data-analysis/015-pandas-numpy-select-where/pandas-and-numpy-select-where.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating conditional columns on Pandas with Numpy select() and where() methods\n",
    "\n",
    "This is a notebook for the medium article [Creating conditional columns on Pandas with Numpy select() and where() methods](https://bindichen.medium.com/creating-conditional-columns-on-pandas-with-numpy-select-and-where-methods-8ee6e2dbd5d5)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Creating a conditional column from 2 datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'fruit': ['Apple', 'Banana', 'Apple', 'Banana'],\n",
    "    'supplier': ['T & C Bro', 'T & C Bro', 'JM Wholesales', 'JM Wholesales'],\n",
    "    'weight (kg)': [1000,2000,3000,4000],\n",
    "    'customer_rating': [4.8,3.2, 4.2, 4.3]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit</th>\n",
       "      <th>supplier</th>\n",
       "      <th>weight (kg)</th>\n",
       "      <th>customer_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Banana</td>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>2000</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>3000</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banana</td>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>4000</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit       supplier  weight (kg)  customer_rating\n",
       "0   Apple      T & C Bro         1000              4.8\n",
       "1  Banana      T & C Bro         2000              3.2\n",
       "2   Apple  JM Wholesales         3000              4.2\n",
       "3  Banana  JM Wholesales         4000              4.3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tc_price = pd.DataFrame({\n",
    "    'fruit': ['Apple', 'Banana', 'Orange', 'Pineapple'],\n",
    "    'price (kg)': [1.1, 2, 2.9, 3.1]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>fruit</th>\n",
       "      <th>price (kg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Banana</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orange</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pineapple</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fruit  price (kg)\n",
       "0      Apple         1.1\n",
       "1     Banana         2.0\n",
       "2     Orange         2.9\n",
       "3  Pineapple         3.1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "jm_price = pd.DataFrame({\n",
    "    'fruit': ['Apple', 'Banana', 'Orange', 'Pineapple'],\n",
    "    'price (kg)': [1.2, 1.8, 4, 6]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>fruit</th>\n",
       "      <th>price (kg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Banana</td>\n",
       "      <td>1.8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orange</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pineapple</td>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "       fruit  price (kg)\n",
       "0      Apple         1.2\n",
       "1     Banana         1.8\n",
       "2     Orange         4.0\n",
       "3  Pineapple         6.0"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "jm_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>supplier</th>\n",
       "      <th>weight (kg)</th>\n",
       "      <th>customer_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>2000</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>3000</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>4000</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             supplier  weight (kg)  customer_rating\n",
       "fruit                                              \n",
       "Apple       T & C Bro         1000              4.8\n",
       "Banana      T & C Bro         2000              3.2\n",
       "Apple   JM Wholesales         3000              4.2\n",
       "Banana  JM Wholesales         4000              4.3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('fruit')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>price (kg)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orange</th>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pineapple</th>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           price (kg)\n",
       "fruit                \n",
       "Apple             1.1\n",
       "Banana            2.0\n",
       "Orange            2.9\n",
       "Pineapple         3.1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc_price = tc_price.set_index('fruit')\n",
    "tc_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>price (kg)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orange</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pineapple</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price (kg)\n",
       "fruit                \n",
       "Apple             1.2\n",
       "Banana            1.8\n",
       "Orange            4.0\n",
       "Pineapple         6.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jm_price = jm_price.set_index('fruit')\n",
    "jm_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supplier</th>\n",
       "      <th>weight (kg)</th>\n",
       "      <th>customer_rating</th>\n",
       "      <th>price (kg)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>2000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>3000</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>4000</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             supplier  weight (kg)  customer_rating  price (kg)\n",
       "fruit                                                          \n",
       "Apple       T & C Bro         1000              4.8         1.1\n",
       "Banana      T & C Bro         2000              3.2         2.0\n",
       "Apple   JM Wholesales         3000              4.2         1.2\n",
       "Banana  JM Wholesales         4000              4.3         1.8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price (kg)'] = np.where(\n",
    "    df['supplier'] == 'T & C Bro', \n",
    "    tc_price.loc[df.index]['price (kg)'], \n",
    "    jm_price.loc[df.index]['price (kg)'],\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Creating a conditional column from 3 datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3 = pd.DataFrame({\n",
