{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ch06-figures-axes-title",
   "metadata": {},
   "source": [
    "# Figures and Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ch06-shared-setup",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:28.702253Z",
     "iopub.status.busy": "2026-08-31T18:39:28.702001Z",
     "iopub.status.idle": "2026-08-31T18:39:29.574543Z",
     "shell.execute_reply": "2026-08-31T18:39:29.574285Z"
    },
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66cc8e47",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ch06-subplots-parent",
   "metadata": {},
   "source": [
    "```{index} object-oriented interface, figure, axes\n",
    "```\n",
    "## Object-Oriented Plotting with `plt.subplots()`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47b2307",
   "metadata": {},
   "source": [
    "\n",
    "::: {note}\n",
    "But what's the difference between a **figure** and an **axes**?\n",
    "\n",
    "| | **Figure** | **Axes** |\n",
    "|---|---|---|\n",
    "| Analogy | The full canvas/window | One plot region on that canvas |\n",
    "| What it is | The top-level container | The area where data is drawn |\n",
    "| Contains | One or more Axes | X/Y axis, ticks, lines, labels, title, legend |\n",
    "| Commonly created by | `plt.figure()` or `plt.subplots()` | returned by `plt.subplots()` or added to a figure |\n",
    "\n",
    "A good mental model is:\n",
    "\n",
    "```text\n",
    "Figure\n",
    "└── Axes (a.k.a. one plot)\n",
    "    ├── XAxis\n",
    "    ├── YAxis\n",
    "    ├── Title\n",
    "    ├── Lines / Bars / etc.\n",
    "    └── Legend\n",
    "```\n",
    ":::\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9edf2ba9",
   "metadata": {},
   "source": [
    "\n",
    "Here we will learn two OO workflows:\n",
    "\n",
    "- `plt.subplots()` for the standard, everyday workflow\n",
    "- `plt.figure()` + `fig.add_axes()` for manual placement and inset-style layouts\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bd6ab7",
   "metadata": {},
   "source": [
    "\n",
    "```{index} subplots (plt.subplots), tight layout (tight_layout), figure title (suptitle), shared axis (sharey)\n",
    "```\n",
    "### `plt.subplots()`\n",
    "\n",
    "`plt.subplots()` creates the **figure** and **axes** objects in one step and returns them as a tuple.\n",
    "\n",
    "- If you create a single subplot, you typically get one `Axes` object.\n",
    "- If you create multiple subplots, you get a NumPy array of `Axes` objects.\n",
    "\n",
    "Typical syntax:\n",
    "\n",
    "`fig, ax = plt.subplots()`\n",
    "\n",
    "The example below shows the standard one-axes case.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8eb7c447",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.576502Z",
     "iopub.status.busy": "2026-08-31T18:39:29.576367Z",
     "iopub.status.idle": "2026-08-31T18:39:29.629799Z",
     "shell.execute_reply": "2026-08-31T18:39:29.629548Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = np.arange(1, 7)                               ### months 1 to 6 (Jan to Jun)\n",
    "revenue = np.array([110, 104, 118, 121, 125, 119])    ### monthly revenue in $K\n",
    "\n",
    "# fig, ax = plt.subplots()              ### single Axes case\n",
    "# fig, axes = plt.subplots(1, 2)        ### multiple Axes case\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,3))   ### unpacking in one step\n",
    "ax.plot(month, revenue, 'g--')          ### plot on that axes\n",
    "\n",
    "### common axes methods\n",
    "ax.set_xlabel('Month')\n",
    "ax.set_ylabel('Revenue ($K)')\n",
    "ax.set_title('Monthly Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c28105",
   "metadata": {},
   "source": [
    "With `subplots()`, you can specify the number of rows and columns using the `nrows=` and `ncols=` parameters when creating the subplots() object. The example below creates an empty canvas of 1-row by 2-cols subplots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6efedeca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.631121Z",
     "iopub.status.busy": "2026-08-31T18:39:29.631041Z",
     "iopub.status.idle": "2026-08-31T18:39:29.675848Z",
     "shell.execute_reply": "2026-08-31T18:39:29.675626Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6, 2))  ### similar to plt.subplot(1, 2, ith axes)\n",
