Pandas_DataFrame_Finisher-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d9600b1",
   "metadata": {},
   "source": [
    "# Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3fe039f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b98e989",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3\n",
       "0     1     4     7\n",
       "1     2     5     5\n",
       "2     3     6     8\n",
       "3     4     9    12\n",
       "4     7     5     1\n",
       "5    11     0    11"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q61\n",
    "d = {'col1': [1, 2, 3, 4, 7, 11], 'col2': [4, 5, 6, 9, 5, 0], 'col3': [7, 5, 8, 12, 1,11]}\n",
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb3f19a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    11\n",
       "4     7\n",
       "3     4\n",
       "Name: col1, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df['col1'].nlargest(n=3)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2dfe7729",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   col1  col2  col3\n",
       "3     4     9    12\n",
       "2     3     6     8\n",
       "1     2     5     5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.nlargest(3, 'col2')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58a262f5",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "   col1  col2  col3\n",
       "3     4     9    12\n",
       "5    11     0    11\n",
       "2     3     6     8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df.nlargest(3, 'col3') \n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5022dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>col3</th>\n",
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       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   col1  col2  col3\n",
       "3     4     9    12\n",
       "4     7     5     1\n",
       "5    11     0    11"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#62\n",
    "n_row = df.iloc[3:]\n",
    "n_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f77f2ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "   col1  col2  col3\n",
       "0     1     4     7\n",
       "1     2     5     5\n",
       "2     3     6     8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#q63\n",
    "n_last = df.iloc[:3]\n",
    "n_last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3098dba",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>68</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
       "      <td>94</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>96</td>\n",
       "      <td>89</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    W   X   Y   Z\n",
       "0  68  78  84  86\n",
       "1  75  85  94  97\n",
       "2  86  96  89  96\n",
       "3  80  80  83  72\n",
       "4  66  86  86  83"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q64\n",
    "df = pd.DataFrame({'W':[68,75,86,80,66],'X':[78,85,96,80,86], 'Y':[84,94,89,83,86],'Z':[86,97,96,72,83]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d9560ac4",
   "metadata": {},
   "outputs": [
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       "      <th>A_W</th>\n",
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       "      <td>75</td>\n",
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       "      <td>97</td>\n",
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       "      <th>3</th>\n",
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       "      <td>83</td>\n",
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      "text/plain": [
       "   A_W  A_X  A_Y  A_Z\n",
       "0   68   78   84   86\n",
       "1   75   85   94   97\n",
       "2   86   96   89   96\n",
       "3   80   80   83   72\n",
       "4   66   86   86   83"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.add_prefix('A_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b125847e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>W_1</th>\n",
       "      <th>X_1</th>\n",
       "      <th>Y_1</th>\n",
       "      <th>Z_1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68</td>\n",
       "      <td>78</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
       "      <td>94</td>\n",
       "      <td>97</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>96</td>\n",
