03-group-apply-answers.ipynb

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   "source": [
    "(c) 2016 - present. Enplus Advisors, Inc."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "setup"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "setup"
    ]
   },
   "outputs": [],
   "source": [
    "dat = pd.read_csv('starmine.csv', parse_dates=['date'])\n",
    "dat = dat.set_index(['date', 'symbol'], verify_integrity=True).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Calculate the average `smi` by sector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "grp = dat.groupby('sector')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector\n",
       "Durbl    41.495868\n",
       "Enrgy    37.072148\n",
       "HiTec    61.005006\n",
       "Hlth     51.898917\n",
       "Manuf    54.563836\n",
       "Name: smi, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res1 = grp['smi'].mean()\n",
    "res1.head() # only show first 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Compute summary statistics on `smi` and `cap_usd` using \n",
    "the `describe` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sum_stats = grp[['smi', 'cap_usd']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "By month and sector, calculate the median of `size` and standard deviation of `liq`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "grp2 = dat.groupby(['date', 'sector'])\n",
    "mth_sector = grp2.agg({\n",
    "    'size': 'median',\n",
    "    'liq': 'std'\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "By month, calculate quantiles for smi using the quantiles defined in `breaks`. \n",
    "\n",
    "Use either the built-in `pandas` quantile approximation method defined\n",
    "a `Series` or the `numpy` quantile algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "outputs": [],
   "source": [
    "breaks = [0.01, 0.25, 0.5, 0.75, 0.99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>smi</th>\n",
       "      <th>0.01</th>\n",
       "      <th>0.25</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.75</th>\n",
       "      <th>0.99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-02-28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>99.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-04-30</th>\n",
       "      <td>2.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-06-30</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-07-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-08-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-09-30</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-10-31</th>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-11-30</th>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "smi         0.01  0.25  0.50  0.75    0.99\n",
       "date                                      \n",
       "1995-01-31   2.0  28.0  51.0  78.0  100.00\n",
       "1995-02-28   2.0  28.0  49.0  78.0  100.00\n",
       "1995-03-31   2.0  26.0  49.0  73.0   99.46\n",
       "1995-04-30   2.0  26.0  48.0  77.0  100.00\n",
       "1995-05-31   2.0  28.0  49.0  75.0  100.00\n",
       "1995-06-30   2.0  28.0  51.0  74.0  100.00\n",
       "1995-07-31   2.0  28.0  52.0  78.0  100.00\n",
       "1995-08-31   2.0  28.0  50.0  77.0   99.00\n",
       "1995-09-30   2.0  28.0  50.0  74.0   99.00\n",
       "1995-10-31   2.0  28.0  52.0  78.0  100.00\n",
       "1995-11-30   2.0  29.0  50.0  72.0   99.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp3 = dat.groupby('date')\n",
    "smi_quantiles = grp3.apply(lambda x: x.smi.quantile(breaks))\n",
    "smi_quantiles"
   ]
  }
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