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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Date axis\n\nLoad a numpy record array from yahoo csv data with fields date, open, close,\nvolume, adj_close from the mpl-data/example directory. The record array\nstores the date as an np.datetime64 with a day unit ('D') in the date column.\n"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport matplotlib.cbook as cbook\nimport matplotlib\n\nyears = mdates.YearLocator()  # every year\nmonths = mdates.MonthLocator()  # every month\nyearsFmt = mdates.DateFormatter(\"%Y\")\n\n\nif matplotlib.__version__ < \"3.3.0\":\n    with cbook.get_sample_data(\"goog.npz\") as datafile:\n        r = np.load(datafile)\nelse:\n    r = cbook.get_sample_data(\"goog.npz\", np_load=True)\n\nr = r[\"price_data\"].view(np.recarray)\n\nfig, ax = plt.subplots()\nax.plot(r.date, r.adj_close)\n\n# format the ticks\nax.xaxis.set_major_locator(years)\nax.xaxis.set_major_formatter(yearsFmt)\nax.xaxis.set_minor_locator(months)\n\n# round to nearest years...\ndatemin = np.datetime64(r.date[0], \"Y\")\ndatemax = np.datetime64(r.date[-1], \"Y\") + np.timedelta64(1, \"Y\")\nax.set_xlim(datemin, datemax)\n\n\n# format the coords message box\ndef price(x):\n    return \"$%1.2f\" % x\n\n\nax.format_xdata = mdates.DateFormatter(\"%Y-%m-%d\")\nax.format_ydata = price\nax.grid(True)\n\n# rotates and right aligns the x labels, and moves the bottom of the\n# axes up to make room for them\nfig.autofmt_xdate()\n\nplt.show()"
      ]
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