       "      <td>89</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>83</td>\n",
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      "text/plain": [
       "   W_1  X_1  Y_1  Z_1\n",
       "0   68   78   84   86\n",
       "1   75   85   94   97\n",
       "2   86   96   89   96\n",
       "3   80   80   83   72\n",
       "4   66   86   86   83"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.add_suffix('_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bac9c634",
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "      <td>94</td>\n",
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       "      <td>75</td>\n",
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       "      <td>86</td>\n",
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       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Z   Y   X   W\n",
       "0  86  84  78  68\n",
       "1  97  94  85  75\n",
       "2  96  89  96  86\n",
       "3  72  83  80  80\n",
       "4  83  86  86  66"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#65\n",
    "df.iloc[:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90f8fdbe",
   "metadata": {},
   "outputs": [
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       "    W   X   Y   Z\n",
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       "1  75  85  94  97\n",
       "0  68  78  84  86"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   "source": [
    "df.iloc[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04920085",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    W   X   Y   Z\n",
       "0  66  86  86  83\n",
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       "3  75  85  94  97\n",
       "4  68  78  84  86"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df.iloc[::-1].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38637f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>18.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gino Mcneill</td>\n",
       "      <td>16/02/1999</td>\n",
       "      <td>21.2</td>\n",
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       "      <th>2</th>\n",
       "      <td>Ryan Parkes</td>\n",
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       "      <th>4</th>\n",
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       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "             name date_of_birth   age\n",
       "0  Alberto Franco    17/05/2002  18.5\n",
       "1    Gino Mcneill    16/02/1999  21.2\n",
       "2     Ryan Parkes    25/09/1998  22.5\n",
       "3    Eesha Hinton    11/05/2002  22.0\n",
       "4    Syed Wharton    15/09/1997  23.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q66\n",
    "df = pd.DataFrame({'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],\n",
    "    'date_of_birth': ['17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],\n",
    "    'age': [18.5, 21.2, 22.5, 22, 23]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9169bcb",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    age\n",
       "0  18.5\n",
       "1  21.2\n",
       "2  22.5\n",
       "3  22.0\n",
       "4  23.0"
      ]
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "df.select_dtypes(include='number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2fedd8a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>16/02/1999</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Parkes</td>\n",
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       "      <th>3</th>\n",
       "      <td>Eesha Hinton</td>\n",
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       "      <td>Syed Wharton</td>\n",
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      "text/plain": [
       "             name date_of_birth\n",
       "0  Alberto Franco    17/05/2002\n",
       "1    Gino Mcneill    16/02/1999\n",
       "2     Ryan Parkes    25/09/1998\n",
       "3    Eesha Hinton    11/05/2002\n",
       "4    Syed Wharton    15/09/1997"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ef3d223b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Parkes</td>\n",
       "      <td>25/09/1998</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Syed Wharton</td>\n",
       "      <td>15/09/1997</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eesha Hinton</td>\n",
