Named colors and hex codes in Matplotlib - Deep Dive

Overview - Named colors and hex codes

What is it?

Named colors and hex codes are ways to specify colors in data visualizations. Named colors use simple words like 'red' or 'blue' to pick a color. Hex codes are six-digit codes starting with # that represent colors using numbers and letters. Both help you choose exact colors for charts and graphs.

Why it matters

Colors make data easier to understand and more attractive. Without named colors or hex codes, you would struggle to pick consistent colors or explain which colors to use. This would make charts confusing and less effective at showing patterns or differences.

Where it fits

You should know basic plotting with matplotlib before learning colors. After this, you can learn about color maps and customizing styles to make your visuals clearer and more professional.

Mental Model

Core Idea

Colors in matplotlib can be chosen by simple names or precise hex codes to control how your data looks.

Think of it like...

Choosing a color is like picking paint for a room: named colors are like calling it 'sky blue', while hex codes are like giving the exact paint formula to get the perfect shade.

Colors selection
┌───────────────┐
│ Named Colors  │  e.g., 'red', 'green', 'blue'
├───────────────┤
│ Hex Codes     │  e.g., '#FF0000', '#00FF00', '#0000FF'
└───────────────┘
Use in matplotlib:
plt.plot(x, y, color='red') or plt.plot(x, y, color='#FF0000')

Build-Up - 7 Steps

1

Foundation

What are Named Colors

Concept: Named colors are simple words that matplotlib understands as colors.

Matplotlib has a list of color names like 'red', 'blue', 'green', 'yellow', and many more. You can use these names directly in your plotting commands to set colors. For example, plt.plot(x, y, color='red') will draw a red line.

Result

The plot line appears in the color named 'red'.

Knowing named colors lets you quickly pick common colors without remembering codes.

2

Foundation

Understanding Hex Codes

Concept: Hex codes are a way to describe colors using six characters representing red, green, and blue parts.

A hex code starts with # and has six characters, each pair shows how much red, green, and blue is in the color. For example, '#FF0000' means full red, no green, no blue, which is pure red. You can use hex codes in matplotlib like plt.plot(x, y, color='#FF0000').

Result

The plot line appears in the exact color defined by the hex code.

Hex codes give you precise control over colors beyond simple names.

3

Intermediate

Comparing Named Colors and Hex Codes

🤔Before reading on: Do you think named colors and hex codes can represent the same colors exactly? Commit to your answer.

Concept: Named colors are a subset of colors, while hex codes can represent millions of colors.

Named colors are easy to remember but limited in number. Hex codes can represent any color by mixing red, green, and blue in different amounts. For example, 'red' is '#FF0000', but you can also use '#FA8072' for a salmon shade which has no simple name.

Result

You can choose between easy names or precise hex codes depending on your needs.

Understanding the difference helps you decide when to use simple names or exact colors.

4

Intermediate

Using Colors in Matplotlib Plots

🤔Before reading on: Do you think you can use both named colors and hex codes interchangeably in matplotlib? Commit to your answer.

Concept: Matplotlib accepts both named colors and hex codes in the same places for color arguments.

You can pass either a named color or a hex code to the color parameter in plotting functions. For example, plt.plot(x, y, color='blue') or plt.plot(x, y, color='#0000FF') both produce blue lines. This flexibility helps you use whichever is easier or more precise.

Result

Plots show the color you specify, whether by name or hex code.

Knowing this interchangeability makes your code more flexible and readable.

5

Intermediate

Finding Named Colors and Hex Codes

Concept: Matplotlib provides a list of named colors and ways to find hex codes for custom colors.

You can see all named colors using matplotlib's documentation or by running code: import matplotlib.colors as mcolors; print(mcolors.CSS4_COLORS). For hex codes, you can use color pickers or online tools to find the exact code you want.

Result

You have a resource to pick colors by name or hex code for your plots.

Having a color reference helps you pick consistent and attractive colors.

6

Advanced

Using RGBA Hex Codes for Transparency

🤔Before reading on: Do you think hex codes can include transparency information? Commit to your answer.

Concept: Hex codes can include an alpha channel to control transparency using 8 characters instead of 6.

Besides the usual 6-character hex code, you can add two more characters for alpha (transparency). For example, '#FF000080' is red with 50% transparency. Matplotlib supports this in color arguments, letting you make colors see-through.

Result

Plots can have colors with transparency, allowing layered visuals.

Knowing about alpha in hex codes lets you create richer, clearer visualizations.

7

Expert

How Matplotlib Maps Colors Internally

🤔Before reading on: Do you think matplotlib stores colors as names or converts all to numeric RGB values internally? Commit to your answer.

Concept: Matplotlib converts all color inputs, named or hex, into numeric RGB or RGBA arrays for rendering.

When you specify a color, matplotlib looks up the name or parses the hex code and converts it into a tuple of numbers between 0 and 1 for red, green, blue, and optionally alpha. This numeric form is what the computer uses to draw pixels on the screen.

Result

All colors are handled uniformly inside matplotlib regardless of input format.

Understanding this conversion explains why different color formats work seamlessly and how to debug color issues.

Under the Hood

Matplotlib accepts color inputs as strings (named colors or hex codes). It uses a color parsing system to convert these strings into normalized numeric arrays representing red, green, blue, and alpha channels. These arrays have values from 0 to 1. The rendering engine then uses these numeric values to paint pixels on the plot canvas.

