Day 25, 26, 27 - 50 Days of Data Analysis .ipynb

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   "source": [
    "## 1. \n",
    "\n",
    "#### Import the retail_shop_data file and view the first 5 rows. Write another code to check how many rows and columns are in the Data Frame. Are there any duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "174596ac",
   "metadata": {},
   "outputs": [
    {
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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>Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 01 2023</td>\n",
       "      <td>1001</td>\n",
       "      <td>101</td>\n",
       "      <td>T-Shirt</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 01 2023</td>\n",
       "      <td>1002</td>\n",
       "      <td>102</td>\n",
       "      <td>Jeans</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 01 2023</td>\n",
       "      <td>1003</td>\n",
       "      <td>103</td>\n",
       "      <td>Hoodie</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>Sneakers</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1005</td>\n",
       "      <td>105</td>\n",
       "      <td>Sunglasses</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        Date  Customer ID  Product ID Product Name  Cost  Price  Quantity   \n",
       "0  1 01 2023         1001         101      T-Shirt    18     20         2  \\\n",
       "1  1 01 2023         1002         102        Jeans    35     50         1   \n",
       "2  2 01 2023         1003         103       Hoodie    25     30         3   \n",
       "3  3 01 2023         1004         104     Sneakers    55     70         2   \n",
       "4  3 01 2023         1005         105   Sunglasses    17     25         1   \n",
       "\n",
       "   Total  \n",
       "0     40  \n",
       "1     50  \n",
       "2     90  \n",
       "3    140  \n",
       "4     25  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"retail_shop_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a0cc7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of rows in the dataset\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fde1cf30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Checking number of columns:\n",
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab262317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicates\n",
    "df.duplicated('Product Name').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cd0247",
   "metadata": {},
   "source": [
    "## 2.\n",
    "\n",
    "#### Write another code to rename columns: \"Total\" to \"Total Revenue\", \"Price\" to \"Price Per Product\" and \"Cost\" to \"Cost Per Product\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b14c06d8",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Cost Per Product</th>\n",
       "      <th>Price Per Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Revenue</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 01 2023</td>\n",
       "      <td>1001</td>\n",
       "      <td>101</td>\n",
       "      <td>T-Shirt</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 01 2023</td>\n",
       "      <td>1002</td>\n",
       "      <td>102</td>\n",
       "      <td>Jeans</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 01 2023</td>\n",
       "      <td>1003</td>\n",
       "      <td>103</td>\n",
       "      <td>Hoodie</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>Sneakers</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1005</td>\n",
       "      <td>105</td>\n",
       "      <td>Sunglasses</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "        Date  Customer ID  Product ID Product Name  Cost Per Product   \n",
       "0  1 01 2023         1001         101      T-Shirt                18  \\\n",
       "1  1 01 2023         1002         102        Jeans                35   \n",
       "2  2 01 2023         1003         103       Hoodie                25   \n",
       "3  3 01 2023         1004         104     Sneakers                55   \n",
       "4  3 01 2023         1005         105   Sunglasses                17   \n",
       "\n",
       "   Price Per Product  Quantity  Revenue  \n",
       "0                 20         2       40  \n",
       "1                 50         1       50  \n",
       "2                 30         3       90  \n",
       "3                 70         2      140  \n",
       "4                 25         1       25  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'Total':'Revenue','Cost': 'Cost Per Product', \n",
    "                   'Price': 'Price Per Product'}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29722d54",
   "metadata": {},
   "source": [
    "## 3\n",
    "\n",
    "#### Calculate the total cost per product and add this as a column to the DataFrame. Name this column: Total Costs. Calculate the difference between Revenue and Expenses. Name this column, Profit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "536d552f",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Cost Per Product</th>\n",
       "      <th>Price Per Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Costs</th>\n",
       "      <th>Profit</th>\n",
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       "      <th>0</th>\n",
       "      <td>1 01 2023</td>\n",
       "      <td>1001</td>\n",
       "      <td>101</td>\n",
       "      <td>T-Shirt</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
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       "      <td>1 01 2023</td>\n",
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       "      <td>Jeans</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 01 2023</td>\n",
