{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Dario Radečić",
  "title_listed": "How to Create Custom Color Palettes in Matplotlib - Discrete vs. Linear Colormaps, Explained",
  "keywords": "Matplotlib,颜色映射",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/how-to-create-custom-color-palettes-in-matplotlib-discrete-vs-linear-colormaps-explained-cfe4c5ba1215/",
  "publish_date": "2024-08",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "1db74e81cf27",
  "collected_at": "2026-10-01T20:54:41",
  "title": "How to Create Custom Color Palettes in Matplotlib - Discrete vs. Linear Colormaps, Explained | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# How to Create Custom Color Palettes in Matplotlib - Discrete vs. Linear Colormaps, Explained\nActionable guide on how to bring custom colors to personalize your charts\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nAugust 29, 20246 min read\n![Article thumbnail \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/11DPJ4Mcsr5AWiwSqFShEgQ.png)\nArticle thumbnail (image by author)\nIf there's one thing that'll make a good chart great, it's the color choice.\nYou can turn any set of hex color codes into a color palette with Matplotlib, and this article will show you how. You'll also learn the difference between discrete and linear color palettes, and the reasons why one is better than the other.\nIf you want to get the same data visualization quality I have, follow the steps from this article before proceeding:\n  * [3 Key Things You Must Change Right Now To Make Your Charts Stand Out](https://darioradecic.substack.com/p/you-need-to-fix-these-3-things-right)\n\n\n* * *\n## How to Create Custom Colormaps in Matplotlib\nThese are the libraries you'll need to follow along:\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Machine Learning](https://roadmap.sh/machine-learning) and [Data Analyst](https://roadmap.sh/data-analyst) roadmaps.\npython\n\n```\nimport numpy as np\nimport pandas as pd\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n```\n\nMatplotlib allows you to create two types of color palettes:\n  * **Discrete** - The palette has a finite number of color values. Great for categorical data, but you need to make sure the palette has at least as many colors as you have distinct categories.\n  * **Linear (continuous)** - The palette has an \"infinite\" number of values. Great for continuous and categorical data. You can specify just two colors, and the palette will automatically include all the values between them (think: gradient in Photoshop).\n\n\nTo create a discrete color palette in Matplotlib, run the following:\npython\n\n```\ncmap_discrete = mpl.colors.ListedColormap(\n    name=\"discrete-monochromatic-blue\",\n    colors=[\"#051923\", \"#003554\", \"#006494\", \"#0582ca\", \"#00a6fb\"]\n)\ncmap_discrete\n```\n\n![Image 1 - Discrete colormap \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1BBRNfYcxEfWwkb_iqAqOVg.png)\nImage 1 - Discrete colormap (image by author)\nAnd to create a linear (continuous) color palette, run this code snippet:\npython\n\n```\ncmap_linear = mpl.colors.LinearSegmentedColormap.from_list(\n    name=\"linear-monochromatic-blue\", \n    colors=[\"#051923\", \"#003554\", \"#006494\", \"#0582ca\", \"#00a6fb\"]\n)\ncmap_linear\n```\n\n![Image 2 - Linear colormap \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1DCEAf_4TRdYiQjKAahDykQ.png)\nImage 2 - Linear colormap (image by author)\nYou can see how the discrete palette has 5 distinct colors, while the linear palette takes a continuous range of values.\n**What's the difference when visualizing data?** That's what you'll learn next.\n## Comparison - Discrete vs. Linear Colormap on Continuous Data\nIn this section, you'll create a 10x10 matrix of random numbers [0.0, 1.0) and visualize it as an image.\nRun the following snippet to create the data:\npython\n\n```\ndata = np.random.random(100).reshape(10, 10)\ndata\n```\n\n![Image 3 - Random normal 10x10 matrix \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1mXdV098MFy0fkRfUeN7m9A.png)\nImage 3 - Random normal 10x10 matrix (image by author)\nAs for the visual, you'll want to create a 1x2 grid and show the identical dataset colored through a discrete colormap on the left, and a linear colormap on the right:\npython\n\n```\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n\ncax1 = ax1.imshow(data, cmap=cmap_discrete)\nax1.set_title(\"Discrete Colormap\", loc=\"left\", fontdict={\"weight\": \"bold\"}, y=1.04)\nfig.colorbar(cax1, ax=ax1)\n\ncax2 = ax2.imshow(data, cmap=cmap_linear)\nax2.set_title(\"Linear Colormap\", loc=\"left\", fontdict={\"weight\": \"bold\"}, y=1.04)\nfig.colorbar(cax2, ax=ax2)\n\nplt.tight_layout()\nplt.show()\n```\n\n![Image 4 - Discrete vs. linear colormap \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1i3y0tqycUMt69-GFaepe_w.png)\nImage 4 - Discrete vs. linear colormap (image by author)\nThe underlying data is the same, but the plot on the left can take up to 5 possible color values. The one on the right has a much wider range.