Prerequisites.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"mobile_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "re = df.loc[\"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhoneId</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>...</th>\n",
       "      <th>os_name_Tizen</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>403</td>\n",
       "      <td>6.26</td>\n",
       "      <td>182</td>\n",
       "      <td>4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.9</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>6.20</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.2</td>\n",
       "      <td>32</td>\n",
       "      <td>4230</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>409</td>\n",
       "      <td>6.30</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83.68</td>\n",
       "      <td>157.0</td>\n",
       "      <td>32</td>\n",
       "      <td>3500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>411</td>\n",
       "      <td>6.00</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.78</td>\n",
       "      <td>159.8</td>\n",
       "      <td>64</td>\n",
       "      <td>3300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>396</td>\n",
       "      <td>6.50</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>84.23</td>\n",
       "      <td>160.4</td>\n",
       "      <td>64</td>\n",
       "      <td>3750</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
       "0        0            403         6.26     182    4                  1.8   \n",
       "1        1            271         6.20     168    3                  1.8   \n",
       "2        2            409         6.30     168    3                  2.1   \n",
       "3        4            411         6.00     169    4                  2.2   \n",
       "4        5            396         6.50     175    4                  2.2   \n",
       "\n",
       "   Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  ...  \\\n",
       "0                              80.68   157.9               64      4000  ...   \n",
       "1                              80.85   156.2               32      4230  ...   \n",
       "2                              83.68   157.0               32      3500  ...   \n",
       "3                              74.78   159.8               64      3300  ...   \n",
       "4                              84.23   160.4               64      3750  ...   \n",
       "\n",
       "   os_name_Tizen  os_name_iOS  SIM Slot(s)_Dual SIM, GSM+CDMA  \\\n",
       "0              0            0                               0   \n",
       "1              0            0                               0   \n",
       "2              0            0                               0   \n",
       "3              0            0                               0   \n",
       "4              0            0                               0   \n",
       "\n",
       "   SIM Slot(s)_Dual SIM, GSM+GSM  SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  \\\n",
       "0                              0                                          1   \n",
       "1                              0                                          1   \n",
       "2                              0                                          1   \n",
       "3                              1                                          0   \n",
       "4                              0                                          1   \n",
       "\n",
       "   SIM Slot(s)_Single SIM, GSM  Sim1_2G  Sim1_3G  Sim1_4G  Rating  \n",
       "0                            0        0        0        1     4.5  \n",
       "1                            0        0        0        1     4.5  \n",
       "2                            0        0        0        1     4.4  \n",
       "3                            0        0        0        1     4.3  \n",
       "4                            0        0        0        1     4.4  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.DataFrame({'A': ['Samsung', 'Redmi', 'Nokia', 'Vivo', 'RealMe', 'Apple', 'OnePlus'], 'Weight' : [1.5,1.25,1.3,1.4,1.5,1.6,1.7]})\n",
    "#test = pd.DataFrame({'A': ['a', 'b', 'a']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict = {'A': ['Samsung', 'Redmi', 'Nokia', 'Vivo', 'RealMe', 'Apple', 'OnePlus'], 'Weight' : [1.5,1.25,1.3,1.4,1.5,1.6,1.7]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': ['Samsung', 'Redmi', 'Nokia', 'Vivo', 'RealMe', 'Apple', 'OnePlus'],\n",
       " 'Weight': [1.5, 1.25, 1.3, 1.4, 1.5, 1.6, 1.7]}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samsung</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Redmi</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nokia</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vivo</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RealMe</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OnePlus</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A  Weight\n",
       "0  Samsung    1.50\n",
       "1    Redmi    1.25\n",
       "2    Nokia    1.30\n",
       "3     Vivo    1.40\n",
       "4   RealMe    1.50\n",
       "5    Apple    1.60\n",
       "6  OnePlus    1.70"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test\n",
    "#test['Brand']=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = [\"abc\", \"xyz\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa=test.loc[test['A']==\"Vivo\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>col1_Apple</th>\n",
       "      <th>col1_Nokia</th>\n",
       "      <th>col1_OnePlus</th>\n",
       "      <th>col1_RealMe</th>\n",
       "      <th>col1_Redmi</th>\n",
       "      <th>col1_Samsung</th>\n",
       "      <th>col1_Vivo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  col1_Apple  col1_Nokia  col1_OnePlus  col1_RealMe  col1_Redmi  \\\n",
       "0    1.50           0           0             0            0           0   \n",
       "1    1.25           0           0             0            0           1   \n",
       "2    1.30           0           1             0            0           0   \n",
       "3    1.40           0           0             0            0           0   \n",
       "4    1.50           0           0             0            1           0   \n",
       "5    1.60           1           0             0            0           0   \n",
       "6    1.70           0           0             1            0           0   \n",
       "\n",
       "   col1_Samsung  col1_Vivo  \n",
       "0             1          0  \n",
       "1             0          0  \n",
       "2             0          0  \n",
       "3             0          1  \n",
       "4             0          0  \n",
       "5             0          0  \n",
       "6             0          0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.get_dummies(test, prefix=['col1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1 entries, 3 to 3\n",
      "Data columns (total 2 columns):\n",
      "A         1 non-null object\n",
      "Weight    1 non-null float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 24.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "aa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2 = pd.get_dummies(test, prefix=\"Brand\", columns = ['A'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_RealMe</th>\n",
       "      <th>Brand_Redmi</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Brand_Apple  Brand_Nokia  Brand_OnePlus  Brand_RealMe  Brand_Redmi  \\\n",
       "0    1.50            0            0              0             0            0   \n",
       "1    1.25            0            0              0             0            1   \n",
       "2    1.30            0            1              0             0            0   \n",
       "3    1.40            0            0              0             0            0   \n",
       "4    1.50            0            0              0             1            0   \n",
       "5    1.60            1            0              0             0            0   \n",
       "6    1.70            0            0              1             0            0   \n",
