1. Data Manipulation with Python Pandas.ipynb

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   "source": [
    "#IMPORT LIBRARY\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Empty df\n",
    "df = pd.DataFrame()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26fa356c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 0 entries\n",
      "Empty DataFrame"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "396755c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "city = ['Berlin','Elrangen', 'Zurich']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e6e98ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Berlin', 'Elrangen', 'Zurich']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city"
   ]
  },
  {
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   "execution_count": 6,
   "id": "f267b25b",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elrangen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zurich</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0    Berlin\n",
       "1  Elrangen\n",
       "2    Zurich"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(city)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60bb2693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
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     "execution_count": 7,
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     "output_type": "execute_result"
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   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50cb56fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "type(city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a77cd77",
   "metadata": {},
   "outputs": [],
   "source": [
    "row, col = df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf2f7f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
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     "execution_count": 10,
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   "source": [
    "row"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "id": "e2a35a22",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
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     "execution_count": 11,
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   ],
   "source": [
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "205794bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "706ba63f",
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   "outputs": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elrangen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zurich</td>\n",
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      "text/plain": [
       "          0\n",
       "0    Berlin\n",
       "1  Elrangen\n",
       "2    Zurich"
      ]
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     "execution_count": 13,
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "486c3743",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_in_Europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elrangen</td>\n",
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       "      <td>Zurich</td>\n",
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      "text/plain": [
       "  City_in_Europe\n",
       "0         Berlin\n",
       "1       Elrangen\n",
       "2         Zurich"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame(city, columns=['City_in_Europe'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "64b7370d",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_in_Europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>Berlin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <td>Elrangen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>Zurich</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   City_in_Europe\n",
       "c1         Berlin\n",
       "c2       Elrangen\n",
       "c3         Zurich"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.DataFrame(city, columns=['City_in_Europe'], index=['c1','c2','c3'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d1c94ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "city2 = ['Dortmund','Paris','Potsdam']\n",
    "df2 = pd.DataFrame(zip(city, city2), columns=['City1', 'City2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62da3963",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>City1</th>\n",
       "      <th>City2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>Dortmund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elrangen</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zurich</td>\n",
       "      <td>Potsdam</td>\n",
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      ],
      "text/plain": [
       "      City1     City2\n",
       "0    Berlin  Dortmund\n",
       "1  Elrangen     Paris\n",
       "2    Zurich   Potsdam"
      ]
     },
     "execution_count": 17,
     "metadata": {},
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    "df2"
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   "cell_type": "code",
   "execution_count": 18,
   "id": "8e647093",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e78e3a35",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City1</th>\n",
       "      <th>City2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>Dortmund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elrangen</td>\n",
       "      <td>Paris</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zurich</td>\n",
       "      <td>Potsdam</td>\n",
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      ],
      "text/plain": [
       "      City1     City2\n",
       "0    Berlin  Dortmund\n",
       "1  Elrangen     Paris\n",
       "2    Zurich   Potsdam"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7c02358c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "51142b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Bavaria', 'Erlangen'], ['NRW', 'Dortmund']]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city3 = [['Bavaria', 'Erlangen'], ['NRW', 'Dortmund']]\n",
    "city3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb3cc481",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     State      City\n",
       "0  Bavaria  Erlangen\n",
       "1      NRW  Dortmund"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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    }
   ],
   "source": [
    "df4 = pd.DataFrame(city3, columns=['State','City'])\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da1e3dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict1 = {\n",
    "    'city1': city,\n",
    "    'city2': city2\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f07e61ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'city1': ['Berlin', 'Elrangen', 'Zurich'],\n",
       " 'city2': ['Dortmund', 'Paris', 'Potsdam']}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5dca0a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = pd.DataFrame(dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d887de0c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city1</th>\n",
       "      <th>city2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>Dortmund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Elrangen</td>\n",
       "      <td>Paris</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Zurich</td>\n",
       "      <td>Potsdam</td>\n",
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      "text/plain": [
       "      city1     city2\n",
       "0    Berlin  Dortmund\n",
       "1  Elrangen     Paris\n",
       "2    Zurich   Potsdam"
      ]
     },
     "execution_count": 26,
     "metadata": {},
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    "df5"
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  {
   "cell_type": "markdown",
   "id": "f2fcd08d",
   "metadata": {},
   "source": [
    "# Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f5b831b7",
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>04/18/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>123</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>04/19/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>112</td>\n",
       "      <td>52</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>04/20/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>101</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>04/21/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0      0  04/17/19  Wednesday                 187                 89   \n",
       "1      1  04/18/19   Thursday                 123                 78   \n",
       "2      2  04/19/19     Friday                 112                 52   \n",