\n**To conclude** - linear color palettes can be used to visualize both continuous and categorical data, while discrete color palettes can only do the latter without information loss.\n## Real-World Plots - How to Add Custom Colors to Your Charts\nYou'll now see how to apply a custom color palette to bar charts in Matplotlib.\nTo start, copy the following code snippet to create mock employee count data across 10 departments and 5 office locations:\npython\n\n```\ndf = pd.DataFrame({\n    \"HR\": [50, 63, 40, 68, 35],\n    \"Engineering\": [77, 85, 62, 89, 58],\n    \"Marketing\": [50, 35, 79, 43, 67],\n    \"Sales\": [59, 62, 33, 77, 72],\n    \"Customer Service\": [31, 34, 61, 70, 39],\n    \"Distribution\": [35, 21, 66, 90, 31],\n    \"Logistics\": [50, 54, 13, 71, 32],\n    \"Production\": [22, 51, 54, 28, 40],\n    \"Maintenance\": [50, 32, 61, 69, 50],\n    \"Quality Control\": [20, 21, 88, 89, 39]\n}, index=[\"New York\", \"San Francisco\", \"Los Angeles\", \"Chicago\", \"Miami\"])\n\ndf = df.T\ndf = df.loc[df.sum(axis=1).sort_values().index]\n\ndf\n```\n\n![Image 5 - Custom employee dataset \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1zXZUXloPAZtI_6jVVGZTFQ.png)\nImage 5 - Custom employee dataset (image by author)\nSince there are 5 office locations, and our discrete color palette has 5 colors, it's a perfect match for visualization.\nThe only new parameter you need to know in `plot()` is `colormap`. You'll have to provide your palette variable. I've added employee counts to each bar segment, but consider this part optional:\npython\n\n```\nax = df.plot(kind=\"barh\", colormap=cmap_discrete, width=0.8, edgecolor=\"#000000\", stacked=True)\n\nfor container in ax.containers:\n    ax.bar_label(container, label_type=\"center\", fontsize=10, color=\"#FFFFFF\", fontweight=\"bold\")\n\nplt.title(\"Employee Count Per Location And Department\", loc=\"left\", fontdict={\"weight\": \"bold\"}, y=1.06)\nplt.xlabel(\"Office Location\")\nplt.ylabel(\"Count\")\n\nplt.show()\n```\n\n![Image 6 - Stacked horizontal bar chart with a custom discrete colormap \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1Y8hpEjZF2XAUdZ1JrM5tNQ.png)\nImage 6 - Stacked horizontal bar chart with a custom discrete colormap (image by author)\nLooks great, doesn't it?\nYou shouldn't see any difference when comparing discrete and linear color palettes for this visualization. **Why?** Because the discrete palette has 5 colors, and you have 5 groups in the data.\nStill, let's define a function that will compare the two palette categories:\npython\n\n```\ndef plot_employee_count_comparison(df, cmap1, cmap2):\n    _, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 7), sharex=True, sharey=True)\n\n    df.plot(kind=\"barh\", colormap=cmap1, width=0.8, edgecolor=\"#000000\", stacked=True, ax=ax1)\n    for container in ax1.containers:\n        ax1.bar_label(container, label_type=\"center\", fontsize=10, color=\"#FFFFFF\", fontweight=\"bold\")\n    ax1.set_title(\"Employee Count (Discrete Colormap)\", loc=\"left\", fontdict={\"weight\": \"bold\"}, y=1.06)\n    ax1.set_xlabel(\"Count\")\n    ax1.set_ylabel(\"Department\")\n\n    df.plot(kind=\"barh\", colormap=cmap2, width=0.8, edgecolor=\"#000000\", stacked=True, ax=ax2)\n    for container in ax2.containers:\n        ax2.bar_label(container, label_type=\"center\", fontsize=10, color=\"#FFFFFF\", fontweight=\"bold\")\n    ax2.set_title(\"Employee Count (Linear Colormap)\", loc=\"left\", fontdict={\"weight\": \"bold\"}, y=1.06)\n    ax2.set_xlabel(\"Count\")\n    ax2.set_ylabel(\"Department\")\n\n    # Adjust layout\n    plt.tight_layout()\n    plt.show()\n\nplot_employee_count_comparison(df=df, cmap1=cmap_discrete, cmap2=cmap_linear)\n```\n\n![Image 7 - Discrete vs. linear colormap \\(identical results\\) \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1u9zDbSGpZu-rzo3m_A2FTQ.png)\nImage 7 - Discrete vs. linear colormap (identical results) (image by author)\nBoth plots are identical. But that won't always be the case.\n### What Happens When You Have More Categories Then Colors in A Discrete Color Palette?