    "# fig, axes = plt.subplots(1, 2)            ### same as above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7148fd4",
   "metadata": {},
   "source": [
    "In this example above, `axes` is an array variable containing two `Axes` objects. Looping over that array is a common pattern when you want to apply the same formatting to each subplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccca6e56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.677327Z",
     "iopub.status.busy": "2026-08-31T18:39:29.677247Z",
     "iopub.status.idle": "2026-08-31T18:39:29.679043Z",
     "shell.execute_reply": "2026-08-31T18:39:29.678854Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<Axes: > <Axes: >]\n"
     ]
    }
   ],
   "source": [
    "print(axes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41162bb3",
   "metadata": {},
   "source": [
    "Now we have access over the two Axes individually using **indexing** (`[0] and [1]`). We may plot them separately. Plotting functions from pandas and Seaborn (Chapter 07) also accept an `ax=` argument, so you can draw their charts into one of these panels, for example `df.plot(ax=axes[0])`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c39215d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.680755Z",
     "iopub.status.busy": "2026-08-31T18:39:29.680668Z",
     "iopub.status.idle": "2026-08-31T18:39:29.742362Z",
     "shell.execute_reply": "2026-08-31T18:39:29.742066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = np.arange(1, 7)\n",
    "store_a = np.array([62, 58, 66, 70, 73, 69])          ### monthly sales in $K\n",
    "store_b = np.array([48, 51, 47, 55, 58, 60])\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6, 2))\n",
    "\n",
    "axes[0].plot(month, store_a, 'r--')    ### plot on the first axes\n",
    "axes[0].set_title('Store A')           ### set title for the first axes\n",
    "\n",
    "axes[1].plot(month, store_b, 'g*-')    ### plot on the second axes\n",
    "axes[1].set_title('Store B')           ### set title for the second axes\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b9da12",
   "metadata": {},
   "source": [
    "It's not uncommon to loop through the Axes object then fine tune the plots. `zip()` pairs each axes with its data and title, so one loop formats both charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d790301",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.744079Z",
     "iopub.status.busy": "2026-08-31T18:39:29.743972Z",
     "iopub.status.idle": "2026-08-31T18:39:29.792792Z",
     "shell.execute_reply": "2026-08-31T18:39:29.792547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = np.arange(1, 7)\n",
    "store_a = np.array([62, 58, 66, 70, 73, 69])          ### monthly sales in $K\n",
    "store_b = np.array([48, 51, 47, 55, 58, 60])\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6, 2))\n",
    "\n",
    "for ax, sales, name in zip(axes, [store_a, store_b], ['Store A', 'Store B']):\n",
    "    ax.plot(month, sales, 'b')\n",
    "    ax.set_xlabel('Month')\n",
    "    ax.set_ylabel('Sales ($K)')\n",
    "    ax.set_title(name)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6bbdd3f",
   "metadata": {},
   "source": [
    "\n",
    "A common issue in Matplotlib is overlapping subplot labels or titles. `plt.tight_layout()` can automatically adjust spacing so the figure is easier to read. (compare with the plots above)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b72224b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.794405Z",
     "iopub.status.busy": "2026-08-31T18:39:29.794300Z",
     "iopub.status.idle": "2026-08-31T18:39:29.855732Z",
     "shell.execute_reply": "2026-08-31T18:39:29.855502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = np.arange(1, 7)\n",
    "store_a = np.array([62, 58, 66, 70, 73, 69])          ### monthly sales in $K\n",
    "store_b = np.array([48, 51, 47, 55, 58, 60])\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6, 2))\n",
    "\n",
    "for ax, sales, name in zip(axes, [store_a, store_b], ['Store A', 'Store B']):\n",
    "    ax.plot(month, sales, 'g--')\n",
    "    ax.set_xlabel('Month')\n",
    "    ax.set_ylabel('Sales ($K)')\n",
    "    ax.set_title(name)\n",
    "\n",