    "    'fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple', 'Banana'],\n",
    "    'supplier': ['T & C Bro', 'T & C Bro', 'JM Wholesales', 'JM Wholesales', 'Star Ltd.', 'Star Ltd.'],\n",
    "    'buy (kg)': [1000,2000,3000,4000, 2000, 1500],\n",
    "    'customer_rating': [4.8,3.2, 4.2, 4.3, 5, 4.8]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>supplier</th>\n",
       "      <th>buy (kg)</th>\n",
       "      <th>customer_rating</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Banana</td>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>2000</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>3000</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banana</td>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>4000</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Star Ltd.</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Banana</td>\n",
       "      <td>Star Ltd.</td>\n",
       "      <td>1500</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fruit       supplier  buy (kg)  customer_rating\n",
       "0   Apple      T & C Bro      1000              4.8\n",
       "1  Banana      T & C Bro      2000              3.2\n",
       "2   Apple  JM Wholesales      3000              4.2\n",
       "3  Banana  JM Wholesales      4000              4.3\n",
       "4   Apple      Star Ltd.      2000              5.0\n",
       "5  Banana      Star Ltd.      1500              4.8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_price = pd.DataFrame({\n",
    "    'fruit': ['Apple', 'Banana', 'Orange', 'Pineapple'],\n",
    "    'T & C Bro': [1.1, 2, 2.9, 3.1],\n",
    "    'JM Wholesales': [1.2, 1.8, 4, 6],\n",
    "    'Star Ltd.': [0.9, 1.5, 3.8, 5.5]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fruit</th>\n",
       "      <th>T &amp; C Bro</th>\n",
       "      <th>JM Wholesales</th>\n",
       "      <th>Star Ltd.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Banana</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orange</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pineapple</td>\n",
       "      <td>3.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fruit  T & C Bro  JM Wholesales  Star Ltd.\n",
       "0      Apple        1.1            1.2        0.9\n",
       "1     Banana        2.0            1.8        1.5\n",
       "2     Orange        2.9            4.0        3.8\n",
       "3  Pineapple        3.1            6.0        5.5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3 = df_3.set_index('fruit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supplier</th>\n",
       "      <th>buy (kg)</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>2000</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>3000</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>4000</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>Star Ltd.</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>Star Ltd.</td>\n",
       "      <td>1500</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             supplier  buy (kg)  customer_rating\n",
       "fruit                                           \n",
       "Apple       T & C Bro      1000              4.8\n",
       "Banana      T & C Bro      2000              3.2\n",
       "Apple   JM Wholesales      3000              4.2\n",
       "Banana  JM Wholesales      4000              4.3\n",
       "Apple       Star Ltd.      2000              5.0\n",
       "Banana      Star Ltd.      1500              4.8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_price = df_price.set_index('fruit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T &amp; C Bro</th>\n",
       "      <th>JM Wholesales</th>\n",
       "      <th>Star Ltd.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>1.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orange</th>\n",
       "      <td>2.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pineapple</th>\n",
       "      <td>3.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           T & C Bro  JM Wholesales  Star Ltd.\n",
       "fruit                                         \n",
       "Apple            1.1            1.2        0.9\n",
       "Banana           2.0            1.8        1.5\n",
       "Orange           2.9            4.0        3.8\n",
       "Pineapple        3.1            6.0        5.5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>supplier</th>\n",
       "      <th>buy (kg)</th>\n",
       "      <th>customer_rating</th>\n",
       "      <th>price (kg)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>T &amp; C Bro</td>\n",
       "      <td>2000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>3000</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>JM Wholesales</td>\n",
       "      <td>4000</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple</th>\n",
       "      <td>Star Ltd.</td>\n",
       "      <td>2000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>Star Ltd.</td>\n",
       "      <td>1500</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             supplier  buy (kg)  customer_rating  price (kg)\n",
       "fruit                                                       \n",
       "Apple       T & C Bro      1000              4.8         1.1\n",
       "Banana      T & C Bro      2000              3.2         2.0\n",
       "Apple   JM Wholesales      3000              4.2         1.2\n",
       "Banana  JM Wholesales      4000              4.3         1.8\n",
       "Apple       Star Ltd.      2000              5.0         0.9\n",
       "Banana      Star Ltd.      1500              4.8         1.5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args = df_price.loc[df_3.index]\n",
    "\n",
    "conds = [\n",
    "    df_3['supplier'] == 'T & C Bro', \n",
    "    df_3['supplier'] == 'JM Wholesales', \n",
    "    df_3['supplier'] == 'Star Ltd.',\n",
    "]\n",
    "\n",
    "choices = [args['T & C Bro'], args['JM Wholesales'], args['Star Ltd.']]\n",
    "\n",
    "\n",
    "df_3['price (kg)'] = np.select(conds, choices)\n",
    "df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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