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   "source": [
    "# Exercise 4.1: Long-term trends in hybridization of Darwin finches\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Peter and Rosemary Grant](https://en.wikipedia.org/wiki/Peter_and_Rosemary_Grant) have been working on the Galápagos island of Daphne Major for over forty years.  During this time, they have collected lots and lots of data about physiological features of finches.  In 2014, they published a book with a summary of some of their major results (Grant P. R., Grant B. R., *40 years of evolution. Darwin's finches on Daphne Major Island*, Princeton University Press, 2014). They made their data from the book publicly available via the [Dryad Digital Repository](http://dx.doi.org/10.5061/dryad.g6g3h).\n",
    "\n",
    "We will investigate their measurements of beak depth (the distance, top to bottom, of a closed beak) and beak length (base to tip on the top) of Darwin's finches.  We will look at data from two species, *Geospiza fortis* and *Geospiza scandens*.  The Grants provided data on the finches of Daphne for the years 1973, 1975, 1987, 1991, and 2012.  I have included the data in the files `grant_1973.csv`, `grant_1975.csv`, `grant_1987.csv`, `grant_1991.csv`, and  `grant_2012.csv`. They are in almost exactly the same format is in the Dryad repository; I have only deleted blank entries at the end of the files.\n",
    "\n",
    "**Note**: If you want to skip the wrangling (which is very valuable experience), you can go directly to part (d). You can load in the data frame you generate in parts (a) through (c) from the file `~/git/bootcamp/data/grant_complete.csv`."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**a)** Load each of the files into separate Pandas data frames.  You might want to inspect the file first to make sure you know what character the comments start with and if there is a header row."
   ]
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   "source": [
    "**b)** We would like to merge these all into one data frame.  The problem is that they have different header names, and only the 1973 file has a year entry (called `yearband`).  This is common with real data.  It is often a bit messy and requires some wrangling.  \n",
    "\n",
    "1. First, change the name of the `yearband` column of the 1973 data to `year`.  Also, make sure the year format is four digits, not two!  \n",
    "2. Next, add a `year` column to the other four data frames.  You want tidy data, so each row in the data frame should have an entry for the year.\n",
    "3. Change the column names so that all the data frames have the same column names.  I would choose column names\n",
    "\n",
    "    `['band', 'species', 'beak length (mm)', 'beak depth (mm)', 'year']`\n",
    "\n",
    "4. Concatenate the data frames into a single data frame. Be careful with indices! If you use `pd.concat()`, you will need to use the `ignore_index=True` kwarg. You might also need to use the `axis` kwarg."
   ]
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   "cell_type": "markdown",
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   "source": [
    "**c)** The `band` field gives the number of the band on the bird's leg that was used to tag it. Are some birds counted twice? Are they counted twice in the same year? Do you think you should drop duplicate birds from the same year? How about different years? My opinion is that you should drop duplicate birds from the same year and keep the others, but I would be open to discussion on that. To practice your Pandas skills, though, let's delete only duplicate birds from the same year from the data frame. When you have made this data frame, save it as a CSV file.\n",
    "\n",
    "*Hint*: The data frame methods `duplicated()` and `drop_duplicates()` will be useful.\n",
    "\n",
    "After doing this work, it is worth saving your tidy data frame in a CSV document. To this using the `to_csv()` method of your data frame. Since the indices are uninformative, you should use the `index=False` kwarg. (I have already done this and saved it as `~/git/bootcamp/data/grant_complete.csv`, which will help you do the rest of the exercise if you have problems with this part.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**d)** Make a plots exploring how beak depth changes over time for each species. Think about what might be effective ways to display the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**e)** It is informative to plot the measurement of each bird's beak as a point in the beak depth-beak length plane.  For the 1987 data, plot beak depth vs. beak width for *Geospiza fortis* and for *Geospiza scandens*. The function you wrote in [Exercise 3.5](exercise_3.5.ipynb) will be useful to do this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**f)** Do part (d) again for all years. _Hint_: To display all of the plots, check out the [Bokeh documentation for layouts](https://bokeh.pydata.org/en/latest/docs/user_guide/layout.html). In your plots, make sure all plots have the same range on the axes. If you want to set two plots, say `p1` and `p2` to have the same axis ranges, you can do the following.\n",