       "3      3  04/20/19   Saturday                 101                 69   \n",
       "4      4  04/21/19     Sunday                  56                 35   \n",
       "\n",
       "   Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                     17     41            22                   0   \n",
       "1                     17      8             9                   0   \n",
       "2                     40      8             4                   0   \n",
       "3                      9     38             2                   0   \n",
       "4                      2     43             3                   0   \n",
       "\n",
       "   Entertainment  Creativity  Yoga  \n",
       "0              0           0     0  \n",
       "1              0           0     0  \n",
       "2              3           0     0  \n",
       "3              3           0     0  \n",
       "4              1           1     0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Screen Time Data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0cefe444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 12)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of df\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc5cf11e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows, cols = df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8c189fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "268f58d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e7deac6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 '04/17/19' 'Wednesday' 187 89 17 41 22 0 0 0 0]\n",
      " [1 '04/18/19' 'Thursday' 123 78 17 8 9 0 0 0 0]\n",
      " [2 '04/19/19' 'Friday' 112 52 40 8 4 0 3 0 0]\n",
      " [3 '04/20/19' 'Saturday' 101 69 9 38 2 0 3 0 0]\n",
      " [4 '04/21/19' 'Sunday' 56 35 2 43 3 0 1 1 0]\n",
      " [5 '04/22/19' 'Monday' 189 68 0 9 3 4 0 0 0]\n",
      " [6 '04/23/19' 'Tuesday' 158 56 18 41 12 15 0 0 0]\n",
      " [7 '04/24/19' 'Wednesday' 135 98 3 33 16 0 0 0 0]\n",
      " [8 '04/25/19' 'Thursday' 52 25 7 3 16 0 0 0 0]\n",
      " [9 '04/26/19' 'Friday' 198 76 8 29 15 0 32 0 0]\n",
      " [10 '04/27/19' 'Saturday' 116 75 10 20 5 0 0 0 0]\n",
      " [11 '04/28/19' 'Sunday' 85 42 22 4 2 0 0 0 0]\n",
      " [12 '04/29/19' 'Monday' 109 46 8 13 9 15 1 0 1]\n",
      " [13 '04/30/19' 'Tuesday' 79 40 2 9 12 0 0 0 1]\n",
      " [14 '05/01/19' 'Wednesday' 127 90 0 10 7 0 0 0 1]\n",
      " [15 '05/02/19' 'Thursday' 170 60 3 2 11 0 0 0 1]\n",
      " [16 '05/03/19' 'Friday' 91 64 2 18 5 1 1 2 1]\n",
      " [17 '05/04/19' 'Saturday' 58 34 4 5 3 0 1 0 1]\n",
      " [18 '05/05/19' 'Sunday' 133 109 5 1 3 0 0 0 1]\n",
      " [19 '05/06/19' 'Monday' 144 81 4 5 3 0 0 0 1]\n",
      " [20 '05/07/19' 'Tuesday' 110 70 5 6 15 0 9 0 1]\n",
      " [21 '05/08/19' 'Wednesday' 122 53 25 26 15 0 0 0 1]\n",
      " [22 '05/09/19' 'Thursday' 96 42 15 16 19 0 0 0 1]\n",
      " [23 '05/10/19' 'Friday' 161 93 13 17 16 1 0 0 1]\n",
      " [24 '05/11/19' 'Saturday' 58 49 1 2 2 0 0 2 1]\n",
      " [25 '05/12/19' 'Sunday' 52 28 1 1 6 0 0 1 1]\n",
      " [26 '05/13/19' 'Monday' 61 37 1 0 4 0 0 0 1]\n",
      " [27 '05/14/19' 'Tuesday' 88 41 2 7 15 0 0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "# Print the values of df\n",
    "print(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd4cb9e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['index', 'Date', 'Week Day', 'Total Screen Time ', 'Social Networking',\n",
      "       'Reading and Reference', 'Other', 'Productivity', 'Health and Fitness',\n",
      "       'Entertainment', 'Creativity', 'Yoga'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print the column index of df\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "97f321ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=28, step=1)\n"
     ]
    }
   ],
   "source": [
    "# Print the row index of df\n",
    "print(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "33244afc",
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>04/18/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>123</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>04/19/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>112</td>\n",
       "      <td>52</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0      0  04/17/19  Wednesday                 187                 89   \n",
       "1      1  04/18/19   Thursday                 123                 78   \n",
       "2      2  04/19/19     Friday                 112                 52   \n",
       "\n",
       "   Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                     17     41            22                   0   \n",
       "1                     17      8             9                   0   \n",
       "2                     40      8             4                   0   \n",
       "\n",
       "   Entertainment  Creativity  Yoga  \n",
       "0              0           0     0  \n",
       "1              0           0     0  \n",
       "2              3           0     0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a4b62b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     04/17/19\n",
       "1     04/18/19\n",
       "2     04/19/19\n",
       "3     04/20/19\n",
       "4     04/21/19\n",
       "5     04/22/19\n",
       "6     04/23/19\n",
       "7     04/24/19\n",
       "8     04/25/19\n",
       "9     04/26/19\n",
       "10    04/27/19\n",
       "11    04/28/19\n",
       "12    04/29/19\n",
       "13    04/30/19\n",
       "14    05/01/19\n",
       "15    05/02/19\n",
       "16    05/03/19\n",
       "17    05/04/19\n",
       "18    05/05/19\n",
       "19    05/06/19\n",
       "20    05/07/19\n",
       "21    05/08/19\n",
       "22    05/09/19\n",
       "23    05/10/19\n",
       "24    05/11/19\n",
       "25    05/12/19\n",
       "26    05/13/19\n",
       "27    05/14/19\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#specific column\n",
    "df.Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4092d633",
   "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>Social Networking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Social Networking\n",
       "0                  89\n",
       "1                  78\n",
       "2                  52\n",
       "3                  69\n",
       "4                  35\n",
       "5                  68\n",
       "6                  56\n",
       "7                  98\n",
       "8                  25\n",
       "9                  76\n",
       "10                 75\n",
       "11                 42\n",
       "12                 46\n",
       "13                 40\n",
       "14                 90\n",
       "15                 60\n",
       "16                 64\n",
       "17                 34\n",
       "18                109\n",
       "19                 81\n",
       "20                 70\n",
       "21                 53\n",
       "22                 42\n",
       "23                 93\n",
       "24                 49\n",
       "25                 28\n",
       "26                 37\n",
       "27                 41"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Social Networking']] # df['Social Networking']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9aee0cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    04/17/19\n",
       "1    04/18/19\n",
       "2    04/19/19\n",
       "3    04/20/19\n",
       "4    04/21/19\n",
       "5    04/22/19\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#specific column\n",
    "df.Date.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c34ca314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    04/17/19\n",
       "1    04/18/19\n",
       "2    04/19/19\n",
       "3    04/20/19\n",
       "4    04/21/19\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "922060e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiple column\n",
    "# df['Date','Week Day'].head() # this is the list of your columns but where is your data frame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a0f00095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04/18/19</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04/19/19</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/20/19</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04/21/19</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date   Week Day\n",
       "0  04/17/19  Wednesday\n",
       "1  04/18/19   Thursday\n",
       "2  04/19/19     Friday\n",
       "3  04/20/19   Saturday\n",