       "      <td>11/05/2002</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name date_of_birth   age\n",
       "2   Ryan Parkes    25/09/1998  22.5\n",
       "4  Syed Wharton    15/09/1997  23.0\n",
       "3  Eesha Hinton    11/05/2002  22.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q67\n",
    "ss = df.sample(frac=0.6)\n",
    "ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "13e43dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4f06b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "ss2 = df.drop(ss.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4800787b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "             name date_of_birth   age\n",
       "0  Alberto Franco    17/05/2002  18.5\n",
       "1    Gino Mcneill    16/02/1999  21.2"
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     "execution_count": 26,
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    "ss2"
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  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f014062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b4e4d1ea",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>Syed Wharton</td>\n",
       "      <td>15/09/1997</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name Date_Of_Birth    Age\n",
       "0  Alberto Franco     17/05/2002  18.5\n",
       "1    Gino Mcneill     16/02/1999  21.2\n",
       "2     Ryan Parkes     25/09/1998  22.5\n",
       "3    Eesha Hinton     11/05/2002  22.0\n",
       "4    Syed Wharton     15/09/1997  23.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q68\n",
    "df = pd.DataFrame({\n",
    "    'Name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],\n",
    "    'Date_Of_Birth ': ['17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],\n",
    "    'Age': [18.5, 21.2, 22.5, 22, 23]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "156582d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'date_of_birth', 'age'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.str.lower().str.rstrip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c04f1e5b",
   "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>Date_Of_Birth</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberto Franco</td>\n",
       "      <td>17/05/2002</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gino Mcneill</td>\n",
       "      <td>16/02/1999</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Parkes</td>\n",
       "      <td>25/09/1998</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eesha Hinton</td>\n",
       "      <td>11/05/2002</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Syed Wharton</td>\n",
       "      <td>15/09/1997</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name Date_Of_Birth    Age\n",
       "0  Alberto Franco     17/05/2002  18.5\n",
       "1    Gino Mcneill     16/02/1999  21.2\n",
       "2     Ryan Parkes     25/09/1998  22.5\n",
       "3    Eesha Hinton     11/05/2002  22.0\n",
       "4    Syed Wharton     15/09/1997  23.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "348da104",
   "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>Date_Of_Birth</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberto Franco</td>\n",
       "      <td>17/05/2002</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gino Mcneill</td>\n",
       "      <td>16/02/1999</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Parkes</td>\n",
       "      <td>25/09/1998</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eesha Hinton</td>\n",
       "      <td>11/05/2002</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Syed Wharton</td>\n",
       "      <td>15/09/1997</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name Date_Of_Birth    Age\n",
       "0  Alberto Franco     17/05/2002  18.5\n",
       "1    Gino Mcneill     16/02/1999  21.2\n",
       "2     Ryan Parkes     25/09/1998  22.5\n",
       "3    Eesha Hinton     11/05/2002  22.0\n",
       "4    Syed Wharton     15/09/1997  23.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q69\n",
    "df = pd.DataFrame({\n",
    "    'Name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],\n",
    "    'Date_Of_Birth ': ['17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],\n",
