data_wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Wrangling- Laptops Pricing dataset**\n",
    "\n",
    "Used to convert data from an initial format to a format that may be better for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WORKFLOW\n",
    "\n",
    "1. Import data\n",
    "2. Identify missing values\n",
    "3. Deal with missing data\n",
    "4. Correct data format\n",
    "5. Data Standardization\n",
    "6. Data Normalization\n",
    "7. Binning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will be using the following libraries:\n",
    "\n",
    "*   [`pandas`](https://pandas.pydata.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for managing the data.\n",
    "*   [`numpy`](https://numpy.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for mathematical operations.\n",
    "*   [`matplotlib`](https://matplotlib.org/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMML0187ENSkillsNetwork31430127-2021-01-01) for additional plotting tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load data into a `pandas.DataFrame`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-Coursera/laptop_pricing_dataset_mod1.csv\"\n",
    "df = pd.read_csv(filepath, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify loading by displaying the dataframe summary using `dataframe.info()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 239 entries, 0 to 238\n",
      "Data columns (total 13 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       238 non-null    float64\n",
      " 1   1       239 non-null    object \n",
      " 2   2       239 non-null    object \n",
      " 3   3       239 non-null    object \n",
      " 4   4       239 non-null    object \n",
      " 5   5       239 non-null    object \n",
      " 6   6       239 non-null    object \n",
      " 7   7       235 non-null    object \n",
      " 8   8       239 non-null    object \n",
      " 9   9       239 non-null    object \n",
      " 10  10      239 non-null    object \n",
      " 11  11      234 non-null    object \n",
      " 12  12      239 non-null    object \n",
      "dtypes: float64(1), object(12)\n",
      "memory usage: 24.4+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the first 5 values of the updated dataframe using `dataframe.head()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>4</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturer</td>\n",
       "      <td>Category</td>\n",
       "      <td>Screen</td>\n",
       "      <td>GPU</td>\n",
       "      <td>OS</td>\n",
       "      <td>CPU_core</td>\n",
       "      <td>Screen_Size_cm</td>\n",
       "      <td>CPU_frequency</td>\n",
       "      <td>RAM_GB</td>\n",
       "      <td>Storage_GB_SSD</td>\n",
       "      <td>Weight_kg</td>\n",
       "      <td>Price</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Acer</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35.56</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.6</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>2.2</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>2.2</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>33.782</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0             1         2          3    4   5         6               7   \\\n",
       "0  NaN  Manufacturer  Category     Screen  GPU  OS  CPU_core  Screen_Size_cm   \n",
       "1  0.0          Acer         4  IPS Panel    2   1         5           35.56   \n",
       "2  1.0          Dell         3    Full HD    1   1         3          39.624   \n",
       "3  2.0          Dell         3    Full HD    1   1         7          39.624   \n",
       "4  3.0          Dell         4  IPS Panel    2   1         5          33.782   \n",
       "\n",
       "              8       9               10         11     12  \n",
       "0  CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_kg  Price  \n",
       "1            1.6       8             256        1.6    978  \n",
       "2            2.0       4             256        2.2    634  \n",
       "3            2.7       8             256        2.2    946  \n",
       "4            1.6       8             128       1.22   1244  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we can update the `Screen_Size_cm` column such that all values are rounded to nearest 2 decimal places by using `numpy.round()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan 'Manufacturer' 'Category' 'Screen' 'GPU' 'OS' 'CPU_core'\n",
      " 'Screen_Size_cm' 'CPU_frequency' 'RAM_GB' 'Storage_GB_SSD' 'Weight_kg'\n",
      " 'Price']\n"
     ]
    }
   ],
   "source": [
    "new_header=df.values[0]\n",
    "print(new_header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>NaN</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Category</th>\n",
       "      <th>Screen</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>CPU_core</th>\n",
       "      <th>Screen_Size_cm</th>\n",
       "      <th>CPU_frequency</th>\n",
       "      <th>RAM_GB</th>\n",