    "\n",
    "```python\n",
    "p1.x_range = p2.x_range\n",
    "p1.y_range = p2.y_range\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
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       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"a985382e-28c5-4944-b750-97757998034d\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"a985382e-28c5-4944-b750-97757998034d\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import iqplot\n",
    "import colorcet\n",
    "\n",
    "import bokeh.io\n",
    "import bokeh.plotting\n",
    "\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon inspecting the files, we see that the comment character is, as usual, `#`. There is also a header row in each file, as the first row, so they files are pretty standard. It is important to note that not all of the column headings are the same, but the units of length in the measurements is millimeters. Let's go ahead and load them in!  We will load them into a list. I will use the `glob` module to load in all the `csv` files with the substring `'grant'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get list of CSV files\n",
    "csv_list = glob.glob('data/grant*19*.csv') + glob.glob('data/grant*20*.csv')\n",
    "\n",
    "# Sort the list so we keep the years in order\n",
    "csv_list.sort()\n",
    "\n",
    "# Initialize list of DataFrames\n",
    "df_list = []\n",
    "\n",
    "# Load in each sequentially.\n",
    "for csv_file in csv_list:\n",
    "    # Read in DataFrame\n",
    "    df = pd.read_csv(csv_file, comment='#')\n",
    "    \n",
    "    # Place in list\n",
    "    df_list.append(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick look at the first entry in the list, just to make sure it loaded ok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band</th>\n",
       "      <th>species</th>\n",
       "      <th>yearband</th>\n",
       "      <th>beak length</th>\n",
       "      <th>beak depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20123</td>\n",
       "      <td>fortis</td>\n",
       "      <td>73</td>\n",
       "      <td>9.25</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20126</td>\n",
       "      <td>fortis</td>\n",
       "      <td>73</td>\n",
       "      <td>11.35</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20128</td>\n",
       "      <td>fortis</td>\n",
       "      <td>73</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20129</td>\n",
       "      <td>fortis</td>\n",
       "      <td>73</td>\n",
       "      <td>9.95</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20133</td>\n",
       "      <td>fortis</td>\n",
       "      <td>73</td>\n",
       "      <td>11.55</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    band species  yearband  beak length  beak depth\n",
       "0  20123  fortis        73         9.25        8.05\n",
       "1  20126  fortis        73        11.35       10.45\n",
       "2  20128  fortis        73        10.15        9.55\n",
       "3  20129  fortis        73         9.95        8.75\n",
       "4  20133  fortis        73        11.55       10.15"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good!\n",
    "\n",
    "**b)** Before moving on, we need to know what year is associated with each data frame. Fortunately, we sorted the list of CSV files, we have the years in order. We can extract the year from the file names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initialize years\n",
    "years = []\n",
    "for csv_file in csv_list:\n",
    "    years.append(int(csv_file[-8:-4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check to make sure we got them, and sort them so we know the order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1973, 1975, 1987, 1991, 2012]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good.  Now, we'll proceed with the steps we need to take to clean things up.  First, we'll change the `'yearband'` column in the data frame from 1973 to `'year'`, and change its year from `73` to `1973`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['band', 'species', 'yearband', 'beak length', 'beak depth'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[0].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band</th>\n",
       "      <th>species</th>\n",
       "      <th>year</th>\n",
       "      <th>beak length</th>\n",
       "      <th>beak depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20123</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "      <td>9.25</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20126</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "      <td>11.35</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20128</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20129</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "      <td>9.95</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20133</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "      <td>11.55</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    band species  year  beak length  beak depth\n",
