{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9336588",
   "metadata": {},
   "source": [
    "# Matplotlib Basics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d74f2d57",
   "metadata": {
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# %pip install matplotlib   ### uncomment to install matplotlib if you don't have it already\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f67915c",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7f9707",
   "metadata": {
    "hide-input": true
   },
   "source": [
    "\n",
    "```{index} Matplotlib\n",
    "```\n",
    "## Introduction\n",
    "\n",
    "Matplotlib is the foundational plotting library in the Python ecosystem, originally developed by John D. Hunter to provide MATLAB-like plotting capabilities in Python {cite}`Hunter_2007`. It offers a rich, programmatic interface for producing publication-quality 2D graphics. For a history and introduction of Matplotlib, see matplotlib.org's [History](https://matplotlib.org/stable/project/history.html) section.\n",
    "\n",
    "For data scientists, Matplotlib is valuable both as a plotting tool and as the underlying engine for fine-grained customization when presentation quality and plot element control matter.\n",
    "\n",
    "Matplotlib sits at a low level in the Python visualization stack:\n",
    "\n",
    "1. Many higher-level libraries, such as Pandas plotting methods and Seaborn, build on top of it.\n",
    "2. It interoperates smoothly with NumPy arrays and the broader scientific Python toolchain.\n",
    "3. It can be used through two main interfaces:\n",
    "   1. The **stateful pyplot API** is convenient for quick, one-off plots.\n",
    "   2. The **object-oriented (OO) API** is the recommended default for most work because it makes figure and axes management explicit.\n",
    "\n",
    "A useful rule of thumb is:\n",
    "\n",
    "- Use `plt.plot()` for quick interactive work.\n",
    "- Use `plt.subplots()` for most real plotting tasks.\n",
    "- Use `fig.add_axes()` when you need manual placement or inset axes.\n",
    "\n",
    "Matplotlib has excellent official documentation and examples at https://matplotlib.org/ (see the [Tutorials](https://matplotlib.org/stable/tutorials/index.html) and [Examples](https://matplotlib.org/stable/gallery/index.html)).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de4e6e58",
   "metadata": {},
   "source": [
    "```{index} installing Matplotlib\n",
    "```\n",
    "### Installation \n",
    "\n",
    "You'll need to install matplotlib first with either using `pip` in the terminal:\n",
    "\n",
    "    pip install matplotlib     ### in terminal with .venv enabled\n",
    "or using `%pip` in the notebook:\n",
    "\n",
    "    %pip install matplotlib    ### in Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f5a10a",
   "metadata": {},
   "source": [
    "Import the `matplotlib.pyplot` module with alias `plt`:\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2daa29",
   "metadata": {},
   "source": [
    "The usual headers for what we are learning so far look like this. The hidden setup cell at the top of this page already runs them; with Live Code active you can install missing packages when needed:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# %pip install matplotlib   ### uncomment to install matplotlib if you don't have it already\n",
    "import matplotlib.pyplot as plt\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36647fd",
   "metadata": {},
   "source": [
    "\n",
    "```{index} stateful interface, object-oriented interface\n",
    "```\n",
    "### Two Styles of Matplotlib Syntax\n",
    "\n",
    "There are two common styles of Matplotlib plotting:\n",
    "\n",
    "1. **Stateful pyplot style**: concise and convenient for quick plots.\n",
    "2. **Object-oriented (OO) style**: more explicit and easier to scale to multi-axes figures.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85edba8",
   "metadata": {},
   "source": [
    "\n",
    "Note on rendering visualizations:\n",
    "\n",
    "- In Jupyter notebooks, plots usually display automatically.\n",
    "- Outside notebooks, you will often need `plt.show()` to display the figure window.\n",
    "- In older Jupyter/IPython setups, `%matplotlib inline` may still be needed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d90e6a",
   "metadata": {},
   "source": [
    "\n",
    "```{index} pyplot, stateful interface\n",
    "```\n",
    "## Stateful Matplotlib\n",
    "\n",
    "Matplotlib has two ways of making plots, and \"stateful\" refers to the older, MATLAB-style one: the pyplot interface (`plt.*` functions) that silently keeps track of a \"current figure\" and \"current axes\" behind the scenes. Each `plt.*` call acts on whatever is current at that moment, so you never hold a reference to the object you're drawing on.\n",