       "\n",
       "   Brand_Samsung  Brand_Vivo  \n",
       "0              1           0  \n",
       "1              0           0  \n",
       "2              0           0  \n",
       "3              0           1  \n",
       "4              0           0  \n",
       "5              0           0  \n",
       "6              0           0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2.loc[test2['Brand_Apple']==1, \"Brand\"] = 'Samsung'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test2[\"Brand\"] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "    }\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>Weight</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_RealMe</th>\n",
       "      <th>Brand_Redmi</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Brand_Apple  Brand_Nokia  Brand_OnePlus  Brand_RealMe  Brand_Redmi  \\\n",
       "0    1.50            0            0              0             0            0   \n",
       "1    1.25            0            0              0             0            1   \n",
       "2    1.30            0            1              0             0            0   \n",
       "3    1.40            0            0              0             0            0   \n",
       "4    1.50            0            0              0             1            0   \n",
       "5    1.60            1            0              0             0            0   \n",
       "6    1.70            0            0              1             0            0   \n",
       "\n",
       "   Brand_Samsung  Brand_Vivo Brand  \n",
       "0              1           0        \n",
       "1              0           0        \n",
       "2              0           0        \n",
       "3              0           1        \n",
       "4              0           0        \n",
       "5              0           0        \n",
       "6              0           0        "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Weight', 'Brand_Apple', 'Brand_Nokia', 'Brand_OnePlus', 'Brand_RealMe',\n",
       "       'Brand_Redmi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2.columns[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in test2.columns[0:7] :\n",
    "    test2.loc[test2[col]==1, \"Brand\"]=col.split('_')[-1]\n",
    "    #print(col.split('_')[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_RealMe</th>\n",
       "      <th>Brand_Redmi</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Samsung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Redmi</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Nokia</td>\n",
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       "      <td>1.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RealMe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>OnePlus</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Brand_Apple  Brand_Nokia  Brand_OnePlus  Brand_RealMe  Brand_Redmi  \\\n",
       "0    1.50            0            0              0             0            0   \n",
       "1    1.25            0            0              0             0            1   \n",
       "2    1.30            0            1              0             0            0   \n",
       "3    1.40            0            0              0             0            0   \n",
       "4    1.50            0            0              0             1            0   \n",
       "5    1.60            1            0              0             0            0   \n",
       "6    1.70            0            0              1             0            0   \n",
       "\n",
       "   Brand_Samsung  Brand_Vivo    Brand  \n",
       "0              1           0  Samsung  \n",
       "1              0           0    Redmi  \n",
       "2              0           0    Nokia  \n",
       "3              0           1           \n",
       "4              0           0   RealMe  \n",
       "5              0           0    Apple  \n",
       "6              0           0  OnePlus  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in test2.columns[0:6] :\n",
    "    del(test2[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhoneId</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>...</th>\n",
       "      <th>os_name_Tizen</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>6.26</td>\n",
       "      <td>182</td>\n",
       "      <td>4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.9</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>6.20</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.2</td>\n",
       "      <td>32</td>\n",
       "      <td>4230</td>\n",
       "      <td>...</td>\n",
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       "      <td>4.5</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>409</td>\n",
       "      <td>6.30</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83.68</td>\n",
       "      <td>157.0</td>\n",
       "      <td>32</td>\n",
       "      <td>3500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>411</td>\n",
       "      <td>6.00</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.78</td>\n",
       "      <td>159.8</td>\n",
       "      <td>64</td>\n",
       "      <td>3300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>396</td>\n",
       "      <td>6.50</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>84.23</td>\n",
       "      <td>160.4</td>\n",
       "      <td>64</td>\n",
       "      <td>3750</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
       "0        0            403         6.26     182    4                  1.8   \n",
       "1        1            271         6.20     168    3                  1.8   \n",
       "2        2            409         6.30     168    3                  2.1   \n",
       "3        4            411         6.00     169    4                  2.2   \n",
       "4        5            396         6.50     175    4                  2.2   \n",
       "\n",
       "   Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  ...  \\\n",
       "0                              80.68   157.9               64      4000  ...   \n",
       "1                              80.85   156.2               32      4230  ...   \n",
       "2                              83.68   157.0               32      3500  ...   \n",
       "3                              74.78   159.8               64      3300  ...   \n",
       "4                              84.23   160.4               64      3750  ...   \n",
       "\n",
       "   os_name_Tizen  os_name_iOS  SIM Slot(s)_Dual SIM, GSM+CDMA  \\\n",
       "0              0            0                               0   \n",
       "1              0            0                               0   \n",
       "2              0            0                               0   \n",
       "3              0            0                               0   \n",
       "4              0            0                               0   \n",
       "\n",
       "   SIM Slot(s)_Dual SIM, GSM+GSM  SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  \\\n",
       "0                              0                                          1   \n",
       "1                              0                                          1   \n",
       "2                              0                                          1   \n",
       "3                              1                                          0   \n",
       "4                              0                                          1   \n",
       "\n",
       "   SIM Slot(s)_Single SIM, GSM  Sim1_2G  Sim1_3G  Sim1_4G  Rating  \n",
       "0                            0        0        0        1     4.5  \n",
       "1                            0        0        0        1     4.5  \n",
       "2                            0        0        0        1     4.4  \n",
       "3                            0        0        0        1     4.3  \n",
       "4                            0        0        0        1     4.4  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Brand\"] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Brand_10.or', 'Brand_Apple', 'Brand_Asus', 'Brand_Billion',\n",