       "4  04/21/19     Sunday"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Date','Week Day']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e26ccf87",
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>04/18/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>123</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>04/19/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>112</td>\n",
       "      <td>52</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>04/20/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>101</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>04/21/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0      0  04/17/19  Wednesday                 187                 89   \n",
       "1      1  04/18/19   Thursday                 123                 78   \n",
       "2      2  04/19/19     Friday                 112                 52   \n",
       "3      3  04/20/19   Saturday                 101                 69   \n",
       "4      4  04/21/19     Sunday                  56                 35   \n",
       "\n",
       "   Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                     17     41            22                   0   \n",
       "1                     17      8             9                   0   \n",
       "2                     40      8             4                   0   \n",
       "3                      9     38             2                   0   \n",
       "4                      2     43             3                   0   \n",
       "\n",
       "   Entertainment  Creativity  Yoga  \n",
       "0              0           0     0  \n",
       "1              0           0     0  \n",
       "2              3           0     0  \n",
       "3              3           0     0  \n",
       "4              1           1     0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "924ce5ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18    109\n",
       "7      98\n",
       "23     93\n",
       "14     90\n",
       "0      89\n",
       "19     81\n",
       "1      78\n",
       "9      76\n",
       "10     75\n",
       "20     70\n",
       "3      69\n",
       "5      68\n",
       "16     64\n",
       "15     60\n",
       "6      56\n",
       "21     53\n",
       "2      52\n",
       "24     49\n",
       "12     46\n",
       "11     42\n",
       "22     42\n",
       "27     41\n",
       "13     40\n",
       "26     37\n",
       "4      35\n",
       "17     34\n",
       "25     28\n",
       "8      25\n",
       "Name: Social Networking, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Social Networking'].sort_values(ascending=False) #shift+tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a36c779c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8      25\n",
       "25     28\n",
       "17     34\n",
       "4      35\n",
       "26     37\n",
       "13     40\n",
       "27     41\n",
       "11     42\n",
       "22     42\n",
       "12     46\n",
       "24     49\n",
       "2      52\n",
       "21     53\n",
       "6      56\n",
       "15     60\n",
       "16     64\n",
       "5      68\n",
       "3      69\n",
       "20     70\n",
       "10     75\n",
       "9      76\n",
       "1      78\n",
       "19     81\n",
       "0      89\n",
       "14     90\n",
       "23     93\n",
       "7      98\n",
       "18    109\n",
       "Name: Social Networking, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Social Networking'].sort_values() #shift+tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "184df862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>05/05/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>133</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>04/24/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>135</td>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>05/10/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>161</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>05/01/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>127</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>05/06/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>144</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>04/18/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>123</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>04/26/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>198</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>04/27/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>116</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>05/07/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>04/20/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>101</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>04/22/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>189</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>05/03/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>91</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>05/02/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>04/23/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>158</td>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>05/08/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>122</td>\n",
       "      <td>53</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>04/19/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>112</td>\n",
       "      <td>52</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>05/11/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>04/29/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>109</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>04/28/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>05/09/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>96</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>05/14/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>88</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>04/30/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>79</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>05/13/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>61</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</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>4</td>\n",
       "      <td>04/21/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>05/04/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>05/12/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>52</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>04/25/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>52</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>16</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": [
       "    index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "18     18  05/05/19     Sunday                 133                109   \n",
       "7       7  04/24/19  Wednesday                 135                 98   \n",
       "23     23  05/10/19     Friday                 161                 93   \n",
       "14     14  05/01/19  Wednesday                 127                 90   \n",
       "0       0  04/17/19  Wednesday                 187                 89   \n",
       "19     19  05/06/19     Monday                 144                 81   \n",
       "1       1  04/18/19   Thursday                 123                 78   \n",
       "9       9  04/26/19     Friday                 198                 76   \n",
       "10     10  04/27/19   Saturday                 116                 75   \n",
       "20     20  05/07/19    Tuesday                 110                 70   \n",
       "3       3  04/20/19   Saturday                 101                 69   \n",
       "5       5  04/22/19     Monday                 189                 68   \n",
       "16     16  05/03/19     Friday                  91                 64   \n",
       "15     15  05/02/19   Thursday                 170                 60   \n",
       "6       6  04/23/19    Tuesday                 158                 56   \n",
       "21     21  05/08/19  Wednesday                 122                 53   \n",
       "2       2  04/19/19     Friday                 112                 52   \n",
       "24     24  05/11/19   Saturday                  58                 49   \n",
       "12     12  04/29/19     Monday                 109                 46   \n",
       "11     11  04/28/19     Sunday                  85                 42   \n",
       "22     22  05/09/19   Thursday                  96                 42   \n",
       "27     27  05/14/19    Tuesday                  88                 41   \n",
