Statistical

Static

48 Python scripts generated for tally chart this week

Tally Chart

Chart overview

Tally charts organize frequency counts in a clear, tabular format.

Key points

Modern implementations use styling libraries to create publication-quality tables that effectively communicate categorical data distributions and survey results.

Python Tutorial

How to create a tally chart in Python

Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.

How to Create a Bar Chart in Python

Example Visualization

Formatted tally chart showing survey response frequencies

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Example AI Prompt

"Create a beautifully formatted tally chart table showing 'Customer Satisfaction Survey Results'. Generate data for 5 response categories with frequencies: 'Very Satisfied' (47 responses, |||| |||| |||| |||| |||| |||| |||| |||| |||), 'Satisfied' (82 responses), 'Neutral' (31 responses), 'Dissatisfied' (15 responses), 'Very Dissatisfied' (5 responses). Display traditional tally marks (groups of 5 with diagonal strike). Include columns: Response, Tally Marks, Frequency, Percentage. Add alternating row colors for readability. Calculate and display total (180 responses). Add a footer row showing cumulative satisfaction rate (Satisfied + Very Satisfied = 71.7%). Use professional table styling with borders. Title: 'Q4 2024 Customer Satisfaction Survey (n=180)'."

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Python Code Example

example.py

# === IMPORTS ===
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, FancyBboxPatch
import matplotlib.patheffects as pe

# === USER-EDITABLE PARAMETERS ===
title = "Customer Satisfaction Survey — Q4 2024"
figsize = (14, 8)

# === EXAMPLE DATASET ===
data = {
    'Response': ['Very Satisfied', 'Satisfied', 'Neutral', 'Dissatisfied', 'Very Dissatisfied'],
    'Frequency': [47, 82, 31, 15, 5],
    'Color': ['#00E676', '#69F0AE', '#FFD54F', '#FF8A65', '#FF5252']
}

total = sum(data['Frequency'])
print("=== Customer Satisfaction Survey Results ===")
print(f"\nTotal Responses: {total}")
for i, (response, freq) in enumerate(zip(data['Response'], data['Frequency'])):
    pct = freq / total * 100
    print(f"  {response}: {freq} ({pct:.1f}%)")

satisfaction_rate = (data['Frequency'][0] + data['Frequency'][1]) / total * 100
print(f"\nSatisfaction Rate: {satisfaction_rate:.1f}%")

# === CREATE TALLY CHART ===
fig, ax = plt.subplots(figsize=figsize, facecolor='#0f0f23')
ax.set_facecolor('#0f0f23')

# Draw tally marks visualization
y_positions = np.arange(len(data['Response']))[::-1]
bar_height = 0.6
tally_width = 0.6

for i, (response, freq, color) in enumerate(zip(data['Response'], data['Frequency'], data['Color'])):
    y = y_positions[i]
    
    # Draw response label
    ax.text(-0.5, y, response, fontsize=14, fontweight='bold', 
            color='white', ha='right', va='center')
    
    # Draw tally marks
    groups_of_5 = freq // 5
    remainder = freq % 5
    x_start = 0
    
    for g in range(groups_of_5):
        # Draw 4 vertical lines
        for j in range(4):
            x = x_start + j * 0.15
            ax.plot([x, x], [y - bar_height/3, y + bar_height/3], 
                   color=color, linewidth=3, solid_capstyle='round')
        
        # Draw diagonal strike-through
        ax.plot([x_start - 0.05, x_start + 0.5], 
               [y - bar_height/3, y + bar_height/3],
               color=color, linewidth=3, solid_capstyle='round')
        
        x_start += tally_width
    
    # Draw remaining marks
    for j in range(remainder):
        x = x_start + j * 0.15
        ax.plot([x, x], [y - bar_height/3, y + bar_height/3], 
               color=color, linewidth=3, solid_capstyle='round')
    
    # Draw frequency and percentage
    pct = freq / total * 100
    x_end = max(groups_of_5 * tally_width + remainder * 0.15, 0.5)
    ax.text(x_end + 0.5, y, f'{freq}', fontsize=16, fontweight='bold',
            color=color, ha='left', va='center')
    ax.text(x_end + 1.2, y, f'({pct:.1f}%)', fontsize=12,
            color='#888', ha='left', va='center')

# Add satisfaction rate highlight
ax.axhline(y=y_positions[2] - 0.5, color='#333', linestyle='--', linewidth=1)

# Satisfaction indicator
sat_box = FancyBboxPatch((8, y_positions[1] - 0.3), 3, 1.5,
                          boxstyle="round,pad=0.1", 
                          facecolor='#1DB954', edgecolor='#00E676',
                          linewidth=2, alpha=0.3)
ax.add_patch(sat_box)
ax.text(9.5, y_positions[1] + 0.4, 'SATISFACTION', fontsize=10, 
        color='#1DB954', ha='center', va='center', fontweight='bold')
ax.text(9.5, y_positions[1], f'{satisfaction_rate:.0f}%', fontsize=28, 
        color='#00E676', ha='center', va='center', fontweight='bold')

# Title
ax.set_title(title, fontsize=22, fontweight='bold', color='white', pad=20,
             path_effects=[pe.withStroke(linewidth=3, foreground='#6c5ce7')])

# Subtitle
ax.text(0.5, 1.02, f'n = {total} respondents', transform=ax.transAxes,
        fontsize=12, color='#888', ha='center', va='bottom')

# Styling
ax.set_xlim(-4, 12)
ax.set_ylim(-0.5, len(data['Response']) - 0.3)
ax.axis('off')

# Legend
legend_text = '|||| = 5 responses (with diagonal strike)'
ax.text(0.02, 0.02, legend_text, transform=ax.transAxes, fontsize=10,
        color='#666', ha='left', va='bottom',
        bbox=dict(boxstyle='round', facecolor='#1a1a3a', alpha=0.8, edgecolor='#333'))

plt.tight_layout()
plt.savefig('chart.png', dpi=150, bbox_inches='tight', facecolor='#0f0f23')
print("Saved: chart.png")
plt.show()
# END-OF-CODE

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Console Output

Output

=== Customer Satisfaction Survey Results ===

Total Responses: 180
  Very Satisfied: 47 (26.1%)
  Satisfied: 82 (45.6%)
  Neutral: 31 (17.2%)
  Dissatisfied: 15 (8.3%)
  Very Dissatisfied: 5 (2.8%)

Satisfaction Rate: 71.7%
Saved: chart.png

Common Use Cases

1Survey result display
2Categorical data counts
3Inventory tracking
4Vote tabulation

Pro Tips

Sort by frequency or alphabetically

Add percentage column

Use color gradients for values

Long-tail keyword opportunities

how to create tally chart in python

tally chart matplotlib

tally chart seaborn

tally chart plotly

tally chart scientific visualization

tally chart publication figure python

High-intent chart variations

Tally Chart with confidence interval overlays

Tally Chart optimized for publication layouts

Tally Chart with category-specific color encoding

Interactive Tally Chart for exploratory analysis

Library comparison for this chart

pandas

Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.

great-tables

Useful in specialized workflows that complement core Python plotting libraries for tally-chart analysis tasks.

plottable

Useful in specialized workflows that complement core Python plotting libraries for tally-chart analysis tasks.

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