7.7 Arithmetic with Dataframe.ipynb

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    "![title](img/cover4.png)"
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   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
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   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
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   "source": [
    "## 7.7 Arithmetic with Dataframe "
   ]
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   "cell_type": "markdown",
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   "source": [
    "First we import numpy and pandas"
   ]
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  {
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   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a dataframe using the function **pd.DataFrame()**:"
   ]
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       "   a   b   c   d\n",
       "0  1   2   3   4\n",
       "1  5   6   7   8\n",
       "2  9  10  11  12"
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   "source": [
    "dt1 = pd.DataFrame(np.arange(1,13).reshape((3,4)), columns = ['a', 'b','c','d'])\n",
    "dt1"
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   "source": [
    "Arithmetic operation can be done for the dataframe like this:"
   ]
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       "          a         b         c         d\n",
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    "1 /dt1"
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       "    a    b    c    d\n",
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   "source": [
    "dt1.pow(2)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply arithmetic operation to a single column like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3\n",
       "1     7\n",
       "2    11\n",
       "Name: a, dtype: int32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dt1['a'].add(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also apply arithmetic operation to **specific rows** like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b    4\n",
       "c    5\n",
       "d    6\n",
       "Name: 0, dtype: int32"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dt1.iloc[0].add(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do arithetic operation among columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    3\n",
       "2    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1['d']- dt1['a']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistics can be aslo calculated for the dataframe like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.0\n",
       "b    6.0\n",
       "c    7.0\n",
       "d    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dt1.mean()"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate a group of statistics using the function **describe()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a     b     c     d\n",
       "count  3.0   3.0   3.0   3.0\n",
       "mean   5.0   6.0   7.0   8.0\n",
       "std    4.0   4.0   4.0   4.0\n",
       "min    1.0   2.0   3.0   4.0\n",
       "25%    3.0   4.0   5.0   6.0\n",
       "50%    5.0   6.0   7.0   8.0\n",
       "75%    7.0   8.0   9.0  10.0\n",
       "max    9.0  10.0  11.0  12.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arithmetic operations can be aslo applied to statistics like this example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    8\n",
       "b    8\n",
       "c    8\n",
       "d    8\n",
       "dtype: int32"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1.max() - dt1.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here is an example to normalize the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hide_input": false
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b    c    d\n",
       "0 -0.5 -0.5 -0.5 -0.5\n",
       "1  0.0  0.0  0.0  0.0\n",
       "2  0.5  0.5  0.5  0.5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_dt1 = (dt1-dt1.mean())/(dt1.max()-dt1.min())\n",
    "normalized_dt1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a series using the function **pd.Series()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     16\n",
       "1     49\n",
       "2     42\n",
       "3     49\n",
       "4     20\n",
       "5     12\n",
       "6     12\n",
       "7     38\n",
       "8     11\n",
       "9     22\n",
       "10    33\n",
       "11    40\n",
       "12    16\n",
       "13    21\n",
       "14    34\n",
       "15    28\n",
       "16    33\n",
       "17    19\n",
       "18    37\n",
       "19    24\n",
       "dtype: int32"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series(np.random.randint(1,50, size = 20))\n",
    "ser1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can normalize the values in this series like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -0.310526\n",
       "1     0.557895\n",
       "2     0.373684\n",
       "3     0.557895\n",
       "4    -0.205263\n",
       "5    -0.415789\n",
       "6    -0.415789\n",
       "7     0.268421\n",
       "8    -0.442105\n",
       "9    -0.152632\n",
       "10    0.136842\n",
       "11    0.321053\n",
       "12   -0.310526\n",
       "13   -0.178947\n",
       "14    0.163158\n",
       "15    0.005263\n",
       "16    0.136842\n",
       "17   -0.231579\n",
       "18    0.242105\n",
       "19   -0.100000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_ser1 = (ser1-ser1.mean())/(ser1.max()-ser1.min())\n",
    "norm_ser1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
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