main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research Question:\n",
    "\n",
    "if there is a correlation between the variables in the dataset and if they may change according to age and gender\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing OS library "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (3.1.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from matplotlib) (2.4.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from matplotlib) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.11 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from matplotlib) (1.17.1)\n",
      "Requirement already satisfied: setuptools in c:\\program files (x86)\\python37-32\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib) (40.8.0)\n",
      "Requirement already satisfied: six in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
      "Requirement already satisfied: pandas in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (0.25.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from pandas) (1.17.1)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from pandas) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from pandas) (2019.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\najam\\appdata\\roaming\\python\\python37\\site-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\n",
      "Collecting csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  ERROR: Could not find a version that satisfies the requirement csv (from versions: none)\n",
      "ERROR: No matching distribution found for csv\n"
     ]
    }
   ],
   "source": [
    "#installing required libraries\n",
    "\n",
    "#!python -m pip install scipy --user\n",
    "!python -m pip install matplotlib --user\n",
    "!python -m pip install pandas --user\n",
    "!python -m pip install csv --user\n",
    "\n",
    "#importing libraries\n",
    "import os\n",
    "import os.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uising the csv reader and pandas library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "      Q1A Q1I    Q1E Q2A Q2I   Q2E Q3A Q3I    Q3E Q4A  ... screensize  \\\n",
       "0       4  28   3890   4  25  2122   2  16   1944   4  ...          1   \n",
       "1       4   2   8118   1  36  2890   2  35   4777   3  ...          2   \n",
       "2       3   7   5784   1  33  4373   4  41   3242   1  ...          2   \n",
       "3       2  23   5081   3  11  6837   2  37   5521   1  ...          2   \n",
       "4       2  36   3215   2  13  7731   3   5   4156   4  ...          2   \n",
       "...    ..  ..    ...  ..  ..   ...  ..  ..    ...  ..  ...        ...   \n",
       "39770   2  31   3287   1   5  2216   3  29   3895   2  ...          2   \n",
       "39771   3  14   4792   4  41  2604   3  15   2668   4  ...          1   \n",
       "39772   2   1  25147   1   4  4555   2  14   3388   1  ...          2   \n",
       "39773   3  36   4286   1  34  2736   2  10   5968   2  ...          2   \n",
       "39774   2  28  32251   1  22  3317   2   4  11734   1  ...          1   \n",
       "\n",
       "      uniquenetworklocation hand religion orientation race voted married  \\\n",
       "0                         1    1       12           1   10     2       1   \n",
       "1                         1    2        7           0   70     2       1   \n",
       "2                         1    1        4           3   60     1       1   \n",
       "3                         1    2        4           5   70     2       1   \n",
       "4                         2    3       10           1   10     2       1   \n",
       "...                     ...  ...      ...         ...  ...   ...     ...   \n",
       "39770                     1    1        2           4   60     2       1   \n",
       "39771                     1    1       10           0   10     2       1   \n",
       "39772                     1    1        7           1   30     1       2   \n",
       "39773                     1    1        6           1   60     1       1   \n",
       "39774                     1    1       10           1   10     1       1   \n",
       "\n",
       "      familysize              major  \n",
       "0              2                     \n",
       "1              4                     \n",
       "2              3                     \n",
       "3              5            biology  \n",
       "4              4         Psychology  \n",
       "...          ...                ...  \n",
       "39770          2                     \n",
       "39771          4         Mathematic  \n",
       "39772          3   Computer Science  \n",
       "39773          2            History  \n",
       "39774          4  Cognitive Science  \n",
       "\n",
       "[39775 rows x 172 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv as csv\n",
    "import pandas as pd\n",
    "import read as R\n",
    "#IMPORTING custom made LIBRARY\n",
    "df=R.createDf('DASS1.csv',csv,pd)\n",
    "df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>667</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>9373</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10171</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>10117</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1280</td>\n",