       "13     13  04/30/19    Tuesday                  79                 40   \n",
       "26     26  05/13/19     Monday                  61                 37   \n",
       "4       4  04/21/19     Sunday                  56                 35   \n",
       "17     17  05/04/19   Saturday                  58                 34   \n",
       "25     25  05/12/19     Sunday                  52                 28   \n",
       "8       8  04/25/19   Thursday                  52                 25   \n",
       "\n",
       "    Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "18                      5      1             3                   0   \n",
       "7                       3     33            16                   0   \n",
       "23                     13     17            16                   1   \n",
       "14                      0     10             7                   0   \n",
       "0                      17     41            22                   0   \n",
       "19                      4      5             3                   0   \n",
       "1                      17      8             9                   0   \n",
       "9                       8     29            15                   0   \n",
       "10                     10     20             5                   0   \n",
       "20                      5      6            15                   0   \n",
       "3                       9     38             2                   0   \n",
       "5                       0      9             3                   4   \n",
       "16                      2     18             5                   1   \n",
       "15                      3      2            11                   0   \n",
       "6                      18     41            12                  15   \n",
       "21                     25     26            15                   0   \n",
       "2                      40      8             4                   0   \n",
       "24                      1      2             2                   0   \n",
       "12                      8     13             9                  15   \n",
       "11                     22      4             2                   0   \n",
       "22                     15     16            19                   0   \n",
       "27                      2      7            15                   0   \n",
       "13                      2      9            12                   0   \n",
       "26                      1      0             4                   0   \n",
       "4                       2     43             3                   0   \n",
       "17                      4      5             3                   0   \n",
       "25                      1      1             6                   0   \n",
       "8                       7      3            16                   0   \n",
       "\n",
       "    Entertainment  Creativity  Yoga  \n",
       "18              0           0     1  \n",
       "7               0           0     0  \n",
       "23              0           0     1  \n",
       "14              0           0     1  \n",
       "0               0           0     0  \n",
       "19              0           0     1  \n",
       "1               0           0     0  \n",
       "9              32           0     0  \n",
       "10              0           0     0  \n",
       "20              9           0     1  \n",
       "3               3           0     0  \n",
       "5               0           0     0  \n",
       "16              1           2     1  \n",
       "15              0           0     1  \n",
       "6               0           0     0  \n",
       "21              0           0     1  \n",
       "2               3           0     0  \n",
       "24              0           2     1  \n",
       "12              1           0     1  \n",
       "11              0           0     0  \n",
       "22              0           0     1  \n",
       "27              0           0     1  \n",
       "13              0           0     1  \n",
       "26              0           0     1  \n",
       "4               1           1     0  \n",
       "17              1           0     1  \n",
       "25              0           1     1  \n",
       "8               0           0     0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Social Networking', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d0e26162",
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>04/25/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>52</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>05/12/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>52</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>05/04/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>04/21/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>05/13/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>61</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>04/30/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>79</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>05/14/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>88</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>04/28/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>05/09/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>96</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>04/29/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>109</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>05/11/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>04/19/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>112</td>\n",
       "      <td>52</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>05/08/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>122</td>\n",
       "      <td>53</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>04/23/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>158</td>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>05/02/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>05/03/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>91</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>04/22/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>189</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>04/20/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>101</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>05/07/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>04/27/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>116</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>04/26/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>198</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>04/18/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>123</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>05/06/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>144</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>05/01/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>127</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>05/10/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>161</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>04/24/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>135</td>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>05/05/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>133</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "8       8  04/25/19   Thursday                  52                 25   \n",
       "25     25  05/12/19     Sunday                  52                 28   \n",
       "17     17  05/04/19   Saturday                  58                 34   \n",
       "4       4  04/21/19     Sunday                  56                 35   \n",
       "26     26  05/13/19     Monday                  61                 37   \n",
       "13     13  04/30/19    Tuesday                  79                 40   \n",
       "27     27  05/14/19    Tuesday                  88                 41   \n",
       "11     11  04/28/19     Sunday                  85                 42   \n",
       "22     22  05/09/19   Thursday                  96                 42   \n",
       "12     12  04/29/19     Monday                 109                 46   \n",
       "24     24  05/11/19   Saturday                  58                 49   \n",
       "2       2  04/19/19     Friday                 112                 52   \n",
       "21     21  05/08/19  Wednesday                 122                 53   \n",
       "6       6  04/23/19    Tuesday                 158                 56   \n",
       "15     15  05/02/19   Thursday                 170                 60   \n",