       "      <th>Storage_GB_SSD</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Acer</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35.56</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>1.6</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>2.2</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>2.2</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>33.782</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>HP</td>\n",
       "      <td>4</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.91</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NaN Manufacturer Category     Screen GPU OS CPU_core Screen_Size_cm  \\\n",
       "1  0.0         Acer        4  IPS Panel   2  1        5          35.56   \n",
       "2  1.0         Dell        3    Full HD   1  1        3         39.624   \n",
       "3  2.0         Dell        3    Full HD   1  1        7         39.624   \n",
       "4  3.0         Dell        4  IPS Panel   2  1        5         33.782   \n",
       "5  4.0           HP        4    Full HD   2  1        7         39.624   \n",
       "\n",
       "  CPU_frequency RAM_GB Storage_GB_SSD Weight_kg Price  \n",
       "1           1.6      8            256       1.6   978  \n",
       "2           2.0      4            256       2.2   634  \n",
       "3           2.7      8            256       2.2   946  \n",
       "4           1.6      8            128      1.22  1244  \n",
       "5           1.8      8            256      1.91   837  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=new_header\n",
    "df.drop(df.index[0],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NaN</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Category</th>\n",
       "      <th>Screen</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>CPU_core</th>\n",
       "      <th>Screen_Size_cm</th>\n",
       "      <th>CPU_frequency</th>\n",
       "      <th>RAM_GB</th>\n",
       "      <th>Storage_GB_SSD</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Acer</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35.56</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.6</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>2.2</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>2.7</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>2.2</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>4</td>\n",
       "      <td>IPS Panel</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>33.782</td>\n",
       "      <td>1.6</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>HP</td>\n",
       "      <td>4</td>\n",
       "      <td>Full HD</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>1.8</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1.91</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NaN Manufacturer Category     Screen GPU OS CPU_core Screen_Size_cm  \\\n",
       "1  0.0         Acer        4  IPS Panel   2  1        5          35.56   \n",
       "2  1.0         Dell        3    Full HD   1  1        3         39.624   \n",
       "3  2.0         Dell        3    Full HD   1  1        7         39.624   \n",
       "4  3.0         Dell        4  IPS Panel   2  1        5         33.782   \n",
       "5  4.0           HP        4    Full HD   2  1        7         39.624   \n",
       "\n",
       "  CPU_frequency RAM_GB Storage_GB_SSD Weight_kg Price  \n",
       "1           1.6      8            256       1.6   978  \n",
       "2           2.0      4            256       2.2   634  \n",
       "3           2.7      8            256       2.2   946  \n",
       "4           1.6      8            128      1.22  1244  \n",
       "5           1.8      8            256      1.91   837  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Screen_Size_cm']] = np.round(df[['Screen_Size_cm']],2)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Identify missing values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values are converted by default. Use the following functions to identify these missing values. You can use two methods to detect missing data:\n",
    "\n",
    "1. isnull()\n",
    "2. notnull()\n",
    "   \n",
    "**The output is a boolean value indicating whether the value that is passed into the argument is in fact missing data**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        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>NaN</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Category</th>\n",
       "      <th>Screen</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>CPU_core</th>\n",
       "      <th>Screen_Size_cm</th>\n",
       "      <th>CPU_frequency</th>\n",
       "      <th>RAM_GB</th>\n",
       "      <th>Storage_GB_SSD</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NaN  Manufacturer  Category  Screen    GPU     OS  CPU_core  \\\n",
       "1    False         False     False   False  False  False     False   \n",
       "2    False         False     False   False  False  False     False   \n",
       "3    False         False     False   False  False  False     False   \n",
       "4    False         False     False   False  False  False     False   \n",
       "5    False         False     False   False  False  False     False   \n",
       "..     ...           ...       ...     ...    ...    ...       ...   \n",