       "      <td>720</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>international relations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>9465</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5284</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>8872</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>360</td>\n",
       "      <td>640</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73484</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>9925</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>14218</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>25499</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2048</td>\n",
       "      <td>1080</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73485</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>5816</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4368</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>7246</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>sociology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73486</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>5353</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>6846</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4920</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1280</td>\n",
       "      <td>1024</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73487</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>7974</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7901</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>8647</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>414</td>\n",
       "      <td>736</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73488</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>7567</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5766</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>37103</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73489 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Q1A Q1I    Q1E Q2A Q2I    Q2E Q3A Q3I    Q3E Q4A  ... screenw screenh  \\\n",
       "0       3   6  21017   3   7  18600   5  20  14957   2  ...    1440     900   \n",
       "1       5  17   3818   5   9   7850   1  16   5902   3  ...    1536     864   \n",
       "2       5  16   4186   5  12   2900   1   2   7160   1  ...     375     667   \n",
       "3       2  12   9373   4   1  10171   2   7  10117   1  ...    1280     720   \n",
       "4       5  13   9465   5   7   5284   2  19   8872   1  ...     360     640   \n",
       "...    ..  ..    ...  ..  ..    ...  ..  ..    ...  ..  ...     ...     ...   \n",
       "73484   2  17   9925   1   6  14218   2  16  25499   4  ...    2048    1080   \n",
       "73485   5   9   5816   5   4   4368   1  15   7246   1  ...    1440     900   \n",
       "73486   2  17   5353   1  18   6846   1   9   4920   1  ...    1280    1024   \n",
       "73487   4  15   7974   3   9   7901   5  17   8647   3  ...     414     736   \n",
       "73488   3  12   7567   1  10   5766   5  13  37103   1  ...    1366     768   \n",
       "\n",
       "      hand religion orientation race voted married familysize  \\\n",
       "0        1        7           1   30     1       2          5   \n",
       "1        1        1           1   60     2       1          2   \n",
       "2        1        2           2   10     2       1          2   \n",
       "3        1        6           1   60     1       3          2   \n",
       "4        1        4           3   60     1       1          2   \n",
       "...    ...      ...         ...  ...   ...     ...        ...   \n",
       "73484    1       12           1   10     2       2          1   \n",
       "73485    1        2           3   60     2       1          2   \n",
       "73486    1        1           1   60     2       1          3   \n",
       "73487    1        3           1   10     1       1          4   \n",
       "73488    1        4           1   70     2       1          4   \n",
       "\n",
       "                         major  \n",
       "0                   Marketing   \n",
       "1                  mathematics  \n",
       "2                    Chemistry  \n",
       "3      international relations  \n",
       "4                   Management  \n",
       "...                        ...  \n",
       "73484                 business  \n",
       "73485                sociology  \n",
       "73486                           \n",
       "73487                 Business  \n",
       "73488                           \n",
       "\n",
       "[73489 rows x 105 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import read as R\n",
    "df2=R.createDf('MACHIAVELLIAN1.csv',csv,pd)\n",
    "df2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1</th>\n",
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       "      <th>Q10</th>\n",
       "      <th>...</th>\n",
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       "      <th>race_other</th>\n",
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       "      <th>ASD</th>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>25454</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25455</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25456</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>4</td>\n",
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       "    </tr>\n",