    "plt.tight_layout()                     ### fix overlapping labels and titles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38aac65",
   "metadata": {},
   "source": [
    "Each `ax.set_title()` names one axes. To give the whole figure one title, use `fig.suptitle()`, which places the title above all the subplots. This is common in dashboards, where several charts support one message. The x values here are quarter labels rather than numbers; Matplotlib places text labels along the axis in the order given. `sharey=True` gives every subplot the same y-axis range. Without it, each subplot scales to its own data, and a small region can look as large as a big one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c56be00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "north = [150, 162, 158, 171]\n",
    "south = [120, 118, 131, 127]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharey=True)   ### sharey: same y scale for a fair comparison\n",
    "\n",
    "axes[0].plot(quarters, north, marker='o')\n",
    "axes[0].set_title('North')\n",
    "\n",
    "axes[1].plot(quarters, south, marker='o')\n",
    "axes[1].set_title('South')\n",
    "\n",
    "fig.suptitle('Quarterly Revenue by Region ($K)')   ### one title for the whole figure\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e205fa13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.857020Z",
     "iopub.status.busy": "2026-08-31T18:39:29.856927Z",
     "iopub.status.idle": "2026-08-31T18:39:29.858570Z",
     "shell.execute_reply": "2026-08-31T18:39:29.858341Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Side-by-Side Subplots with plt.subplots()\n",
    "#   1. Create month = np.arange(1, 7),\n",
    "#      east = np.array([95, 99, 104, 101, 110, 116]), and\n",
    "#      west = np.array([70, 68, 74, 79, 77, 83]).\n",
    "#   2. Use plt.subplots(1, 2, figsize=(8, 3)) to create a figure and two axes.\n",
    "#   3. On axes[0], plot month vs east with 'b-', title \"East\",\n",
    "#      x-label \"Month\", and y-label \"Revenue ($K)\".\n",
    "#   4. On axes[1], plot month vs west with 'r--', title \"West\",\n",
    "#      x-label \"Month\", and y-label \"Revenue ($K)\".\n",
    "#   5. Call plt.tight_layout() at the end.\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f50b512f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.859730Z",
     "iopub.status.busy": "2026-08-31T18:39:29.859657Z",
     "iopub.status.idle": "2026-08-31T18:39:29.919328Z",
     "shell.execute_reply": "2026-08-31T18:39:29.919091Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "month = np.arange(1, 7)\n",
    "east = np.array([95, 99, 104, 101, 110, 116])\n",
    "west = np.array([70, 68, 74, 79, 77, 83])\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
    "axes[0].plot(month, east, 'b-')\n",
    "axes[0].set_title('East')\n",
    "axes[0].set_xlabel('Month')\n",
    "axes[0].set_ylabel('Revenue ($K)')\n",
    "axes[1].plot(month, west, 'r--')\n",
    "axes[1].set_title('West')\n",
    "axes[1].set_xlabel('Month')\n",
    "axes[1].set_ylabel('Revenue ($K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a50a6b2",
   "metadata": {},
   "source": [
    "\n",
    "```{index} add axes (add_axes), inset axes\n",
    "```\n",
    "## Manual Placement with `add_axes()`\n",
    "\n",
    "`fig.add_axes()` is a more manual OO technique than `plt.subplots()`. It is most useful when you want precise axes placement, such as inset plots or custom layouts.\n",
    "\n",
    "The process of using `add_axes()` to create plots is:\n",
    "\n",
    "1. Create a **Figure** object.\n",
    "2. Add one or more **Axes** using normalized figure coordinates `[left, bottom, width, height]`.\n",
    "3. Plot and format each axes separately.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1483be4",
   "metadata": {},
   "source": [
    "`add_axes()` uses normalized figure coordinates. For example, `[0.1, 0.1, 0.8, 0.8]` means:\n",
    "\n",
    "- left edge at 10% of the figure width\n",
    "- bottom edge at 10% of the figure height\n",
    "- axes width equal to 80% of the figure width\n",
    "- axes height equal to 80% of the figure height\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31d4640b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.279750Z",
     "iopub.status.busy": "2026-08-31T18:39:33.279669Z",
     "iopub.status.idle": "2026-08-31T18:39:33.307685Z",