    "    'Age': [18.5, 21.2, 22.5, 22, 23]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "37b51188",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "New DataFrames:\n",
      "           Name Date_Of_Birth    Age\n",
      "2   Ryan Parkes     25/09/1998  22.5\n",
      "3  Eesha Hinton     11/05/2002  22.0\n",
      "4  Syed Wharton     15/09/1997  23.0\n",
      "             Name Date_Of_Birth    Age\n",
      "0  Alberto Franco     17/05/2002  18.5\n",
      "1    Gino Mcneill     16/02/1999  21.2\n",
      "3    Eesha Hinton     11/05/2002  22.0\n",
      "4    Syed Wharton     15/09/1997  23.0\n",
      "\n",
      "\"one_to_one”: check if merge keys are unique in both left and right datasets:\"\n",
      "           Name Date_Of_Birth    Age\n",
      "0  Eesha Hinton     11/05/2002  22.0\n",
      "1  Syed Wharton     15/09/1997  23.0\n",
      "\n",
      "\"one_to_many” or “1:m”: check if merge keys are unique in left dataset:\n",
      "           Name Date_Of_Birth    Age\n",
      "0  Eesha Hinton     11/05/2002  22.0\n",
      "1  Syed Wharton     15/09/1997  23.0\n",
      "“many_to_one” or “m:1”: check if merge keys are unique in right dataset:\n",
      "           Name Date_Of_Birth    Age\n",
      "0  Eesha Hinton     11/05/2002  22.0\n",
      "1  Syed Wharton     15/09/1997  23.0\n"
     ]
    }
   ],
   "source": [
    "df1=  df.copy(deep = True)\n",
    "df = df.drop([0, 1])\n",
    "df1 = df1.drop([2])\n",
    "print(\"\\nNew DataFrames:\")\n",
    "print(df)\n",
    "print(df1)\n",
    "print('\\n\"one_to_one”: check if merge keys are unique in both left and right datasets:\"')\n",
    "df_one_to_one = pd.merge(df, df1, validate = \"one_to_one\")\n",
    "print(df_one_to_one)\n",
    "print('\\n\"one_to_many” or “1:m”: check if merge keys are unique in left dataset:')\n",
    "df_one_to_many = pd.merge(df, df1, validate = \"one_to_many\")\n",
    "print(df_one_to_many)\n",
    "print('“many_to_one” or “m:1”: check if merge keys are unique in right dataset:')\n",
    "df_many_to_one = pd.merge(df, df1, validate = \"many_to_one\")\n",
    "print(df_many_to_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8e25babd",
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberto Franco</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gino Mcneill</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Parkes</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eesha Hinton</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Syed Wharton</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kierra Gentry</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  age\n",
       "0  Alberto Franco   18\n",
       "1    Gino Mcneill   22\n",
       "2     Ryan Parkes   85\n",
       "3    Eesha Hinton   50\n",
       "4    Syed Wharton   80\n",
       "5   Kierra Gentry    5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q70\n",
    "df = pd.DataFrame({\n",
    "    'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton', 'Kierra Gentry'],\n",
    "      'age': [18, 22, 85, 50, 80, 5]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bec93336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       kids\n",
       "1      adult\n",
       "2    elderly\n",
       "3      adult\n",
       "4    elderly\n",
       "5       kids\n",
       "Name: age_groups, dtype: category\n",
       "Categories (3, object): ['kids' < 'adult' < 'elderly']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"age_groups\"] = pd.cut(df[\"age\"], bins = [0, 18, 65, 99], labels = [\"kids\", \"adult\", \"elderly\"])\n",
    "df['age_groups']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cf1e9aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "             Name Date_Of_Birth    Age\n",
      "0  Alberto Franco     17/05/2002  18.5\n",
      "1    Gino Mcneill     16/02/1999  21.2\n",
      "2     Ryan Parkes     25/09/1998  22.5\n",
      "3    Eesha Hinton     11/05/2002  22.0\n",
      "4    Syed Wharton     15/09/1997  23.0\n",
      "\n",
      "Global usage of memory of the DataFrame:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Name            5 non-null      object \n",
      " 1   Date_Of_Birth   5 non-null      object \n",
      " 2   Age             5 non-null      float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 849.0 bytes\n",
