{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Dario Radečić",
  "title_listed": "3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready",
  "keywords": "Matplotlib,可视化",
  "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/3-key-tweaks-that-will-make-your-matplotlib-charts-publication-ready-18e41eaac589/",
  "publish_date": "2024-08",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "ae366926a1a3",
  "collected_at": "2026-10-01T20:54:41",
  "title": "3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready | 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# 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready\nMatplotlib charts are an eyesore by default - here's what to do about it.\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nAugust 8, 20244 min read\n![Article thumbnail \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1ROFoeqwdhTpxmaAo0ZkMBw.png)\nArticle thumbnail (image by author)\nData visualization offers much deeper insights than looking at raw, numerical data.\nHowever, creating appealing charts takes time and effort. Matplotlib is a de facto standard library for data visualization in Python. It's simple, has been used for decades, and anything you're looking for is one web search away.\nBut it's not all sunshine and rainbows. **Matplotlib visualizations look horrendous by default** , and you as a data professional will have to turn many cogs to get something usable. Getting you there is the goal of today's article.\nBy the end, you'll have a code snippet you can stick to any Jupyter Notebook.\n## What's Wrong with Matplotlib's Default Styles?\nYou won't need to download any dataset to follow along. You'll create a synthetic time series dataset with increasing trend and repeatable seasonal patterns:\npython\n\n```\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Single season multiplier factors - for seasonality effect\nseasonal_multipliers = [1.1, 1.3, 1.2, 1.5, 1.9, 2.3, 2.1, 2.8, 2.0, 1.7, 1.5, 1.2]\n# Immitate 10 years of data\nxs = np.arange(1, 121)\n\ntime_series = []\n# Split to 10 chunks - 1 year each\nfor chunk in np.split(xs, 10):\n    for i, val in enumerate(chunk):\n        # Multiply value with seasonal scalar\n        time_series.append(float(val * seasonal_multipliers[i]))\n\nx = pd.date_range(start=\"2015-01-01\", freq=\"MS\", periods=120)\ny = time_series      \n\nprint(x[-10:])\nprint(y[-10:])\n```\n\n![Image 1 - Time series data \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1nRZscGFbYPVi8g8nO1w61A.png)\nImage 1 - Time series data (image by author)\nSince the `dataset` has dates for an index, and float values for the only attribute, you can plot the entire thing directly via `plt.plot()`:\npython\n\n```\nplt.figure(figsize=(9, 6))\nplt.plot(x, y)\nplt.show()\n```\n\n![Image 2 - Default matplotlib chart \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1KhQVI_2JZRVDAtzvAubBLA.png)\nImage 2 - Default matplotlib chart (image by author)\n**Everything about it screams 2002**. Low resolution. The surrounding box. The font size.\nNothing a couple of tweaks can't fix.\n## Tweak #1 - Adjust rcParams to Set the Overall Theme\nTweaking every chart by hand is a sure way to waste your time.\nAfter all, most charts you make will have an underlying **theme**. It makes sense to declare it once and reuse it everywhere. That's the role of `rcParams`.\nThe following code snippet changes a whole bunch of them and ensure your charts are rendered as SVGs. This last bit won't matter if you explicitly save your charts to disk, but it will make a huge difference in a notebook environment:\npython\n\n```\nimport matplotlib_inline\nmatplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n\nplt.rcParams.update({\n    \"figure.figsize\": (9, 6),\n    \"axes.spines.top\": False,\n    \"axes.spines.right\": False,\n    \"font.size\": 14,\n    \"figure.titlesize\": \"xx-large\",\n    \"xtick.labelsize\": \"medium\",\n    \"ytick.labelsize\": \"medium\",\n    \"axes.axisbelow\": True\n})\n```\n\nAnd now, when you repeat the call to `plt.plot()`, the chart will look somewhat presentable:\npython\n\n```\nplt.plot(x, y)\nplt.title(\"Sales Over Time\")\nplt.xlabel(\"Time Period\")\nplt.ylabel(\"Sales in 000\")\nplt.show()\n```\n\n![Image 3 - Adjusting chart theme \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1dfK0ItUboRpGszLUerboRA.png)\nImage 3 - Adjusting chart theme (image by author)\nNot quite there yet, but the idea was just to set an underlying theme. You shouldn't include chart-specific instructions in `rcParams`.\n## Tweak #2 - Bring the Font to the 21st Century\nAnother thing you can change in `rcParams` is the font.\nYou can download any TTF font from the internet, and load it via Matplotlib's `font_manager`. I'll use [Roboto Condensed](https://fonts.google.com/specimen/Roboto+Condensed), but feel free to go with anything you like:\npython\n\n```\nimport matplotlib.font_manager as font_manager\n\nfont_dir = [\"/path/to/Roboto_Condensed\"]\nfor font in font_manager.findSystemFonts(font_dir):\n    font_manager.fontManager.addfont(font)\n\nplt.rcParams.update({\n    \"font.family\": \"Roboto Condensed\"\n})\n```\n\nTo verify the font has changed, simply rerun the plotting snippet from earlier:\npython\n\n```\nplt.plot(x, y)\nplt.title(\"Sales Over Time\")\nplt.xlabel(\"Time Period\")\nplt.ylabel(\"Sales in 000\")\nplt.show()\n```\n\n![Image 4 - Changing the font \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1-uZUec_6Um5HqwpigFMGuA.png)\nImage 4 - Changing the font (image by author)\nAnd that's about all we'll discuss regarding overall theme changes. Up next, let's get specific.\n## Tweak #3 - Make Micro Adjustment Specific to Your Chart Type\nDifferent chart types will have different go-to approaches when it comes to fine-tuning.\nFor line charts, you can change the line color and width, and potentially even add a filled area section to make the chart look more dashboardy.\nThe other changes made in the below snippet are purely cosmetic - title location and y-axis limit:\npython\n\n```\n# 1. Line color and width\nplt.plot(x, y, color=\"#1C3041\", linewidth=2)\n# 2. Add shaded area below the line\nplt.fill_between(x, y, color=\"#1C3041\", alpha=0.3)\n# 3. Change title location and font weight\nplt.title(\"Sales Over Time\", loc=\"left\", fontdict={\"weight\": \"bold\"}, y=1.06)\n\nplt.xlabel(\"Time Period\")\nplt.ylabel(\"Sales in 000\")\nplt.show()\n```\n\n![Image 5 - Finalized chart \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/08/1LydYpqOD7Js29feVqkYP1g.png)\nImage 5 - Finalized chart (image by author)\nNow this is almost a publication-ready visualization!\nA couple of specific made all the difference, but that wouldn't be possible without setting a strong foundation.\n## Wrapping Up\nMany data professionals disregard Matplotlib entirely because of how it looks by default.\nThey think \"It's blurry and awful, I can't send a visual like this to my boss.\" The reality couldn't be further away from the truth. You can change a bunch of parameters and end up with a code block you can bring anywhere - to every script, notebook, and environment.\nI encourage you to play around with the parameters to further personalize the overall look and feel, and share your preferences 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/you-need-to-fix-these-3-things-right) _._\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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