       "       'Brand_Blackberry', 'Brand_Comio', 'Brand_Coolpad', 'Brand_Do',\n",
       "       'Brand_Gionee', 'Brand_Google', 'Brand_HTC', 'Brand_Honor',\n",
       "       'Brand_Huawei', 'Brand_InFocus', 'Brand_Infinix', 'Brand_Intex',\n",
       "       'Brand_Itel', 'Brand_Jivi', 'Brand_Karbonn', 'Brand_LG', 'Brand_Lava',\n",
       "       'Brand_LeEco', 'Brand_Lenovo', 'Brand_Lephone', 'Brand_Lyf',\n",
       "       'Brand_Meizu', 'Brand_Micromax', 'Brand_Mobiistar', 'Brand_Moto',\n",
       "       'Brand_Motorola', 'Brand_Nokia', 'Brand_Nubia', 'Brand_OPPO',\n",
       "       'Brand_OnePlus', 'Brand_Oppo', 'Brand_Panasonic', 'Brand_Razer',\n",
       "       'Brand_Realme', 'Brand_Reliance', 'Brand_Samsung', 'Brand_Sony',\n",
       "       'Brand_Spice', 'Brand_Tecno', 'Brand_Ulefone', 'Brand_VOTO',\n",
       "       'Brand_Vivo', 'Brand_Xiaomi', 'Brand_Xiaomi Poco', 'Brand_Yu',\n",
       "       'Brand_iVooMi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[23:73]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[23:73] :\n",
    "    df.loc[df[col]==1, \"Brand\"] = col.split('_')[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>PhoneId</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>...</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>403</td>\n",
       "      <td>6.26</td>\n",
       "      <td>182</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.90</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Xiaomi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>6.20</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.20</td>\n",
       "      <td>32</td>\n",
       "      <td>4230</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Realme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>409</td>\n",
       "      <td>6.30</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>2.10</td>\n",
       "      <td>83.68</td>\n",
       "      <td>157.00</td>\n",
       "      <td>32</td>\n",
       "      <td>3500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Realme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>411</td>\n",
       "      <td>6.00</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>74.78</td>\n",
       "      <td>159.80</td>\n",
       "      <td>64</td>\n",
       "      <td>3300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Samsung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>396</td>\n",
       "      <td>6.50</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>84.23</td>\n",
       "      <td>160.40</td>\n",
       "      <td>64</td>\n",
       "      <td>3750</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Honor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>402</td>\n",
       "      <td>6.41</td>\n",
       "      <td>156</td>\n",
       "      <td>6</td>\n",
       "      <td>2.20</td>\n",
       "      <td>84.98</td>\n",
       "      <td>157.90</td>\n",
       "      <td>64</td>\n",
       "      <td>3400</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Vivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>295</td>\n",
       "      <td>5.45</td>\n",
       "      <td>145</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>72.68</td>\n",
       "      <td>147.50</td>\n",
       "      <td>16</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Xiaomi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>432</td>\n",
       "      <td>5.84</td>\n",
       "      <td>178</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>79.24</td>\n",
       "      <td>149.30</td>\n",
       "      <td>32</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Xiaomi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>271</td>\n",
       "      <td>6.20</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>1.80</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.20</td>\n",
       "      <td>16</td>\n",
       "      <td>4230</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>OPPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>403</td>\n",
       "      <td>6.18</td>\n",
       "      <td>182</td>\n",
       "      <td>6</td>\n",
       "      <td>2.80</td>\n",
       "      <td>82.20</td>\n",
       "      <td>155.50</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Xiaomi Poco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>402</td>\n",
       "      <td>6.41</td>\n",
       "      <td>185</td>\n",
       "      <td>6</td>\n",
       "      <td>2.80</td>\n",
       "      <td>85.43</td>\n",
       "      <td>157.50</td>\n",
       "      <td>128</td>\n",
       "      <td>3700</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>OnePlus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>403</td>\n",
       "      <td>5.99</td>\n",
       "      <td>168</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>77.38</td>\n",
       "      <td>158.70</td>\n",
       "      <td>64</td>\n",
       "      <td>3010</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Xiaomi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17</td>\n",
       "      <td>432</td>\n",
       "      <td>5.84</td>\n",
       "      <td>152</td>\n",
       "      <td>3</td>\n",
       "      <td>2.36</td>\n",
       "      <td>79.07</td>\n",
       "      <td>149.20</td>\n",
       "      <td>32</td>\n",
       "      <td>3000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Honor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18</td>\n",
       "      <td>400</td>\n",
       "      <td>6.30</td>\n",
       "      <td>150</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>84.87</td>\n",
       "      <td>154.81</td>\n",
       "      <td>64</td>\n",
       "      <td>3260</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Vivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>270</td>\n",
       "      <td>6.22</td>\n",
       "      <td>161</td>\n",
       "      <td>4</td>\n",
       "      <td>2.00</td>\n",
       "      <td>82.60</td>\n",
       "      <td>155.10</td>\n",
       "      <td>32</td>\n",
       "      <td>4030</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Vivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21</td>\n",
       "      <td>435</td>\n",
       "      <td>5.80</td>\n",
       "      <td>151</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>80.05</td>\n",
       "      <td>147.20</td>\n",
       "      <td>64</td>\n",
       "      <td>3060</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Nokia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22</td>\n",
       "      <td>403</td>\n",
       "      <td>6.18</td>\n",
       "      <td>180</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>82.03</td>\n",
       "      <td>154.80</td>\n",
       "      <td>64</td>\n",
       "      <td>3500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Nokia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23</td>\n",
       "      <td>270</td>\n",
       "      <td>6.22</td>\n",
       "      <td>161</td>\n",
       "      <td>4</td>\n",
       "      <td>1.95</td>\n",
       "      <td>82.60</td>\n",
       "      <td>155.10</td>\n",
       "      <td>64</td>\n",
       "      <td>4030</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Vivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>26</td>\n",
       "      <td>409</td>\n",
       "      <td>6.30</td>\n",
       "      <td>176</td>\n",
       "      <td>4</td>\n",
       "      <td>2.36</td>\n",
       "      <td>82.98</td>\n",
       "      <td>157.90</td>\n",
       "      <td>64</td>\n",
       "      <td>3750</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Honor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>28</td>\n",
       "      <td>269</td>\n",
       "      <td>6.26</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>80.64</td>\n",
       "      <td>158.40</td>\n",
       "      <td>32</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Asus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30</td>\n",
       "      <td>271</td>\n",
       "      <td>6.20</td>\n",
