03-group-apply-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\n",
    "\n",
    "pd.set_option('display.float_format', '{:,.1f}'.format)"
   ]
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    "dat = pd.read_csv('data/weather-6m.csv')"
   ]
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     "exercise"
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   "source": [
    "**Exercise:**\n",
    "\n",
    "Calculate the average `air_temp` by `month`."
   ]
  },
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   "source": [
    "**Exercise:**\n",
    "\n",
    "Compute summary statistics on `air_temp` and `dew_point` using \n",
    "the `describe` method."
   ]
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   "source": [
    "**Exercise:**\n",
    "\n",
    "For January and February and 0 - 11 hours, calculate the average and standard deviation of `air_temp` grouping by month and hour of the day. Name your result columns `air_temp_mean` and `air_temp_sd`.\n",
    "\n",
    "Your result `DataFrame` should have 24 rows, the number of months (2) times the number of hours (12). \n",
    "\n",
    "$2 * 12 = 24$\n",
    "\n"
   ]
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   "source": [
    "**Exercise:**\n",
    "\n",
    "By month, calculate quantiles for `air_temp` using the quantiles defined in `breaks`. \n",
    "\n",
    "Hint: Use the `quantile` method defined on a `Series` (`pd.Series.quantile`).\n"
   ]
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   "source": [
    "breaks = [0.01, 0.25, 0.5, 0.75, 0.99]"
   ]
  }
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