    "\n",
    "These functions, such as `plt.plot()`, `plt.xlabel()`, and `plt.title()`, build a figure step by step. This style is useful for quick exploration, but it becomes harder to manage as figures grow more complex.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1349471",
   "metadata": {},
   "source": [
    "```{index} pyplot commands, pandas plotting\n",
    "```\n",
    "### Basic Matplotlib Commands\n",
    "\n",
    "The table below compares common `plt.plot()` tasks with the corresponding Pandas `df.plot()` usage.\n",
    "\n",
    "| Task     | Matplotlib plt.plot()  | Pandas df.plot()                      | Notes                                                           |\n",
    "|----------|------------------------|---------------------------------------|---------------------------------------------------------------|\n",
    "| figsize  | `plt.figure(figsize)`  | `df.plot(figsize=(m, n))`             | Use `plt.figure()` to setup figure size before `plt.plot()`     |\n",
    "| To plot  | `plt.plot(x, y)`       | `df.plot(x=\"x\", y=\"y\")` or `s.plot()` | Uses DataFrame/Series directly; x defaults to index if omitted. |\n",
    "| X label  | `plt.xlabel(\"X\")`      | `df.plot(xlabel=...)`                 | `df.plot(...)` returns an `Axes`; set labels on it.             |\n",
    "| Y label  | `plt.ylabel(\"Y\")`      | `df.plot(ylabel=...)`                 | Same pattern as label.                                          |\n",
    "| Title    | `plt.title(\"My Plot\")` | `df.plot(title=\"My Plot\")`            | Pandas supports `title=` in `plot`                              |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb4db6a",
   "metadata": {},
   "source": [
    "Pandas `.plot()` is a convenience wrapper around Matplotlib for quick DataFrame visualization. Compared with `plt.plot()`, the main differences are:\n",
    "\n",
    "| Feature | `plt.plot()` | Pandas `.plot()` |\n",
    "|---------|--------------|-------------|\n",
    "| Typical use | Fine-grained, figure-level control | Fast EDA from DataFrame/Series |\n",
    "| Built on | Core matplotlib | Wrapper around matplotlib |\n",
    "| Customization | Full control | Limited, but can access underlying axes |\n",
    "| Data Input | Expects x/y arrays | Pandas columns/index |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41400eb0",
   "metadata": {},
   "source": [
    "```{index} line plot (plt.plot), scatter plot (plt.scatter)\n",
    "```\n",
    "### First Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091717eb",
   "metadata": {},
   "source": [
    "Let's walk through a simple example: six months of revenue for a small retailer, stored in two NumPy arrays. Lists work too, but you'll usually pass NumPy arrays or Pandas columns, which behave like arrays. The data we want to plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab7c72e7",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.066805Z",
     "iopub.status.busy": "2026-08-31T18:39:27.066682Z",
     "iopub.status.idle": "2026-08-31T18:39:27.068732Z",
     "shell.execute_reply": "2026-08-31T18:39:27.068517Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month <class 'numpy.ndarray'>: [1 2 3 4 5 6]\n",
      "revenue <class 'numpy.ndarray'>: [110 104 118 121 125 119]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\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",
    "print(f\"month {type(month)}: {month}\")\n",
    "print(f\"revenue {type(revenue)}: {revenue}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "769a057d",
   "metadata": {},
   "source": [
    "The following code creates a basic line plot:\n",
    "- `plt.figure()`\n",
    "- `plt.plot()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03987ef4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.069927Z",
     "iopub.status.busy": "2026-08-31T18:39:27.069855Z",
     "iopub.status.idle": "2026-08-31T18:39:27.115299Z",
     "shell.execute_reply": "2026-08-31T18:39:27.115018Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3))\n",
    "plt.plot(month, revenue)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ac382f",
   "metadata": {},
   "source": [
    "You can also create a scatter plot with `plt.scatter()`. A scatter plot suits pairs of values, such as each month's ad spend and sales:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cb4d67d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.116659Z",
     "iopub.status.busy": "2026-08-31T18:39:27.116576Z",
     "iopub.status.idle": "2026-08-31T18:39:27.146621Z",
