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   "source": [
    "# Exercise 1.1: Practice with Pandas and Palmer's Penguins\n",
    "\n",
    "[Data set download](https://s3.amazonaws.com/bebi103.caltech.edu/data/penguins_subset.csv)\n",
    "\n",
    "<hr />"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Palmer penguins data set](https://towardsdatascience.com/penguins-dataset-overview-iris-alternative-9453bb8c8d95) is a nice data set with which to practice various data science skills. For this exercise, we will use as subset of it, which you can download here: [https://s3.amazonaws.com/bebi103.caltech.edu/data/penguins_subset.csv](https://s3.amazonaws.com/bebi103.caltech.edu/data/penguins_subset.csv). The data set consists of measurements of three different species of penguins acquired at the [Palmer Station in Antarctica](https://en.wikipedia.org/wiki/Palmer_Station). The measurements were made between 2007 and 2009 by [Kristen Gorman](https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php).\n",
    "\n",
    "**a)** Load the data set into a Pandas `DataFrame` called `df`. You will need to use the `header=[0,1]` kwarg of `pd.read_csv()` to load the data set in properly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**b)** Take a look at `df`. Is it tidy? Why or why not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**c)** Perform the following operations to make a new `DataFrame` from the original one you loaded in exercise 1 to generate a new `DataFrame`. You do not need to worry about what these operations do (you can learn about tidying data frames [here](http://bebi103.caltech.edu.s3-website-us-east-1.amazonaws.com/2020a/lessons/08/index.html)), just do them to answer this question: Is the resulting data frame `df_tidy` tidy? Why or why not?\n",
    "\n",
    "```python\n",
    "df_tidy = df.stack(\n",
    "    level=0\n",
    ").sort_index(\n",
    "    level=1\n",
    ").reset_index(\n",
    "    level=1\n",
    ").rename(\n",
    "    columns={\"level_1\": \"species\"}\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**d)** Using `df_tidy`, slice out all of the bill lengths for *Gentoo* penguins as a Numpy array. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**e)** Make a new data frame containing the mean measured bill depth, bill length, body mass in kg, and flipper length for each species. You can use millimeters for all length measurements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**f)** Save `df_tidy` as a file named `penguins_subset_tidy.csv`."
   ]
  }
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