       "      <td>1003</td>\n",
       "      <td>103</td>\n",
       "      <td>Hoodie</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
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       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>Sneakers</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>110</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1005</td>\n",
       "      <td>105</td>\n",
       "      <td>Sunglasses</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "        Date  Customer ID  Product ID Product Name  Cost Per Product   \n",
       "0  1 01 2023         1001         101      T-Shirt                18  \\\n",
       "1  1 01 2023         1002         102        Jeans                35   \n",
       "2  2 01 2023         1003         103       Hoodie                25   \n",
       "3  3 01 2023         1004         104     Sneakers                55   \n",
       "4  3 01 2023         1005         105   Sunglasses                17   \n",
       "\n",
       "   Price Per Product  Quantity  Revenue  Costs  Profit  \n",
       "0                 20         2       40     36       4  \n",
       "1                 50         1       50     35      15  \n",
       "2                 30         3       90     75      15  \n",
       "3                 70         2      140    110      30  \n",
       "4                 25         1       25     17       8  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the total costs column to the DataFrame\n",
    "df[\"Costs\"] = df[\"Cost Per Product\"] * df[\"Quantity\"]\n",
    "\n",
    "# Adding the profit column\n",
    "df['Profit'] = df['Revenue'] - df['Costs']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afeb37b",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### Add another column called Filter, this column should check for products that have profit margin of $15 and above. If a product has profit over 15 it should return True, otherwise False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7beff8a",
   "metadata": {},
   "outputs": [
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       "      <th>Product Name</th>\n",
       "      <th>Cost Per Product</th>\n",
       "      <th>Price Per Product</th>\n",
       "      <th>Quantity</th>\n",
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       "      <th>Filter</th>\n",
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       "      <td>1 01 2023</td>\n",
       "      <td>1001</td>\n",
       "      <td>101</td>\n",
       "      <td>T-Shirt</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
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       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>1 01 2023</td>\n",
       "      <td>1002</td>\n",
       "      <td>102</td>\n",
       "      <td>Jeans</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 01 2023</td>\n",
       "      <td>1003</td>\n",
       "      <td>103</td>\n",
       "      <td>Hoodie</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>Sneakers</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>110</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1005</td>\n",
       "      <td>105</td>\n",
       "      <td>Sunglasses</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Customer ID  Product ID Product Name  Cost Per Product   \n",
       "0  1 01 2023         1001         101      T-Shirt                18  \\\n",
       "1  1 01 2023         1002         102        Jeans                35   \n",
       "2  2 01 2023         1003         103       Hoodie                25   \n",
       "3  3 01 2023         1004         104     Sneakers                55   \n",
       "4  3 01 2023         1005         105   Sunglasses                17   \n",
       "\n",
       "   Price Per Product  Quantity  Revenue  Costs  Profit  Filter  \n",
       "0                 20         2       40     36       4   False  \n",
       "1                 50         1       50     35      15   False  \n",
       "2                 30         3       90     75      15   False  \n",
       "3                 70         2      140    110      30    True  \n",
       "4                 25         1       25     17       8   False  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Filter'] = df[\"Profit\"] > 15\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdc95b1",
   "metadata": {},
   "source": [
    "## 5. \n",
    "\n",
    "#### Which products have profit margin of over $15? Use pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91af0e3e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Customer ID</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Cost Per Product</th>\n",
       "      <th>Price Per Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Costs</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 01 2023</td>\n",
       "      <td>1004</td>\n",
       "      <td>104</td>\n",
       "      <td>Sneakers</td>\n",
       "      <td>55</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>110</td>\n",
       "      <td>30</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6 01 2023</td>\n",
       "      <td>1008</td>\n",
       "      <td>108</td>\n",
       "      <td>Jacket</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>63</td>\n",
       "      <td>17</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9 01 2023</td>\n",
       "      <td>1008</td>\n",
       "      <td>108</td>\n",
       "      <td>Jacket</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>126</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Customer ID  Product ID Product Name  Cost Per Product   \n",