       "      <td>168</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>81.22</td>\n",
       "      <td>155.90</td>\n",
       "      <td>64</td>\n",
       "      <td>4230</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>OPPO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31</td>\n",
       "      <td>403</td>\n",
       "      <td>6.26</td>\n",
       "      <td>182</td>\n",
       "      <td>6</td>\n",
       "      <td>1.80</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.90</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Xiaomi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>33</td>\n",
       "      <td>270</td>\n",
       "      <td>6.22</td>\n",
       "      <td>143</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>82.66</td>\n",
       "      <td>155.00</td>\n",
       "      <td>16</td>\n",
       "      <td>3260</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Vivo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>34</td>\n",
       "      <td>287</td>\n",
       "      <td>5.86</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>1.80</td>\n",
       "      <td>79.34</td>\n",
       "      <td>149.50</td>\n",
       "      <td>32</td>\n",
       "      <td>3060</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Nokia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>36</td>\n",
       "      <td>274</td>\n",
       "      <td>6.00</td>\n",
       "      <td>178</td>\n",
       "      <td>2</td>\n",
       "      <td>1.40</td>\n",
       "      <td>73.94</td>\n",
       "      <td>161.40</td>\n",
       "      <td>32</td>\n",
       "      <td>3300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Samsung</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
       "0         0            403         6.26     182    4                 1.80   \n",
       "1         1            271         6.20     168    3                 1.80   \n",
       "2         2            409         6.30     168    3                 2.10   \n",
       "3         4            411         6.00     169    4                 2.20   \n",
       "4         5            396         6.50     175    4                 2.20   \n",
       "5         7            402         6.41     156    6                 2.20   \n",
       "6         8            295         5.45     145    2                 2.00   \n",
       "7         9            432         5.84     178    3                 2.00   \n",
       "8        10            271         6.20     168    2                 1.80   \n",
       "9        12            403         6.18     182    6                 2.80   \n",
       "10       14            402         6.41     185    6                 2.80   \n",
       "11       15            403         5.99     168    4                 2.20   \n",
       "12       17            432         5.84     152    3                 2.36   \n",
       "13       18            400         6.30     150    4                 2.20   \n",
       "14       20            270         6.22     161    4                 2.00   \n",
       "15       21            435         5.80     151    4                 1.80   \n",
       "16       22            403         6.18     180    4                 2.20   \n",
       "17       23            270         6.22     161    4                 1.95   \n",
       "18       26            409         6.30     176    4                 2.36   \n",
       "19       28            269         6.26     160    3                 1.80   \n",
       "20       30            271         6.20     168    4                 1.80   \n",
       "21       31            403         6.26     182    6                 1.80   \n",
       "22       33            270         6.22     143    2                 2.00   \n",
       "23       34            287         5.86     151    3                 1.80   \n",
       "24       36            274         6.00     178    2                 1.40   \n",
       "\n",
       "    Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  ...  \\\n",
       "0                               80.68  157.90               64      4000  ...   \n",
       "1                               80.85  156.20               32      4230  ...   \n",
       "2                               83.68  157.00               32      3500  ...   \n",
       "3                               74.78  159.80               64      3300  ...   \n",
       "4                               84.23  160.40               64      3750  ...   \n",
       "5                               84.98  157.90               64      3400  ...   \n",
       "6                               72.68  147.50               16      3000  ...   \n",
       "7                               79.24  149.30               32      4000  ...   \n",
       "8                               80.85  156.20               16      4230  ...   \n",
       "9                               82.20  155.50               64      4000  ...   \n",
       "10                              85.43  157.50              128      3700  ...   \n",
       "11                              77.38  158.70               64      3010  ...   \n",
       "12                              79.07  149.20               32      3000  ...   \n",
       "13                              84.87  154.81               64      3260  ...   \n",
       "14                              82.60  155.10               32      4030  ...   \n",
       "15                              80.05  147.20               64      3060  ...   \n",
       "16                              82.03  154.80               64      3500  ...   \n",
       "17                              82.60  155.10               64      4030  ...   \n",
       "18                              82.98  157.90               64      3750  ...   \n",
       "19                              80.64  158.40               32      4000  ...   \n",
       "20                              81.22  155.90               64      4230  ...   \n",
       "21                              80.68  157.90               64      4000  ...   \n",
       "22                              82.66  155.00               16      3260  ...   \n",
       "23                              79.34  149.50               32      3060  ...   \n",
       "24                              73.94  161.40               32      3300  ...   \n",
       "\n",
       "    os_name_iOS  SIM Slot(s)_Dual SIM, GSM+CDMA  \\\n",
       "0             0                               0   \n",
       "1             0                               0   \n",
       "2             0                               0   \n",
       "3             0                               0   \n",
       "4             0                               0   \n",
       "5             0                               0   \n",
       "6             0                               0   \n",
       "7             0                               0   \n",
       "8             0                               0   \n",
       "9             0                               0   \n",
       "10            0                               0   \n",
       "11            0                               0   \n",
       "12            0                               0   \n",
       "13            0                               0   \n",
       "14            0                               0   \n",
       "15            0                               0   \n",
       "16            0                               0   \n",
       "17            0                               0   \n",
       "18            0                               0   \n",
       "19            0                               0   \n",
       "20            0                               0   \n",
       "21            0                               0   \n",
       "22            0                               0   \n",
       "23            0                               0   \n",
       "24            0                               0   \n",
       "\n",
       "    SIM Slot(s)_Dual SIM, GSM+GSM  SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  \\\n",
       "0                               0                                          1   \n",