     "shell.execute_reply": "2026-08-31T18:39:33.307405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,3))             ### 1. create an empty figure\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])     ### 2. add one axes using normalized coordinates\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d14412",
   "metadata": {},
   "source": [
    "\n",
    "While slightly more involved, `add_axes()` gives you precise control over where each axes sits in the figure. That makes it a good choice for inset plots and manually arranged layouts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c09414a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.308903Z",
     "iopub.status.busy": "2026-08-31T18:39:33.308835Z",
     "iopub.status.idle": "2026-08-31T18:39:33.372788Z",
     "shell.execute_reply": "2026-08-31T18:39:33.372539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day = np.arange(1, 31)                  ### days in June\n",
    "daily_sales = np.array([41, 39, 43, 40, 42, 44, 38, 41, 45, 43,\n",
    "                        40, 42, 44, 46, 61, 68, 72, 75, 70, 66,\n",
    "                        63, 47, 44, 42, 45, 43, 41, 44, 46, 42])   ### $K; promotion on days 15-21\n",
    "\n",
    "fig = plt.figure(figsize=(4,3))         ### create empty canvas on the figure (object)\n",
    "\n",
    "### add two axes to the figure\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])     ### main axes\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.25, 0.3])    ### inset axes, over the flat early days\n",
    "\n",
    "### main axes: the whole month\n",
    "axes1.plot(day, daily_sales, 'b')\n",
    "axes1.set_xlabel('Day')\n",
    "axes1.set_ylabel('Sales ($K)')\n",
    "axes1.set_title('Daily Sales in June')\n",
    "\n",
    "### inset axes: zoom in on the promotion week\n",
    "axes2.plot(day[14:21], daily_sales[14:21], 'r')\n",
    "axes2.set_title('Promotion Week')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8dec854b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.373963Z",
     "iopub.status.busy": "2026-08-31T18:39:33.373893Z",
     "iopub.status.idle": "2026-08-31T18:39:33.375396Z",
     "shell.execute_reply": "2026-08-31T18:39:33.375191Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Figure with Main Plot and Inset using plt.figure()\n",
    "#   1. Create month = np.arange(1, 7),\n",
    "#      units = np.array([540, 510, 580, 600, 620, 590]), and\n",
    "#      returns = np.array([22, 25, 19, 18, 21, 17]).\n",
    "#   2. Create a figure with figsize=(4, 3).\n",
    "#   3. Add a main axes at [0.1, 0.1, 0.8, 0.8] and plot month vs units in blue.\n",
    "#      Set the title to \"Units Sold\".\n",
    "#   4. Add an inset axes at [0.55, 0.15, 0.3, 0.3] and plot month vs returns in red.\n",
    "#      Set the title to \"Returns\".\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2de8b254",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.376433Z",
     "iopub.status.busy": "2026-08-31T18:39:33.376352Z",
     "iopub.status.idle": "2026-08-31T18:39:33.426252Z",
     "shell.execute_reply": "2026-08-31T18:39:33.426024Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "month = np.arange(1, 7)\n",
    "units = np.array([540, 510, 580, 600, 620, 590])\n",
    "returns = np.array([22, 25, 19, 18, 21, 17])\n",
    "\n",
    "fig = plt.figure(figsize=(4, 3))\n",
    "main_ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "main_ax.plot(month, units, color='blue')\n",
    "main_ax.set_title('Units Sold')\n",
    "inset_ax = fig.add_axes([0.55, 0.15, 0.3, 0.3])\n",
    "inset_ax.plot(month, returns, color='red')\n",
    "inset_ax.set_title('Returns')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c2ffa8",
   "metadata": {},
   "source": [
    "```{index} figure-level settings\n",
    "```\n",
    "## Figure Size, Resolution, and Saving\n",
    "\n",
    "Three figure-level settings decide how a chart looks outside the notebook: its size in inches, its resolution in dots per inch, and the file it is saved to. These matter most when a chart goes into a report or slide deck.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e85c46",
   "metadata": {},
   "source": [
    "```{index} figure size (figsize)\n",
    "```\n",
    "### `figsize=`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967742f7",
   "metadata": {},