       "3   3 01 2023         1004         104     Sneakers                55  \\\n",
       "7   6 01 2023         1008         108       Jacket                63   \n",
       "13  9 01 2023         1008         108       Jacket                63   \n",
       "\n",
       "    Price Per Product  Quantity  Revenue  Costs  Profit  Filter  \n",
       "3                  70         2      140    110      30    True  \n",
       "7                  80         1       80     63      17    True  \n",
       "13                 80         2      160    126      34    True  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using filter to filter True values from DataFrame\n",
    "df.query(\"Filter == True\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88895bb4",
   "metadata": {},
   "source": [
    "Another method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d880869d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3     Sneakers\n",
      "7       Jacket\n",
      "13      Jacket\n",
      "Name: Product Name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df[df['Filter'] == True]['Product Name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3316310f",
   "metadata": {},
   "source": [
    "# DAY 26"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11845665",
   "metadata": {},
   "source": [
    "## 1. \n",
    "\n",
    "#### Using pandas, write a code to check the difference in profit between Jackets and Sneakers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dab698ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The profit difference between Jackets and Sneakers 21\n"
     ]
    }
   ],
   "source": [
    "# Group data by products and sum profit\n",
    "df_data = df.groupby(\"Product Name\")[\"Profit\"].sum()\n",
    "\n",
    "jackets_sneakers_profit = abs(df_data.loc[\"Jacket\"] - df_data.loc[\"Sneakers\"])\n",
    "print(f'The profit difference between Jackets and Sneakers {jackets_sneakers_profit}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab75234",
   "metadata": {},
   "source": [
    "## 2. \n",
    "#### Using pandas, what is the difference in costs between the most profitable product and the least profitable product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8daf044d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference in costs between the most profitable product and least profitable is 98\n"
     ]
    }
   ],
   "source": [
    "# Sort DataFrame by profit\n",
    "df.sort_values(\"Profit\", inplace=True)\n",
    "cost_diff = df.iloc[-1][\"Costs\"] - df.iloc[0][\"Costs\"]\n",
    "\n",
    "print('The difference in costs between the most profitable', \n",
    "      'product and least profitable is', cost_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ff8a57",
   "metadata": {},
   "source": [
    "## 3. \n",
    "\n",
    "#### Write a code to access the total costs of jackets (using loc method)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b58e2ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The costs of Jackets is 189\n"
     ]
    }
   ],
   "source": [
    "# Group data by Product Name and sum costs\n",
    "df_data = df.groupby(\"Product Name\")[\"Costs\"].sum()\n",
    "\n",
    "# Use loc to find the product costs\n",
    "df_costs_of_jeans = df_data.loc[\"Jacket\"]\n",
    "\n",
    "print(\"The costs of Jackets is\", df_costs_of_jeans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9de0fa",
   "metadata": {},
   "source": [
    "## 4. \n",
    "#### Using Matplotlib, create a bar stack graph of the sales, costs and profits of the 5 least profitable products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7de6ce71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_least_5_products = df.sort_values(by = \"Profit\", ascending=True)[0:6]\n",
    "\n",
    "# Create data\n",
    "products = df_least_5_products[\"Product Name\"]\n",
    "sales = df_least_5_products[\"Revenue\"]\n",
    "costs = df_least_5_products[\"Costs\"]\n",
    "profit = df_least_5_products[\"Profit\"]\n",
    "\n",
    "# Create figure and axes\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "# Plotting the data\n",
    "ax.bar(products, sales,  label=\"Sales\")\n",
    "ax.bar(products, costs,  label=\"Costs\", bottom = sales)\n",
    "ax.bar(products, profit, label=\"Profit\", bottom = sales + costs)\n",
    "\n",
    "ax.set_ylabel(\"Revenue\", fontsize = 15)\n",
    "ax.set_xlabel(\"Products\", fontsize = 15)\n",
    "plt.xticks(rotation = 0)\n",
    "ax.legend()\n",
    "plt.title(\"6 Least Profitable Products\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0fe650",
   "metadata": {},
   "source": [
    "# Day 27"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef5ef2ac",
   "metadata": {},
   "source": [
    "## 1. \n",
    "\n",
    "#### Using loc method , calculate the profit of the sunglasses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74485b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profit on Sunglasses is 8 dollars\n"
     ]
    }
   ],
   "source": [
    "# Find the batteries sales value\n",
    "sales_sunglasses = df.loc[df[\"Product Name\"]==\"Sunglasses\",\"Revenue\"]\n",
    "# Find the batteries costs values\n",
    "costs_sunglasses = df.loc[df[\"Product Name\"]==\"Sunglasses\",\"Costs\"]\n",
    "\n",
    "# Calculate the difference to find profit.\n",
    "profit_of_sunglasses =  sales_sunglasses - costs_sunglasses\n",
    "print(f\"Profit on Sunglasses is {profit_of_sunglasses.iloc[0]} dollars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac115679",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### Use loc to return the profit of hoodies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bbc09ebb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The profit of hoodies is: 15\n"