      "None\n",
      "\n",
      "The usage of memory of every column of the said DataFrame:\n",
      "Index             128\n",
      "Name              346\n",
      "Date_Of_Birth     335\n",
      "Age                40\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#71\n",
    "df = pd.DataFrame({\n",
    "    'Name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],\n",
    "    'Date_Of_Birth ': ['17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],\n",
    "    'Age': [18.5, 21.2, 22.5, 22, 23]\n",
    "})\n",
    "print(\"Original DataFrame:\")\n",
    "print(df)\n",
    "print(\"\\nGlobal usage of memory of the DataFrame:\")\n",
    "print(df.info(memory_usage = \"deep\"))\n",
    "print(\"\\nThe usage of memory of every column of the said DataFrame:\")\n",
    "print(df.memory_usage(deep = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cfd769e6",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>php</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>python</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>java</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c#</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c++</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0  1\n",
       "0     php  1\n",
       "1  python  2\n",
       "2    java  3\n",
       "3      c#  4\n",
       "4     c++  5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#72\n",
    "sr1 = pd.Series(['php', 'python', 'java', 'c#', 'c++'])\n",
    "sr2 = pd.Series([1, 2, 3, 4, 5])\n",
    "ser_df = pd.concat([sr1,sr2], axis = 1)\n",
    "ser_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b26adbba",
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>php</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>python</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>java</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c#</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c++</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     col1  col2\n",
       "0     php     1\n",
       "1  python     2\n",
       "2    java     3\n",
       "3      c#     4\n",
       "4     c++     5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.DataFrame({\"col1\":sr1, \"col2\":sr2})\n",
    "ser.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f657c5ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame: Contains random values:\n",
      "                   A         B         C         D\n",
      "qOC3E1qla0  0.047846 -1.991869  0.865249  0.338796\n",
      "hCJPcBBV1f -0.294396  0.927071 -2.217706 -0.016243\n",
      "4GPWXcDY29  1.097098  1.476247  0.900262 -0.258701\n",
      "69EujqdusY  0.267570 -0.096644  0.142681 -0.112199\n",
      "erTeSFtWHf  0.122068  0.363914 -1.703563  0.898166\n",
      "TXCohzlued -1.264579  0.466866 -0.072798  2.075657\n",
      "PVIjrtSog5  0.953076  0.754174  0.236926  0.402469\n",
      "dR4VmaXtFL  0.271288  0.993901  0.887719  0.122350\n",
      "plYSo2cUI3  0.802560 -1.127319  0.711616  0.406709\n",
      "IayBXM6wIX -1.019520 -0.249567  1.113960 -1.027704\n",
      "ptQN9XUbSt  0.771019  0.337710  0.000586 -1.024045\n",
      "6ukQXy56sI  1.149282  0.621633 -2.150642 -0.111043\n",
      "jerOHOkBBA -1.621845 -0.502249  1.158184 -0.489027\n",
      "EtQEgEa6km -0.854476 -0.713116 -0.417633  1.823510\n",
      "B7bi8wumr2  0.685047  1.760848  0.026994  1.296305\n",
      "pqgnSwXTX2 -0.657143  0.119415 -0.304219  0.333964\n",
      "obLAHrWvjQ -0.205920 -1.009185 -0.040617 -2.535441\n",
      "R7alfPlTuj  1.201022  0.645332  0.865360 -0.876944\n",
      "slkngrhJS0 -1.261043 -0.013490 -1.172840  0.948291\n",
      "xlEA0yPrBv  0.673965 -0.679471 -1.065031 -0.943019\n",
      "h9re0WGJXv -0.848039  1.346196 -0.767247  0.805858\n",
      "yk7bDDHCpI -0.796511  0.093408  2.185244 -1.946190\n",
      "rRtlMPrnH9  0.193218  0.309727  0.632193  0.876053\n",
      "tChRJf02tC  1.559848  0.603598 -0.904579 -1.084291\n",
      "KWCZotdi6P  1.613194 -0.770245 -0.116431 -2.002015\n",
      "byhQPJn2gZ -1.234317 -2.384185 -0.117440  0.442530\n",
      "UVrKwOxMy9 -0.219881 -0.087788 -0.700977 -0.872885\n",
      "q0hynppFtH -1.496854  1.592159 -0.452053 -1.261169\n",
      "Pzw1em4SEU  0.347315 -0.111134  0.321904 -1.627736\n",
