01-intro-to-pandas-part-1-answers.ipynb

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    "(c) 2016 - present. Enplus Advisors, Inc."
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
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   "source": [
    "**Exercise:**\n",
    "\n",
    "* Create a `Series` and assign it to `s1a` from the integers 8, 6, 7, 5\n",
    "* From a `dict`, create an integer `Series` and assign it to `s1b` with values 8, 6, 7, 5 named `s1b` and a string index 'a', 'b', 'c', and 'd'\n",
    "* Convert the `s1b` values to 64-bit floating point values and assign it to `s1c`\n",
    "* Extract only the values from the `s1c` as a `PandasArray`"
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    "s1a = pd.Series([8, 6, 7, 5])"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "s1b = pd.Series({'a': 8, 'b': 6, 'c': 7, 'd': 5})"
   ]
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    "s1c = s1b.astype(np.float64)"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PandasArray>\n",
       "[8.0, 6.0, 7.0, 5.0]\n",
       "Length: 4, dtype: float64"
      ]
     },
     "execution_count": 5,
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     "output_type": "execute_result"
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   "source": [
    "s1c.array # s1c.values for pandas < 0.24.0"
   ]
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "data": {
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       "array([8., 6., 7., 5.])"
      ]
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   "source": [
    "s1c.to_numpy() # to convert to a `numpy.ndarray`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
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   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "* Select the first element of `s2` using integer based lookup (`iloc`).\n",
    "* Select the first element of the `s2` using label indexing (`loc`).\n",
    "* Select all elements greater than 6 in `s2` using a boolean `Series`"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "source": [
    "s2 = pd.Series([6, 8, 7, 5], index=list('abcd'), dtype='Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    {
     "data": {
      "text/plain": [
       "6"
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     "execution_count": 8,
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   "source": [
    "s2.iloc[0] # also s2.iat[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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     "data": {
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       "6"
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     "execution_count": 9,
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   "source": [
    "s2.loc['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    8\n",
       "c    7\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "s2[s2 > 6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "exercise"
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   "source": [
    "**Exercise**\n",
    "\n",
    "* Select all non-NaN values in `s3`\n",
    "* What will the result of adding `s2` and `s3` together be? \n",
    "  Figure it out on paper then check in the notebook with `s2 + s3`"
   ]
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
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   "source": [
    "s3 = pd.Series([9., 100., np.nan], index=list('ayz'), dtype='Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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     "data": {
      "text/plain": [
       "a      9\n",
       "y    100\n",
       "dtype: Int64"
      ]
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     "execution_count": 12,
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   "source": [
    "s3[s3.notnull()]"
   ]
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      15\n",
       "b    <NA>\n",
       "c    <NA>\n",
       "d    <NA>\n",
       "y    <NA>\n",
       "z    <NA>\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "s2 + s3"
   ]
  }
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