       "0  20123  fortis  1973         9.25        8.05\n",
       "1  20126  fortis  1973        11.35       10.45\n",
       "2  20128  fortis  1973        10.15        9.55\n",
       "3  20129  fortis  1973         9.95        8.75\n",
       "4  20133  fortis  1973        11.55       10.15"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename to year\n",
    "df_list[0] = df_list[0].rename(columns={'yearband': 'year'})\n",
    "\n",
    "# No worries about Y2K\n",
    "df_list[0]['year'] += 1900\n",
    "\n",
    "# Check it out\n",
    "df_list[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Let's proceed to add a `year` column to all of the other data frames. As we do it, we'll just reassign the 1973 year in that data frame, but that's no big deal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i, df in enumerate(df_list):\n",
    "    df_list[i]['year'] = years[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check one to make sure it makes sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band</th>\n",
       "      <th>species</th>\n",
       "      <th>blength</th>\n",
       "      <th>bdepth</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2639</td>\n",
       "      <td>fortis</td>\n",
       "      <td>10.30</td>\n",
       "      <td>8.95</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2666</td>\n",
       "      <td>fortis</td>\n",
       "      <td>12.81</td>\n",
       "      <td>9.30</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2753</td>\n",
       "      <td>fortis</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10.35</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2776</td>\n",
       "      <td>fortis</td>\n",
       "      <td>11.30</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4229</td>\n",
       "      <td>fortis</td>\n",
       "      <td>10.05</td>\n",
       "      <td>8.62</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   band species  blength  bdepth  year\n",
       "0  2639  fortis    10.30    8.95  1991\n",
       "1  2666  fortis    12.81    9.30  1991\n",
       "2  2753  fortis    10.89   10.35  1991\n",
       "3  2776  fortis    11.30   10.00  1991\n",
       "4  4229  fortis    10.05    8.62  1991"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[3].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. Now, we need to change the column names so they are all the same for the respective data frames. We have few enough data frames that we could do that by hand, but it is more instructive (and re-usable) if we automate it.  We will write a function to rename the columns. It first sniffs out which column should be `'band'`, which should be `'species'`, and so on. We can do this with Pandas's convenient `.str` methods, which enable us to use string methods on many entries at once. This is perhaps best seen by example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['band', 'species', 'blength', 'bdepth', 'year'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a DataFrame to try it on.\n",
    "df = df_list[3]\n",
    "\n",
    "# Look at the columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, if we are interested in the beak length column, we want to find a column heading that contains `'len'`, since pretty much anything that is about beak length would have the substring. We can use the convenient `str.contains()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False, False])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See which column had 'len' in it\n",
    "df.columns.str.contains('len')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can slice out the column heading that has `'len'` in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['blength'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[df.columns.str.contains('len')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we just want the string, so we do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'blength'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[df.columns.str.contains('len')][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use this to identify the current column headings and then change them to what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def rename_cols(df):\n",
    "    \"\"\"Rename columns so all DataFrames have same column headings.\"\"\"\n",
    "    # Sniff out the key names from names that are close\n",
    "    band_key = df.columns[df.columns.str.contains(\"and\")][0]\n",
    "    species_key = df.columns[df.columns.str.contains(\"ecies\")][0]\n",
    "    length_key = df.columns[df.columns.str.contains(\"len\")][0]\n",
    "    depth_key = df.columns[df.columns.str.contains(\"dep\")][0]\n",
    "    year_key = df.columns[df.columns.str.contains(\"year\")][0]\n",
    "\n",
    "    # Rename the columns using renaming dictionary\n",
    "    return df.rename(\n",
    "        columns={\n",
    "            band_key: \"band\",\n",
    "            species_key: \"species\",\n",