       "16     16  05/03/19     Friday                  91                 64   \n",
       "5       5  04/22/19     Monday                 189                 68   \n",
       "3       3  04/20/19   Saturday                 101                 69   \n",
       "20     20  05/07/19    Tuesday                 110                 70   \n",
       "10     10  04/27/19   Saturday                 116                 75   \n",
       "9       9  04/26/19     Friday                 198                 76   \n",
       "1       1  04/18/19   Thursday                 123                 78   \n",
       "19     19  05/06/19     Monday                 144                 81   \n",
       "0       0  04/17/19  Wednesday                 187                 89   \n",
       "14     14  05/01/19  Wednesday                 127                 90   \n",
       "23     23  05/10/19     Friday                 161                 93   \n",
       "7       7  04/24/19  Wednesday                 135                 98   \n",
       "18     18  05/05/19     Sunday                 133                109   \n",
       "\n",
       "    Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "8                       7      3            16                   0   \n",
       "25                      1      1             6                   0   \n",
       "17                      4      5             3                   0   \n",
       "4                       2     43             3                   0   \n",
       "26                      1      0             4                   0   \n",
       "13                      2      9            12                   0   \n",
       "27                      2      7            15                   0   \n",
       "11                     22      4             2                   0   \n",
       "22                     15     16            19                   0   \n",
       "12                      8     13             9                  15   \n",
       "24                      1      2             2                   0   \n",
       "2                      40      8             4                   0   \n",
       "21                     25     26            15                   0   \n",
       "6                      18     41            12                  15   \n",
       "15                      3      2            11                   0   \n",
       "16                      2     18             5                   1   \n",
       "5                       0      9             3                   4   \n",
       "3                       9     38             2                   0   \n",
       "20                      5      6            15                   0   \n",
       "10                     10     20             5                   0   \n",
       "9                       8     29            15                   0   \n",
       "1                      17      8             9                   0   \n",
       "19                      4      5             3                   0   \n",
       "0                      17     41            22                   0   \n",
       "14                      0     10             7                   0   \n",
       "23                     13     17            16                   1   \n",
       "7                       3     33            16                   0   \n",
       "18                      5      1             3                   0   \n",
       "\n",
       "    Entertainment  Creativity  Yoga  \n",
       "8               0           0     0  \n",
       "25              0           1     1  \n",
       "17              1           0     1  \n",
       "4               1           1     0  \n",
       "26              0           0     1  \n",
       "13              0           0     1  \n",
       "27              0           0     1  \n",
       "11              0           0     0  \n",
       "22              0           0     1  \n",
       "12              1           0     1  \n",
       "24              0           2     1  \n",
       "2               3           0     0  \n",
       "21              0           0     1  \n",
       "6               0           0     0  \n",
       "15              0           0     1  \n",
       "16              1           2     1  \n",
       "5               0           0     0  \n",
       "3               3           0     0  \n",
       "20              9           0     1  \n",
       "10              0           0     0  \n",
       "9              32           0     0  \n",
       "1               0           0     0  \n",
       "19              0           0     1  \n",
       "0               0           0     0  \n",
       "14              0           0     1  \n",
       "23              0           0     1  \n",
       "7               0           0     0  \n",
       "18              0           0     1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Social Networking',ascending=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ffd2d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>113.250000</td>\n",
       "      <td>60.714286</td>\n",
       "      <td>8.714286</td>\n",
       "      <td>14.821429</td>\n",
       "      <td>9.071429</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>1.821429</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.225975</td>\n",
       "      <td>43.562322</td>\n",
       "      <td>22.853571</td>\n",
       "      <td>9.340700</td>\n",
       "      <td>13.754605</td>\n",
       "      <td>6.097532</td>\n",
       "      <td>3.952094</td>\n",
       "      <td>6.188721</td>\n",
       "      <td>0.568112</td>\n",
       "      <td>0.503953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.750000</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>41.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.500000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20.250000</td>\n",
       "      <td>137.250000</td>\n",
       "      <td>76.500000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           index  Total Screen Time   Social Networking  \\\n",
       "count  28.000000           28.000000          28.000000   \n",
       "mean   13.500000          113.250000          60.714286   \n",
       "std     8.225975           43.562322          22.853571   \n",
       "min     0.000000           52.000000          25.000000   \n",
       "25%     6.750000           83.500000          41.750000   \n",
       "50%    13.500000          111.000000          58.000000   \n",
       "75%    20.250000          137.250000          76.500000   \n",
       "max    27.000000          198.000000         109.000000   \n",
       "\n",
       "       Reading and Reference      Other  Productivity  Health and Fitness  \\\n",
       "count              28.000000  28.000000     28.000000           28.000000   \n",
       "mean                8.714286  14.821429      9.071429            1.285714   \n",
       "std                 9.340700  13.754605      6.097532            3.952094   \n",
       "min                 0.000000   0.000000      2.000000            0.000000   \n",
       "25%                 2.000000   4.750000      3.000000            0.000000   \n",
       "50%                 5.000000   9.000000      8.000000            0.000000   \n",
       "75%                13.500000  21.500000     15.000000            0.000000   \n",
       "max                40.000000  43.000000     22.000000           15.000000   \n",
       "\n",
       "       Entertainment  Creativity       Yoga  \n",
       "count      28.000000   28.000000  28.000000  \n",
       "mean        1.821429    0.214286   0.571429  \n",
       "std         6.188721    0.568112   0.503953  \n",
       "min         0.000000    0.000000   0.000000  \n",
       "25%         0.000000    0.000000   0.000000  \n",
       "50%         0.000000    0.000000   1.000000  \n",
       "75%         1.000000    0.000000   1.000000  \n",
       "max        32.000000    2.000000   1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Statistical Analysis\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "98216cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.344952</td>\n",
       "      <td>-0.218093</td>\n",
       "      <td>-0.360073</td>\n",
       "      <td>-0.537331</td>\n",
       "      <td>0.012553</td>\n",
       "      <td>-0.178863</td>\n",
       "      <td>-0.113858</td>\n",
       "      <td>0.221908</td>\n",
       "      <td>0.857690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Screen Time</th>\n",
