<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Start Date</th>
      <th>End Date</th>
      <th>Response Type</th>
      <th>Progress</th>
      <th>Duration (in seconds)</th>
      <th>Finished</th>
      <th>Recorded Date</th>
      <th>Response ID</th>
      <th>consent</th>
      <th>stood_out</th>
      <th>...</th>
      <th>sexual_orientation</th>
      <th>social_media_accounts</th>
      <th>social_media_used</th>
      <th>social_political_ideology</th>
      <th>state</th>
      <th>vehicle_types</th>
      <th>voter_registration_usa</th>
      <th>yob</th>
      <th>yob_and_gender_of_children</th>
      <th>zip</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2025-03-22 09:37:15</td>
      <td>2025-03-22 10:06:03</td>
      <td>0</td>
      <td>100</td>
      <td>1727</td>
      <td>1</td>
      <td>2025-03-22 10:06:03.466</td>
      <td>R_59vk9us5U8IP3gf</td>
      <td>1</td>
      <td>What stood out to me the most about the clip w...</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>13;18;3;10;5;4;9;2;8;11;1;19</td>
      <td>2.0</td>
      <td>NJ</td>
      <td>1</td>
      <td>1.0</td>
      <td>1993</td>
      <td>99</td>
      <td>8093.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2025-03-22 09:37:17</td>
      <td>2025-03-22 09:58:44</td>
      <td>0</td>
      <td>100</td>
      <td>1286</td>
      <td>1</td>
      <td>2025-03-22 09:58:44.783</td>
      <td>R_6JggRfb6ow9nZfc</td>
      <td>1</td>
      <td>I was more interested in the Inuet culture and...</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>5.0</td>
      <td>NJ</td>
      <td>NaN</td>
      <td>1.0</td>
      <td>1986</td>
      <td>99</td>
      <td>7035.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2025-03-22 09:37:37</td>
      <td>2025-03-22 09:57:50</td>
      <td>0</td>
      <td>100</td>
      <td>1213</td>
      <td>1</td>
      <td>2025-03-22 09:57:55.730</td>
      <td>R_5tMxxNsXZ6QDuLj</td>
      <td>1</td>
      <td>-99</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>4;10;19;11</td>
      <td>4.0</td>
      <td>IL</td>
      <td>1</td>
      <td>1.0</td>
      <td>1996</td>
      <td>22019;22018</td>
      <td>62203.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2025-03-22 09:37:47</td>
      <td>2025-03-22 10:01:50</td>
      <td>0</td>
      <td>100</td>
      <td>1443</td>
      <td>1</td>
      <td>2025-03-22 10:01:50.785</td>
      <td>R_5lKeFwUGE7lDwGh</td>
      <td>1</td>
      <td>The red frame that was used to isolate differe...</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>10;13;16;3;18;2;9;8;4;11;19</td>
      <td>3.0</td>
      <td>TX</td>
      <td>1</td>
      <td>1.0</td>
      <td>1998</td>
      <td>12021</td>
      <td>76031.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2025-03-22 09:37:48</td>
      <td>2025-03-22 09:53:02</td>
      <td>0</td>
      <td>100</td>
      <td>914</td>
      <td>1</td>
      <td>2025-03-22 09:53:03.277</td>
      <td>R_6uPZbRrXC8qBe2Z</td>
      <td>1</td>
      <td>What stood out to me the most is that we are n...</td>
      <td>...</td>
      <td>1.0</td>
      <td>11;2;3;4;13;19;9;10</td>
      <td>4;19;18;5;2;12;3;9;13;11;10</td>
      <td>4.0</td>
      <td>WI</td>
      <td>1</td>
      <td>1.0</td>
      <td>1974</td>
      <td>99</td>
      <td>53206.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 92 columns</p>
</div>

           Start Date            End Date  Response Type  Progress  \
0 2025-03-22 09:37:15 2025-03-22 10:06:03              0       100   
1 2025-03-22 09:37:17 2025-03-22 09:58:44              0       100   
2 2025-03-22 09:37:37 2025-03-22 09:57:50              0       100   
3 2025-03-22 09:37:47 2025-03-22 10:01:50              0       100   
4 2025-03-22 09:37:48 2025-03-22 09:53:02              0       100   