    "            depth_key: \"beak depth (mm)\",\n",
    "            length_key: \"beak length (mm)\",\n",
    "            year_key: \"year\",\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can loop through the data frames and rename the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band</th>\n",
       "      <th>species</th>\n",
       "      <th>beak length (mm)</th>\n",
       "      <th>beak depth (mm)</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2639</td>\n",
       "      <td>fortis</td>\n",
       "      <td>10.30</td>\n",
       "      <td>8.95</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2666</td>\n",
       "      <td>fortis</td>\n",
       "      <td>12.81</td>\n",
       "      <td>9.30</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2753</td>\n",
       "      <td>fortis</td>\n",
       "      <td>10.89</td>\n",
       "      <td>10.35</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2776</td>\n",
       "      <td>fortis</td>\n",
       "      <td>11.30</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4229</td>\n",
       "      <td>fortis</td>\n",
       "      <td>10.05</td>\n",
       "      <td>8.62</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   band species  beak length (mm)  beak depth (mm)  year\n",
       "0  2639  fortis             10.30             8.95  1991\n",
       "1  2666  fortis             12.81             9.30  1991\n",
       "2  2753  fortis             10.89            10.35  1991\n",
       "3  2776  fortis             11.30            10.00  1991\n",
       "4  4229  fortis             10.05             8.62  1991"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, df in enumerate(df_list):\n",
    "    df_list[i] = rename_cols(df)\n",
    "    \n",
    "# Check the result\n",
    "df_list[3].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we do the concatenation using `pd.concat()`. We want to ignore the indices because they are not important identifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band</th>\n",
       "      <th>beak depth (mm)</th>\n",
       "      <th>beak length (mm)</th>\n",
       "      <th>species</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20123</td>\n",
       "      <td>8.05</td>\n",
       "      <td>9.25</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20126</td>\n",
       "      <td>10.45</td>\n",
       "      <td>11.35</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20128</td>\n",
       "      <td>9.55</td>\n",
       "      <td>10.15</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20129</td>\n",
       "      <td>8.75</td>\n",
       "      <td>9.95</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20133</td>\n",
       "      <td>10.15</td>\n",
       "      <td>11.55</td>\n",
       "      <td>fortis</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>21295</td>\n",
       "      <td>9.30</td>\n",
       "      <td>14.20</td>\n",
       "      <td>scandens</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>21297</td>\n",
       "      <td>9.80</td>\n",
       "      <td>13.00</td>\n",
       "      <td>scandens</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>21340</td>\n",
       "      <td>8.90</td>\n",
       "      <td>14.60</td>\n",
       "      <td>scandens</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>21342</td>\n",
       "      <td>9.80</td>\n",
       "      <td>13.10</td>\n",
       "      <td>scandens</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>21347</td>\n",
       "      <td>9.10</td>\n",
       "      <td>15.20</td>\n",
       "      <td>scandens</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2304 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       band  beak depth (mm)  beak length (mm)   species  year\n",
       "0     20123             8.05              9.25    fortis  1973\n",
       "1     20126            10.45             11.35    fortis  1973\n",
       "2     20128             9.55             10.15    fortis  1973\n",
       "3     20129             8.75              9.95    fortis  1973\n",
       "4     20133            10.15             11.55    fortis  1973\n",
       "...     ...              ...               ...       ...   ...\n",
       "2299  21295             9.30             14.20  scandens  2012\n",
       "2300  21297             9.80             13.00  scandens  2012\n",
       "2301  21340             8.90             14.60  scandens  2012\n",
       "2302  21342             9.80             13.10  scandens  2012\n",
       "2303  21347             9.10             15.20  scandens  2012\n",
       "\n",
       "[2304 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat(df_list, axis=0, ignore_index=True, sort=True)\n",
    "\n",
    "# Take a look\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great!  We now have one convenient tidy data frame to work with.\n",
    "\n",