     "shell.execute_reply": "2026-08-31T18:39:27.146386Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ad_spend = np.array([5, 10, 15, 20, 25, 30])        ### monthly ad spend in $K\n",
    "sales = np.array([60, 85, 110, 120, 150, 165])      ### monthly sales in $K\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.scatter(ad_spend, sales)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16ea295",
   "metadata": {},
   "source": [
    "```{index} title, axis label, legend\n",
    "```\n",
    "### Labels/Title/Legend"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560c55bf",
   "metadata": {},
   "source": [
    "Common plot customizations include:\n",
    "\n",
    "- color\n",
    "- label (legend information)\n",
    "- x-axis and y-axis labels\n",
    "- title\n",
    "- legend (built from labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3fb52c6",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.147966Z",
     "iopub.status.busy": "2026-08-31T18:39:27.147887Z",
     "iopub.status.idle": "2026-08-31T18:39:27.187432Z",
     "shell.execute_reply": "2026-08-31T18:39:27.187189Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.plot(month, revenue, 'r')       ### 'r' means color red\n",
    "# plt.plot(month, revenue, c='r')     ### same as above, we saw this in Pandas plot\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.plot(month, revenue, c='red', label='Revenue')   ### label= is what the legend shows\n",
    "plt.xlabel('Month')                  ### in Pandas plot, this is automatic from column names\n",
    "plt.ylabel('Revenue ($K)')           ### in Pandas plot, this is automatic from column names\n",
    "plt.title('Monthly Revenue')         ### in Pandas plot, this is \"title=\"\n",
    "plt.legend()                         ### to show the label we set above\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d554c81",
   "metadata": {},
   "source": [
    "The same plot can be created with Pandas plotting:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab5b7642",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.188778Z",
     "iopub.status.busy": "2026-08-31T18:39:27.188699Z",
     "iopub.status.idle": "2026-08-31T18:39:27.237159Z",
     "shell.execute_reply": "2026-08-31T18:39:27.236850Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'month': month, 'revenue': revenue})   ### DataFrame with two columns\n",
    "\n",
    "# df.plot(x='month', y='revenue', kind=\"line\")   ### line plot is default in Pandas; axes is automatically created\n",
    "# df.plot.line(x='month', y='revenue')           ### same as above\n",
    "# df.plot(x='month', y='revenue')                ### same as above\n",
    "\n",
    "df.plot(x='month', y='revenue', xlabel='Month', ylabel='Revenue ($K)', title='Monthly Revenue', figsize=(4,3))\n",
    "### all labels and title can be set in df.plot() directly or from dataframe column names\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c7896ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.238500Z",
     "iopub.status.busy": "2026-08-31T18:39:27.238427Z",
     "iopub.status.idle": "2026-08-31T18:39:27.240116Z",
     "shell.execute_reply": "2026-08-31T18:39:27.239888Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Basic plt.plot() with Customization\n",
    "#   1. Create week = np.arange(1, 9) and\n",
    "#      visits = np.array([820, 870, 910, 890, 960, 1010, 990, 1080]).\n",
    "#   2. Use plt.figure(figsize=(4, 3)) to set the figure size.\n",
    "#   3. Plot week vs visits in red with the label \"Visits\".\n",
    "#   4. Add an x-label \"Week\", y-label \"Website Visits\", and title \"Weekly Website Visits\".\n",
    "#   5. Call plt.legend() to show the label.\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "795ac12c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.241241Z",
     "iopub.status.busy": "2026-08-31T18:39:27.241176Z",
     "iopub.status.idle": "2026-08-31T18:39:27.270766Z",
     "shell.execute_reply": "2026-08-31T18:39:27.270506Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "week = np.arange(1, 9)\n",
    "visits = np.array([820, 870, 910, 890, 960, 1010, 990, 1080])\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.plot(week, visits, 'r-', label='Visits')\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Website Visits')\n",
    "plt.title('Weekly Website Visits')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e0c22c",
   "metadata": {},
   "source": [
    "\n",
    "```{index} subplot (plt.subplot)\n",
    "```\n",
    "## Multiple Plots with `plt.subplot()`\n",
    "\n",