     ]
    }
   ],
   "source": [
    "# Locating the profit of hoodies\n",
    "profit_hoodies = df.loc[df[\"Product Name\"] == \"Hoodie\", \"Profit\"]\n",
    "print(\"The profit of hoodies is:\", profit_hoodies.iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b0eb41",
   "metadata": {},
   "source": [
    "## 3.\n",
    "\n",
    "#### Using iloc return the names of the two least profitable products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "531fafe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16    Wallet\n",
       "12    Beanie\n",
       "Name: Product Name, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort products by profit in ascending order\n",
    "df.sort_values(\"Profit\", inplace=True)\n",
    "\n",
    "# Use iloc to find 2 least profitable products.\n",
    "least_profitable_products = df.iloc[:2,3]\n",
    "least_profitable_products"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53118c52",
   "metadata": {},
   "source": [
    "## 4. \n",
    "#### Using loc return the name of most profitable product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e9c7c26",
   "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>Customer ID</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Cost Per Product</th>\n",
       "      <th>Price Per Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Costs</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9 01 2023</td>\n",
       "      <td>1008</td>\n",
       "      <td>108</td>\n",
       "      <td>Jacket</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>126</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Customer ID  Product ID Product Name  Cost Per Product   \n",
       "13  9 01 2023         1008         108       Jacket                63  \\\n",
       "\n",
       "    Price Per Product  Quantity  Revenue  Costs  Profit  Filter  \n",
       "13                 80         2      160    126      34    True  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_most_profit = df.loc[df[\"Profit\"]==max(df[\"Profit\"])]\n",
    "df_most_profit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c5bbc57",
   "metadata": {},
   "source": [
    "## 5. \n",
    "#### Using seaborn, create a scatter plot to visualize the relationship between the sales and costs for each product. Is there any noticeable correlation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e85d4349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.scatterplot(x=df[\"Revenue\"], y=df[\"Costs\"], data=df)\n",
    "plt.title('Costs versus Profit', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c29319e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSV dataset generated successfully.\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import random\n",
    "\n",
    "# List of names\n",
    "names = [\"Alice\", \"Bob\", \"Charlie\", \"David\", \"Eve\", \"Frank\", \"Grace\", \"Henry\", \"Ivy\", \"Jack\",\n",
    "         \"Kate\", \"Liam\", \"Mia\", \"Noah\", \"Olivia\", \"Peter\", \"Quinn\", \"Rachel\", \"Samuel\", \"Tara\",\n",
    "         \"Uma\", \"Victor\", \"Wendy\", \"Xander\", \"Yara\", \"Zara\", \"Alex\", \"Beth\", \"Chris\", \"Diana\"]\n",
    "\n",
    "\n",
    "# Generate random data for distance run and time run\n",
    "data = []\n",
    "for _ in range(30):\n",
    "    name = random.choice(names)\n",
    "    distance = round(random.uniform(1, 10), 2)  # Random distance between 1 and 10\n",
    "    time = round(random.uniform(1, 5), 2)      # Random time between 1 and 5 hours\n",
    "    data.append([name, distance, time])\n",
    "\n",
    "# Write the data to a CSV file\n",
    "with open('running_data.csv', mode='w', newline='') as file:\n",
    "    writer = csv.writer(file)\n",
    "    writer.writerow([\"Name\", \"Distance (km)\", \"Time (hours)\"])  # Write header\n",
    "    writer.writerows(data)  # Write data rows\n",
    "\n",
    "print(\"CSV dataset generated successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "59de66a9",
   "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>Name</th>\n",
       "      <th>Distance (km)</th>\n",
       "      <th>Time (hours)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wendy</td>\n",
       "      <td>3.10</td>\n",
       "      <td>4.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yara</td>\n",
       "      <td>8.61</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grace</td>\n",
       "      <td>7.17</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grace</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>David</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Noah</td>\n",
       "      <td>4.95</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Quinn</td>\n",
       "      <td>3.35</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Uma</td>\n",
       "      <td>4.12</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wendy</td>\n",
       "      <td>2.74</td>\n",
       "      <td>4.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Xander</td>\n",
       "      <td>9.36</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yara</td>\n",
       "      <td>5.46</td>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bob</td>\n",
       "      <td>7.55</td>\n",
       "      <td>3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Diana</td>\n",
       "      <td>3.46</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kate</td>\n",