      "cY89muNdkC  0.529091  1.190997 -0.701958 -0.381440\n",
      "\n",
      "DataFrame: Contains missing values:\n",
      "                   A         B         C         D\n",
      "EiIjJ4foqx -0.602282  0.009971 -1.533778       NaN\n",
      "6Tab4r155I  1.532752 -1.271217  0.199974  0.578852\n",
      "ksenhhvkyQ       NaN  0.161229  0.349396  1.182191\n",
      "IXdTpieBM9 -0.384115 -0.236606  0.297577 -1.935534\n",
      "8rjtLgWBV6  0.357282  1.950793 -0.311008  1.594482\n",
      "Qat2bUnXaE -0.896719  0.258131       NaN -0.062669\n",
      "RGKvDy5cPy  0.788996  0.540441  0.436946  1.770733\n",
      "Rfwqvmhvrn -1.627263       NaN       NaN -0.080892\n",
      "1CoUTy4yQ6 -1.584736 -0.002416  0.809422  0.811640\n",
      "ubIgumzJpi       NaN -1.043728  1.241054  0.334718\n",
      "SfOFwcPg6F       NaN  0.979686 -0.872731  0.005387\n",
      "wDqaA1Ds7M  0.201177  0.268609 -1.080984 -0.682632\n",
      "NJX046fwhF -0.141327       NaN -1.255088  0.394900\n",
      "NuiBmOr39Q  0.495063  1.539868 -0.927428  0.587269\n",
      "MrvSGwXtlV  0.467980 -2.067988  0.657541  0.114316\n",
      "e6HxJwUAw0       NaN  0.674899  0.771832 -0.418385\n",
      "n80xRmMBkh  0.273335 -0.841369       NaN -0.522211\n",
      "rmRay2vgUk -1.790683 -0.264889  1.197359 -0.826362\n",
      "6elSEjvbKz -0.504871 -1.411270  0.863200  0.178097\n",
      "9UZxlpr9ML       NaN  0.805048  0.810531  0.473469\n",
      "izVocm1HkY  0.321367 -0.593297 -1.013192  0.754580\n",
      "t2z7QGk723  1.687303 -0.400465       NaN -1.402999\n",
      "7wJuGteJ7I  1.372795 -1.937681  0.110448  0.321609\n",
      "j4L9qpWuLR  0.836154  0.431760 -0.577324 -0.023962\n",
      "lEJVZ6JR6c  1.121464 -0.809746  0.145045  0.405901\n",
      "Nm9A0DXsJA  2.244094  0.465288  2.313555 -1.170788\n",
      "txevycpkhl  0.828277 -0.785198  1.228538 -1.562028\n",
      "uz81dLOLsU  1.333180 -1.815801 -0.236543 -0.889697\n",
      "bW2LU3JwIh  0.362550  0.699332 -0.276390  1.047902\n",
      "8d3t1TdGuS -1.223794 -0.850173  1.016768 -0.090140\n",
      "\n",
      "DataFrame: Contains datetime values:\n",
      "                   A         B         C         D\n",
      "2000-01-03  0.115934 -2.004566 -0.787604  1.033678\n",
      "2000-01-04 -0.814223 -0.891064  1.368019  2.714167\n",
      "2000-01-05 -0.402787 -0.050343  1.618811  2.207445\n",
      "2000-01-06 -0.111565  2.907007 -2.854857 -2.107732\n",
      "2000-01-07 -0.474969  0.723212 -1.250352  1.493928\n",
      "2000-01-10 -1.679932 -1.160796 -0.005605  0.380423\n",
      "2000-01-11 -1.011007 -0.975106  0.174718 -1.224293\n",
      "2000-01-12 -0.965630  0.600023  1.159723 -0.043530\n",
      "2000-01-13  1.395644  1.152051  1.526339  1.465117\n",
      "2000-01-14 -0.520203  0.768612  0.958670  0.633659\n",
      "2000-01-17 -0.645420 -0.856055  0.024364 -0.076341\n",
      "2000-01-18  0.077543  1.439540 -1.135971  2.067483\n",
      "2000-01-19 -0.197866  0.563057  0.604089  0.209179\n",
      "2000-01-20 -1.387883  1.375536 -0.879382  1.210824\n",
      "2000-01-21 -0.207600  1.102363  0.658476  0.018397\n",
      "2000-01-24 -0.239153 -1.500180  0.145309 -0.238770\n",
      "2000-01-25 -1.271406  2.054513 -0.471555 -1.195169\n",
      "2000-01-26 -0.292622  0.937424  0.408518 -0.769264\n",
      "2000-01-27 -1.396697  0.695864 -0.231657 -1.127429\n",
      "2000-01-28 -0.280239 -0.879820 -0.317526  1.597869\n",
      "2000-01-31 -1.729062  0.410923 -0.582834  0.034565\n",
      "2000-02-01  2.013864 -0.417184  0.200934 -0.162063\n",
      "2000-02-02 -0.578458 -0.448306 -0.674571 -0.991902\n",
      "2000-02-03 -0.327336  0.101503  0.407097 -0.960919\n",
      "2000-02-04  1.082372  2.240264  0.319928 -0.520072\n",
      "2000-02-07  0.533920 -1.690178 -0.665613  0.737714\n",
      "2000-02-08  0.824598 -0.760494  0.295054  0.826895\n",
      "2000-02-09  1.025061 -1.721649 -0.568317 -1.666311\n",
      "2000-02-10  2.226590  1.196824  1.272698 -0.388749\n",
      "2000-02-11  1.609946 -0.505334  3.080261  1.892564\n",
      "\n",
      "DataFrame: Contains mixed values:\n",
      "     A    B     C          D\n",
      "0  0.0  0.0  foo1 2009-01-01\n",
      "1  1.0  1.0  foo2 2009-01-02\n",
      "2  2.0  0.0  foo3 2009-01-05\n",
      "3  3.0  1.0  foo4 2009-01-06\n",
