02-intro-to-pandas-part-2-exercises.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": [
    "df = pd.DataFrame({\n",
    "    'ticker': ['AAPL', 'AAPL', 'MSFT', 'IBM', 'YHOO'],\n",
    "    'date': ['2015-12-30', '2015-12-31', '2015-12-30', '2015-12-30', '2015-12-30'],\n",
    "    'open': [426.23, 427.81, 42.3, 101.65, 35.53]\n",
    "})"
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   "source": [
    "**Exercise:**\n",
    "\n",
    "* Select the `open` column as a `Series` using attribute lookup\n",
    "* Select the `open` column as a `Series` using `dict`-style lookup\n",
    "* Select the `date` column as a `DataFrame`"
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    "**Exercise:**\n",
    "\n",
    "* Select all rows with the `AAPL` ticker and the `date` and `open`\n",
    "  columns.\n",
    "* Assign to the variable `df1` a new `DataFrame` with `ticker` as\n",
    "  the index.\n",
    "* Assign to the variable `df2` a new `DataFrame` with `date` as\n",
    "  the index. Create this `DataFrame` from `df1` with a single\n",
    "  statement.\n",
    "* Sort `df2` by the index values."
   ]
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    "**Exercise:**\n",
    "\n",
    "* Create a copy of `df` called `df3`. Add a new column of `NaNs` \n",
    "  to `df3` called `close`. Assign `close` the same value as `open`\n",
    "  for all `open` values greater than 100.\n",
    "* Sort `df3` by its `close` values."
   ]
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