       "234  False         False     False   False  False  False     False   \n",
       "235  False         False     False   False  False  False     False   \n",
       "236  False         False     False   False  False  False     False   \n",
       "237  False         False     False   False  False  False     False   \n",
       "238  False         False     False   False  False  False     False   \n",
       "\n",
       "     Screen_Size_cm  CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_kg  Price  \n",
       "1             False          False   False           False      False  False  \n",
       "2             False          False   False           False      False  False  \n",
       "3             False          False   False           False      False  False  \n",
       "4             False          False   False           False      False  False  \n",
       "5             False          False   False           False      False  False  \n",
       "..              ...            ...     ...             ...        ...    ...  \n",
       "234           False          False   False           False      False  False  \n",
       "235           False          False   False           False      False  False  \n",
       "236           False          False   False           False      False  False  \n",
       "237           False          False   False           False      False  False  \n",
       "238           False          False   False           False      False  False  \n",
       "\n",
       "[238 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COUNTING MISSING VALUES IN A COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n",
      "nan\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Manufacturer\n",
      "Manufacturer\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Category\n",
      "Category\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Screen\n",
      "Screen\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "GPU\n",
      "GPU\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "OS\n",
      "OS\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "CPU_core\n",
      "CPU_core\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Screen_Size_cm\n",
      "Screen_Size_cm\n",
      "False    234\n",
      "True       4\n",
      "Name: count, dtype: int64\n",
      "\n",
      "CPU_frequency\n",
      "CPU_frequency\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "RAM_GB\n",
      "RAM_GB\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Storage_GB_SSD\n",
      "Storage_GB_SSD\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Weight_kg\n",
      "Weight_kg\n",
      "False    233\n",
      "True       5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Price\n",
      "Price\n",
      "False    238\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "missing_data = df.isnull()\n",
    "for column in missing_data.columns.values.tolist():\n",
    "    print(column)\n",
    "    print (missing_data[column].value_counts())\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Deal with missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace with mean\n",
    "Missing values in attributes that have continuous data are best replaced using Mean value. We note that values in \"Weight_kg\" attribute are continuous in nature, and some values are missing. Therefore, write a code to replace the missing values of weight with the average value of the attribute.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       1.6\n",
       "2       2.2\n",
       "3       2.2\n",
       "4      1.22\n",
       "5      1.91\n",
       "       ... \n",
       "234     1.7\n",
       "235     1.2\n",
       "236    1.36\n",
       "237     2.4\n",
       "238    1.95\n",
       "Name: Weight_kg, Length: 238, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing missing data with mean\n",
    "Weight_mean=df[\"Weight_kg\"].astype(\"float\").mean(axis=0)\n",
    "df['Weight_kg'].replace(np.nan, Weight_mean, inplace=True)\n",
    "# astype() function converts the values to the desired data type\n",
    "# axis=0 indicates that the mean value is to calculated across all column elements in a row.\n",
    "df['Weight_kg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_kg\n",
       "False    238\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check\n",
    "missing_data = df.isnull()\n",
    "missing_data['Weight_kg'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace with the most frequent value\n",
    "Missing values in attributes that have categorical data are best replaced using the most frequent value. We note that values in \"Screen_Size_cm\" attribute are categorical in nature, and some values are missing. Therefore, write a code to replace the missing values of Screen Size with the most frequent value of the attribute.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_screen_size = df['Screen_Size_cm'].value_counts().idxmax()\n",
    "df[\"Screen_Size_cm\"].replace(np.nan, common_screen_size, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Screen_Size_cm\n",
       "False    238\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to check\n",