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       "      <td>25457</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25458</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25459 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10  ... race_arab race_asian race_black  \\\n",
       "0      3  5  3  3  5  5  5  3  5   5  ...         0          0          0   \n",
       "1      4  4  4  3  5  2  5  1  4   4  ...         0          0          0   \n",
       "2      5  5  5  5  5  5  5  5  5   5  ...         0          0          0   \n",
       "3      5  5  5  5  5  5  5  3  5   5  ...         0          0          0   \n",
       "4      4  4  4  4  4  4  4  4  4   5  ...         0          0          0   \n",
       "...   .. .. .. .. .. .. .. .. ..  ..  ...       ...        ...        ...   \n",
       "25454  4  4  4  3  5  4  4  3  4   5  ...         0          0          0   \n",
       "25455  1  2  4  2  5  5  5  1  2   1  ...         0          1          0   \n",
       "25456  4  5  2  4  2  4  5  4  2   4  ...         0          1          0   \n",
       "25457  5  5  5  5  5  4  5  5  3   4  ...         0          0          0   \n",
       "25458  5  3  5  5  5  4  5  3  4   5  ...         0          0          0   \n",
       "\n",
       "      race_white race_hispanic race_nativeam race_nativeau race_other nerdy  \\\n",
       "0              1             0             0             0          0     7   \n",
       "1              1             0             0             0          0     6   \n",
       "2              1             0             0             0          0     7   \n",
       "3              1             0             0             0          0     7   \n",
       "4              1             0             0             0          0     6   \n",
       "...          ...           ...           ...           ...        ...   ...   \n",
       "25454          1             0             0             0          0     5   \n",
       "25455          0             0             0             0          0     6   \n",
       "25456          0             0             0             0          0     7   \n",
       "25457          1             0             0             0          0     6   \n",
       "25458          1             0             0             0          0     6   \n",
       "\n",
       "      ASD  \n",
       "0       2  \n",
       "1       2  \n",
       "2       2  \n",
       "3       2  \n",
       "4       2  \n",
       "...    ..  \n",
       "25454   2  \n",
       "25455   2  \n",
       "25456   2  \n",
       "25457   2  \n",
       "25458   2  \n",
       "\n",
       "[25459 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=R.createDf('NERD1.csv',csv,pd)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().values.any() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isnull().values.any() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slicing and Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>introelapse</th>\n",
       "      <th>testelapse</th>\n",
       "      <th>surveyelapse</th>\n",
       "      <th>TIPI1</th>\n",
       "      <th>TIPI2</th>\n",
       "      <th>TIPI3</th>\n",
       "      <th>TIPI4</th>\n",
       "      <th>TIPI5</th>\n",
       "      <th>TIPI6</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>engnat</th>\n",
       "      <th>age</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>voted</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>major</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>GB</td>\n",
       "      <td>49</td>\n",
       "      <td>328</td>\n",
       "      <td>426</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>38</td>\n",
       "      <td>143</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>mathematics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>US</td>\n",
       "      <td>4</td>\n",
       "      <td>143</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>CH</td>\n",
       "      <td>60</td>\n",
       "      <td>191</td>\n",
       "      <td>269</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>international relations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NL</td>\n",
       "      <td>37</td>\n",
       "      <td>302</td>\n",
       "      <td>334</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73484</td>\n",
       "      <td>US</td>\n",
       "      <td>6</td>\n",
       "      <td>494</td>\n",
       "      <td>340</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73485</td>\n",
       "      <td>PL</td>\n",
       "      <td>6</td>\n",
       "      <td>295</td>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>sociology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73486</td>\n",
       "      <td>RO</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73487</td>\n",
       "      <td>NONE</td>\n",
       "      <td>56</td>\n",
       "      <td>269</td>\n",
       "      <td>198</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73488</td>\n",
       "      <td>US</td>\n",
       "      <td>24</td>\n",
       "      <td>198</td>\n",
       "      <td>197</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73489 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      country introelapse testelapse surveyelapse TIPI1 TIPI2 TIPI3 TIPI4  \\\n",
       "0          GB          49        328          426     6     5     6     1   \n",