       "      <td>-0.344952</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.710864</td>\n",
       "      <td>0.185685</td>\n",
       "      <td>0.333433</td>\n",
       "      <td>0.344195</td>\n",
       "      <td>0.201360</td>\n",
       "      <td>0.343897</td>\n",
       "      <td>-0.409306</td>\n",
       "      <td>-0.258123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social Networking</th>\n",
       "      <td>-0.218093</td>\n",
       "      <td>0.710864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004461</td>\n",
       "      <td>0.234066</td>\n",
       "      <td>0.124539</td>\n",
       "      <td>-0.092968</td>\n",
       "      <td>0.132916</td>\n",
       "      <td>-0.214764</td>\n",
       "      <td>-0.110716</td>\n",
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       "    <tr>\n",
       "      <th>Reading and Reference</th>\n",
       "      <td>-0.360073</td>\n",
       "      <td>0.185685</td>\n",
       "      <td>0.004461</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204553</td>\n",
       "      <td>0.162942</td>\n",
       "      <td>0.091587</td>\n",
       "      <td>0.012539</td>\n",
       "      <td>-0.302112</td>\n",
       "      <td>-0.381038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>-0.537331</td>\n",
       "      <td>0.333433</td>\n",
       "      <td>0.234066</td>\n",
       "      <td>0.204553</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307073</td>\n",
       "      <td>0.236715</td>\n",
       "      <td>0.192795</td>\n",
       "      <td>-0.023360</td>\n",
       "      <td>-0.529736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Productivity</th>\n",
       "      <td>0.012553</td>\n",
       "      <td>0.344195</td>\n",
       "      <td>0.124539</td>\n",
       "      <td>0.162942</td>\n",
       "      <td>0.307073</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032934</td>\n",
       "      <td>0.186832</td>\n",
       "      <td>-0.336026</td>\n",
       "      <td>-0.001722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health and Fitness</th>\n",
       "      <td>-0.178863</td>\n",
       "      <td>0.201360</td>\n",
       "      <td>-0.092968</td>\n",
       "      <td>0.091587</td>\n",
       "      <td>0.236715</td>\n",
       "      <td>0.032934</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.075065</td>\n",
       "      <td>-0.094262</td>\n",
       "      <td>-0.066414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entertainment</th>\n",
       "      <td>-0.113858</td>\n",
       "      <td>0.343897</td>\n",
       "      <td>0.132916</td>\n",
       "      <td>0.012539</td>\n",
       "      <td>0.192795</td>\n",
       "      <td>0.186832</td>\n",
       "      <td>-0.075065</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.083521</td>\n",
       "      <td>-0.203577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creativity</th>\n",
       "      <td>0.221908</td>\n",
       "      <td>-0.409306</td>\n",
       "      <td>-0.214764</td>\n",
       "      <td>-0.302112</td>\n",
       "      <td>-0.023360</td>\n",
       "      <td>-0.336026</td>\n",
       "      <td>-0.094262</td>\n",
       "      <td>-0.083521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yoga</th>\n",
       "      <td>0.857690</td>\n",
       "      <td>-0.258123</td>\n",
       "      <td>-0.110716</td>\n",
       "      <td>-0.381038</td>\n",
       "      <td>-0.529736</td>\n",
       "      <td>-0.001722</td>\n",
       "      <td>-0.066414</td>\n",
       "      <td>-0.203577</td>\n",
       "      <td>0.203286</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          index  Total Screen Time   Social Networking  \\\n",
       "index                  1.000000           -0.344952          -0.218093   \n",
       "Total Screen Time     -0.344952            1.000000           0.710864   \n",
       "Social Networking     -0.218093            0.710864           1.000000   \n",
       "Reading and Reference -0.360073            0.185685           0.004461   \n",
       "Other                 -0.537331            0.333433           0.234066   \n",
       "Productivity           0.012553            0.344195           0.124539   \n",
       "Health and Fitness    -0.178863            0.201360          -0.092968   \n",
       "Entertainment         -0.113858            0.343897           0.132916   \n",
       "Creativity             0.221908           -0.409306          -0.214764   \n",
       "Yoga                   0.857690           -0.258123          -0.110716   \n",
       "\n",
       "                       Reading and Reference     Other  Productivity  \\\n",
       "index                              -0.360073 -0.537331      0.012553   \n",
       "Total Screen Time                   0.185685  0.333433      0.344195   \n",
       "Social Networking                   0.004461  0.234066      0.124539   \n",
       "Reading and Reference               1.000000  0.204553      0.162942   \n",
       "Other                               0.204553  1.000000      0.307073   \n",
       "Productivity                        0.162942  0.307073      1.000000   \n",
       "Health and Fitness                  0.091587  0.236715      0.032934   \n",
       "Entertainment                       0.012539  0.192795      0.186832   \n",
       "Creativity                         -0.302112 -0.023360     -0.336026   \n",
       "Yoga                               -0.381038 -0.529736     -0.001722   \n",
       "\n",
       "                       Health and Fitness  Entertainment  Creativity      Yoga  \n",
       "index                           -0.178863      -0.113858    0.221908  0.857690  \n",
       "Total Screen Time                0.201360       0.343897   -0.409306 -0.258123  \n",
       "Social Networking               -0.092968       0.132916   -0.214764 -0.110716  \n",
       "Reading and Reference            0.091587       0.012539   -0.302112 -0.381038  \n",
       "Other                            0.236715       0.192795   -0.023360 -0.529736  \n",
       "Productivity                     0.032934       0.186832   -0.336026 -0.001722  \n",
       "Health and Fitness               1.000000      -0.075065   -0.094262 -0.066414  \n",
       "Entertainment                   -0.075065       1.000000   -0.083521 -0.203577  \n",
       "Creativity                      -0.094262      -0.083521    1.000000  0.203286  \n",
       "Yoga                            -0.066414      -0.203577    0.203286  1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c68b55ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3c84ab87",
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>04/18/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>123</td>\n",
       "      <td>78</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>04/19/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>112</td>\n",
       "      <td>52</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>04/20/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>101</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>04/21/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0      0  04/17/19  Wednesday                 187                 89   \n",
       "1      1  04/18/19   Thursday                 123                 78   \n",
       "2      2  04/19/19     Friday                 112                 52   \n",
       "3      3  04/20/19   Saturday                 101                 69   \n",
       "4      4  04/21/19     Sunday                  56                 35   \n",
       "\n",
       "   Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                     17     41            22                   0   \n",
       "1                     17      8             9                   0   \n",
       "2                     40      8             4                   0   \n",
       "3                      9     38             2                   0   \n",
       "4                      2     43             3                   0   \n",
       "\n",
       "   Entertainment  Creativity  Yoga  \n",
       "0              0           0     0  \n",
       "1              0           0     0  \n",
       "2              3           0     0  \n",
       "3              3           0     0  \n",
       "4              1           1     0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d29f8401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4     False\n",
       "5      True\n",
       "6      True\n",
       "7      True\n",
       "8     False\n",
       "9      True\n",