Why designed this way?

This design allows users to specify colors in many familiar formats while keeping internal processing consistent and efficient. Named colors provide ease of use, while hex codes offer precision. Converting all colors to numeric arrays simplifies rendering and color blending.

Color Input
  ┌───────────────┐
  │ Named Color   │
  │ (e.g., 'red') │
  └──────┬────────┘
         │
         ▼
  ┌───────────────┐
  │ Hex Code      │
  │ (e.g., '#FF0000')
  └──────┬────────┘
         │
         ▼
  ┌─────────────────────────┐
  │ Color Parser             │
  │ Converts to (R,G,B,A)   │
  │ values between 0 and 1   │
  └────────────┬────────────┘
               │
               ▼
       ┌─────────────┐
       │ Renderer    │
       │ Draws pixels│
       └─────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Do you think 'red' and '#FF0000' always produce exactly the same color in matplotlib? Commit to yes or no.

Common Belief: Named colors and their hex code equivalents are always exactly the same color.

Reality: Most named colors match their hex codes exactly, but some named colors may differ slightly due to color space conversions or legacy definitions.

Why it matters: Assuming exact matches can cause subtle color differences in professional visualizations, affecting brand consistency or visual clarity.

Quick: Can you use any 3-digit hex code like '#F00' in matplotlib? Commit to yes or no.

Common Belief: Matplotlib supports shorthand 3-digit hex codes like '#F00' for colors.

Reality: Matplotlib does not support 3-digit shorthand hex codes; it requires full 6-digit hex codes starting with '#'.

Why it matters: Using shorthand hex codes causes errors or unexpected colors, confusing beginners.

Quick: Do you think transparency can be set using named colors alone? Commit to yes or no.

Common Belief: You can add transparency by just using named colors like 'red' with an alpha value inside the color string.

Reality: Named colors do not include transparency; you must use RGBA tuples or 8-digit hex codes to set transparency.

Why it matters: Trying to add transparency with named colors alone leads to no effect or errors, limiting visualization design.

Quick: Is it true that all colors in matplotlib are stored as strings internally? Commit to yes or no.

Common Belief: Matplotlib stores colors internally as their string names or hex codes.

Reality: Matplotlib converts all colors to numeric RGB(A) arrays internally for efficient rendering.

Why it matters: Misunderstanding this can lead to confusion when debugging color issues or extending matplotlib.

Expert Zone

1

Some named colors come from CSS4 standards, but matplotlib also supports additional legacy names, which can cause confusion.

2

Hex codes can include alpha transparency as the last two characters, but this is less commonly known and used in matplotlib.

3

Matplotlib's color parsing supports many formats beyond named and hex, like RGB tuples and grayscale strings, which interact with named colors.

When NOT to use

For complex color gradients or continuous color scales, using named colors or single hex codes is limiting. Instead, use colormaps or color normalization techniques. Also, for accessibility, relying solely on color names without considering colorblind-friendly palettes is not recommended.

Production Patterns

Professionals often define color palettes as dictionaries mapping names to hex codes for consistent branding. They use hex codes for precise control and transparency in layered plots. Named colors are used for quick prototyping or when exact shades are not critical.

Connections

CSS Color Standards

Named colors in matplotlib are based on CSS4 color names.

Understanding CSS color standards helps you predict which named colors matplotlib supports and their exact shades.

Digital Image Representation

Hex codes represent colors as combinations of red, green, and blue values, similar to how digital images store pixel colors.

Knowing how images use RGB values clarifies why hex codes work and how colors mix on screens.

Paint Mixing in Art

Hex codes combine red, green, and blue light to create colors, analogous to mixing paint colors in art but using light instead of pigment.

This cross-domain link helps understand additive color mixing, which is fundamental to digital color representation.

Common Pitfalls

#1 Using 3-digit hex codes which matplotlib does not support.

Wrong approach:

plt.plot(x, y, color='#F00') # incorrect shorthand hex code

Correct approach:

plt.plot(x, y, color='#FF0000') # full 6-digit hex code

Root cause:

Confusing CSS shorthand hex codes with matplotlib's required full hex code format.

#2 Trying to add transparency by appending alpha to named colors.

Wrong approach:

plt.plot(x, y, color='red80') # invalid color string

Correct approach:

plt.plot(x, y, color='#FF000080') # red with 50% transparency using 8-digit hex

Root cause:

Misunderstanding that named colors cannot include transparency and that alpha must be specified separately.

#3 Assuming named colors cover all needed shades.

Wrong approach:

plt.plot(x, y, color='salmon') # 'salmon' is a named color in matplotlib

Correct approach:

plt.plot(x, y, color='#FA8072') # use hex code for salmon color

Root cause:

Believing all common color names are supported as named colors in matplotlib.

Key Takeaways

Named colors are easy-to-use color names predefined in matplotlib for quick color selection.

Hex codes provide precise control over colors by specifying exact red, green, and blue values.

Matplotlib converts all color inputs into numeric RGB(A) values internally for consistent rendering.

Transparency can be controlled using 8-digit hex codes or RGBA tuples, not by named colors alone.

Knowing when to use named colors versus hex codes helps create clear, consistent, and attractive data visualizations.