       "1          US          38        143          150     2     5     6     2   \n",
       "2          US           4        143          157     1     7     6     7   \n",
       "3          CH          60        191          269     6     5     5     7   \n",
       "4          NL          37        302          334     2     5     5     6   \n",
       "...       ...         ...        ...          ...   ...   ...   ...   ...   \n",
       "73484      US           6        494          340     6     4     5     2   \n",
       "73485      PL           6        295          140     5     7     5     5   \n",
       "73486      RO          11        111          122     1     6     1     5   \n",
       "73487    NONE          56        269          198     5     4     5     7   \n",
       "73488      US          24        198          197     6     7     6     4   \n",
       "\n",
       "      TIPI5 TIPI6  ... gender engnat age hand religion orientation voted  \\\n",
       "0         7     3  ...      1      1  26    1        7           1     1   \n",
       "1         4     6  ...      1      1  18    1        1           1     2   \n",
       "2         5     7  ...      2      1  15    1        2           2     2   \n",
       "3         7     2  ...      2      2  31    1        6           1     1   \n",
       "4         7     6  ...      1      2  20    1        4           3     1   \n",
       "...     ...   ...  ...    ...    ...  ..  ...      ...         ...   ...   \n",
       "73484     6     6  ...      1      2  29    1       12           1     2   \n",
       "73485     5     6  ...      2      2  20    1        2           3     2   \n",
       "73486     5     6  ...      1      2  31    1        1           1     2   \n",
       "73487     7     5  ...      1      2  24    1        3           1     1   \n",
       "73488     7     5  ...      1      1  19    1        4           1     2   \n",
       "\n",
       "      married familysize                    major  \n",
       "0           2          5               Marketing   \n",
       "1           1          2              mathematics  \n",
       "2           1          2                Chemistry  \n",
       "3           3          2  international relations  \n",
       "4           1          2               Management  \n",
       "...       ...        ...                      ...  \n",
       "73484       2          1                 business  \n",
       "73485       1          2                sociology  \n",
       "73486       1          3                           \n",
       "73487       1          4                 Business  \n",
       "73488       1          4                           \n",
       "\n",
       "[73489 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A=df2.loc[:,['country', 'introelapse', 'testelapse', 'surveyelapse', 'TIPI1', 'TIPI2', 'TIPI3', 'TIPI4', 'TIPI5', 'TIPI6', 'TIPI7', 'TIPI8', 'TIPI9', 'TIPI10', 'VCL1', 'VCL2', 'VCL3', 'VCL4', 'VCL5', 'VCL6', 'VCL7', 'VCL8', 'VCL9', 'VCL10', 'VCL11', 'VCL12', 'VCL13', 'VCL14', 'VCL15', 'VCL16', 'education', 'urban', 'gender', 'engnat', 'age', 'hand', 'religion', 'orientation', 'voted', 'married', 'familysize', 'major']]\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>US</td>\n",
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       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>PL</td>\n",
       "      <td>5</td>\n",
       "      <td>271</td>\n",
       "      <td>122</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>3</td>\n",
       "      <td>261</td>\n",
       "      <td>336</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>MY</td>\n",
       "      <td>1766</td>\n",
       "      <td>164</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Psychology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39770</td>\n",
       "      <td>GB</td>\n",
       "      <td>7</td>\n",
       "      <td>143</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39771</td>\n",
       "      <td>MY</td>\n",
       "      <td>10</td>\n",
       "      <td>181</td>\n",
       "      <td>243</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mathematic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39772</td>\n",
       "      <td>US</td>\n",
       "      <td>12</td>\n",
       "      <td>246</td>\n",
       "      <td>257</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Computer Science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39773</td>\n",
       "      <td>US</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>History</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39774</td>\n",
       "      <td>MY</td>\n",
       "      <td>17</td>\n",
       "      <td>427</td>\n",
       "      <td>371</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Cognitive Science</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39775 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      country introelapse testelapse surveyelapse TIPI1 TIPI2 TIPI3 TIPI4  \\\n",
       "0          IN          19        167          166     1     5     7     7   \n",
       "1          US           1        193          186     6     5     4     7   \n",
       "2          PL           5        271          122     2     5     2     2   \n",
       "3          US           3        261          336     1     1     7     4   \n",
