Pandas_DataFrame(Q16-30)-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ae8ef61",
   "metadata": {},
   "source": [
    "# Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7fe6de46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea180838",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "a  Anastasia   12.5         1     yes\n",
       "b       Dima    9.0         3      no\n",
       "c  Katherine   16.5         2     yes\n",
       "d      James    NaN         3      no\n",
       "e      Emily    9.0         2      no\n",
       "f    Michael   20.0         3     yes\n",
       "g    Matthew   14.5         1     yes\n",
       "h      Laura    NaN         1      no\n",
       "i      Kevin    8.0         2      no\n",
       "j      Jonas   19.0         1     yes"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q16\n",
    "exam_data  = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "        'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "        'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "        'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
    "\n",
    "df = pd.DataFrame(exam_data , index=labels)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16ad526c",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
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       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>yes</td>\n",
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       "      <th>g</th>\n",
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       "      <td>14.5</td>\n",
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       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "f    Michael   20.0         3     yes\n",
       "g    Matthew   14.5         1     yes\n",
       "h      Laura    NaN         1      no\n",
       "i      Kevin    8.0         2      no\n",
       "c  Katherine   16.5         2     yes\n",
       "j      Jonas   19.0         1     yes\n",
       "d      James    NaN         3      no\n",
       "e      Emily    9.0         2      no\n",
       "b       Dima    9.0         3      no\n",
       "a  Anastasia   12.5         1     yes"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by = ['name', 'score'], ascending=[False, True])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1ec75c6",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Anastasia</td>\n",
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       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        name  score  attempts  qualify\n",
       "a  Anastasia   12.5         1     True\n",
       "b       Dima    9.0         3    False\n",
       "c  Katherine   16.5         2     True\n",
       "d      James    NaN         3    False\n",
       "e      Emily    9.0         2    False\n",
       "f    Michael   20.0         3     True\n",
       "g    Matthew   14.5         1     True\n",
       "h      Laura    NaN         1    False\n",
       "i      Kevin    8.0         2    False\n",
       "j      Jonas   19.0         1     True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q17\n",
    "df['qualify'] = df['qualify'].map({'yes': True, 'no': False})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c84c216",
   "metadata": {},
   "outputs": [
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       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
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       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
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       "      <td>Dima</td>\n",
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       "      <td>no</td>\n",
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       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Suresh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        name  score  attempts qualify\n",
       "a  Anastasia   12.5         1     yes\n",
       "b       Dima    9.0         3      no\n",
       "c  Katherine   16.5         2     yes\n",
       "d     Suresh    NaN         3      no\n",
       "e      Emily    9.0         2      no\n",
       "f    Michael   20.0         3     yes\n",
       "g    Matthew   14.5         1     yes\n",
       "h      Laura    NaN         1      no\n",
       "i      Kevin    8.0         2      no\n",
       "j      Jonas   19.0         1     yes"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q18\n",
    "df['name'] = df['name'].replace('James', 'Suresh')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3bcb6b2c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>qualify</th>\n",
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       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>Dima</td>\n",
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       "      <td>no</td>\n",
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       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>Suresh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        name  score qualify\n",
       "a  Anastasia   12.5     yes\n",
       "b       Dima    9.0      no\n",
       "c  Katherine   16.5     yes\n",
       "d     Suresh    NaN      no\n",
       "e      Emily    9.0      no\n",
       "f    Michael   20.0     yes\n",
       "g    Matthew   14.5     yes\n",
       "h      Laura    NaN      no\n",
       "i      Kevin    8.0      no\n",
       "j      Jonas   19.0     yes"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q19\n",
    "df.pop('attempts')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e171f530",
   "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>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempts</th>\n",
       "      <th>qualify</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>Anastasia</td>\n",
       "      <td>12.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>Maths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>Dima</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>Science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>Katherine</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>Hindi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>Emily</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>Social Science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>Michael</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>Maths</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>Science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>Laura</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>Kevin</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>Hindi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>Jonas</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>Social Science</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  score  attempts qualify         subject\n",
       "a  Anastasia   12.5         1     yes           Maths\n",
