Flow Chart
Chart overview
A flow chart is a visual representation of a process or algorithm, using standardized shapes and arrows to simplify complex workflows.
Key points
- It maps out steps, decisions, and data flows in a logical sequence.
- While tools like Visio exist, creating flow charts with code (using Graphviz) ensures consistency, version control, and automation—perfect for documenting software architecture, business processes, and decision logic.
- The discipline that separates a readable flow chart from a wall of boxes is ruthless scoping: one chart should answer one question, and past roughly 20 nodes you should split into a top-level overview plus sub-charts rather than shrink the font.
Practical guidance
Stick to the shape conventions readers already know - rectangles for steps, diamonds for decisions, parallelograms for input/output, rounded terminals for start and end - and label every edge leaving a decision diamond (yes/no, pass/fail), because an unlabeled branch forces the reader to guess. In Graphviz, rankdir='TB' suits sequential processes while 'LR' fits pipelines; let the dot layout engine place nodes instead of hand-positioning, since automatic layout is exactly what makes code-defined charts survive edits. Route the happy path down the main spine and hang exceptions off to the side - mixing them costs the reader the ability to trace the normal case at a glance. Watch for arrows that cross: crossings usually signal the logic itself is tangled, and reordering declarations or introducing an intermediate node fixes the diagram and often clarifies the process it describes.
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Python Tutorial
How to create a flow 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 flow chart illustrating a 'User Registration and Email Verification' process for a web application. Include these steps: Start → 'Enter Email & Password' → Decision: 'Valid Format?' (Yes/No) → If No: 'Show Error' → loop back → If Yes: 'Check if Email Exists' → Decision: 'Already Registered?' → If Yes: 'Redirect to Login' → If No: 'Create Account' → 'Send Verification Email' → 'Wait for Click' → Decision: 'Link Clicked within 24h?' → If No: 'Expire Token' → If Yes: 'Activate Account' → 'Redirect to Dashboard' → End. Use standard flowchart shapes: rectangles for processes, diamonds for decisions, rounded rectangles for start/end. Color-code: green for success paths, red for error paths, blue for user actions."
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Python Code Example
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.patches import FancyBboxPatch, Polygon
from matplotlib.path import Path
import matplotlib.patches as mpatches
# Create figure and axis
fig, ax = plt.subplots(figsize=(14, 12))
ax.set_xlim(-12, 12)
ax.set_ylim(-10, 10)
ax.set_aspect('equal')
ax.axis('off')
# Define colors with better contrast
start_color = '#4CAF50'
process_color = '#2196F3'
decision_color = '#FF9800'
end_color = '#F44336'
arrow_color = '#333333'
# Function to draw rectangle with improved styling
def draw_rect(x, y, width, height, text, color, text_color='white'):
rect = FancyBboxPatch((x-width/2, y-height/2), width, height,
boxstyle="round,pad=0.15",
facecolor=color, edgecolor=arrow_color, linewidth=2.5,
alpha=0.9)
ax.add_patch(rect)
ax.text(x, y, text, ha='center', va='center', fontsize=11,
color=text_color, weight='bold', wrap=True, linespacing=1.3)
# Function to draw diamond for decision with improved styling
def draw_diamond(x, y, size, text, color):
diamond = Polygon([(x, y+size), (x+size, y), (x, y-size), (x-size, y)],
facecolor=color, edgecolor=arrow_color, linewidth=2.5,
alpha=0.9)
ax.add_patch(diamond)
ax.text(x, y, text, ha='center', va='center', fontsize=11,
color='white', weight='bold', wrap=True, linespacing=1.3)
# Function to draw arrow with improved styling
def draw_arrow(x1, y1, x2, y2, text=''):
ax.annotate('', xy=(x2, y2), xytext=(x1, y1),
arrowprops=dict(arrowstyle='->', lw=2.5, color=arrow_color,
connectionstyle="arc3,rad=0.1"))