       "4          MY        1766        164          157     2     5     3     6   \n",
       "...       ...         ...        ...          ...   ...   ...   ...   ...   \n",
       "39770      GB           7        143           66     2     2     3     5   \n",
       "39771      MY          10        181          243     4     5     5     7   \n",
       "39772      US          12        246          257     6     6     7     5   \n",
       "39773      US           2        174          113     1     6     5     7   \n",
       "39774      MY          17        427          371     6     2     3     5   \n",
       "\n",
       "      TIPI5 TIPI6  ... gender engnat age hand religion orientation voted  \\\n",
       "0         7     7  ...      2      2  16    1       12           1     2   \n",
       "1         5     4  ...      2      1  16    2        7           0     2   \n",
       "2         5     6  ...      2      2  17    1        4           3     1   \n",
       "3         6     4  ...      2      1  13    2        4           5     2   \n",
       "4         5     5  ...      2      2  19    3       10           1     2   \n",
       "...     ...   ...  ...    ...    ...  ..  ...      ...         ...   ...   \n",
       "39770     6     5  ...      1      1  16    1        2           4     2   \n",
       "39771     4     6  ...      1      2  21    1       10           0     2   \n",
       "39772     6     3  ...      2      1  48    1        7           1     1   \n",
       "39773     3     5  ...      2      1  20    1        6           1     1   \n",
       "39774     6     3  ...      2      2  26    1       10           1     1   \n",
       "\n",
       "      married familysize              major  \n",
       "0           1          2                     \n",
       "1           1          4                     \n",
       "2           1          3                     \n",
       "3           1          5            biology  \n",
       "4           1          4         Psychology  \n",
       "...       ...        ...                ...  \n",
       "39770       1          2                     \n",
       "39771       1          4         Mathematic  \n",
       "39772       2          3   Computer Science  \n",
       "39773       1          2            History  \n",
       "39774       1          4  Cognitive Science  \n",
       "\n",
       "[39775 rows x 42 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B=df.loc[:,['country', 'introelapse', 'testelapse', 'surveyelapse', 'TIPI1', 'TIPI2', 'TIPI3', 'TIPI4', 'TIPI5', 'TIPI6', 'TIPI7', 'TIPI8', 'TIPI9', 'TIPI10', 'VCL1', 'VCL2', 'VCL3', 'VCL4', 'VCL5', 'VCL6', 'VCL7', 'VCL8', 'VCL9', 'VCL10', 'VCL11', 'VCL12', 'VCL13', 'VCL14', 'VCL15', 'VCL16', 'education', 'urban', 'gender', 'engnat', 'age', 'hand', 'religion', 'orientation', 'voted', 'married', 'familysize', 'major']]\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .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>country</th>\n",
       "      <th>introelapse</th>\n",
       "      <th>testelapse</th>\n",
       "      <th>surveyelapse</th>\n",
       "      <th>TIPI1</th>\n",
       "      <th>TIPI2</th>\n",
       "      <th>TIPI3</th>\n",
       "      <th>TIPI4</th>\n",
       "      <th>TIPI5</th>\n",
       "      <th>TIPI6</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>engnat</th>\n",
       "      <th>age</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>voted</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>major</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>41</td>\n",
       "      <td>93</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Studio Art</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>GB</td>\n",
       "      <td>13</td>\n",
       "      <td>131</td>\n",
       "      <td>161</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>biophysics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>PL</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>26</td>\n",
       "      <td>710</td>\n",
       "      <td>228</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Mathematics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>6</td>\n",
       "      <td>195</td>\n",
       "      <td>424</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25454</td>\n",
       "      <td>NL</td>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Philosophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25455</td>\n",
       "      <td>MY</td>\n",
       "      <td>3</td>\n",
       "      <td>135</td>\n",
       "      <td>179</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25456</td>\n",
       "      <td>US</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bioengineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25457</td>\n",
       "      <td>US</td>\n",
       "      <td>3</td>\n",
       "      <td>139</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25458</td>\n",
       "      <td>FR</td>\n",
       "      <td>15</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25459 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      country introelapse testelapse surveyelapse TIPI1 TIPI2 TIPI3 TIPI4  \\\n",
       "0          US          41         93          125     1     1     7     1   \n",
       "1          GB          13        131          161     5     3     5     4   \n",
