ae-01-meet-the-penguins.ipynb

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   "source": [
    "# AE 01: Meet the penguins\n",
    "## Greg Chism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this application exercise, we'll use the pandas and palmerpenguins packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from palmerpenguins import load_penguins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "penguins = load_penguins()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we will visualize is called `penguins`.\n",
    "Let's `.info()` at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "2 * 4"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-   **Your turn:** Replace `#add code here` with the code for \"info\"ing at the data `penguins` data frame – `penguins.info()`. Render the document and view the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 344 entries, 0 to 343\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   species            344 non-null    object \n",
      " 1   island             344 non-null    object \n",
      " 2   bill_length_mm     342 non-null    float64\n",
      " 3   bill_depth_mm      342 non-null    float64\n",
      " 4   flipper_length_mm  342 non-null    float64\n",
      " 5   body_mass_g        342 non-null    float64\n",
      " 6   sex                333 non-null    object \n",
      " 7   year               344 non-null    int64  \n",
      "dtypes: float64(4), int64(1), object(3)\n",
      "memory usage: 21.6+ KB\n"
     ]
    }
   ],
   "source": [
    "penguins.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-   **Demo:** First, replace the blank below with the number of rows in the `penguins` data frame based on the output of the chunk below. Then, replace it with \"inline code\" and render again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "344"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(penguins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 344 penguins in the `penguins` data frame."
   ]
  }
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