   Duration (in seconds)  Finished           Recorded Date        Response ID  \
0                   1727         1 2025-03-22 10:06:03.466  R_59vk9us5U8IP3gf   
1                   1286         1 2025-03-22 09:58:44.783  R_6JggRfb6ow9nZfc   
2                   1213         1 2025-03-22 09:57:55.730  R_5tMxxNsXZ6QDuLj   
3                   1443         1 2025-03-22 10:01:50.785  R_5lKeFwUGE7lDwGh   
4                    914         1 2025-03-22 09:53:03.277  R_6uPZbRrXC8qBe2Z   

   consent                                          stood_out  ...  \
0        1  What stood out to me the most about the clip w...  ...   
1        1  I was more interested in the Inuet culture and...  ...   
2        1                                                -99  ...   
3        1  The red frame that was used to isolate differe...  ...   
4        1  What stood out to me the most is that we are n...  ...   

  sexual_orientation  social_media_accounts             social_media_used  \
0                1.0                    NaN  13;18;3;10;5;4;9;2;8;11;1;19   
1                1.0                    NaN                           NaN   
2                1.0                    NaN                    4;10;19;11   
3                1.0                    NaN   10;13;16;3;18;2;9;8;4;11;19   
4                1.0    11;2;3;4;13;19;9;10   4;19;18;5;2;12;3;9;13;11;10   

  social_political_ideology  state  vehicle_types  voter_registration_usa  \
0                       2.0     NJ              1                     1.0   
1                       5.0     NJ            NaN                     1.0   
2                       4.0     IL              1                     1.0   
3                       3.0     TX              1                     1.0   
4                       4.0     WI              1                     1.0   

    yob  yob_and_gender_of_children      zip  
0  1993                          99   8093.0  
1  1986                          99   7035.0  
2  1996                 22019;22018  62203.0  
3  1998                       12021  76031.0  
4  1974                          99  53206.0  

[5 rows x 92 columns]

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Start Date</th>
      <th>End Date</th>
      <th>Response Type</th>
      <th>Progress</th>
      <th>Duration (in seconds)</th>
      <th>Finished</th>
      <th>Recorded Date</th>
      <th>Response ID</th>
      <th>consent</th>
      <th>stood_out</th>
      <th>...</th>
      <th>sexual_orientation</th>
      <th>social_media_accounts</th>
      <th>social_media_used</th>
      <th>social_political_ideology</th>
      <th>state</th>
      <th>vehicle_types</th>
      <th>voter_registration_usa</th>
      <th>yob</th>
      <th>yob_and_gender_of_children</th>
      <th>zip</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2025-03-22 09:37:15</td>
      <td>2025-03-22 10:06:03</td>
      <td>0</td>
      <td>100</td>
      <td>1727</td>
      <td>1</td>
      <td>2025-03-22 10:06:03.466</td>
      <td>R_59vk9us5U8IP3gf</td>
      <td>1</td>
      <td>What stood out to me the most about the clip w...</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>13;18;3;10;5;4;9;2;8;11;1;19</td>
      <td>2.0</td>
      <td>NJ</td>
      <td>1</td>
      <td>1.0</td>
      <td>1993</td>
      <td>99</td>
      <td>08093</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2025-03-22 09:37:17</td>
      <td>2025-03-22 09:58:44</td>
      <td>0</td>
      <td>100</td>
      <td>1286</td>
      <td>1</td>
      <td>2025-03-22 09:58:44.783</td>
      <td>R_6JggRfb6ow9nZfc</td>
      <td>1</td>
      <td>I was more interested in the Inuet culture and...</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>5.0</td>
      <td>NJ</td>
      <td>NaN</td>
      <td>1.0</td>
      <td>1986</td>
      <td>99</td>
      <td>07035</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2025-03-22 09:37:37</td>
      <td>2025-03-22 09:57:50</td>
      <td>0</td>
      <td>100</td>
      <td>1213</td>
      <td>1</td>
      <td>2025-03-22 09:57:55.730</td>
      <td>R_5tMxxNsXZ6QDuLj</td>
      <td>1</td>
      <td>-99</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>4;10;19;11</td>
      <td>4.0</td>
      <td>IL</td>
      <td>1</td>
      <td>1.0</td>
      <td>1996</td>
      <td>22019;22018</td>
      <td>62203</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2025-03-22 09:37:47</td>
      <td>2025-03-22 10:01:50</td>
      <td>0</td>
      <td>100</td>
      <td>1443</td>
      <td>1</td>
      <td>2025-03-22 10:01:50.785</td>
      <td>R_5lKeFwUGE7lDwGh</td>
      <td>1</td>
      <td>The red frame that was used to isolate differe...</td>
      <td>...</td>
      <td>1.0</td>
      <td>NaN</td>
      <td>10;13;16;3;18;2;9;8;4;11;19</td>
      <td>3.0</td>
      <td>TX</td>
      <td>1</td>
      <td>1.0</td>
      <td>1998</td>
      <td>12021</td>
      <td>76031</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2025-03-22 09:37:48</td>
      <td>2025-03-22 09:53:02</td>
      <td>0</td>
      <td>100</td>
      <td>914</td>
      <td>1</td>
      <td>2025-03-22 09:53:03.277</td>
      <td>R_6uPZbRrXC8qBe2Z</td>
      <td>1</td>
      <td>What stood out to me the most is that we are n...</td>
      <td>...</td>
      <td>1.0</td>
      <td>11;2;3;4;13;19;9;10</td>
      <td>4;19;18;5;2;12;3;9;13;11;10</td>
      <td>4.0</td>
      <td>WI</td>
      <td>1</td>
      <td>1.0</td>
      <td>1974</td>
      <td>99</td>
      <td>53206</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 92 columns</p>
</div>