    "**c)** First, let's look for duplicate band numbers. There are many, so we'll just write out how many. The `df.duplicated()` method returns True for each row if it is a duplicate. We will get all duplicates in the `'band'` column, and then get the unique values in the list of all duplicated. This will tell us how many birds were measured more than once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There were 350 birds that were measured more than once.\n",
      "There were 1954 total birds measured.\n"
     ]
    }
   ],
   "source": [
    "# Stats about how many birds were measured more than once\n",
    "print(\n",
    "    \"There were\",\n",
    "    len(df[\"band\"][df[\"band\"].duplicated()].unique()),\n",
    "    \"birds that were measured more than once.\",\n",
    ")\n",
    "\n",
    "print(\"There were\", len(df[\"band\"].unique()), \"total birds measured.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, most birds were only measured once. Nonetheless, let's eliminate duplicates of birds that were measured twice. When we drop the duplicates, we will keep the first measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Drop all rows with matching year and band (keep first)\n",
    "df = df.drop_duplicates(subset=['year', 'band'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will save the data frame as a CSV file using the `df.to_csv()` method. We do not want to print the indices (they are meaningless)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv('data/grant_complete.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**d)** As a first pass, I will use a strip plot showing both species by year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"f8940d13-f586-4eab-af4b-8bce9cdd5809\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n"
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     "output_type": "display_data"
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      "application/vnd.bokehjs_exec.v0+json": ""
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      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1002"
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     "output_type": "display_data"
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   ],
   "source": [
    "p = iqplot.strip(\n",
    "    data=df,\n",
    "    q='beak depth (mm)',\n",
    "    cats=['year', 'species'],\n",
    "    color_column='species',\n",
    "    spread='jitter',\n",
    "    marker_kwargs=dict(alpha=0.3),\n",
    "    frame_height=550,\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot immediately shows that there are more *G. fortis* measurements and they are more broadly distributed. There seems to be a big jump toward shallower beaks from 1991 to 2012 for *G. fortis*, but the *G. scandens* beak depth stays relatively constant.\n",
    "\n",
    "To better see the distributions, we can plot ECDFs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "    }, 10, root)\n",
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       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
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     "output_type": "display_data"
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   ],
   "source": [
    "palette_fortis = bokeh.palettes.Blues9\n",
    "p_fortis = iqplot.ecdf(\n",
    "    data=df.loc[df['species']=='fortis', :],\n",
    "    q='beak depth (mm)',\n",
    "    cats='year',\n",
    "    palette=palette_fortis,\n",
    "    frame_height=250,\n",
    "    title='fortis',\n",
    ")\n",
    "\n",
    "palette_scandens = bokeh.palettes.Oranges9\n",
    "p_scandens = iqplot.ecdf(\n",
    "    data=df.loc[df['species']=='scandens', :],\n",
    "    q='beak depth (mm)',\n",
    "    cats='year',\n",
    "    palette=palette_scandens,\n",
    "    frame_height=250,\n",
    "    title='scandens',\n",
    ")\n",
    "\n",
    "p_scandens.x_range = p_fortis.x_range\n",
    "\n",
    "bokeh.io.show(bokeh.layouts.column([p_fortis, p_scandens]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The big jump from 1991 to 2012 for *G. fortis* is really clear in the ECDF. In both cases, there appears to be a jump toward shallower beaks between 1973 and 1975, but there are not very many data points in 1973, so it is difficult to tell. (We will learn how to quantify this uncertainty in future lessons when we do bootstrapping.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**e)** Now let's make a plot of the 1987 data in the beak depth-beak length plane. We can use the function we wrote in [Exercise 3.5](../exercises/exercise_3.5.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  const render_items = [{\"docid\":\"39a57117-2049-45d4-aac9-7edd1656440b\",\"roots\":{\"p1884\":\"a441fac0-8aff-4a4e-9b3f-b281fcf1943e\"},\"root_ids\":[\"p1884\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1884"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def scatter(\n",
    "    data=None,\n",
    "    cat=None,\n",
    "    x=None,\n",
    "    y=None,\n",
    "    p=None,\n",
    "    palette=None,\n",
    "    show_legend=True,\n",
    "    click_policy=\"hide\",\n",
    "    marker_kwargs={},\n",
    "    **kwargs,\n",
    "):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : Pandas DataFrame\n",