       "1                               0                                          1   \n",
       "2                               0                                          1   \n",
       "3                               1                                          0   \n",
       "4                               0                                          1   \n",
       "5                               0                                          1   \n",
       "6                               0                                          1   \n",
       "7                               1                                          0   \n",
       "8                               1                                          0   \n",
       "9                               0                                          1   \n",
       "10                              1                                          0   \n",
       "11                              0                                          1   \n",
       "12                              1                                          0   \n",
       "13                              1                                          0   \n",
       "14                              0                                          1   \n",
       "15                              0                                          1   \n",
       "16                              0                                          1   \n",
       "17                              1                                          0   \n",
       "18                              0                                          1   \n",
       "19                              0                                          1   \n",
       "20                              1                                          0   \n",
       "21                              0                                          1   \n",
       "22                              1                                          0   \n",
       "23                              0                                          1   \n",
       "24                              1                                          0   \n",
       "\n",
       "    SIM Slot(s)_Single SIM, GSM  Sim1_2G  Sim1_3G  Sim1_4G  Rating  \\\n",
       "0                             0        0        0        1     4.5   \n",
       "1                             0        0        0        1     4.5   \n",
       "2                             0        0        0        1     4.4   \n",
       "3                             0        0        0        1     4.3   \n",
       "4                             0        0        0        1     4.4   \n",
       "5                             0        0        0        1     4.5   \n",
       "6                             0        0        0        1     4.3   \n",
       "7                             0        0        0        1     4.1   \n",
       "8                             0        0        0        1     4.3   \n",
       "9                             0        0        0        1     4.5   \n",
       "10                            0        0        0        1     4.5   \n",
       "11                            0        0        0        1     4.0   \n",
       "12                            0        0        0        1     4.4   \n",
       "13                            0        0        0        1     4.4   \n",
       "14                            0        0        0        1     4.4   \n",
       "15                            0        0        0        1     4.4   \n",
       "16                            0        0        0        1     4.5   \n",
       "17                            0        0        0        1     4.4   \n",
       "18                            0        0        0        1     4.4   \n",
       "19                            0        0        0        1     4.4   \n",
       "20                            0        0        0        1     4.4   \n",
       "21                            0        0        0        1     4.5   \n",
       "22                            0        0        0        1     4.4   \n",
       "23                            0        0        0        1     4.3   \n",
       "24                            0        0        0        1     4.2   \n",
       "\n",
       "          Brand  \n",
       "0        Xiaomi  \n",
       "1        Realme  \n",
       "2        Realme  \n",
       "3       Samsung  \n",
       "4         Honor  \n",
       "5          Vivo  \n",
       "6        Xiaomi  \n",
       "7        Xiaomi  \n",
       "8          OPPO  \n",
       "9   Xiaomi Poco  \n",
       "10      OnePlus  \n",
       "11       Xiaomi  \n",
       "12        Honor  \n",
       "13         Vivo  \n",
       "14         Vivo  \n",
       "15        Nokia  \n",
       "16        Nokia  \n",
       "17         Vivo  \n",
       "18        Honor  \n",
       "19         Asus  \n",
       "20         OPPO  \n",
       "21       Xiaomi  \n",
       "22         Vivo  \n",
       "23        Nokia  \n",
       "24      Samsung  \n",
       "\n",
       "[25 rows x 89 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "        vertical-align: top;\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>PhoneId</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>...</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>403</td>\n",
       "      <td>6.26</td>\n",
       "      <td>182</td>\n",
       "      <td>4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.9</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Xiaomi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>6.20</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.2</td>\n",
       "      <td>32</td>\n",
       "      <td>4230</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Realme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>409</td>\n",
       "      <td>6.30</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83.68</td>\n",
       "      <td>157.0</td>\n",
       "      <td>32</td>\n",
       "      <td>3500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Realme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>411</td>\n",
       "      <td>6.00</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.78</td>\n",
       "      <td>159.8</td>\n",
       "      <td>64</td>\n",
       "      <td>3300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Samsung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>396</td>\n",
       "      <td>6.50</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>84.23</td>\n",
       "      <td>160.4</td>\n",
       "      <td>64</td>\n",
       "      <td>3750</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Honor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
       "0        0            403         6.26     182    4                  1.8   \n",
       "1        1            271         6.20     168    3                  1.8   \n",
       "2        2            409         6.30     168    3                  2.1   \n",
       "3        4            411         6.00     169    4                  2.2   \n",
       "4        5            396         6.50     175    4                  2.2   \n",
       "\n",
       "   Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  ...  \\\n",
       "0                              80.68   157.9               64      4000  ...   \n",
       "1                              80.85   156.2               32      4230  ...   \n",
       "2                              83.68   157.0               32      3500  ...   \n",
       "3                              74.78   159.8               64      3300  ...   \n",
       "4                              84.23   160.4               64      3750  ...   \n",
       "\n",
       "   os_name_iOS  SIM Slot(s)_Dual SIM, GSM+CDMA  SIM Slot(s)_Dual SIM, GSM+GSM  \\\n",
       "0            0                               0                              0   \n",
       "1            0                               0                              0   \n",