       "b       Dima    9.0         3      no         Science\n",
       "c  Katherine   16.5         2     yes         English\n",
       "d      James    NaN         3      no           Hindi\n",
       "e      Emily    9.0         2      no  Social Science\n",
       "f    Michael   20.0         3     yes           Maths\n",
       "g    Matthew   14.5         1     yes         Science\n",
       "h      Laura    NaN         1      no         English\n",
       "i      Kevin    8.0         2      no           Hindi\n",
       "j      Jonas   19.0         1     yes  Social Science"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q20\n",
    "sub = ['Maths', 'Science', 'English', 'Hindi', 'Social Science','Maths', 'Science', 'English', 'Hindi', 'Social Science' ]\n",
    "df['subject'] = sub\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1215ccb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anastasia 12.5\n",
      "Dima 9.0\n",
      "Katherine 16.5\n"
     ]
    }
   ],
   "source": [
    "#Q21\n",
    "exam_data = [{'name':'Anastasia', 'score':12.5}, {'name':'Dima','score':9}, {'name':'Katherine','score':16.5}]\n",
    "df = pd.DataFrame(exam_data)\n",
    "\n",
    "for index, row in df.iterrows():\n",
    "    print(row['name'], row['score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87070fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name', 'score', 'attempts', 'qualify']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q22\n",
    "list(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "40c423d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "1     2     5     8\n",
      "2     3     6     9\n",
      "New DataFrame after renaming columns:\n",
      "   Column1  Column2  Column3\n",
      "0        1        4        7\n",
      "1        2        5        8\n",
      "2        3        6        9\n"
     ]
    }
   ],
   "source": [
    "#Q23\n",
    "ori = {'col1': [1, 2, 3], 'col2': [4, 5, 6], 'col3': [7, 8, 9]}\n",
    "df = pd.DataFrame(data=ori)\n",
    "print(\"Original DataFrame\")\n",
    "print(df)\n",
    "\n",
    "df.columns = ['Column1', 'Column2', 'Column3']\n",
    "df = df.rename(columns={'col1': 'Column1', 'col2': 'Column2', 'col3': 'Column3'})\n",
    "print(\"New DataFrame after renaming columns:\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0314d0e1",
   "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>Column1</th>\n",
       "      <th>Column2</th>\n",
       "      <th>Column3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column1  Column2  Column3\n",
       "1        2        5        8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q24\n",
    "df.loc[df['Column1'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42049faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "1     2     5     8\n",
      "2     3     6     9\n",
      "After altering col1 and col3\n",
      "   col3  col2  col1\n",
      "0     7     4     1\n",
      "1     8     5     2\n",
      "2     9     6     3\n"
     ]
    }
   ],
   "source": [
    "#Q25\n",
    "ori = {'col1': [1, 2, 3], 'col2': [4, 5, 6], 'col3': [7, 8, 9]}\n",
    "df = pd.DataFrame(data=ori)\n",
    "print(\"Original DataFrame\")\n",
    "print(df)\n",
    "print('After altering col1 and col3')\n",
    "df = df[['col3', 'col2', 'col1']]\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cdd85202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "1     4     5     8\n",
      "2     3     6     9\n",
      "3     4     7     0\n",
      "4     5     8     1\n",
      "After add one row:\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "1     4     5     8\n",
      "2     3     6     9\n",
      "3     4     7     0\n",
      "4     5     8     1\n",
      "5    10    11    12\n"
     ]
    }
   ],
   "source": [
    "#Q26\n",
    "ori = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}\n",
    "df = pd.DataFrame(data=ori)\n",
    "print(\"Original DataFrame\")\n",
    "print(df)\n",
    "print('After add one row:')\n",
    "df2 = {'col1': 10, 'col2': 11, 'col3': 12}\n",
    "df = df.append(df2, ignore_index=True)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a7f63347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "1     4     5     8\n",
      "2     3     6     9\n",
      "3     4     7     0\n",
      "4     5     8     1\n",
      "Data from new_file.csv file:\n",
      "  col1\\tcol2\\tcol3\n",
      "0          1\\t4\\t7\n",
      "1          4\\t5\\t8\n",
      "2          3\\t6\\t9\n",
      "3          4\\t7\\t0\n",
      "4          5\\t8\\t1\n"
     ]
    }
   ],
   "source": [
    "#Q27\n",
    "ori = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}\n",
    "df = pd.DataFrame(data=ori)\n",
    "print(\"Original DataFrame\")\n",
    "print(df)\n",
    "print('Data from new_file.csv file:')\n",
    "df.to_csv('new_file.csv', sep='\\t', index=False)\n",
    "new_df = pd.read_csv('new_file.csv')\n",
    "print(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b3c9ef39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          city  Number of people\n",
      "0   California                 4\n",
      "1      Georgia                 2\n",
      "2  Los Angeles                 4\n"
     ]
    }
   ],
   "source": [
    "#28\n",
    "df1 = pd.DataFrame({'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "'city': ['California', 'Los Angeles', 'California', 'California', 'California', 'Los Angeles', 'Los Angeles', 'Georgia', 'Georgia', 'Los Angeles']})\n",
    "g1 = df1.groupby([\"city\"]).size().reset_index(name='Number of people')\n",
    "print(g1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "52868967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "1     4     5     8\n",
      "2     3     6     9\n",
      "3     4     7     0\n",
      "4     5     8     1\n",
      "New DataFrame\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "2     3     6     9\n",
      "3     4     7     0\n",
      "4     5     8     1\n"
     ]
    }
   ],
   "source": [
    "#Q29\n",
    "d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}\n",
    "df = pd.DataFrame(data=d)\n",
    "print(\"Original DataFrame\")\n",
    "print(df)\n",
    "df = df[df.col2 != 5]\n",
    "print(\"New DataFrame\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6d0cedc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame\n",
      "   col1  col2  col3\n",
      "0     1     4     7\n",
      "1     4     5     8\n",
      "2     3     6     9\n",
      "3     4     7     0\n",
      "4     5     8     1\n"
     ]
    }
   ],
   "source": [
    "#Q30\n",
    "d = {'col1': [1, 4, 3, 4, 5], 'col2': [4, 5, 6, 7, 8], 'col3': [7, 8, 9, 0, 1]}\n",
    "df = pd.DataFrame(data=d)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "print(\"Original DataFrame\")\n",
    "print(df)"
   ]
  }
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