    "        DataFrame containing tidy data for plotting.\n",
    "    cat : hashable\n",
    "        Name of column to use as categorical variable.\n",
    "    x : hashable\n",
    "        Name of column to use as x-axis.\n",
    "    y : hashable\n",
    "        Name of column to use as y-axis.\n",
    "    p : bokeh.plotting.Figure instance, or None (default)\n",
    "        If None, create a new figure. Otherwise, populate the existing\n",
    "        figure `p`.\n",
    "    palette : list of strings of hex colors, or single hex string\n",
    "        If a list, color palette to use. If a single string representing\n",
    "        a hex color, all glyphs are colored with that color. Default is\n",
    "        the Glasbey Catagory 10.\n",
    "    show_legend : bool, default False\n",
    "        If True, show legend.\n",
    "    tooltips : list of 2-tuples\n",
    "        Specification for tooltips as per Bokeh specifications. For\n",
    "        example, if we want `col1` and `col2` tooltips, we can use\n",
    "        `tooltips=[('label 1': '@col1'), ('label 2': '@col2')]`. Ignored\n",
    "        if `formal` is True.\n",
    "    show_legend : bool, default False\n",
    "        If True, show a legend.\n",
    "    click_policy : str, default \"hide\"\n",
    "        How to display points when their legend entry is clicked.\n",
    "    marker_kwargs : dict\n",
    "        kwargs to be passed to `p.circle()` when making the scatter plot.\n",
    "    kwargs\n",
    "        Any kwargs to be passed to `bokeh.plotting.figure()` when making \n",
    "        the plot.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    output : bokeh.plotting.Figure instance\n",
    "        Plot populated with jitter plot or box plot.\n",
    "    \"\"\"\n",
    "    # Automatically name the axes\n",
    "    if \"x_axis_label\" not in kwargs:\n",
    "        kwargs[\"x_axis_label\"] = x\n",
    "    if \"y_axis_label\" not in kwargs:\n",
    "        kwargs[\"y_axis_label\"] = y\n",
    "\n",
    "    # Default palette\n",
    "    if palette is None:\n",
    "        palette = colorcet.b_glasbey_category10\n",
    "    elif type(palette) == str:\n",
    "        palette = [palette]\n",
    "\n",
    "    # Instantiate figure\n",
    "    if p is None:\n",
    "        p = bokeh.plotting.figure(**kwargs)\n",
    "\n",
    "    # Build plot (not using color factors) to enable click policies\n",
    "    for i, (name, g) in enumerate(data.groupby(cat, sort=False)):\n",
    "        marker_kwargs[\"color\"] = palette[i % len(palette)]\n",
    "        marker_kwargs[\"legend_label\"] = str(name)\n",
    "        p.circle(source=g, x=x, y=y, **marker_kwargs)\n",
    "\n",
    "    if show_legend:\n",
    "        p.legend.click_policy = click_policy\n",
    "    else:\n",
    "        p.legend.visible = False\n",
    "\n",
    "    return p\n",
    "\n",
    "p = scatter(\n",
    "    data=df.loc[df['year']==1987, :],\n",
    "    x='beak length (mm)',\n",
    "    y='beak depth (mm)',\n",
    "    cat='species',\n",
    "    marker_kwargs=dict(alpha=0.3),\n",
    "    height=450,\n",
    "    width=450,\n",
    ")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**f)** To generate all of the plots, we can loop through a GroupBy object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"f5a2e15f-dc12-4187-866b-b293c6231738\" data-root-id=\"p2536\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  const render_items = [{\"docid\":\"180444ba-2d0c-4b35-b110-da42c102a196\",\"roots\":{\"p2536\":\"f5a2e15f-dc12-4187-866b-b293c6231738\"},\"root_ids\":[\"p2536\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p2536"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots = []\n",
    "for name, group in df.groupby('year'):\n",
    "    p = scatter(\n",
    "        data=group,\n",
    "        x='beak length (mm)',\n",
    "        y='beak depth (mm)',\n",
    "        cat='species',\n",
    "        show_legend=False,\n",
    "        marker_kwargs=dict(alpha=0.3),\n",
    "        title=str(name),\n",
    "        frame_height=250,\n",
    "        frame_width=250,\n",
    "    )\n",
    "    plots.append(p)\n",
    "    \n",
    "# Link the ranges\n",
    "for i in range(1, len(plots)):\n",
    "    plots[i].x_range = plots[0].x_range\n",
    "    plots[i].y_range = plots[0].y_range\n",
    "    \n",
    "bokeh.io.show(bokeh.layouts.column(plots))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we look at the data this way, we see the the species coming together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.3\n",
      "IPython version      : 8.12.0\n",
      "\n",
      "pandas    : 1.5.3\n",
      "bokeh     : 3.1.1\n",
      "iqplot    : 0.3.3\n",
      "jupyterlab: 3.6.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p pandas,bokeh,iqplot,jupyterlab"
   ]
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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   "codemirror_mode": {
    "name": "ipython",
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