       "10     True\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14     True\n",
       "15     True\n",
       "16     True\n",
       "17    False\n",
       "18     True\n",
       "19     True\n",
       "20     True\n",
       "21     True\n",
       "22    False\n",
       "23     True\n",
       "24    False\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "Name: Social Networking, dtype: bool"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subseting rows\n",
    "df['Social Networking'] > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4f001916",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Date</th>\n",
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       "      <th>Total Screen Time</th>\n",
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       "      <td>04/20/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>101</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
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       "      <th>5</th>\n",
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       "      <td>04/22/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>189</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>04/23/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>158</td>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>04/24/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>135</td>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>04/26/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>198</td>\n",
       "      <td>76</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>04/27/19</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>116</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>14</td>\n",
       "      <td>05/01/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>127</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
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       "      <td>05/02/19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
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       "      <td>91</td>\n",
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       "      <td>05/05/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>133</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>05/06/19</td>\n",
       "      <td>Monday</td>\n",
       "      <td>144</td>\n",
       "      <td>81</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>05/07/19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
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       "      <td>1</td>\n",
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       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>05/08/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>122</td>\n",
       "      <td>53</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>05/10/19</td>\n",
       "      <td>Friday</td>\n",
       "      <td>161</td>\n",
       "      <td>93</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0       0  04/17/19  Wednesday                 187                 89   \n",
       "1       1  04/18/19   Thursday                 123                 78   \n",
       "2       2  04/19/19     Friday                 112                 52   \n",
       "3       3  04/20/19   Saturday                 101                 69   \n",
       "5       5  04/22/19     Monday                 189                 68   \n",
       "6       6  04/23/19    Tuesday                 158                 56   \n",
       "7       7  04/24/19  Wednesday                 135                 98   \n",
       "9       9  04/26/19     Friday                 198                 76   \n",
       "10     10  04/27/19   Saturday                 116                 75   \n",
       "14     14  05/01/19  Wednesday                 127                 90   \n",
       "15     15  05/02/19   Thursday                 170                 60   \n",
       "16     16  05/03/19     Friday                  91                 64   \n",
       "18     18  05/05/19     Sunday                 133                109   \n",
       "19     19  05/06/19     Monday                 144                 81   \n",
       "20     20  05/07/19    Tuesday                 110                 70   \n",
       "21     21  05/08/19  Wednesday                 122                 53   \n",
       "23     23  05/10/19     Friday                 161                 93   \n",
       "\n",
       "    Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                      17     41            22                   0   \n",
       "1                      17      8             9                   0   \n",
       "2                      40      8             4                   0   \n",
       "3                       9     38             2                   0   \n",
       "5                       0      9             3                   4   \n",
       "6                      18     41            12                  15   \n",
       "7                       3     33            16                   0   \n",
       "9                       8     29            15                   0   \n",
       "10                     10     20             5                   0   \n",
       "14                      0     10             7                   0   \n",
       "15                      3      2            11                   0   \n",
       "16                      2     18             5                   1   \n",
       "18                      5      1             3                   0   \n",
       "19                      4      5             3                   0   \n",
       "20                      5      6            15                   0   \n",
       "21                     25     26            15                   0   \n",
       "23                     13     17            16                   1   \n",
       "\n",
       "    Entertainment  Creativity  Yoga  \n",
       "0               0           0     0  \n",
       "1               0           0     0  \n",
       "2               3           0     0  \n",
       "3               3           0     0  \n",
       "5               0           0     0  \n",
       "6               0           0     0  \n",
       "7               0           0     0  \n",
       "9              32           0     0  \n",
       "10              0           0     0  \n",
       "14              0           0     1  \n",
       "15              0           0     1  \n",
       "16              1           2     1  \n",
       "18              0           0     1  \n",
       "19              0           0     1  \n",
       "20              9           0     1  \n",
       "21              0           0     1  \n",
       "23              0           0     1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subseting rows\n",
    "df[df['Social Networking'] > 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "182f7e63",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0       0  04/17/19  Wednesday                 187                 89   \n",
       "7       7  04/24/19  Wednesday                 135                 98   \n",
       "14     14  05/01/19  Wednesday                 127                 90   \n",
       "21     21  05/08/19  Wednesday                 122                 53   \n",
       "\n",
       "    Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                      17     41            22                   0   \n",
       "7                       3     33            16                   0   \n",
       "14                      0     10             7                   0   \n",
       "21                     25     26            15                   0   \n",
       "\n",
       "    Entertainment  Creativity  Yoga  \n",
       "0               0           0     0  \n",
       "7               0           0     0  \n",
       "14              0           0     1  \n",
       "21              0           0     1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subseting rows\n",
    "df[df['Week Day'] == 'Wednesday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c2fe07b2",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Week Day</th>\n",
       "      <th>Total Screen Time</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Reading and Reference</th>\n",
       "      <th>Other</th>\n",
       "      <th>Productivity</th>\n",
       "      <th>Health and Fitness</th>\n",
       "      <th>Entertainment</th>\n",
       "      <th>Creativity</th>\n",
