0.guides/1.get-top-10-highest-lowest-values-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33a0cd01",
   "metadata": {},
   "source": [
    "# How to Get Top 10 Values in Pandas\n",
    "\n",
    "https://datascientyst.com/get-top-10-highest-lowest-values-pandas/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f54ec963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Magnitude Type</th>\n",
       "      <th>Type</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/02/1965</td>\n",
       "      <td>13:44:18</td>\n",
       "      <td>131.60</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/04/1965</td>\n",
       "      <td>11:29:49</td>\n",
       "      <td>80.00</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/05/1965</td>\n",
       "      <td>18:05:58</td>\n",
       "      <td>20.00</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/08/1965</td>\n",
       "      <td>18:49:43</td>\n",
       "      <td>15.00</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/09/1965</td>\n",
       "      <td>13:32:50</td>\n",
       "      <td>15.00</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23407</th>\n",
       "      <td>12/28/2016</td>\n",
       "      <td>08:22:12</td>\n",
       "      <td>12.30</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23408</th>\n",
       "      <td>12/28/2016</td>\n",
       "      <td>09:13:47</td>\n",
       "      <td>8.80</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23409</th>\n",
       "      <td>12/28/2016</td>\n",
       "      <td>12:38:51</td>\n",
       "      <td>10.00</td>\n",
       "      <td>MWW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23410</th>\n",
       "      <td>12/29/2016</td>\n",
       "      <td>22:30:19</td>\n",
       "      <td>79.00</td>\n",
       "      <td>MWW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23411</th>\n",
       "      <td>12/30/2016</td>\n",
       "      <td>20:08:28</td>\n",
       "      <td>11.94</td>\n",
       "      <td>MB</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23412 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Time   Depth Magnitude Type        Type  Magnitude\n",
       "0      01/02/1965  13:44:18  131.60             MW  Earthquake        6.0\n",
       "1      01/04/1965  11:29:49   80.00             MW  Earthquake        5.8\n",
       "2      01/05/1965  18:05:58   20.00             MW  Earthquake        6.2\n",
       "3      01/08/1965  18:49:43   15.00             MW  Earthquake        5.8\n",
       "4      01/09/1965  13:32:50   15.00             MW  Earthquake        5.8\n",
       "...           ...       ...     ...            ...         ...        ...\n",
       "23407  12/28/2016  08:22:12   12.30             ML  Earthquake        5.6\n",
       "23408  12/28/2016  09:13:47    8.80             ML  Earthquake        5.5\n",
       "23409  12/28/2016  12:38:51   10.00            MWW  Earthquake        5.9\n",
       "23410  12/29/2016  22:30:19   79.00            MWW  Earthquake        6.3\n",
       "23411  12/30/2016  20:08:28   11.94             MB  Earthquake        5.5\n",
       "\n",
       "[23412 rows x 6 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "cols = ['Date', 'Time', 'Depth', 'Magnitude Type', 'Type', 'Magnitude']\n",
    "df = pd.read_csv(f'../data/earthquakes_1965_2016_database.csv.zip')[cols]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a4d3d1",
   "metadata": {},
   "source": [
    "## Step 2: Get Top 10 biggest/lowest values for single column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec767478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17083    9.1\n",
       "20501    9.1\n",
       "19928    8.8\n",
       "16       8.7\n",
       "17329    8.6\n",
       "21219    8.6\n",
       "15440    8.4\n",
       "18615    8.4\n",
       "12119    8.3\n",
       "16446    8.3\n",
       "Name: Magnitude, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Magnitude'].nlargest(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0463323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17083    9.1\n",
       "20501    9.1\n",
       "19928    8.8\n",
       "16       8.7\n",
       "17329    8.6\n",
       "Name: Magnitude, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Magnitude'].nlargest(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c0f4599",
   "metadata": {},
   "source": [
    "## Step 3: Get Top 10 biggest/lowest values - duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8f993cd5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Magnitude Type</th>\n",
       "      <th>Type</th>\n",
       "      <th>Magnitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10978</th>\n",
       "      <td>06/28/1992</td>\n",
       "      <td>12:00:45</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10977</th>\n",
       "      <td>06/28/1992</td>\n",
       "      <td>11:57:34</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8099</th>\n",
       "      <td>07/21/1986</td>\n",
       "      <td>22:07:16</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>02/16/1973</td>\n",
       "      <td>05:02:58</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MB</td>\n",
       "      <td>Explosion</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>07/23/1973</td>\n",
       "      <td>01:22:58</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MB</td>\n",
       "      <td>Nuclear Explosion</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             Date      Time  Depth Magnitude Type               Type  \\\n",
       "10978  06/28/1992  12:00:45 -1.100             ML         Earthquake   \n",
       "10977  06/28/1992  11:57:34 -0.097             MW         Earthquake   \n",
       "8099   07/21/1986  22:07:16 -0.076             ML         Earthquake   \n",
       "2614   02/16/1973  05:02:58  0.000             MB          Explosion   \n",
       "2797   07/23/1973  01:22:58  0.000             MB  Nuclear Explosion   \n",
       "\n",
       "       Magnitude  \n",
       "10978       5.77  \n",