      "4  4.0  0.0  foo5 2009-01-07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\singh\\anaconda3\\lib\\site-packages\\pandas\\util\\__init__.py:15: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing\n"
     ]
    }
   ],
   "source": [
    "#73\n",
    "#The pd.util.testing.makeDataFrame() function is a utility function that generates a DataFrame\n",
    "#with 30 rows and 4 columns, each with a random name and random values. \n",
    "#This function is mainly used for testing purposes, and not for real data analysis.\n",
    "\n",
    "print(\"DataFrame: Contains random values:\")\n",
    "df1 = pd.util.testing.makeDataFrame() # contains random values\n",
    "print(df1)\n",
    "print(\"\\nDataFrame: Contains missing values:\")\n",
    "df2 = pd.util.testing.makeMissingDataframe() # contains missing values\n",
    "print(df2)\n",
    "print(\"\\nDataFrame: Contains datetime values:\")\n",
    "df3 = pd.util.testing.makeTimeDataFrame() # contains datetime values\n",
    "print(df3)\n",
    "print(\"\\nDataFrame: Contains mixed values:\")\n",
    "df4 = pd.util.testing.makeMixedDataFrame() # contains mixed values\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7bd6ead2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "               c1    c2\n",
      "2000-01-03  120.0   7.0\n",
      "2000-01-04  130.0   NaN\n",
      "2000-01-05  140.0  10.0\n",
      "2000-01-06  150.0   NaN\n",
      "2000-01-07    NaN   5.5\n",
      "2000-01-10  170.0  16.5\n",
      "\n",
      "DataFrame after interpolate:\n",
      "               c1     c2\n",
      "2000-01-03  120.0   7.00\n",
      "2000-01-04  130.0   8.50\n",
      "2000-01-05  140.0  10.00\n",
      "2000-01-06  150.0   7.75\n",
      "2000-01-07  160.0   5.50\n",
      "2000-01-10  170.0  16.50\n"
     ]
    }
   ],
   "source": [
    "#74\n",
    "sdata = {\"c1\":[120, 130 ,140, 150, np.nan, 170], \"c2\":[7, np.nan, 10, np.nan, 5.5, 16.5]}\n",
    "df = pd.DataFrame(sdata)\n",
    "df.index = pd.util.testing.makeDateIndex()[0:6]\n",
    "print(\"Original DataFrame:\")\n",
    "print(df)\n",
    "print(\"\\nDataFrame after interpolate:\")\n",
    "print(df.interpolate())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4eae1f6f",
   "metadata": {},
   "outputs": [
    {
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       "    W   X   Y   Z\n",
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      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#75\n",
    "df = pd.DataFrame({'W':[68,75,86,80,66],'X':[78,85,96,80,86], 'Y':[84,94,89,83,86],'Z':[86,97,96,72,83]});\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bc5daf76",
   "metadata": {},
   "outputs": [
    {
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       "4  66  86  86  83"
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     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.query('W < 70')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b056d36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataframe:\n",
      "  agent  purchase\n",
      "0  a001    4500.0\n",
      "1  a002    7500.0\n",
      "2  a003  $3000.25\n",
      "3  a003  $1250.35\n",
      "4  a004   9000.00\n",
      "\n",
      "Data Types:\n",
      "0    <class 'float'>\n",
      "1    <class 'float'>\n",
      "2      <class 'str'>\n",
      "3      <class 'str'>\n",
      "4      <class 'str'>\n",
      "Name: purchase, dtype: object\n",
      "\n",
      "New Data Types:\n",
      "0    <class 'float'>\n",
      "1    <class 'float'>\n",
      "2    <class 'float'>\n",
      "3    <class 'float'>\n",
      "4    <class 'float'>\n",
      "Name: purchase, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#76\n",
    "d = {\"agent\": [\"a001\", \"a002\", \"a003\", \"a003\", \"a004\"], \"purchase\":[4500.00, 7500.00, \"$3000.25\", \"$1250.35\", \"9000.00\"]}\n",
    "df = pd.DataFrame(d)\n",
    "print(\"Original dataframe:\")\n",
    "print(df)\n",
    "print(\"\\nData Types:\")\n",
    "print(df[\"purchase\"].apply(type))\n",
    "df[\"purchase\"] = df[\"purchase\"].replace(\"[$,]\", \"\", regex = True).astype(\"float\")\n",
    "print(\"\\nNew Data Types:\")\n",
    "print(df[\"purchase\"].apply(type))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9372fd9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "             Name Date_Of_Birth    Age\n",