       "      <th>Yoga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>04/17/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>187</td>\n",
       "      <td>89</td>\n",
       "      <td>17</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>04/21/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>04/24/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>135</td>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>04/28/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>05/01/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>127</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>05/05/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>133</td>\n",
       "      <td>109</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>05/08/19</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>122</td>\n",
       "      <td>53</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>05/12/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>52</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0       0  04/17/19  Wednesday                 187                 89   \n",
       "4       4  04/21/19     Sunday                  56                 35   \n",
       "7       7  04/24/19  Wednesday                 135                 98   \n",
       "11     11  04/28/19     Sunday                  85                 42   \n",
       "14     14  05/01/19  Wednesday                 127                 90   \n",
       "18     18  05/05/19     Sunday                 133                109   \n",
       "21     21  05/08/19  Wednesday                 122                 53   \n",
       "25     25  05/12/19     Sunday                  52                 28   \n",
       "\n",
       "    Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                      17     41            22                   0   \n",
       "4                       2     43             3                   0   \n",
       "7                       3     33            16                   0   \n",
       "11                     22      4             2                   0   \n",
       "14                      0     10             7                   0   \n",
       "18                      5      1             3                   0   \n",
       "21                     25     26            15                   0   \n",
       "25                      1      1             6                   0   \n",
       "\n",
       "    Entertainment  Creativity  Yoga  \n",
       "0               0           0     0  \n",
       "4               1           1     0  \n",
       "7               0           0     0  \n",
       "11              0           0     0  \n",
       "14              0           0     1  \n",
       "18              0           0     1  \n",
       "21              0           0     1  \n",
       "25              0           1     1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subseting rows\n",
    "df[(df['Week Day'] == 'Wednesday') | (df['Week Day'] == 'Sunday')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e16bd72c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4      True\n",
       "5     False\n",
       "6     False\n",
       "7      True\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11     True\n",
       "12    False\n",
       "13    False\n",
       "14     True\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18     True\n",
       "19    False\n",
       "20    False\n",
       "21     True\n",
       "22    False\n",
       "23    False\n",
       "24    False\n",
       "25     True\n",
       "26    False\n",
       "27    False\n",
       "Name: Week Day, dtype: bool"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Subsetting using isin()\n",
    "df['Week Day'].isin(['Wednesday', 'Sunday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8a9901c6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Wednesday</td>\n",
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       "      <td>05/05/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>133</td>\n",
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       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "    index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0       0  04/17/19  Wednesday                 187                 89   \n",
       "4       4  04/21/19     Sunday                  56                 35   \n",
       "7       7  04/24/19  Wednesday                 135                 98   \n",
       "11     11  04/28/19     Sunday                  85                 42   \n",
       "14     14  05/01/19  Wednesday                 127                 90   \n",
       "18     18  05/05/19     Sunday                 133                109   \n",
       "21     21  05/08/19  Wednesday                 122                 53   \n",
       "25     25  05/12/19     Sunday                  52                 28   \n",
       "\n",
       "    Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                      17     41            22                   0   \n",
       "4                       2     43             3                   0   \n",
       "7                       3     33            16                   0   \n",
       "11                     22      4             2                   0   \n",
       "14                      0     10             7                   0   \n",
       "18                      5      1             3                   0   \n",
       "21                     25     26            15                   0   \n",
       "25                      1      1             6                   0   \n",
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       "    Entertainment  Creativity  Yoga  \n",
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       "18              0           0     1  \n",
       "21              0           0     1  \n",
       "25              0           1     1  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Subsetting using isin()\n",
    "df[df['Week Day'].isin(['Wednesday', 'Sunday'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5fb0251c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>05/05/19</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>133</td>\n",
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      ],
      "text/plain": [
       "    index      Date   Week Day  Total Screen Time   Social Networking  \\\n",
       "0       0  04/17/19  Wednesday                 187                 89   \n",
       "4       4  04/21/19     Sunday                  56                 35   \n",
       "7       7  04/24/19  Wednesday                 135                 98   \n",
       "11     11  04/28/19     Sunday                  85                 42   \n",
       "14     14  05/01/19  Wednesday                 127                 90   \n",
       "18     18  05/05/19     Sunday                 133                109   \n",
       "21     21  05/08/19  Wednesday                 122                 53   \n",
       "25     25  05/12/19     Sunday                  52                 28   \n",
       "\n",
       "    Reading and Reference  Other  Productivity  Health and Fitness  \\\n",
       "0                      17     41            22                   0   \n",
       "4                       2     43             3                   0   \n",
       "7                       3     33            16                   0   \n",
       "11                     22      4             2                   0   \n",
       "14                      0     10             7                   0   \n",
       "18                      5      1             3                   0   \n",
       "21                     25     26            15                   0   \n",
       "25                      1      1             6                   0   \n",
       "\n",
       "    Entertainment  Creativity  Yoga  \n",
       "0               0           0     0  \n",
       "4               1           1     0  \n",
       "7               0           0     0  \n",
       "11              0           0     0  \n",
       "14              0           0     1  \n",
       "18              0           0     1  \n",
       "21              0           0     1  \n",
       "25              0           1     1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subseting rows\n",
    "df[(df['Week Day'] == 'Wednesday') | (df['Week Day'] == 'Sunday')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e110260",
   "metadata": {},
   "outputs": [],
   "source": [
    "Watch this video: https://youtu.be/_OFZtGaKHeE"
   ]
  }
 ],
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