       "2            0                               0                              0   \n",
       "3            0                               0                              1   \n",
       "4            0                               0                              0   \n",
       "\n",
       "   SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  SIM Slot(s)_Single SIM, GSM  \\\n",
       "0                                          1                            0   \n",
       "1                                          1                            0   \n",
       "2                                          1                            0   \n",
       "3                                          0                            0   \n",
       "4                                          1                            0   \n",
       "\n",
       "   Sim1_2G  Sim1_3G  Sim1_4G  Rating    Brand  \n",
       "0        0        0        1     4.5   Xiaomi  \n",
       "1        0        0        1     4.5   Realme  \n",
       "2        0        0        1     4.4   Realme  \n",
       "3        0        0        1     4.3  Samsung  \n",
       "4        0        0        1     4.4    Honor  \n",
       "\n",
       "[5 rows x 89 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['os_name_Android', 'os_name_Blackberry', 'os_name_KAI', 'os_name_Nokia',\n",
       "       'os_name_Other', 'os_name_Tizen', 'os_name_iOS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[73:80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mob' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-2307c97bd490>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmob\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'mob' is not defined"
     ]
    }
   ],
   "source": [
    "mob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[73:80]:\n",
    "    df.loc[df[col]==1, \"OS Name\"] = col.split('name_')[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Sim 1\"] = ''\n",
    "df[\"sim 2\"] = \"\"\n",
    "df.columns[84:87]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[84:87]:\n",
    "    df.loc[df[col]==1, \"Sim 1\"] = col.split('_')[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns[11:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[11:15]:\n",
    "    df.loc[df[col]==1, \"Sim 2\"] = col.split('_')[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df[\"sim 2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[23:73] :\n",
    "    del(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns[11:19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[34:37] :\n",
    "    del(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[23:30] :\n",
    "    del(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[11:15] :\n",
    "    del(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[19:23]:\n",
    "    df.loc[df[col]==1, \"SIM Slot\"] = col.split('_')[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[19:23] :\n",
    "    del(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[11:19]:\n",
    "    df.loc[df[col]==1, \"Processesor\"] = col.split('cores_')[-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns[11:19] :\n",
    "    del(df[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Mobile_Data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import IntProgress\n",
    "from IPython.display import display\n",
    "import time\n",
    "\n",
    "max_count = 100\n",
    "\n",
    "f = IntProgress(min=0, max=max_count) # instantiate the bar\n",
    "display(f) # display the bar\n",
    "\n",
    "count = 0\n",
    "while count <= max_count:\n",
    "    f.value += 1 # signal to increment the progress bar\n",
    "    time.sleep(.1)\n",
    "    count += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "tqdm.pandas()\n",
    "a = 4**456\n",
    "df.progress_apply(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib  \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"weather.csv\",\"r\")as weather:\n",
    "    print(weather.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"weather.csv\")\n",
    "df.head(45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Day,Temperature ,Wind Speed,Event\n",
      "01/01/2018,35,320,Rainy\n",
      "02/01/2018,28,123,cloud\n",
      "03/01/2018,26,156,Hot\n",
      "04/01/2018,29,174,not available \n",
      "05/01/2018,30,185,Rainy\n",
      "06/01/2018,31,169,cloud\n",
      "07/01/2018,32,245,Hot\n",
      "08/01/2018,35,123,not available \n",
      "09/01/2018,40,156,cloud\n",
      "10/01/2018,22,147,Hot\n",
      "11/01/2018,35,563,not available \n",
      "12/01/2018,28,156,cloud\n",
      "13/01/2018,24,412,Hot\n",
      "14/01/2018,26,457,Rainy\n",
      "01/02/2018,32,423,cloud\n",
      "02/02/2018,35,156,Hot\n",
      "03/02/2018,36,148,not available \n",
      "04/02/2018,37,175,not available \n",
      "05/02/2018,35,245,Rainy\n",
      "06/02/2018,22,265,Hot\n",
      "07/02/2018,25,245,Hot\n",
      "08/02/2018,29,231,not available \n",
      "09/02/2018,30,269,Hot\n",
      "10/02/2018,32,269,Rainy\n",
      "11/02/2018,32,247,Rainy\n",
      "01/03/2018,31,258,Rainy\n",
      "02/03/2018,41,265,Rainy\n",
      "03/03/2018,42,236,Hot\n",
      "04/03/2018,43,254,Hot\n",
      "05/03/2018,25,258,cloud\n",
      "06/03/2018,28,236,not available \n",
      "07/03/2018,21,254,\n",
      "08/03/2018,20,245,Rainy\n",
      "09/03/2018,35,285,cloud\n",
      "10/03/2018,45,269,cloud\n",
      "01/04/2018,40,247,Rainy\n",
      "02/04/2018,41,321,cloud\n",
      "03/04/2018,32,321,cloud\n",
      "04/04/2018,35,351,cloud\n",
      "05/04/2018,22,302,cloud\n",
      "06/04/2018,21,306,not available \n",
      "07/04/2018,23,385,not available \n",
      "08/04/2018,24,300,not available \n",
      "09/04/2018,35,213,not available \n",
      "10/04/2018,22,265,not available \n",
      "\n"
     ]
    },
    {
     "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>Day</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>320</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/2018</td>\n",
       "      <td>28</td>\n",
       "      <td>123</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/2018</td>\n",
       "      <td>26</td>\n",
       "      <td>156</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/01/2018</td>\n",
       "      <td>29</td>\n",
       "      <td>174</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/01/2018</td>\n",
       "      <td>30</td>\n",
       "      <td>185</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06/01/2018</td>\n",
       "      <td>31</td>\n",
       "      <td>169</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07/01/2018</td>\n",
       "      <td>32</td>\n",
       "      <td>245</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08/01/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>123</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09/01/2018</td>\n",
       "      <td>40</td>\n",
       "      <td>156</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10/01/2018</td>\n",
       "      <td>22</td>\n",
       "      <td>147</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11/01/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>563</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12/01/2018</td>\n",
       "      <td>28</td>\n",
       "      <td>156</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13/01/2018</td>\n",
       "      <td>24</td>\n",
       "      <td>412</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14/01/2018</td>\n",
       "      <td>26</td>\n",
       "      <td>457</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01/02/2018</td>\n",
       "      <td>32</td>\n",
       "      <td>423</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>02/02/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>156</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>03/02/2018</td>\n",
       "      <td>36</td>\n",
       "      <td>148</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>04/02/2018</td>\n",
       "      <td>37</td>\n",
       "      <td>175</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>05/02/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>245</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>06/02/2018</td>\n",