       "      <td>5.66</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jack</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Uma</td>\n",
       "      <td>3.38</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Yara</td>\n",
       "      <td>5.60</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Alice</td>\n",
       "      <td>5.41</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Frank</td>\n",
       "      <td>6.25</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bob</td>\n",
       "      <td>5.33</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Samuel</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Olivia</td>\n",
       "      <td>8.63</td>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Jack</td>\n",
       "      <td>2.32</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Eve</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Alex</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Uma</td>\n",
       "      <td>3.17</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Eve</td>\n",
       "      <td>6.51</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Victor</td>\n",
       "      <td>3.89</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Grace</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Jack</td>\n",
       "      <td>4.54</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Distance (km)  Time (hours)\n",
       "0    Wendy           3.10          4.24\n",
       "1     Yara           8.61          4.16\n",
       "2    Grace           7.17          2.94\n",
       "3    Grace           1.36          2.78\n",
       "4    David           3.96          3.46\n",
       "5     Noah           4.95          3.51\n",
       "6    Quinn           3.35          4.29\n",
       "7      Uma           4.12          3.15\n",
       "8    Wendy           2.74          4.08\n",
       "9   Xander           9.36          2.71\n",
       "10    Yara           5.46          3.27\n",
       "11     Bob           7.55          3.51\n",
       "12   Diana           3.46          2.05\n",
       "13    Kate           5.66          4.39\n",
       "14    Jack           2.80          2.61\n",
       "15     Uma           3.38          4.27\n",
       "16    Yara           5.60          3.70\n",
       "17   Alice           5.41          4.49\n",
       "18   Frank           6.25          1.11\n",
       "19     Bob           5.33          4.95\n",
       "20  Samuel           2.59          1.24\n",
       "21  Olivia           8.63          2.37\n",
       "22    Jack           2.32          2.50\n",
       "23     Eve           2.84          1.60\n",
       "24    Alex           1.47          1.83\n",
       "25     Uma           3.17          2.64\n",
       "26     Eve           6.51          1.35\n",
       "27  Victor           3.89          1.20\n",
       "28   Grace           3.04          3.18\n",
       "29    Jack           4.54          2.77"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"running_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a570d7f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rest dataset generated successfully.\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import random\n",
    "\n",
    "# List of names from the previous dataset\n",
    "names = [\"Alice\", \"Bob\", \"Charlie\", \"David\", \"Eve\", \"Frank\", \"Grace\", \"Henry\", \"Ivy\", \"Jack\",\n",
    "         \"Kate\", \"Liam\", \"Mia\", \"Noah\", \"Olivia\", \"Peter\", \"Quinn\", \"Rachel\", \"Samuel\", \"Tara\",\n",
    "         \"Uma\", \"Victor\", \"Wendy\", \"Xander\", \"Yara\", \"Zara\", \"Alex\", \"Beth\", \"Chris\", \"Diana\"]\n",
    "\n",
    "# Select 10 random runners for rest times\n",
    "rest_data = []\n",
    "for _ in range(10):\n",
    "    name = random.choice(names)\n",
    "    rest_time = round(random.uniform(0.5, 3), 2)  # Random rest time between 0.5 and 3 hours\n",
    "    rest_data.append([name, rest_time])\n",
    "\n",
    "# Write the rest data to a CSV file\n",
    "with open('runner_rest.csv', mode='w', newline='') as file:\n",
    "    writer = csv.writer(file)\n",
    "    writer.writerow([\"Name\", \"Rest Time (hours)\"])  # Write header\n",
    "    writer.writerows(rest_data)  # Write data rows\n",
    "\n",
    "print(\"Rest dataset generated successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d02816af",
   "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>Name</th>\n",
       "      <th>Rest Time (hours)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grace</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Frank</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alice</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noah</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Victor</td>\n",
       "      <td>1.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alice</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mia</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Noah</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Rest Time (hours)\n",
       "0   Grace               2.73\n",
       "1   Chris               1.91\n",
       "2   Frank               0.58\n",
       "3   Alice               2.47\n",
       "4    Noah               1.62\n",
       "5  Victor               1.95\n",
       "6   Alice               1.91\n",
       "7     Mia               2.98\n",
       "8     Bob               2.01\n",
       "9    Noah               1.33"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"runner_rest.csv\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40bb6673",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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