       "10977       7.30  \n",
       "8099        5.60  \n",
       "2614        5.60  \n",
       "2797        6.30  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nsmallest(n=5, columns=['Depth'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9676088e",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Magnitude Type</th>\n",
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       "      <th>Magnitude</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10978</th>\n",
       "      <td>06/28/1992</td>\n",
       "      <td>12:00:45</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10977</th>\n",
       "      <td>06/28/1992</td>\n",
       "      <td>11:57:34</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>7.30</td>\n",
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       "    <tr>\n",
       "      <th>8099</th>\n",
       "      <td>07/21/1986</td>\n",
       "      <td>22:07:16</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>02/16/1973</td>\n",
       "      <td>05:02:58</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MB</td>\n",
       "      <td>Explosion</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>07/23/1973</td>\n",
       "      <td>01:22:58</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MB</td>\n",
       "      <td>Nuclear Explosion</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12613</th>\n",
       "      <td>08/17/1995</td>\n",
       "      <td>00:59:58</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MB</td>\n",
       "      <td>Nuclear Explosion</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13054</th>\n",
       "      <td>06/08/1996</td>\n",
       "      <td>02:55:58</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MB</td>\n",
       "      <td>Nuclear Explosion</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14841</th>\n",
       "      <td>05/26/2000</td>\n",
       "      <td>01:28:23</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MWC</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16668</th>\n",
       "      <td>02/24/2004</td>\n",
       "      <td>02:27:46</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MWB</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22363</th>\n",
       "      <td>09/01/2014</td>\n",
       "      <td>11:41:10</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MWW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Time  Depth Magnitude Type               Type  \\\n",
       "10978  06/28/1992  12:00:45 -1.100             ML         Earthquake   \n",
       "10977  06/28/1992  11:57:34 -0.097             MW         Earthquake   \n",
       "8099   07/21/1986  22:07:16 -0.076             ML         Earthquake   \n",
       "2614   02/16/1973  05:02:58  0.000             MB          Explosion   \n",
       "2797   07/23/1973  01:22:58  0.000             MB  Nuclear Explosion   \n",
       "...           ...       ...    ...            ...                ...   \n",
       "12613  08/17/1995  00:59:58  0.000             MB  Nuclear Explosion   \n",
       "13054  06/08/1996  02:55:58  0.000             MB  Nuclear Explosion   \n",
       "14841  05/26/2000  01:28:23  0.000            MWC         Earthquake   \n",
       "16668  02/24/2004  02:27:46  0.000            MWB         Earthquake   \n",
       "22363  09/01/2014  11:41:10  0.000            MWW         Earthquake   \n",
       "\n",
       "       Magnitude  \n",
       "10978       5.77  \n",
       "10977       7.30  \n",
       "8099        5.60  \n",
       "2614        5.60  \n",
       "2797        6.30  \n",
       "...          ...  \n",
       "12613       6.00  \n",
       "13054       5.90  \n",
       "14841       5.60  \n",
       "16668       6.40  \n",
       "22363       5.50  \n",
       "\n",
       "[173 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nsmallest(n=5, columns=['Depth'], keep='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aeda5602",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Depth</th>\n",
       "      <th>Magnitude Type</th>\n",
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       "      <th>10978</th>\n",
       "      <td>06/28/1992</td>\n",
       "      <td>12:00:45</td>\n",
       "      <td>-1.100</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.77</td>\n",
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       "    <tr>\n",
       "      <th>10977</th>\n",
       "      <td>06/28/1992</td>\n",
       "      <td>11:57:34</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8099</th>\n",
       "      <td>07/21/1986</td>\n",
       "      <td>22:07:16</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>ML</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22363</th>\n",
       "      <td>09/01/2014</td>\n",
       "      <td>11:41:10</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MWW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16668</th>\n",
       "      <td>02/24/2004</td>\n",
       "      <td>02:27:46</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MWB</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Time  Depth Magnitude Type        Type  Magnitude\n",
       "10978  06/28/1992  12:00:45 -1.100             ML  Earthquake       5.77\n",
       "10977  06/28/1992  11:57:34 -0.097             MW  Earthquake       7.30\n",
       "8099   07/21/1986  22:07:16 -0.076             ML  Earthquake       5.60\n",
       "22363  09/01/2014  11:41:10  0.000            MWW  Earthquake       5.50\n",
       "16668  02/24/2004  02:27:46  0.000            MWB  Earthquake       6.40"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nsmallest(n=5, columns=['Depth'], keep='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9493721a",
   "metadata": {},
   "source": [
    "## Step 4: Get Top N values in multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "037caea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Magnitude Type</th>\n",
       "      <th>Type</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17083</th>\n",
       "      <td>12/26/2004</td>\n",