       "      <td>22</td>\n",
       "      <td>265</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>07/02/2018</td>\n",
       "      <td>25</td>\n",
       "      <td>245</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>08/02/2018</td>\n",
       "      <td>29</td>\n",
       "      <td>231</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>09/02/2018</td>\n",
       "      <td>30</td>\n",
       "      <td>269</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10/02/2018</td>\n",
       "      <td>32</td>\n",
       "      <td>269</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11/02/2018</td>\n",
       "      <td>32</td>\n",
       "      <td>247</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>01/03/2018</td>\n",
       "      <td>31</td>\n",
       "      <td>258</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>02/03/2018</td>\n",
       "      <td>41</td>\n",
       "      <td>265</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>03/03/2018</td>\n",
       "      <td>42</td>\n",
       "      <td>236</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>04/03/2018</td>\n",
       "      <td>43</td>\n",
       "      <td>254</td>\n",
       "      <td>Hot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>05/03/2018</td>\n",
       "      <td>25</td>\n",
       "      <td>258</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>06/03/2018</td>\n",
       "      <td>28</td>\n",
       "      <td>236</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>07/03/2018</td>\n",
       "      <td>21</td>\n",
       "      <td>254</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>08/03/2018</td>\n",
       "      <td>20</td>\n",
       "      <td>245</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>09/03/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>285</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>10/03/2018</td>\n",
       "      <td>45</td>\n",
       "      <td>269</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>01/04/2018</td>\n",
       "      <td>40</td>\n",
       "      <td>247</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>02/04/2018</td>\n",
       "      <td>41</td>\n",
       "      <td>321</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>03/04/2018</td>\n",
       "      <td>32</td>\n",
       "      <td>321</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>04/04/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>351</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>05/04/2018</td>\n",
       "      <td>22</td>\n",
       "      <td>302</td>\n",
       "      <td>cloud</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>06/04/2018</td>\n",
       "      <td>21</td>\n",
       "      <td>306</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>07/04/2018</td>\n",
       "      <td>23</td>\n",
       "      <td>385</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>08/04/2018</td>\n",
       "      <td>24</td>\n",
       "      <td>300</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>09/04/2018</td>\n",
       "      <td>35</td>\n",
       "      <td>213</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>10/04/2018</td>\n",
       "      <td>22</td>\n",
       "      <td>265</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Day  Temperature   Wind Speed           Event\n",
       "0   01/01/2018            35         320           Rainy\n",
       "1   02/01/2018            28         123           cloud\n",
       "2   03/01/2018            26         156             Hot\n",
       "3   04/01/2018            29         174  not available \n",
       "4   05/01/2018            30         185           Rainy\n",
       "5   06/01/2018            31         169           cloud\n",
       "6   07/01/2018            32         245             Hot\n",
       "7   08/01/2018            35         123  not available \n",
       "8   09/01/2018            40         156           cloud\n",
       "9   10/01/2018            22         147             Hot\n",
       "10  11/01/2018            35         563  not available \n",
       "11  12/01/2018            28         156           cloud\n",
       "12  13/01/2018            24         412             Hot\n",
       "13  14/01/2018            26         457           Rainy\n",
       "14  01/02/2018            32         423           cloud\n",
       "15  02/02/2018            35         156             Hot\n",
       "16  03/02/2018            36         148  not available \n",
       "17  04/02/2018            37         175  not available \n",
       "18  05/02/2018            35         245           Rainy\n",
       "19  06/02/2018            22         265             Hot\n",
       "20  07/02/2018            25         245             Hot\n",
       "21  08/02/2018            29         231  not available \n",
       "22  09/02/2018            30         269             Hot\n",
       "23  10/02/2018            32         269           Rainy\n",
       "24  11/02/2018            32         247           Rainy\n",
       "25  01/03/2018            31         258           Rainy\n",
       "26  02/03/2018            41         265           Rainy\n",
       "27  03/03/2018            42         236             Hot\n",
       "28  04/03/2018            43         254             Hot\n",
       "29  05/03/2018            25         258           cloud\n",
       "30  06/03/2018            28         236  not available \n",
       "31  07/03/2018            21         254             NaN\n",
       "32  08/03/2018            20         245           Rainy\n",
       "33  09/03/2018            35         285           cloud\n",
       "34  10/03/2018            45         269           cloud\n",
       "35  01/04/2018            40         247           Rainy\n",
       "36  02/04/2018            41         321           cloud\n",
       "37  03/04/2018            32         321           cloud\n",
       "38  04/04/2018            35         351           cloud\n",
       "39  05/04/2018            22         302           cloud\n",
       "40  06/04/2018            21         306  not available \n",
       "41  07/04/2018            23         385  not available \n",
       "42  08/04/2018            24         300  not available \n",
       "43  09/04/2018            35         213  not available \n",
       "44  10/04/2018            22         265  not available "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib  \n",
    "import pandas as pd\n",
    "\n",
    "with open(\"weather.csv\",\"r\")as weather:\n",
    "    print(weather.read())\n",
    "\n",
    "df = pd.read_csv(\"weather.csv\")\n",
    "df.head(45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Day  Wind Speed\n",
      "0  01/01/2018         320\n",
      "1  02/01/2018         123\n",
      "2  03/01/2018         156\n",
      "3  04/01/2018         174\n",
      "0     01/01/2018\n",
      "1     02/01/2018\n",
      "2     03/01/2018\n",
      "3     04/01/2018\n",
      "4     05/01/2018\n",
      "5     06/01/2018\n",
      "6     07/01/2018\n",
      "7     08/01/2018\n",
      "8     09/01/2018\n",
      "9     10/01/2018\n",
      "10    11/01/2018\n",
      "11    12/01/2018\n",
      "12    13/01/2018\n",
      "13    14/01/2018\n",
      "14    01/02/2018\n",
      "15    02/02/2018\n",
      "16    03/02/2018\n",
      "17    04/02/2018\n",
      "18    05/02/2018\n",
      "19    06/02/2018\n",
      "20    07/02/2018\n",
      "21    08/02/2018\n",
      "22    09/02/2018\n",
      "23    10/02/2018\n",
      "24    11/02/2018\n",
      "25    01/03/2018\n",
      "26    02/03/2018\n",
      "27    03/03/2018\n",
      "28    04/03/2018\n",
      "29    05/03/2018\n",
      "30    06/03/2018\n",
      "31    07/03/2018\n",
      "32    08/03/2018\n",
      "33    09/03/2018\n",
      "34    10/03/2018\n",
      "35    01/04/2018\n",
      "36    02/04/2018\n",
      "37    03/04/2018\n",
      "38    04/04/2018\n",
      "39    05/04/2018\n",
      "40    06/04/2018\n",