       "2          PL           2         49           87     1     6     3     7   \n",
       "3          US          26        710          228     7     2     7     4   \n",
       "4          US           6        195          424     2     5     4     3   \n",
       "...       ...         ...        ...          ...   ...   ...   ...   ...   \n",
       "25454      NL           3        134          215     2     5     3     6   \n",
       "25455      MY           3        135          179     6     6     7     7   \n",
       "25456      US           2        124          224     6     5     6     3   \n",
       "25457      US           3        139          203     1     5     3     6   \n",
       "25458      FR          15         83          156     1     5     7     1   \n",
       "\n",
       "      TIPI5 TIPI6  ... gender engnat age hand religion orientation voted  \\\n",
       "0         6     7  ...      2      1  69    1        6           1     1   \n",
       "1         5     5  ...      2      1  50    1        1           1     1   \n",
       "2         6     7  ...      2      2  22    1        1           2     1   \n",
       "3         7     2  ...      1      1  44    1        2           1     2   \n",
       "4         7     6  ...      2      1  17    1        7           4     2   \n",
       "...     ...   ...  ...    ...    ...  ..  ...      ...         ...   ...   \n",
       "25454     4     5  ...      2      2  23    1        1           1     1   \n",
       "25455     5     1  ...      2      2  32    1       10           1     1   \n",
       "25456     5     2  ...      2      1  16    1        2           2     2   \n",
       "25457     3     3  ...      1      1  17    1        5           4     2   \n",
       "25458     6     7  ...      1      2  20    1        2           1     1   \n",
       "\n",
       "      married familysize           major  \n",
       "0           3          4      Studio Art  \n",
       "1           1          3      biophysics  \n",
       "2           1          2         biology  \n",
       "3           3          4     Mathematics  \n",
       "4           1          1                  \n",
       "...       ...        ...             ...  \n",
       "25454       1          4      Philosophy  \n",
       "25455       3          4        Medicine  \n",
       "25456       1          2  Bioengineering  \n",
       "25457       1          5                  \n",
       "25458       1          3                  \n",
       "\n",
       "[25459 rows x 42 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C=df3.loc[:,['country', 'introelapse', 'testelapse', 'surveyelapse', 'TIPI1', 'TIPI2', 'TIPI3', 'TIPI4', 'TIPI5', 'TIPI6', 'TIPI7', 'TIPI8', 'TIPI9', 'TIPI10', 'VCL1', 'VCL2', 'VCL3', 'VCL4', 'VCL5', 'VCL6', 'VCL7', 'VCL8', 'VCL9', 'VCL10', 'VCL11', 'VCL12', 'VCL13', 'VCL14', 'VCL15', 'VCL16', 'education', 'urban', 'gender', 'engnat', 'age', 'hand', 'religion', 'orientation', 'voted', 'married', 'familysize', 'major']]\n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merging/Combining datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>country</th>\n",
       "      <th>introelapse</th>\n",
       "      <th>testelapse</th>\n",
       "      <th>surveyelapse</th>\n",
       "      <th>TIPI1</th>\n",
       "      <th>TIPI2</th>\n",
       "      <th>TIPI3</th>\n",
       "      <th>TIPI4</th>\n",
       "      <th>TIPI5</th>\n",
       "      <th>TIPI6</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>engnat</th>\n",
       "      <th>age</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>voted</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>major</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>GB</td>\n",
       "      <td>49</td>\n",
       "      <td>328</td>\n",
       "      <td>426</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>US</td>\n",
       "      <td>38</td>\n",
       "      <td>143</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>mathematics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>US</td>\n",
       "      <td>4</td>\n",
       "      <td>143</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>CH</td>\n",
       "      <td>60</td>\n",
       "      <td>191</td>\n",
       "      <td>269</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>international relations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NL</td>\n",
       "      <td>37</td>\n",
       "      <td>302</td>\n",
       "      <td>334</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25454</td>\n",
       "      <td>NL</td>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "      <td>215</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Philosophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25455</td>\n",
       "      <td>MY</td>\n",
       "      <td>3</td>\n",
       "      <td>135</td>\n",
       "      <td>179</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25456</td>\n",
       "      <td>US</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bioengineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25457</td>\n",
       "      <td>US</td>\n",
       "      <td>3</td>\n",
       "      <td>139</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25458</td>\n",
       "      <td>FR</td>\n",
       "      <td>15</td>\n",
