python code does the following: 1. Imports Required Libraries: ...