   "source": [
    "Figure size and DPI are two of the most common figure-level controls.\n",
    "\n",
    "- `figsize=(width, height)` uses inches.\n",
    "\n",
    "This can be set with `plt.figure(...)` or `plt.subplots(...)`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75f140d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:32.154505Z",
     "iopub.status.busy": "2026-08-31T18:39:32.154430Z",
     "iopub.status.idle": "2026-08-31T18:39:32.195679Z",
     "shell.execute_reply": "2026-08-31T18:39:32.195459Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = np.arange(1, 7)                               ### months 1 to 6 (Jan to Jun)\n",
    "revenue = np.array([110, 104, 118, 121, 125, 119])    ### monthly revenue in $K\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(4,3))\n",
    "\n",
    "### peripherals for the axes object\n",
    "axes.plot(month, revenue, 'r')\n",
    "axes.set_xlabel('Month')\n",
    "axes.set_ylabel('Revenue ($K)')\n",
    "axes.set_title('Monthly Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9289006c",
   "metadata": {},
   "source": [
    "```{index} DPI, resolution\n",
    "```\n",
    "### DPI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb99562d",
   "metadata": {},
   "source": [
    "- `dpi` sets dots per inch, which affects rendered resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb363303",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.485961Z",
     "iopub.status.busy": "2026-08-31T18:39:33.485879Z",
     "iopub.status.idle": "2026-08-31T18:39:33.511868Z",
     "shell.execute_reply": "2026-08-31T18:39:33.511628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 150x50 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = np.arange(1, 7)                               ### months 1 to 6 (Jan to Jun)\n",
    "revenue = np.array([110, 104, 118, 121, 125, 119])    ### monthly revenue in $K\n",
    "\n",
    "fig = plt.figure(figsize=(3,1), dpi=50)          ### low resolution\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "axes.plot(month, revenue, 'r')\n",
    "axes.set_xlabel('Month')\n",
    "axes.set_ylabel('$K')\n",
    "axes.set_title('Monthly Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4c3791d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.513129Z",
     "iopub.status.busy": "2026-08-31T18:39:33.513052Z",
     "iopub.status.idle": "2026-08-31T18:39:33.541990Z",
     "shell.execute_reply": "2026-08-31T18:39:33.541756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month = np.arange(1, 7)                               ### months 1 to 6 (Jan to Jun)\n",
    "revenue = np.array([110, 104, 118, 121, 125, 119])    ### monthly revenue in $K\n",
    "\n",
    "fig = plt.figure(figsize=(3,1), dpi=100)         ### same size, twice the resolution\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "axes.plot(month, revenue, 'r')\n",
    "axes.set_xlabel('Month')\n",
    "axes.set_ylabel('$K')\n",
    "axes.set_title('Monthly Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d91b743a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.543286Z",
     "iopub.status.busy": "2026-08-31T18:39:33.543208Z",
     "iopub.status.idle": "2026-08-31T18:39:33.544707Z",
     "shell.execute_reply": "2026-08-31T18:39:33.544513Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Setting Figure Size and DPI\n",
    "#   1. Create month = np.arange(1, 7) and\n",
    "#      revenue = np.array([110, 104, 118, 121, 125, 119]).\n",
    "#   2. Create a figure with figsize=(4, 3) and dpi=150.\n",
    "#   3. Add axes using fig.add_axes([0.1, 0.1, 0.8, 0.8]).\n",
    "#   4. Plot month vs revenue with a green line.\n",
    "#   5. Set the title to \"Monthly Revenue\".\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "815d1844",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:33.545712Z",
     "iopub.status.busy": "2026-08-31T18:39:33.545644Z",
     "iopub.status.idle": "2026-08-31T18:39:33.586148Z",
     "shell.execute_reply": "2026-08-31T18:39:33.585911Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "month = np.arange(1, 7)\n",
    "revenue = np.array([110, 104, 118, 121, 125, 119])\n",
    "\n",
    "fig = plt.figure(figsize=(4, 3), dpi=150)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "ax.plot(month, revenue, color='green')\n",