       "      <td>83</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138723 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      country introelapse testelapse surveyelapse TIPI1 TIPI2 TIPI3 TIPI4  \\\n",
       "0          GB          49        328          426     6     5     6     1   \n",
       "1          US          38        143          150     2     5     6     2   \n",
       "2          US           4        143          157     1     7     6     7   \n",
       "3          CH          60        191          269     6     5     5     7   \n",
       "4          NL          37        302          334     2     5     5     6   \n",
       "...       ...         ...        ...          ...   ...   ...   ...   ...   \n",
       "25454      NL           3        134          215     2     5     3     6   \n",
       "25455      MY           3        135          179     6     6     7     7   \n",
       "25456      US           2        124          224     6     5     6     3   \n",
       "25457      US           3        139          203     1     5     3     6   \n",
       "25458      FR          15         83          156     1     5     7     1   \n",
       "\n",
       "      TIPI5 TIPI6  ... gender engnat age hand religion orientation voted  \\\n",
       "0         7     3  ...      1      1  26    1        7           1     1   \n",
       "1         4     6  ...      1      1  18    1        1           1     2   \n",
       "2         5     7  ...      2      1  15    1        2           2     2   \n",
       "3         7     2  ...      2      2  31    1        6           1     1   \n",
       "4         7     6  ...      1      2  20    1        4           3     1   \n",
       "...     ...   ...  ...    ...    ...  ..  ...      ...         ...   ...   \n",
       "25454     4     5  ...      2      2  23    1        1           1     1   \n",
       "25455     5     1  ...      2      2  32    1       10           1     1   \n",
       "25456     5     2  ...      2      1  16    1        2           2     2   \n",
       "25457     3     3  ...      1      1  17    1        5           4     2   \n",
       "25458     6     7  ...      1      2  20    1        2           1     1   \n",
       "\n",
       "      married familysize                    major  \n",
       "0           2          5               Marketing   \n",
       "1           1          2              mathematics  \n",
       "2           1          2                Chemistry  \n",
       "3           3          2  international relations  \n",
       "4           1          2               Management  \n",
       "...       ...        ...                      ...  \n",
       "25454       1          4               Philosophy  \n",
       "25455       3          4                 Medicine  \n",
       "25456       1          2           Bioengineering  \n",
       "25457       1          5                           \n",
       "25458       1          3                           \n",
       "\n",
       "[138723 rows x 42 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [A, B, C]\n",
    "\n",
    "result = pd.concat(frames)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotter\n",
    "import matplotlib.pyplot as plt\n",
    "#result=result.astype(float)\n",
    "\n",
    "#converting column values to numeric\n",
    "result.gender=pd.to_numeric(result.gender)\n",
    "result.age=pd.to_numeric(result.age)\n",
    "result.married=pd.to_numeric(result.married)\n",
    "result.orientation=pd.to_numeric(result.orientation)\n",
    "result.familysize=pd.to_numeric(result.familysize)\n",
    "result.TIPI1=pd.to_numeric(result.TIPI1)\n",
    "result.TIPI5=pd.to_numeric(result.TIPI5)\n",
    "result.VCL6=pd.to_numeric(result.VCL6)\n",
    "\n",
    "#changing size of plots\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Marital Status "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h5=plotter.histogram('married',[0.5,1.5,2.5,3.5],(1, 2, 3),(\"Never married\", \"Currently married\", \"Previously married\"),0.8)\n",
    "h5.plotHist(plt,result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ages of People in the survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result[['age']].plot(kind='hist',bins=[0,20,40,60,80,100],rwidth=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gender Distribution in the survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h5=plotter.histogram('gender',[0,2,4,6],(1, 2, 3),(\"male\", \"\", \"female\"),0.8)\n",
    "h5.plotHist(plt,result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h4=plotter.histogram('orientation',[0.5,1.5,2.5,3.5,4.5],(1, 2, 3,4,5),( \"Heterosexual\", \"Bisexual\", \"Homosexual\", \"Asexual\", \"Other\"),0.5)\n",
    "h4.plotHist(plt,result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#converting column values to numeric\n",
    "\n",
    "result[['familysize']].plot(kind='hist',bins=[1,2,3,4,5],rwidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of people about how they see themselves. Are they Extraverted, enthusiastic ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h3=plotter.histogram('TIPI1',[0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5],(1, 2, 3,4,5,6,7),( \"Disagree strongly\",\n",
    "\"Disagree moderately\",\n",
    "\"Disagree a little\",\n",
    "\"Neither agree nor disagree\",\n",
    "\"Agree a little\",\n",