      "0  Alberto Franco     17/05/2002  18.5\n",
      "1    Gino Mcneill     16/02/1999  21.2\n",
      "2     Ryan Parkes     25/09/1998  22.5\n",
      "3    Eesha Hinton     11/05/2002  22.0\n",
      "4    Gino Mcneill     15/09/1997  23.0\n",
      "\n",
      "Numeric representation of an array by identifying distinct values:\n",
      "[0 1 2 3 1]\n",
      "Index(['Alberto Franco', 'Gino Mcneill', 'Ryan Parkes', 'Eesha Hinton'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#77\n",
    "df = pd.DataFrame({\n",
    "    'Name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Gino Mcneill'],\n",
    "    'Date_Of_Birth ': ['17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],\n",
    "    'Age': [18.5, 21.2, 22.5, 22, 23]\n",
    "})\n",
    "print(\"Original DataFrame:\")\n",
    "print(df)\n",
    "label1, unique1 = pd.factorize(df['Name'])\n",
    "print(\"\\nNumeric representation of an array by identifying distinct values:\")\n",
    "print(label1)\n",
    "print(unique1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3664a100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DataFrame:\n",
      "    rnum\n",
      "0     23\n",
      "1     21\n",
      "2     27\n",
      "3     22\n",
      "4     34\n",
      "5     33\n",
      "6     34\n",
      "7     31\n",
      "8     25\n",
      "9     22\n",
      "10    34\n",
      "11    19\n",
      "12    31\n",
      "13    32\n",
      "14    19\n",
      "\n",
      "Replace current value in a dataframe column based on last largest value:\n",
      "    rnum\n",
      "0     23\n",
      "1      0\n",
      "2     27\n",
      "3      0\n",
      "4     34\n",
      "5      0\n",
      "6     34\n",
      "7      0\n",
      "8      0\n",
      "9      0\n",
      "10    34\n",
      "11     0\n",
      "12     0\n",
      "13     0\n",
      "14     0\n"
     ]
    }
   ],
   "source": [
    "#78\n",
    "df1=pd.DataFrame({'rnum':[23, 21, 27, 22, 34, 33, 34, 31, 25, 22, 34, 19, 31, 32, 19]})\n",
    "print(\"Original DataFrame:\")\n",
    "print(df1)\n",
    "df1['rnum']=df1.rnum.where(df1.rnum.eq(df1.rnum.cummax()),0)\n",
    "print(\"\\nReplace current value in a dataframe column based on last largest value:\")\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b85becc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#79\n",
    "df = pd.read_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0197efb8",
   "metadata": {},
   "outputs": [
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       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       W      X      Y      Z\n",
       "0   True  False  False  False\n",
       "1  False  False   True  False\n",
       "2  False   True  False  False\n",
       "3  False  False  False  False\n",
       "4   True   True  False  False"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#80\n",
    "df1 = pd.DataFrame({'W':[68,75,86,80,None],'X':[78,85,None,80,86], 'Y':[84,94,89,83,86],'Z':[86,97,96,72,83]});\n",
    "df2 = pd.DataFrame({'W':[78,75,86,80,None],'X':[78,85,96,80,76], 'Y':[84,84,89,83,86],'Z':[86,97,96,72,83]});\n",
    "df1.ne(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "041f0779",
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3\n",
       "0     1     4     7\n",
       "1     2     5     5\n",
       "2     3     6     8\n",
       "3     4     9    12\n",
       "4     7     5     1\n",
       "5    11     0    11"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#81\n",
    "d = {'col1': [1, 2, 3, 4, 7, 11], 'col2': [4, 5, 6, 9, 5, 0], 'col3': [7, 5, 8, 12, 1,11]}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "725f43c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: col1, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df['col1'].nsmallest(n=3)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7ac8ac5a",
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3\n",
       "5    11     0    11\n",
       "0     1     4     7\n",
       "1     2     5     5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.nsmallest(3, 'col2')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2d57e938",
   "metadata": {},
   "outputs": [],
   "source": [
    "####FINISH#####\n",
    "#:D"
   ]
  }
 ],
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