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.
- A tally is the right choice when the exact count per category is the message and the number of categories is small enough to scan — think Likert-scale survey responses, defect-type counts, or vote totals.
- Once you care about the shape of the distribution rather than individual numbers, a bar chart reads faster; tallies excel at precision, bars at comparison.
Practical guidance
In Python, great_tables and plottable turn a pandas value_counts() Series into a styled table with data bars, percentage columns, and conditional color scales, so you keep the exactness of a table while borrowing the at-a-glance encoding of a chart. Sort rows by frequency (not alphabetically) unless the categories have a natural order like age bands, add a total row, and include a percentage-of-total column so readers do not have to do mental arithmetic. For counts that will be published, right-align numbers and use a monospaced or tabular-figures font so digits line up column-wise.
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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 PythonExample Visualization
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View example 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
# === 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
Opens the Analyze page with this code pre-loaded and ready to execute
Console 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
Frequently asked questions
When should you use a tally chart?
Tally charts organize frequency counts in a clear, tabular format. Modern implementations use styling libraries to create publication-quality tables that effectively communicate categorical data distributions and survey results. Common applications include survey result display, categorical data counts, and inventory tracking.
Which Python libraries can create a tally chart?
A tally chart can be built in Python with pandas, great-tables, and plottable — pandas for quick plots straight from a DataFrame, great-tables, and plottable. In Plotivy you describe the figure and it writes the pandas code for you.
Can I make a tally chart without writing Python code?
Yes. Describe the tally chart you need in plain language and upload your dataset — Plotivy's AI writes the Python code and renders a publication-ready figure. You still get the full, editable pandas source, so nothing is locked in a black box.
What are best practices for a clear tally chart?
Sort by frequency or alphabetically. Add percentage column.
Long-tail keyword opportunities
High-intent chart variations
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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