    "\"Agree moderately\",\n",
    "\"Agree strongly\"),0.1)\n",
    "h3.plotHist(plt,result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of people about how they see themselves. Are they Open to new experiences, complex. ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using custom class\n",
    "h2=plotter.histogram('TIPI5',[0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5],(1, 2, 3,4,5,6,7),( \"Disagree strongly\",\n",
    "\"Disagree moderately\",\n",
    "\"Disagree a little\",\n",
    "\"Neither agree nor disagree\",\n",
    "\"Agree a little\",\n",
    "\"Agree moderately\",\n",
    "\"Agree strongly\"),0.5)\n",
    "h2.plotHist(plt,result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of invalid and valid people in the survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "h1=plotter.histogram('VCL6',[0.5,1.5,2.5],(1, 2),( \"valid entry\", \"invalid entry\"),0.5)\n",
    "h1.plotHist(plt,result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "finding correlation between variables using pearson method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>TIPI1</th>\n",
       "      <th>TIPI5</th>\n",
       "      <th>VCL6</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>orientation</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TIPI1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295923</td>\n",
       "      <td>0.027517</td>\n",
       "      <td>-0.007704</td>\n",
       "      <td>-0.004204</td>\n",
       "      <td>-0.077028</td>\n",
       "      <td>0.052919</td>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TIPI5</td>\n",
       "      <td>0.295923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060585</td>\n",
       "      <td>-0.056453</td>\n",
       "      <td>-0.008320</td>\n",
       "      <td>-0.020070</td>\n",
       "      <td>0.051589</td>\n",
       "      <td>-0.002399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VCL6</td>\n",
       "      <td>0.027517</td>\n",
       "      <td>0.060585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034915</td>\n",
       "      <td>-0.000905</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.049501</td>\n",
       "      <td>0.008539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gender</td>\n",
       "      <td>-0.007704</td>\n",
       "      <td>-0.056453</td>\n",
       "      <td>-0.034915</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>0.204987</td>\n",
       "      <td>-0.008629</td>\n",
       "      <td>-0.003013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>-0.004204</td>\n",
       "      <td>-0.008320</td>\n",
       "      <td>-0.000905</td>\n",
       "      <td>0.006662</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.008454</td>\n",
       "      <td>0.000407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>orientation</td>\n",
       "      <td>-0.077028</td>\n",
       "      <td>-0.020070</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.204987</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.110396</td>\n",
       "      <td>0.008318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>married</td>\n",
       "      <td>0.052919</td>\n",
       "      <td>0.051589</td>\n",
       "      <td>0.049501</td>\n",
       "      <td>-0.008629</td>\n",
       "      <td>0.008454</td>\n",
       "      <td>-0.110396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>familysize</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>-0.002399</td>\n",
       "      <td>0.008539</td>\n",
       "      <td>-0.003013</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.008318</td>\n",
       "      <td>-0.001288</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TIPI1     TIPI5      VCL6    gender       age  orientation  \\\n",
       "TIPI1        1.000000  0.295923  0.027517 -0.007704 -0.004204    -0.077028   \n",
       "TIPI5        0.295923  1.000000  0.060585 -0.056453 -0.008320    -0.020070   \n",
       "VCL6         0.027517  0.060585  1.000000 -0.034915 -0.000905     0.000578   \n",
       "gender      -0.007704 -0.056453 -0.034915  1.000000  0.006662     0.204987   \n",
       "age         -0.004204 -0.008320 -0.000905  0.006662  1.000000     0.008323   \n",
       "orientation -0.077028 -0.020070  0.000578  0.204987  0.008323     1.000000   \n",
       "married      0.052919  0.051589  0.049501 -0.008629  0.008454    -0.110396   \n",
       "familysize   0.000177 -0.002399  0.008539 -0.003013  0.000407     0.008318   \n",
       "\n",
       "              married  familysize  \n",
       "TIPI1        0.052919    0.000177  \n",
       "TIPI5        0.051589   -0.002399  \n",
       "VCL6         0.049501    0.008539  \n",
       "gender      -0.008629   -0.003013  \n",
       "age          0.008454    0.000407  \n",
       "orientation -0.110396    0.008318  \n",
       "married      1.000000   -0.001288  \n",
       "familysize  -0.001288    1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CM=result.corr(method ='pearson') \n",
    "CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "f = plt.figure(figsize=(19, 15))\n",
    "plt.matshow(CM.corr(), fignum=f.number)\n",
    "plt.xticks(range(CM.shape[1]), CM.columns, fontsize=14, rotation=45)\n",
    "plt.yticks(range(CM.shape[1]), CM.columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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