       "      <td>00:58:53</td>\n",
       "      <td>30.0</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20501</th>\n",
       "      <td>03/11/2011</td>\n",
       "      <td>05:46:24</td>\n",
       "      <td>29.0</td>\n",
       "      <td>MWW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19928</th>\n",
       "      <td>02/27/2010</td>\n",
       "      <td>06:34:12</td>\n",
       "      <td>22.9</td>\n",
       "      <td>MWW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>02/04/1965</td>\n",
       "      <td>05:01:22</td>\n",
       "      <td>30.3</td>\n",
       "      <td>MW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17329</th>\n",
       "      <td>03/28/2005</td>\n",
       "      <td>16:09:37</td>\n",
       "      <td>30.0</td>\n",
       "      <td>MWW</td>\n",
       "      <td>Earthquake</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Time  Depth Magnitude Type        Type  Magnitude\n",
       "17083  12/26/2004  00:58:53   30.0             MW  Earthquake        9.1\n",
       "20501  03/11/2011  05:46:24   29.0            MWW  Earthquake        9.1\n",
       "19928  02/27/2010  06:34:12   22.9            MWW  Earthquake        8.8\n",
       "16     02/04/1965  05:01:22   30.3             MW  Earthquake        8.7\n",
       "17329  03/28/2005  16:09:37   30.0            MWW  Earthquake        8.6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nlargest(n=5, columns=['Magnitude', 'Depth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164f7a28",
   "metadata": {},
   "source": [
    "## Step 5: How do `nsmallest` and `nlargest` work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "487b0359",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Column 'Magnitude Type' has dtype object, cannot use method 'nlargest' with this dtype",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_22551/3997848027.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Magnitude Type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Depth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/PycharmProjects/datascientyst/venv/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mnlargest\u001b[0;34m(self, n, columns, keep)\u001b[0m\n\u001b[1;32m   6629\u001b[0m         \u001b[0mBrunei\u001b[0m      \u001b[0;36m434000\u001b[0m    \u001b[0;36m12128\u001b[0m      \u001b[0mBN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6630\u001b[0m         \"\"\"\n\u001b[0;32m-> 6631\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSelectNFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6633\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mnsmallest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"first\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/PycharmProjects/datascientyst/venv/lib/python3.8/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36mnlargest\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1219\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nlargest\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/PycharmProjects/datascientyst/venv/lib/python3.8/site-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, method)\u001b[0m\n\u001b[1;32m   1349\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_valid_dtype_n_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1351\u001b[0;31m                 raise TypeError(\n\u001b[0m\u001b[1;32m   1352\u001b[0m                     \u001b[0;34mf\"Column {repr(column)} has dtype {dtype}, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1353\u001b[0m                     \u001b[0;34mf\"cannot use method {repr(method)} with this dtype\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Column 'Magnitude Type' has dtype object, cannot use method 'nlargest' with this dtype"
     ]
    }
   ],
   "source": [
    "df.nlargest(n=5, columns=['Magnitude Type', 'Depth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d637ea7d",
   "metadata": {},
   "source": [
    "## Step 6: Display Top values for all numeric columns in DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b4ba2b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>Magnitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>700.0</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>691.6</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>690.0</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>688.0</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>687.6</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>682.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>678.9</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Depth  Magnitude\n",
       "0  700.0        9.1\n",
       "1  691.6        9.1\n",
       "2  690.0        8.8\n",
       "3  688.0        8.7\n",
       "4  687.6        8.6\n",
       "5  682.2        8.6\n",
       "6  678.9        8.4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import is_numeric_dtype\n",
    "\n",
    "dfs = []\n",
    "\n",
    "for col in df.columns:\n",
    "    top_values = []\n",
    "    if is_numeric_dtype(df[col]):\n",
    "        top_values = df[col].nlargest(n=7)\n",
    "        dfs.append(pd.DataFrame({col: top_values}).reset_index(drop=True))\n",
    "pd.concat(dfs, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6107fed6",
   "metadata": {},
   "source": [
    "## Step 7:  `nsmallest` and `nlargest` and datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "38368dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'], utc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1643125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23411   2016-12-30 00:00:00+00:00\n",
       "23410   2016-12-29 00:00:00+00:00\n",
       "23406   2016-12-28 00:00:00+00:00\n",
       "23407   2016-12-28 00:00:00+00:00\n",
       "23408   2016-12-28 00:00:00+00:00\n",
       "Name: Date, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].nlargest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c77e524c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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