      "41    07/04/2018\n",
      "42    08/04/2018\n",
      "43    09/04/2018\n",
      "44    10/04/2018\n",
      "Name: Day, dtype: object\n"
     ]
    }
   ],
   "source": [
    "len(df)\n",
    "\n",
    "df.shape \n",
    "\n",
    "df.iloc[[4],[2]]\n",
    "#checking StaDs of a Specified date\n",
    "\n",
    "df_short=df[0:10]\n",
    "\n",
    "df_short.shape\n",
    "\n",
    "df_thin = df.loc[0:3,['Day','Wind Speed']]\n",
    "\n",
    "df_thin.shape\n",
    "\n",
    "print(df_thin)\n",
    "\n",
    "df.columns\n",
    "\n",
    "df.loc[df[\"Event\"] == \"Rainy\"]\n",
    "\n",
    "print(df[\"Day\"])\n",
    "\n",
    "df.sort_values(\"Event\", ascending=False)\n",
    "\n",
    "df.describe()\n",
    "\n",
    "df.mean()\n",
    "\n",
    "df[\"Wind Speed\"].mean()\n",
    "\n",
    "df[\"Wind Speed\"].describe()\n",
    "\n",
    "df.index\n",
    "\n",
    "df = pd.read_csv(\"weather.csv\",header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#confusion matrix to visualize matrices\n",
    "from sklearn.metrics import confusion_matrix\n",
    "#accuracy_score to visualize accuracy of our model\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#original output\n",
    "actual = [1,0,2,1]\n",
    "#predicted output\n",
    "predicted = [1,0,8,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      "[[1 0 0 0 0]\n",
      " [0 1 0 1 0]\n",
      " [0 0 0 0 1]\n",
      " [0 0 0 0 0]\n",
      " [0 0 0 0 0]]\n",
      "Accuracy score :  0.5\n"
     ]
    }
   ],
   "source": [
    "#fitting our actal and prediction to confusion matrix model\n",
    "results= confusion_matrix(actual,predicted)\n",
    "print(\"Confusion Matrix : \")\n",
    "print(results)\n",
    "#printing accuracy scores\n",
    "print(\"Accuracy score : \",accuracy_score(actual,predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'x': [12, 20, 28, 18, 29, 33, 24, 45, 45, 52, 51, 52, 55, 53, 55, 61, 64, 69, 72,45,12,78,56,23,78,12,78,25,45],\n",
    "    'y': [39, 36, 30, 52, 54, 46, 55, 59, 63, 70, 66, 63, 58, 23, 14, 8, 19, 7, 24,9,78,12,43,47,20,45,96,41,78]\n",
    "})\n",
    "\n",
    "\n",
    "np.random.seed(200)\n",
    "k = 3\n",
    "# centroids[i] = [x, y]\n",
    "centroids = {\n",
    "    i+1: [np.random.randint(0, 80), np.random.randint(0, 80)]\n",
    "    for i in range(k)\n",
    "}\n",
    "    \n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "plt.scatter(df['x'], df['y'], color='k')\n",
    "colmap = {1: 'r', 2: 'g', 3: 'b'}\n",
    "for i in centroids.keys():\n",
    "    plt.scatter(*centroids[i], color=colmap[i])\n",
    "plt.xlim(0, 80)\n",
    "plt.ylim(0, 80)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"mobile_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhoneId</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>Weight</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>...</th>\n",
       "      <th>os_name_Tizen</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>403</td>\n",
       "      <td>6.26</td>\n",
       "      <td>182</td>\n",
       "      <td>4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.9</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>6.20</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.2</td>\n",
       "      <td>32</td>\n",
       "      <td>4230</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>409</td>\n",
       "      <td>6.30</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>83.68</td>\n",
       "      <td>157.0</td>\n",
       "      <td>32</td>\n",
       "      <td>3500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>411</td>\n",
       "      <td>6.00</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>74.78</td>\n",
       "      <td>159.8</td>\n",
       "      <td>64</td>\n",
       "      <td>3300</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>396</td>\n",
       "      <td>6.50</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>84.23</td>\n",
       "      <td>160.4</td>\n",
       "      <td>64</td>\n",
       "      <td>3750</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhoneId  Pixel Density  Screen Size  Weight  RAM  Processor_frequency  \\\n",
       "0        0            403         6.26     182    4                  1.8   \n",
       "1        1            271         6.20     168    3                  1.8   \n",
       "2        2            409         6.30     168    3                  2.1   \n",
       "3        4            411         6.00     169    4                  2.2   \n",
       "4        5            396         6.50     175    4                  2.2   \n",
       "\n",
       "   Screen to Body Ratio (calculated)  Height  Internal Memory  Capacity  ...  \\\n",
       "0                              80.68   157.9               64      4000  ...   \n",
       "1                              80.85   156.2               32      4230  ...   \n",
       "2                              83.68   157.0               32      3500  ...   \n",
       "3                              74.78   159.8               64      3300  ...   \n",
       "4                              84.23   160.4               64      3750  ...   \n",
       "\n",
       "   os_name_Tizen  os_name_iOS  SIM Slot(s)_Dual SIM, GSM+CDMA  \\\n",
       "0              0            0                               0   \n",
       "1              0            0                               0   \n",
       "2              0            0                               0   \n",
       "3              0            0                               0   \n",
       "4              0            0                               0   \n",
       "\n",
       "   SIM Slot(s)_Dual SIM, GSM+GSM  SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE  \\\n",
       "0                              0                                          1   \n",
       "1                              0                                          1   \n",
       "2                              0                                          1   \n",
       "3                              1                                          0   \n",
       "4                              0                                          1   \n",
       "\n",
       "   SIM Slot(s)_Single SIM, GSM  Sim1_2G  Sim1_3G  Sim1_4G  Rating  \n",
       "0                            0        0        0        1     4.5  \n",
       "1                            0        0        0        1     4.5  \n",
       "2                            0        0        0        1     4.4  \n",
       "3                            0        0        0        1     4.3  \n",
       "4                            0        0        0        1     4.4  \n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No axis named Screen Size for object type <class 'type'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-866120e954b1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Screen Size\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, axis)\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m             \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m         \u001b[0mnew_self\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnew_self\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_axis_number\u001b[1;34m(cls, axis)\u001b[0m\n\u001b[0;32m    359\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m         raise ValueError('No axis named {0} for object type {1}'\n\u001b[1;32m--> 361\u001b[1;33m                          .format(axis, type(cls)))\n\u001b[0m\u001b[0;32m    362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    363\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: No axis named Screen Size for object type <class 'type'>"
     ]
    }
   ],
   "source": [
    "df.loc(\"Screen Size\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "list = np.array([1,45,12,989,23,45]).reshape(2,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list[:, 1:3]\n",
    "y = list[:,0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1],\n",
       "       [989]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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