MeltExercise.ipynb

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    "## Melt Exercise\n",
    "\n",
    "Example taken from Tidy Data in Python\n",
    "by [Jean-Nicholas Hould](http://www.jeannicholashould.com/) from the blog post of the same name\n",
    "[http://www.jeannicholashould.com/tidy-data-in-python.html](http://www.jeannicholashould.com/tidy-data-in-python.html)\n",
    "\n",
    "*Don't look yet, but the solutions are in [MeltExerciseSolutions](MeltExerciseSolutions.ipynb)*"
   ]
  },
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   "source": [
    "### Pew Research Center Dataset\n",
    "\n",
    "This dataset explores the relationship between income and religion.\n",
    "\n",
    "Problem: \n",
    "\n",
    "- The columns headers are composed of the possible income range values."
   ]
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   "source": [
    "import pandas as pd"
   ]
  },
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   "source": [
    "#### Read in the data\n",
    "\n",
    "- Read data in from the CSV file `pew-raw.csv` in the `data` directory, \n",
    "- and print out the table"
   ]
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    "# Read in data from CSV file\n",
    "df = pd.r____\n",
    "\n",
    "# and display the original table\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot/melt\n",
    "\n",
    "- Pivot/melt every column except for `religion`,\n",
    "- make a new column called `\"income\"` based on the old column headers\n",
    "- and a new column called `\"freq\"` based on the frequency values under those headers"
   ]
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    "tidy_df = df.____(____, _____=\"income\", _____=\"freq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort temporarily\n",
    "\n",
    "- Display the first 15 values, sorted by `religion`\n",
    "- *Don't actually permanently sort the DataFrame yet!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "tidy_df.s____(____).h____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort and store the results\n",
    "\n",
    "- Now sort again, this time storing the values\n",
    "- this time **descending** by `\"freq\"` so the highest frequency values are at the top\n",
    "- *The default is NOT to sort \"in place\", so need to reassign to keep sorting results*\n",
    "- Display the first 15 results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tidy_sorted_df = tidy_df.s____\n",
    "\n",
    "# display the results\n",
    "tidy_sorted_df.____(__)"
   ]
  }
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