Solution/Day_10_Solution_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P6xk2_jLsvFF"
   },
   "source": [
    "# [作業目標]\n",
    "\n",
    "1. [簡答題] 請問下列四種不同的 DataFrame 選取結果有什麼差異？\n",
    "\n",
    "```\n",
    "df.loc[ '2013-01-01', 'A'] \n",
    "df.loc[ '2013-01-01', ['A', 'B'] ] \n",
    "df.loc[ '2013-01-01':'2013-01-02', 'A' ] \n",
    "df.loc[ '2013-01-01':'2013-01-05', 'A':'C'] \n",
    "```\n",
    "\n",
    "\n",
    "2. 請根據提供的資料，選擇出下列的要求：\n",
    "\n",
    "```\n",
    "# - select the first 3 rows.\n",
    "# - select the odd rows. (index = 1, 3, 5)\n",
    "# - select the last 2 columns.\n",
    "# - select the even columns. (index = 0, 2, 4)\n",
    "```\n",
    "\n",
    "3. 請根據提供的資料，選擇出下列的要求：\n",
    "\n",
    "```\n",
    "# - 1. filtered by first column > 20?\n",
    "# - 2. filtered by first column + second column > 50\n",
    "# - 3. filtered by first column < 30 or second column > 30\n",
    "# - 4. filtered by total sum of row > 100\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uXGll28asvFS"
   },
   "source": [
    "# 作業 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. [簡答題] 請問下列四種不同的 DataFrame 選取結果有什麼差異？\n",
    "\n",
    "```\n",
    "df.loc[ '2013-01-01', 'A'] \n",
    "df.loc[ '2013-01-01', ['A', 'B'] ] \n",
    "df.loc[ '2013-01-01':'2013-01-02', 'A' ] \n",
    "df.loc[ '2013-01-01':'2013-01-05', 'A':'C'] \n",
    "```\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5QrYoyNWsvFS"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 請根據提供的資料，選擇出下列的要求：\n",
    "\n",
    "```\n",
    "# - select the first 3 rows.\n",
    "# - select the odd rows. (index = 1, 3, 5)\n",
    "# - select the last 2 columns.\n",
    "# - select the even columns. (index = 0, 2, 4)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 記得先 Import 正確的套件\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e1gVI0tvsvFY"
   },
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.824286</td>\n",
       "      <td>0.264754</td>\n",
       "      <td>-0.887365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.044179</td>\n",
       "      <td>0.151320</td>\n",
       "      <td>-0.975283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.812146</td>\n",
       "      <td>0.995687</td>\n",
       "      <td>2.556824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.746811</td>\n",
       "      <td>0.183326</td>\n",
       "      <td>-1.529704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.992052</td>\n",
       "      <td>1.578329</td>\n",
       "      <td>-0.858061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.387535</td>\n",
       "      <td>0.339512</td>\n",
       "      <td>0.714364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col1      col2      col3\n",
       "0  0.824286  0.264754 -0.887365\n",
       "1 -1.044179  0.151320 -0.975283\n",
       "2 -1.812146  0.995687  2.556824\n",
       "3  1.746811  0.183326 -1.529704\n",
       "4  0.992052  1.578329 -0.858061\n",
       "5  0.387535  0.339512  0.714364"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,3),columns=['col1','col2','col3'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>col1</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.824286</td>\n",
       "      <td>-0.887365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.044179</td>\n",
       "      <td>-0.975283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.812146</td>\n",
       "      <td>2.556824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.746811</td>\n",
       "      <td>-1.529704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.992052</td>\n",
       "      <td>-0.858061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.387535</td>\n",
       "      <td>0.714364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       col1      col3\n",
       "0  0.824286 -0.887365\n",
       "1 -1.044179 -0.975283\n",
       "2 -1.812146  2.556824\n",
       "3  1.746811 -1.529704\n",
       "4  0.992052 -0.858061\n",
       "5  0.387535  0.714364"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 參考解答\n",
    "\n",
    "# - select the first 3 rows.\n",
    "df[0:3]\n",
    "df.iloc[0:3,:]\n",
    "df.iloc[0:3]\n",
    "\n",
    "# - select the odd rows. (index = 1, 3, 5)\n",
    "df[1::2]\n",
    "df.iloc[1::2, :]\n",
    "df.iloc[[1,3,5], :]\n",
    "\n",
    "# - select the last 2 columns.\n",
    "df.iloc[:, -2:]\n",
    "\n",
    "# - select the even columns. (index = 0, 2)\n",
    "df.iloc[:, 0::2]\n",
    "df.iloc[:, [0, 2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 請根據提供的資料，選擇出下列的要求：\n",
    "\n",
    "```\n",
    "# - 1. filtered by first column > 20?\n",
    "# - 2. filtered by first column + second column > 50\n",
    "# - 3. filtered by first column < 30 or second column > 30\n",
    "# - 4. filtered by total sum of row > 100\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>19</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1   2   3\n",
       "0   24  32  33  17\n",
       "1   37  32  26  15\n",
       "2   15  27  21  36\n",
       "3   34  30  27  24\n",
       "4   34  20  38  27\n",
       "5   16  39  28  16\n",
       "6   39  35  38  24\n",
       "7   35  19  37  12\n",
       "8   16  17  37  32\n",
       "9   38  38  38  17\n",
       "10  10  21  15  39\n",
       "11  25  35  27  22\n",
       "12  33  20  15  37\n",
       "13  27  29  13  19\n",
       "14  32  15  13  25"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randint(10, 40, 60).reshape(-1, 4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>38</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>38</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>19</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1   2   3\n",
       "0   24  32  33  17\n",
       "1   37  32  26  15\n",
       "3   34  30  27  24\n",
       "4   34  20  38  27\n",
       "6   39  35  38  24\n",
       "7   35  19  37  12\n",
       "8   16  17  37  32\n",
       "9   38  38  38  17\n",
       "11  25  35  27  22\n",
       "12  33  20  15  37"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 參考解答\n",
    "\n",
    "# - 1. filtered by first column > 20?\n",
    "\n",
    "df.loc[df[0] > 20]\n",
    "df[df[0] > 20]\n",
    "\n",
    "# - 2. filtered by first column + second column > 50\n",
    "\n",
    "df[df[0] + df[1] > 50]\n",
    "df.loc[df[0] + df[1] > 50]\n",
    "\n",
    "# - 3. filtered by first column < 30 or second column > 30\n",
    "\n",
    "df[np.logical_or(df[0] < 30, df[1] > 30)]\n",
    "df[(df[0] < 30) | (df[1] > 30)]\n",
    "\n",
    "# - 4. filtered by total sum of row > 100\n",
    "\n",
    "df[df[0] + df[1] + df[2] + df[3] > 100]\n",
    "df[df.sum(axis=1) > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Day_023_Reduce_Skewness_Ans.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}