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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>1</th>
      <th>1.1</th>
      <th>16600</th>
      <th>29</th>
      <th>1.2</th>
      <th>4</th>
      <th>1.3</th>
      <th>3</th>
      <th>4.1</th>
      <th>4.2</th>
      <th>...</th>
      <th>2.2</th>
      <th>2.3</th>
      <th>2.4</th>
      <th>3.1</th>
      <th>2.5</th>
      <th>2.6</th>
      <th>3.2</th>
      <th>1.4</th>
      <th>1.5</th>
      <th>7</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2</td>
      <td>1</td>
      <td>14600</td>
      <td>26</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>...</td>
      <td>2</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>8</td>
    </tr>
    <tr>
      <th>1</th>
      <td>3</td>
      <td>1</td>
      <td>17800</td>
      <td>40</td>
      <td>5</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>...</td>
      <td>2</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>16400</td>
      <td>46</td>
      <td>15</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>4</td>
      <td>...</td>
      <td>2</td>
      <td>3</td>
      <td>2</td>
      <td>2</td>
      <td>3</td>
      <td>2</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2</td>
      <td>2</td>
      <td>18600</td>
      <td>63</td>
      <td>36</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>...</td>
      <td>3</td>
      <td>4</td>
      <td>5</td>
      <td>5</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>5</td>
      <td>3</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1</td>
      <td>1</td>
      <td>16000</td>
      <td>54</td>
      <td>31</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>3</td>
      <td>3</td>
      <td>...</td>
      <td>2</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 23 columns</p>
</div>

   1  1.1  16600  29  1.2  4  1.3  3  4.1  4.2  ...  2.2  2.3  2.4  3.1  2.5  \
0  2    1  14600  26    5  2    1  1    2    3  ...    2    3    4    4    4   
1  3    1  17800  40    5  4    4  4    4    1  ...    2    2    1    2    3   
2  3    1  16400  46   15  2    2  5    2    4  ...    2    3    2    2    3   
3  2    2  18600  63   36  4    3  4    4    1  ...    3    4    5    5    4   
4  1    1  16000  54   31  2    2  5    3    3  ...    2    4    4    4    4   

   2.6  3.2  1.4  1.5  7  
0    1    3    4    4  8  
1    1    4    3    4  0  
2    2    3    3    4  4  
3    1    3    5    3  0  
4    1    1    3    4  1  

[5 rows x 23 columns]

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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ID</th>
      <th>Gender</th>
      <th>Income</th>
      <th>Age</th>
      <th>Experience</th>
      <th>Ethnic_Group</th>
      <th>Job_Satisfaction</th>
      <th>C7</th>
      <th>C8</th>
      <th>C9</th>
      <th>...</th>
      <th>C13</th>
      <th>C14</th>
      <th>C15</th>
      <th>C16</th>
      <th>C17</th>
      <th>C18</th>
      <th>C19</th>
      <th>C20</th>
      <th>C21</th>
      <th>Other</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2</td>
      <td>1</td>
      <td>14600</td>
      <td>26</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>...</td>
      <td>2</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>8</td>
    </tr>
    <tr>
      <th>1</th>
      <td>3</td>
      <td>1</td>
      <td>17800</td>
      <td>40</td>
      <td>5</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>...</td>
      <td>2</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>16400</td>
      <td>46</td>
      <td>15</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>4</td>
      <td>...</td>
      <td>2</td>
      <td>3</td>
      <td>2</td>
      <td>2</td>
      <td>3</td>
      <td>2</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2</td>
      <td>2</td>
      <td>18600</td>
      <td>63</td>
      <td>36</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>...</td>
      <td>3</td>
      <td>4</td>
      <td>5</td>
      <td>5</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>5</td>
      <td>3</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1</td>
      <td>1</td>
      <td>16000</td>
      <td>54</td>
      <td>31</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>3</td>
      <td>3</td>
      <td>...</td>
      <td>2</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 23 columns</p>
</div>

   ID  Gender  Income  Age  Experience  Ethnic_Group  Job_Satisfaction  C7  \
0   2       1   14600   26           5             2                 1   1   
1   3       1   17800   40           5             4                 4   4   
2   3       1   16400   46          15             2                 2   5   
3   2       2   18600   63          36             4                 3   4   
4   1       1   16000   54          31             2                 2   5   

   C8  C9  ...  C13  C14  C15  C16  C17  C18  C19  C20  C21  Other  
0   2   3  ...    2    3    4    4    4    1    3    4    4      8  
1   4   1  ...    2    2    1    2    3    1    4    3    4      0  
2   2   4  ...    2    3    2    2    3    2    3    3    4      4  
3   4   1  ...    3    4    5    5    4    1    3    5    3      0  
4   3   3  ...    2    4    4    4    4    1    1    3    4      1  

[5 rows x 23 columns]

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<table border="1" class="dataframe">
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      <th></th>
      <th>Job_Satisfaction</th>
      <th>Age</th>
      <th>Gender</th>
      <th>Experience</th>
      <th>Ethnic_Group</th>
      <th>Income</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Job_Satisfaction</th>
      <td>1.000000</td>
      <td>0.436887</td>
      <td>-1.757544e-02</td>
      <td>0.148243</td>
      <td>4.836305e-01</td>
      <td>0.289249</td>
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      <th>Age</th>
      <td>0.436887</td>
      <td>1.000000</td>
      <td>2.484586e-03</td>
      <td>0.795599</td>
      <td>3.135652e-01</td>
      <td>0.534118</td>
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      <th>Gender</th>
      <td>-0.017575</td>
      <td>0.002485</td>
      <td>1.000000e+00</td>
      <td>0.089905</td>
      <td>-4.098231e-17</td>
      <td>-0.120916</td>
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      <th>Experience</th>
      <td>0.148243</td>
      <td>0.795599</td>
      <td>8.990457e-02</td>
      <td>1.000000</td>
      <td>2.303891e-01</td>
      <td>0.302794</td>
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    <tr>
      <th>Ethnic_Group</th>
      <td>0.483630</td>
      <td>0.313565</td>
      <td>-4.098231e-17</td>
      <td>0.230389</td>
      <td>1.000000e+00</td>
      <td>0.362071</td>
    </tr>
    <tr>
      <th>Income</th>
      <td>0.289249</td>
      <td>0.534118</td>
      <td>-1.209157e-01</td>
      <td>0.302794</td>
      <td>3.620715e-01</td>
      <td>1.000000</td>
    </tr>
  </tbody>
</table>
</div>

                  Job_Satisfaction       Age        Gender  Experience  \
Job_Satisfaction          1.000000  0.436887 -1.757544e-02    0.148243   
Age                       0.436887  1.000000  2.484586e-03    0.795599   
Gender                   -0.017575  0.002485  1.000000e+00    0.089905   
Experience                0.148243  0.795599  8.990457e-02    1.000000   
Ethnic_Group              0.483630  0.313565 -4.098231e-17    0.230389   
Income                    0.289249  0.534118 -1.209157e-01    0.302794   

                  Ethnic_Group    Income  
Job_Satisfaction  4.836305e-01  0.289249  
Age               3.135652e-01  0.534118  
Gender           -4.098231e-17 -0.120916  
Experience        2.303891e-01  0.302794  
Ethnic_Group      1.000000e+00  0.362071  
Income            3.620715e-01  1.000000  

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Vizly: AI-powered data analysis

Exploring Correlations Between Job Satisfaction and Demographic Variables

Determine the correlation between job satisfaction and age, gender, experience, ethnic group, income

Correlation Summary

Visualize this for me