\nGood question. Let's answer it by expanding the Pandas DataFrame to include two additional office locations:\npython\n\n```\ndf = pd.DataFrame({\n    \"HR\": [50, 63, 40, 68, 35, 44, 31],\n    \"Engineering\": [77, 85, 62, 89, 58, 56, 59],\n    \"Marketing\": [50, 35, 79, 43, 67, 31, 24],\n    \"Sales\": [59, 62, 33, 77, 72, 55, 66],\n    \"Customer Service\": [31, 34, 61, 70, 39, 49, 81],\n    \"Distribution\": [35, 21, 66, 90, 31, 67, 81],\n    \"Logistics\": [50, 54, 13, 71, 32, 58, 51],\n    \"Production\": [22, 51, 54, 28, 40, 41, 62],\n    \"Maintenance\": [50, 32, 61, 69, 50, 49, 41],\n    \"Quality Control\": [20, 21, 88, 89, 39, 66, 32]\n}, index=[\"New York\", \"San Francisco\", \"Los Angeles\", \"Chicago\", \"Miami\", \"Las Vegas\", \"Boston\"])\n\ndf = df.T\ndf = df.loc[df.sum(axis=1).sort_values().index]\n\ndf\n```\n\n![Image 8 - Custom employee dataset \\(2\\) \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1b5C5hsjR578tegABmPa2ew.png)\nImage 8 - Custom employee dataset (2) (image by author)\nIn theory, a discrete color palette should fail since it only has 5 color values and the dataset has 7 categories.\nLet's use the `plot_employee_count_comparison()` function to see the differences:\npython\n\n```\nplot_employee_count_comparison(df=df, cmap1=cmap_discrete, cmap2=cmap_linear)\n```\n\n![Image 9 - Discrete vs. linear colormap \\(different results\\) \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1eJaNmpuL-JLW47FwW4N2MA.png)\nImage 9 - Discrete vs. linear colormap (different results) (image by author)\nThe left chart is unusable.\nJust look at the first column for employee count combinations (77, 85) and (56, 59). **They use the same color!** Matplotlib uses the colors provided in the center of the chart, and the edges take values from the neighboring points.\nIn short, not what you want.\nYou don't get this type of issue with linear color palettes, even if you construct it from two colors only.\n* * *\n## Wrapping up\nTo summarize, a custom color palette might be just what your chart needs to make it publication-worthy.\nAlso, if you're working at a company, chances are they already have a set of colors defined. Using them from the get-go is a guaranteed way to remove at least one submission iteration.\nMatplotlib makes creating custom discrete and linear color palettes a breeze. Both can be used on categorical data, but only the latter works properly on continuous data. In this use case, a discrete color palette is obsolete, as you can get the same result (and more) with a linear palette.\n_What are your favorite color combinations for data visualization?_ Please share in the comment section below.\n* * *\n[_Looking to level up your data visualization game? Join thousands of like-minded people on my Substack for expert tips and exclusive content._](https://darioradecic.substack.com/)\n> [**Data Doodles with Python | Dario Radecic | Substack**](https://darioradecic.substack.com/)\n_Originally published at_[ _https://darioradecic.substack.com_](https://darioradecic.substack.com/p/how-to-create-custom-color-palettes?r=48ma0u) _._\n* * *\n![](https://assets.insightmediagroup.io/media/1787657095692_93hujy.png)\nWritten By\n## [Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nhttps://writingfordevs.substack.com\n[See all from Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Data Visualization](https://towardsdatascience.com/tag/data-visualization/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Matplotlib](https://towardsdatascience.com/tag/matplotlib/)[Python](https://towardsdatascience.com/tag/python/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-create-custom-color-palettes-in-matplotlib-discrete-vs-linear-colormaps-explained-cfe4c5ba1215%2F&title=How%20to%20Create%20Custom%20Color%20Palettes%20in%20Matplotlib%20-%20Discrete%20vs.%20Linear%20Colormaps%2C%20Explained)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-create-custom-color-palettes-in-matplotlib-discrete-vs-linear-colormaps-explained-cfe4c5ba1215%2F&title=How%20to%20Create%20Custom%20Color%20Palettes%20in%20Matplotlib%20-%20Discrete%20vs.%20Linear%20Colormaps%2C%20Explained)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-create-custom-color-palettes-in-matplotlib-discrete-vs-linear-colormaps-explained-cfe4c5ba1215%2F&text=How%20to%20Create%20Custom%20Color%20Palettes%20in%20Matplotlib%20-%20Discrete%20vs.%20Linear%20Colormaps%2C%20Explained)\n\n\nTowards Data Science is a community publication. 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