course/introduction/lesson-5.ipynb

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    "# Constructing DataFrames from Series\n",
    "\n",
    "This lesson introduced method to construct a DataFrame from multiple\n",
    "Series.\n",
    "\n",
    "This first block loads the variables created in an earlier lesson.  A\n",
    "later lesson will cover loading and saving data."
   ]
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    "# Setup: Load data created in an earlier lesson\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "hdf_file = \"data/dataframes.h5\"\n",
    "\n",
    "sep_04 = pd.read_hdf(hdf_file, \"sep_04\")\n",
    "sep_05 = pd.read_hdf(hdf_file, \"sep_05\")\n",
    "sep_06 = pd.read_hdf(hdf_file, \"sep_06\")\n",
    "sep_07 = pd.read_hdf(hdf_file, \"sep_07\")\n",
    "sep_10 = pd.read_hdf(hdf_file, \"sep_10\")\n",
    "sep_11 = pd.read_hdf(hdf_file, \"sep_11\")\n",
    "sep_12 = pd.read_hdf(hdf_file, \"sep_12\")\n",
    "sep_13 = pd.read_hdf(hdf_file, \"sep_13\")\n",
    "sep_14 = pd.read_hdf(hdf_file, \"sep_14\")\n",
    "sep_17 = pd.read_hdf(hdf_file, \"sep_17\")\n",
    "sep_18 = pd.read_hdf(hdf_file, \"sep_18\")\n",
    "sep_19 = pd.read_hdf(hdf_file, \"sep_19\")\n",
    "\n",
    "spy = pd.read_hdf(hdf_file, \"spy\")\n",
    "aapl = pd.read_hdf(hdf_file, \"aapl\")\n",
    "goog = pd.read_hdf(hdf_file, \"goog\")\n",
    "\n",
    "dates = pd.to_datetime(pd.read_hdf(hdf_file, \"dates\"))\n",
    "\n",
    "prices = pd.read_hdf(hdf_file, \"prices\")"
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    "## Problem: Construct a DataFrame from rows\n",
    "\n",
    "Create a DataFrame named `prices_row` from the row vectors previously\n",
    "entered such that the results are identical to prices. For example, the first\n",
    "two days worth of data are:\n",
    "\n",
    "```python\n",
    "dates_2 = pd.to_datetime([\"1998-09-04\", \"1998-09-05\"])\n",
    "prices_row = pd.DataFrame([sep_04, sep_05])\n",
    "# Set the index after using concat to join\n",
    "prices_row.index = dates_2\n",
    "```\n",
    "\n",
    "Verify that the DataFrame identical by printing the difference with\n",
    "`prices` \n",
    "\n",
    "```python\n",
    "print(prices_row - prices)\n",
    "```"
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    "## Problem: Construct a DataFrame from columns\n",
    "\n",
    "Create a DataFrame named `prices_col` from the 3 column vectors entered\n",
    "such that the results are identical to prices.\n",
    "\n",
    "*Note*: `.T` transposes a 2-d array since `DataFrame` builds the\n",
    "array by rows.\n",
    "\n",
    "Verify that the DataFrame identical by printing the difference with\n",
    "`prices` "
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    "## Problem: Construct a DataFrame from a dictionary\n",
    "\n",
    "Create a DataFrame named `prices_dict` from the 3 column vectors entered\n",
    "such that the results are identical to prices\n",
    "\n",
    "Verify that the DataFrame identical by printing the difference with\n",
    "`prices` "
   ]
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    "## Exercises\n",
    "\n",
    "### Exercise: Create a DataFrame from rows\n",
    "\n",
    "Use the three series populated below to create a DataFrame using each\n",
    "as a row.\n",
    "\n",
    "**Note**: Notice what happens in the resulting `DataFrame` since one of the\n",
    "`Series` has 4 elements while the others have 3."
   ]
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    "# Setup: Data for the Exercises\n",
    "import pandas as pd\n",
    "index = [\"Num\", \"Let\", \"Date\"]\n",
    "a = pd.Series([1, \"A\", pd.Timestamp(2018,12,31)], name=\"a\", index=index)\n",
    "b = pd.Series([2, \"B\", pd.Timestamp(2018,12,31)], name=\"b\", index=index)\n",
    "index = [\"Num\", \"Let\", \"Date\", \"Float\"]\n",
    "c = pd.Series([3, \"C\", pd.Timestamp(2018,12,31), 3.0], name=\"c\", index=index)"
   ]
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   "source": [
    "### Exercise: Build a DataFrame from Columns\n",
    "\n",
    "Build a `DataFrame` from the three series where each is used as a column.\n"
   ]
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