    "`plt.subplot()` is the older pyplot-style way to place multiple axes in one figure. It is still useful to recognize, but for new code you will usually prefer `plt.subplots()`, which is clearer and fits the OO workflow better.\n",
    "\n",
    "Syntax:\n",
    "\n",
    "`plt.subplot(nrows, ncols, plot_number)`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73852272",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.310143Z",
     "iopub.status.busy": "2026-08-31T18:39:27.310069Z",
     "iopub.status.idle": "2026-08-31T18:39:27.360218Z",
     "shell.execute_reply": "2026-08-31T18:39:27.359983Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plt.subplot(nrows, ncols, plot_number)   ### when you want multiple plots in one figure\n",
    "### order is important, plot after subplot\n",
    "month = np.arange(1, 7)\n",
    "revenue = np.array([110, 104, 118, 121, 125, 119])   ### $K\n",
    "units = np.array([540, 510, 580, 600, 620, 590])     ### units sold\n",
    "\n",
    "plt.figure(figsize=(8,3))          ### create a figure first with specified size\n",
    "\n",
    "plt.subplot(1, 2, 1)               ### subplot (axes) 1 of 1 row, 2 columns\n",
    "plt.plot(month, revenue, 'r--')    ### 'r--' means red dashed line\n",
    "plt.title('Revenue ($K)')\n",
    "\n",
    "plt.subplot(1, 2, 2)               ### subplot (axes) 2 of 1 row, 2 columns\n",
    "plt.plot(month, units, 'g*-')      ### 'g*-' means green line with star markers\n",
    "plt.title('Units Sold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "573b0b73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.361469Z",
     "iopub.status.busy": "2026-08-31T18:39:27.361389Z",
     "iopub.status.idle": "2026-08-31T18:39:27.363026Z",
     "shell.execute_reply": "2026-08-31T18:39:27.362837Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Exercise: Multiple Subplots with plt.subplot()\n",
    "#   1. Create month = np.arange(1, 7),\n",
    "#      online = np.array([82, 90, 97, 105, 118, 126]), and\n",
    "#      in_store = np.array([140, 136, 131, 129, 124, 120]).\n",
    "#   2. Use plt.figure(figsize=(8, 3)) to set the figure size.\n",
    "#   3. In subplot position (1, 2, 1), plot month vs online with 'b-' and title \"Online Sales\".\n",
    "#   4. In subplot position (1, 2, 2), plot month vs in_store with 'g--' and title \"In-Store Sales\".\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d97fb69d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T18:39:27.364263Z",
     "iopub.status.busy": "2026-08-31T18:39:27.364184Z",
     "iopub.status.idle": "2026-08-31T18:39:27.426644Z",
     "shell.execute_reply": "2026-08-31T18:39:27.426415Z"
    },
    "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",
    "online = np.array([82, 90, 97, 105, 118, 126])\n",
    "in_store = np.array([140, 136, 131, 129, 124, 120])\n",
    "\n",
    "plt.figure(figsize=(8, 3))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(month, online, 'b-')\n",
    "plt.title('Online Sales')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(month, in_store, 'g--')\n",
    "plt.title('In-Store Sales')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d7acfa",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- Matplotlib is the plotting library underneath pandas and Seaborn charts. Import its plotting module as `plt` with `import matplotlib.pyplot as plt`.\n",
    "- Matplotlib has two styles. The stateful pyplot style (`plt.plot()`, `plt.title()`) acts on whatever figure and axes are current; the object-oriented style keeps the figure and axes in variables and is covered in the next section.\n",
    "- `plt.plot(x, y)` draws a line plot and `plt.scatter(x, y)` draws a scatter plot. `color`/`c`, `label`, `plt.xlabel()`, `plt.ylabel()`, `plt.title()`, and `plt.legend()` turn a default plot into a readable chart.\n",
    "- Pandas `df.plot()` is a wrapper around Matplotlib: the same chart can be drawn with `xlabel=`, `ylabel=`, and `title=` arguments.\n",
    "- `plt.subplot(nrows, ncols, index)` places charts in a grid one at a time. For new code, prefer `plt.subplots()`, introduced next."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1beb38d7",
   "metadata": {},
   "source": [
    "## Further Reading\n",
    "\n",
    "- [Pyplot tutorial](https://matplotlib.org/stable/tutorials/pyplot.html): the stateful interface used in this section, with more customization options.\n",
    "- [Quick start guide](https://matplotlib.org/stable/users/explain/quick_start.html): the parts of a figure and a side-by-side view of the pyplot and object-oriented styles.\n",
    "- [History of Matplotlib](https://matplotlib.org/stable/project/history.html): why Matplotlib was built and how it grew."
   ]
  }
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