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   "source": [
    "# Answers - In Class Activity - Pandas and data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1a34fa",
   "metadata": {},
   "source": [
    "First, let's import our packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27fec022",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.random as npr\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5c7ba4",
   "metadata": {},
   "source": [
    "The code below creates a pandas dataframe named `df` that stores information about several items, each of which is a shape. Each shape has a `type` (just rectangle or circle), a `width`, and the rectangles also have a `height` (for circles, this is not-a-valid-number (NaN) since circles must have same width and height). Note that pandas and numpy work together pretty well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "193b2fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mytype = np.array(['rectangle','circle','rectangle','rectangle','circle','rectangle','circle','rectangle','circle','circle'])\n",
    "width = npr.rand(len(mytype))*10.\n",
    "height = npr.rand(len(mytype))*10.\n",
    "height[mytype=='circle']=np.nan  \n",
    "df = pd.DataFrame({\"type\":mytype, \"width\":width, \"height\":height})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd755d44",
   "metadata": {},
   "source": [
    "### Problem 0: Changing entries in the table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e11322",
   "metadata": {},
   "source": [
    "Your first task is to manually change some of the entries in the dataframe. For the last 'rectangle' in the table, please change it's width to `5.0` and it's height to be `2.0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a0496aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer goes here\n",
    "df.at[7,'width'] = 5.0\n",
    "df.at[7,'height'] = 2.0\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec6b441",
   "metadata": {},
   "source": [
    "### Problem 1: Droppings rows with missing data, and computing a mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dcb7d61",
   "metadata": {},
   "source": [
    "Using pandas function `dropna`, drop any rows that have missing data / a `NaN` value (in essence, all the circles in the table will be dropped). Save the resulting dataframe to a new variable `df2`. \n",
    "\n",
    "Next, compute the average height of the items in `df2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9b3d082",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer here\n",
    "df2 = df.dropna()\n",
    "df2.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c2970a",
   "metadata": {},
   "source": [
    "### Problem 2: Computing the area and creating a new column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b90d30d",
   "metadata": {},
   "source": [
    "Forget about `df2`, let's go back to the original table `df`. We want to create a new column of `df` that lists the area of each shape. Please write code that does this--both creating the column and computing the area of each shape. Remember, the formula for the area of a circle is $\\Pi r^2$ for radius $r$. In our case, it is s $\\Pi (w/2)^2$ for width $w$.\n",
    "\n",
    "Note, if you haven't read through Chapter 6.10 yet, it's a good time to do so. Especially see 6.10.4 on \"Selecting\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7bd9e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer here (this answer uses a for-loop)\n",
    "df['area'] = df['width']*df['height']\n",
    "n = df.shape[0]\n",
    "for i in range(n):\n",
    "    if df.at[i,'type'] == 'circle':\n",
    "        df.at[i,'area'] = math.pi * (df.at[i,'width']/2) ** 2\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c5ba1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your answer here (this answer doesn't use a for-loop)\n",
    "df['area'] = np.nan\n",
    "df['area'] = df['width']*df['height']\n",
    "sel = df['type'] == 'circle'\n",
    "df.loc[sel,'area'] = math.pi * (df.loc[sel,'width']/2) ** 2\n",
    "df"
   ]
  }
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