    "ax.set_title('Monthly Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcde5f9e",
   "metadata": {},
   "source": [
    "```{index} saving figures (savefig)\n",
    "```\n",
    "### Saving Figures\n",
    "\n",
    "Matplotlib can save high-quality output in formats such as PNG, JPG, SVG, and PDF.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e80ed4c",
   "metadata": {},
   "source": [
    "Use the figure's `.savefig()` method. The file type follows the extension, such as `.png`, `.pdf`, or `.svg`. Use `dpi=` to set the resolution of the saved file: the default is about 100 dots per inch, which looks fine on screen, while 200 to 300 is typical for printed reports. In the example below, we create a small figure explicitly before saving it:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6ab424f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:29.992689Z",
     "iopub.status.busy": "2026-08-31T18:39:29.992592Z",
     "iopub.status.idle": "2026-08-31T18:39:30.020170Z",
     "shell.execute_reply": "2026-08-31T18:39:30.019942Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: ../../_build/generated/monthly_revenue.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "output_dir = '../../_build/generated'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "output_file = os.path.join(output_dir, 'monthly_revenue.png')\n",
    "\n",
    "month = np.arange(1, 7)                               ### months 1 to 6 (Jan to Jun)\n",
    "revenue = np.array([110, 104, 118, 121, 125, 119])    ### monthly revenue in $K\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 3))\n",
    "ax.plot(month, revenue)\n",
    "ax.set_title('Monthly Revenue')\n",
    "\n",
    "fig.savefig(output_file, dpi=200)     ### dpi= sets the resolution of the saved file\n",
    "print(f\"Saved: {output_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9ed7b52d",
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Save a Chart for a Report\n",
    "#   1. Create quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"] and revenue = [120, 135, 128, 150].\n",
    "#   2. Use fig, ax = plt.subplots(figsize=(6, 3)) and plot quarters vs revenue\n",
    "#      with circle markers (marker='o').\n",
    "#   3. Set the title \"Quarterly Revenue\".\n",
    "#   4. Save the figure as \"quarterly_revenue.png\" with dpi=150.\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "75b6e41e",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "revenue = [120, 135, 128, 150]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "ax.plot(quarters, revenue, marker='o')\n",
    "ax.set_title(\"Quarterly Revenue\")\n",
    "fig.savefig(\"quarterly_revenue.png\", dpi=150)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb7a95e",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- A **figure** is the whole canvas; an **axes** is one chart on it. Most formatting is done with axes methods such as `ax.set_title()`, `ax.set_xlabel()`, and `ax.set_ylabel()`.\n",
    "- `fig, ax = plt.subplots()` creates one chart. `fig, axes = plt.subplots(nrows, ncols)` returns an array of axes that you can index (`axes[0]`) or loop over with `zip()`.\n",
    "- `plt.tight_layout()` fixes overlapping labels, `fig.suptitle()` titles the whole figure, and `sharey=True` puts subplots on the same scale so they compare fairly.\n",
    "- `fig.add_axes([left, bottom, width, height])` places an axes by fractions of the figure, which is how inset charts are made.\n",
    "- `figsize=` sets size in inches and `dpi=` sets resolution. `fig.savefig(\"file.png\", dpi=200)` saves a chart for a report; the file extension sets the format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "171f5aaa",
   "metadata": {},
   "source": [
    "## Further Reading\n",
    "\n",
    "- [Arranging multiple Axes in a Figure](https://matplotlib.org/stable/users/explain/axes/arranging_axes.html): grids, shared axes, and more layout options than `plt.subplots()`.\n",
    "- [`Figure.add_axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.add_axes.html): the full reference for manual axes placement.\n",
    "- [`Figure.savefig`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.savefig.html): file formats, resolution, transparent backgrounds, and cropping options."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.13.7)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