    "missing_data = df.isnull()\n",
    "missing_data['Screen_Size_cm'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Correct data format\n",
    "Both \"Weight_kg\" and \"Screen_Size_cm\" are seen to have the data type \"Object\", while both of them should be having a data type of \"float\". Write a code to fix the data type of these two columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['Weight_kg','Screen_Size_cm','CPU_frequency','Price']] = df[['Weight_kg',\"Screen_Size_cm\",'CPU_frequency','Price']].astype(\"float\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Data Standardization\n",
    "The value of Screen_size usually has a standard unit of inches. Similarly, weight of the laptop is needed to be in pounds. Use the below mentioned units of conversion and write a code to modify the columns of the dataframe accordingly. Update their names as well.\n",
    "\n",
    "```{math}\n",
    "1 inch = 2.54 cm\n",
    "1 kg   = 2.205 pounds\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Weight_kg\"] = df[\"Weight_kg\"]*2.205\n",
    "df.rename(columns={'Weight_kg':'Weight_pounds'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Data Normalization\n",
    "Often it is required to normalize a continuous data attribute. Write a code to normalize the \"CPU_frequency\" attribute with respect to the maximum value available in the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data normalization is a preprocessing technique used in data mining and machine learning to scale numeric data attributes to a standard range. Normalization transforms the data to have a mean of 0 and a standard deviation of 1 or to scale it between 0 and 1.\n",
    "\n",
    "Normalization is important because it ensures that each feature contributes equally to the analysis and prevents certain features from dominating due to their larger scales. It can also help algorithms converge faster during training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Common methods of normalization include:\n",
    "\n",
    "1. Min-Max Scaling: Scales the data to a fixed range, typically between 0 and 1. The formula is:\n",
    "$$X_norm=X-X_min/X_max-X_min$$\n",
    "\n",
    "2.Z-score Standardization: Standardizes the data to have a mean of 0 and a standard deviation of 1. The formula is:\n",
    "$$X_norm=(X-\\mu)/\\sigma$$.where μ is the mean of the data and σ is the standard deviation.\n",
    "\n",
    "3.Decimal Scaling: Shifts the decimal point of the data to the left or right, making the absolute values fall between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0.551724\n",
       "2      0.689655\n",
       "3      0.931034\n",
       "4      0.551724\n",
       "5      0.620690\n",
       "         ...   \n",
       "234    0.896552\n",
       "235    0.827586\n",
       "236    0.896552\n",
       "237    0.862069\n",
       "238    0.793103\n",
       "Name: CPU_frequency, Length: 238, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CPU_frequency']= df['CPU_frequency']/df['CPU_frequency'].max()\n",
    "df['CPU_frequency']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Binning\n",
    "Binning is a process of creating a categorical attribute which splits the values of a continuous data into a specified number of groups. In this case, write a code to create 3 bins for the attribute \"Price\". These bins would be named \"Low\", \"Medium\" and \"High\". The new attribute will be named \"Price-binned\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 527.        , 1621.33333333, 2715.66666667, 3810.        ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.linspace(min(df[\"Price\"]),max(df[\"Price\"]), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         Low\n",
       "2         Low\n",
       "3         Low\n",
       "4         Low\n",
       "5         Low\n",
       "        ...  \n",
       "234    Medium\n",
       "235    Medium\n",
       "236    Medium\n",
       "237       Low\n",
       "238       Low\n",
       "Name: Price-binned, Length: 238, dtype: category\n",
       "Categories (3, object): ['Low' < 'Medium' < 'High']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.linspace(min(df[\"Price\"]),max(df[\"Price\"]), 4)\n",
    "group_names = ['Low', 'Medium', 'High']#labelling \n",
    "df['Price-binned'] = pd.cut(df['Price'], bins, labels=group_names, include_lowest=True)#assigning label to a values\n",
    "df['Price-binned']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3810.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(df[\"Price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, plot the bar graph of these bins.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price-binned\n",
       "Low       160\n",
       "Medium     72\n",
       "High        6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Price-binned\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Price bins')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(group_names, df[\"Price-binned\"].value_counts())\n",
    "plt.xlabel(\"Price\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Price bins\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indicator variables\n",