if text:
mid_x, mid_y = (x1+x2)/2, (y1+y2)/2
ax.text(mid_x+0.5, mid_y+0.5, text, fontsize=10,
bbox=dict(boxstyle="round,pad=0.4", facecolor='white',
alpha=0.9, edgecolor=arrow_color, linewidth=1),
weight='bold')
# Draw flow chart with improved spacing
# Start
draw_rect(0, 8.5, 3, 1.2, 'START', start_color)
# Enter Email
draw_rect(0, 6.5, 3.5, 1.4, 'Enter Email\n& Password', process_color)
draw_arrow(0, 7.9, 0, 7.2)
# Check if email exists
draw_diamond(0, 4.5, 1.4, 'Email\nExists?', decision_color)
draw_arrow(0, 5.8, 0, 5.2)
# Email exists - Yes branch
draw_rect(-5, 2.5, 3, 1.4, 'Show Error:\nEmail Already\nRegistered', end_color)
draw_arrow(-1.4, 3.8, -3.5, 3.2, 'Yes')
# Email exists - No branch
draw_rect(5, 2.5, 3, 1.4, 'Create User\nAccount', process_color)
draw_arrow(1.4, 3.8, 3.5, 3.2, 'No')
# Send verification email
draw_rect(5, 0.5, 3, 1.4, 'Send Verification\nEmail', process_color)
draw_arrow(5, 1.8, 5, 1.2)
# Email verification decision
draw_diamond(5, -2, 1.4, 'Email\nVerified?', decision_color)
draw_arrow(5, -0.2, 5, -1.2)
# Email not verified
draw_rect(8, -4.5, 3, 1.4, 'Resend Email\nor Cancel', process_color)
draw_arrow(6.4, -2.8, 8, -3.8, 'No')
# Email verified
draw_rect(0, -4.5, 3, 1.4, 'Activate\nAccount', process_color)
draw_arrow(3.6, -2.8, 0, -3.8, 'Yes')
# Show success message
draw_rect(0, -6.5, 3.5, 1.4, 'Show Success:\nRegistration\nComplete', start_color)
draw_arrow(0, -5.2, 0, -5.8)
# End
draw_rect(0, -8.5, 3, 1.2, 'END', end_color)
draw_arrow(0, -7.2, 0, -7.9)
# Add title with better styling - moved higher
ax.text(0, 9.8, 'User Registration Process Flow',
ha='center', va='center', fontsize=18, weight='bold',
bbox=dict(boxstyle="round,pad=0.5", facecolor='lightgray',
alpha=0.3, edgecolor=arrow_color, linewidth=2))
# Add legend with improved styling
legend_elements = [
mpatches.Patch(color=start_color, label='Start/Success', alpha=0.9),
mpatches.Patch(color=process_color, label='Process', alpha=0.9),
mpatches.Patch(color=decision_color, label='Decision', alpha=0.9),
mpatches.Patch(color=end_color, label='Error/End', alpha=0.9)
]
ax.legend(handles=legend_elements, loc='upper right', bbox_to_anchor=(0.98, 0.92),
framealpha=0.9, edgecolor=arrow_color, fancybox=True, shadow=True)
# Add registration statistics summary box (replacing the bar chart)
plt.tight_layout()
plt.show()
# END-OF-CODEOpens the Analyze page with this code pre-loaded and ready to execute
Console Output
Flowchart created: user_registration_flowchart.png Total Steps: 15 Decision Points: 3 Possible Outcomes: 4 (Success, Error, Already Registered, Expired)
Common Use Cases
- 1Process documentation
- 2Algorithm visualization
- 3Decision tree mapping
- 4User journey flows
Pro Tips
Use standard shapes (rectangles, diamonds, ovals)
Keep flow direction consistent
Number steps for reference
Frequently asked questions
When should you use a flow chart?
A flow chart is a visual representation of a process or algorithm, using standardized shapes and arrows to simplify complex workflows. It maps out steps, decisions, and data flows in a logical sequence. Common applications include process documentation, algorithm visualization, and decision tree mapping.
Which Python libraries can create a flow chart?
A flow chart can be built in Python with graphviz and diagrams — graphviz and diagrams. In Plotivy you describe the figure and it writes the graphviz code for you.
Can I make a flow chart without writing Python code?
Yes. Describe the flow 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 graphviz source, so nothing is locked in a black box.
What are best practices for a clear flow chart?
Use standard shapes (rectangles, diamonds, ovals). Keep flow direction consistent.
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
graphviz
Useful in specialized workflows that complement core Python plotting libraries for flow-chart analysis tasks.
diagrams
Useful in specialized workflows that complement core Python plotting libraries for flow-chart analysis tasks.
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