Code-Example-002-1-Pandas-Operations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upgrade your Numpy and Pandas Packages \n",
    "# ! pip install --upgrade pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install --upgrade numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy Version is:  1.24.1\n",
      "Pandas Version is:  1.5.3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "print(\"Numpy Version is: \", np.__version__)\n",
    "print(\"Pandas Version is: \", pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>row-5</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>row-6</th>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "       Col-1  Col-2  Col-3  Col-4\n",
       "row-1      3      2      3      3\n",
       "row-2      1      3      3      2\n",
       "row-3      4      3      4      2\n",
       "row-4      3      2      1      2\n",
       "row-5      3      4      2      1\n",
       "row-6      3      1      4      2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "data = [['row-1', 'row-2', 'row-3', 'row-4', 'row-5', 'row-6']]\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(1,5,(6, 4)), index = data,\n",
    "                 columns = ['Col-1', 'Col-2', 'Col-3', 'Col-4'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add a New Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Col-1</th>\n",
       "      <th>Col-2</th>\n",
       "      <th>Col-3</th>\n",
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       "      <th>Col-5</th>\n",
       "    </tr>\n",
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       "      <th>row-1</th>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>row-2</th>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row-5</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>row-6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "       Col-1  Col-2  Col-3  Col-4  Col-5\n",
       "row-1      3      2      3      3    1.0\n",
       "row-2      1      3      3      2    1.0\n",
       "row-3      4      3      4      2    1.0\n",
       "row-4      3      2      1      2    1.0\n",
       "row-5      3      4      2      1    1.0\n",
       "row-6      3      1      4      2    1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column to Dataframe that includes all ones\n",
    "df['Col-5'] = np.ones(6)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy Columns as grouping keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Col-1\n",
       "1    1\n",
       "3    4\n",
       "4    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group and show the number of each group as a size value\n",
    "# Result of this is a Pandas Series. You can convert it to frames using to_frames() operation\n",
    "df.groupby([\"Col-1\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.25</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.00</td>\n",
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      "text/plain": [
       "       Col-2  Col-3  Col-4  Col-5\n",
       "Col-1                            \n",
       "1       3.00    3.0    2.0    1.0\n",
       "3       2.25    2.5    2.0    1.0\n",
       "4       3.00    4.0    2.0    1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group and calulate mean\n",
    "df.groupby([\"Col-1\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>3</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "       Col-2  Col-3  Col-4  Col-5\n",
       "Col-1                            \n",
       "1          3      3      2    1.0\n",
       "3          9     10      8    4.0\n",
       "4          3      4      2    1.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group and sum the result\n",
    "df.groupby([\"Col-1\"]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Col-3</th>\n",
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       "      <th>Col-5</th>\n",
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       "      <th>Col-1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "             Col-3  Col-4  Col-5\n",
       "Col-1 Col-2                     \n",
       "1     3        3.0    2.0    1.0\n",
       "3     1        4.0    2.0    1.0\n",
       "      2        2.0    2.5    1.0\n",
       "      4        2.0    1.0    1.0\n",
       "4     3        4.0    2.0    1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by two different columns and calulate mean\n",
    "# The combination of these two keys creates a new combined key.\n",
    "df.groupby([\"Col-1\", \"Col-2\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy and Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Col-3</th>\n",
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       "    <tr>\n",
       "      <th>Col-1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <th>row-2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">3</th>\n",
       "      <th>row-1</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row-4</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row-5</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row-6</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>row-3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Col-1     Col-2  Col-3  Col-4  Col-5\n",
       "Col-1                                            \n",
       "1     row-2   1.00  1.000000    1.0  1.000   1.00\n",
       "3     row-1   0.25  0.222222    0.3  0.375   0.25\n",
       "      row-4   0.25  0.222222    0.1  0.250   0.25\n",
       "      row-5   0.25  0.444444    0.2  0.125   0.25\n",
       "      row-6   0.25  0.111111    0.4  0.250   0.25\n",
       "4     row-3   1.00  1.000000    1.0  1.000   1.00"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Col-1\"], group_keys=True).apply(lambda x: x/x.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy and Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
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      "text/plain": [
       "   A  B  C\n",
       "0  a  1  4\n",
       "1  a  2  6\n",
       "2  b  3  5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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    }
   ],
   "source": [
    "# Let us create another DataFrame like this\n",
    "df2 = pd.DataFrame({'A': 'a a b'.split(),\n",
    "                   'B': [1,2,3],\n",
    "                   'C': [4,6,5]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          B    C\n",
       "0  0.333333  0.4\n",
       "1  0.666667  0.6\n",
       "2  1.000000  1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1 = df2.groupby('A', group_keys=False)\n",
    "g1[['B', 'C']].apply(lambda x: x / x.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            B    C\n",
       "A                 \n",
       "a 0  0.333333  0.4\n",
       "  1  0.666667  0.6\n",
       "b 2  1.000000  1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2 = df2.groupby('A', group_keys=True)\n",
    "g2[['B', 'C']].apply(lambda x: x / x.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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