    "Convert the \"Screen\" attribute of the dataset into 2 indicator variables, \"Screen-IPS_panel\" and \"Screen-Full_HD\". Then drop the \"Screen\" attribute from the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      IPS Panel\n",
       "2        Full HD\n",
       "3        Full HD\n",
       "4      IPS Panel\n",
       "5        Full HD\n",
       "         ...    \n",
       "234    IPS Panel\n",
       "235      Full HD\n",
       "236    IPS Panel\n",
       "237      Full HD\n",
       "238      Full HD\n",
       "Name: Screen, Length: 238, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Screen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_variable = pd.get_dummies(df[\"Screen\"])#Convert the \"Screen\" attribute of the dataset into 2 indicator variables, \"Screen-IPS_panel\" and \"Screen-Full_HD\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Full HD</th>\n",
       "      <th>IPS Panel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Full HD  IPS Panel\n",
       "1      False       True\n",
       "2       True      False\n",
       "3       True      False\n",
       "4      False       True\n",
       "5       True      False\n",
       "..       ...        ...\n",
       "234    False       True\n",
       "235     True      False\n",
       "236    False       True\n",
       "237     True      False\n",
       "238     True      False\n",
       "\n",
       "[238 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a dummy dataframe\n",
    "dummy_variable_1 = pd.get_dummies(df[\"Screen\"])\n",
    "dummy_variable_1.rename(columns={'IPS Panel':'Screen-IPS_panel', 'Full HD':'Screen-Full_HD'}, inplace=True)\n",
    "#merging dataframes\n",
    "df = pd.concat([df, dummy_variable_1], axis=1)\n",
    "\n",
    "# drop original column \"Screen\" from \"df\"\n",
    "df.drop(\"Screen\", axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This version of the dataset, now finalized, is the one you'll be using in all subsequent modules. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the content of dataframe.head() to verify the changes that were made to the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>NaN</th>\n",
       "      <th>Manufacturer</th>\n",
       "      <th>Category</th>\n",
       "      <th>GPU</th>\n",
       "      <th>OS</th>\n",
       "      <th>CPU_core</th>\n",
       "      <th>Screen_Size_cm</th>\n",
       "      <th>CPU_frequency</th>\n",
       "      <th>RAM_GB</th>\n",
       "      <th>Storage_GB_SSD</th>\n",
       "      <th>Weight_pounds</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price-binned</th>\n",
       "      <th>Screen-Full_HD</th>\n",
       "      <th>Screen-IPS_panel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Acer</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>35.560</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>3.52800</td>\n",
       "      <td>978.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39.624</td>\n",
       "      <td>0.689655</td>\n",
       "      <td>4</td>\n",
       "      <td>256</td>\n",
       "      <td>4.85100</td>\n",
       "      <td>634.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4.85100</td>\n",
       "      <td>946.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Dell</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>33.782</td>\n",
       "      <td>0.551724</td>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>2.69010</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>HP</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.624</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4.21155</td>\n",
       "      <td>837.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NaN Manufacturer Category GPU OS CPU_core  Screen_Size_cm  CPU_frequency  \\\n",
       "1  0.0         Acer        4   2  1        5          35.560       0.551724   \n",
       "2  1.0         Dell        3   1  1        3          39.624       0.689655   \n",
       "3  2.0         Dell        3   1  1        7          39.624       0.931034   \n",
       "4  3.0         Dell        4   2  1        5          33.782       0.551724   \n",
       "5  4.0           HP        4   2  1        7          39.624       0.620690   \n",
       "\n",
       "  RAM_GB Storage_GB_SSD  Weight_pounds   Price Price-binned  Screen-Full_HD  \\\n",
       "1      8            256        3.52800   978.0          Low           False   \n",
       "2      4            256        4.85100   634.0          Low            True   \n",
       "3      8            256        4.85100   946.0          Low            True   \n",
       "4      8            128        2.69010  1244.0          Low           False   \n",
       "5      8            256        4.21155   837.0          Low            True   \n",
       "\n",
       "   Screen-IPS_panel  \n",
       "1              True  \n",
       "2             False  \n",
       "3             False  \n",
       "4              True  \n",
       "5             False  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  }
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