           Start Date            End Date  Response Type  Progress  \
0 2025-03-22 09:37:15 2025-03-22 10:06:03              0       100   
1 2025-03-22 09:37:17 2025-03-22 09:58:44              0       100   
2 2025-03-22 09:37:37 2025-03-22 09:57:50              0       100   
3 2025-03-22 09:37:47 2025-03-22 10:01:50              0       100   
4 2025-03-22 09:37:48 2025-03-22 09:53:02              0       100   

   Duration (in seconds)  Finished           Recorded Date        Response ID  \
0                   1727         1 2025-03-22 10:06:03.466  R_59vk9us5U8IP3gf   
1                   1286         1 2025-03-22 09:58:44.783  R_6JggRfb6ow9nZfc   
2                   1213         1 2025-03-22 09:57:55.730  R_5tMxxNsXZ6QDuLj   
3                   1443         1 2025-03-22 10:01:50.785  R_5lKeFwUGE7lDwGh   
4                    914         1 2025-03-22 09:53:03.277  R_6uPZbRrXC8qBe2Z   

   consent                                          stood_out  ...  \
0        1  What stood out to me the most about the clip w...  ...   
1        1  I was more interested in the Inuet culture and...  ...   
2        1                                                -99  ...   
3        1  The red frame that was used to isolate differe...  ...   
4        1  What stood out to me the most is that we are n...  ...   

  sexual_orientation  social_media_accounts             social_media_used  \
0                1.0                    NaN  13;18;3;10;5;4;9;2;8;11;1;19   
1                1.0                    NaN                           NaN   
2                1.0                    NaN                    4;10;19;11   
3                1.0                    NaN   10;13;16;3;18;2;9;8;4;11;19   
4                1.0    11;2;3;4;13;19;9;10   4;19;18;5;2;12;3;9;13;11;10   

  social_political_ideology  state  vehicle_types  voter_registration_usa  \
0                       2.0     NJ              1                     1.0   
1                       5.0     NJ            NaN                     1.0   
2                       4.0     IL              1                     1.0   
3                       3.0     TX              1                     1.0   
4                       4.0     WI              1                     1.0   

    yob  yob_and_gender_of_children    zip  
0  1993                          99  08093  
1  1986                          99  07035  
2  1996                 22019;22018  62203  
3  1998                       12021  76031  
4  1974                          99  53206  

[5 rows x 92 columns]

0     08093
1     07035
2     62203
3     76031
4     53206
      ...  
95    14619
96    85051
97    75134
98    75160
99    44506
Name: zip, Length: 100, dtype: object

Index(['Start Date', 'End Date', 'Response Type', 'Progress',
       'Duration (in seconds)', 'Finished', 'Recorded Date', 'Response ID',
       'consent', 'stood_out', 'character_impressions', 'identify_closed',
       'identified_with', 'why_not_identify', 'involved_experiences',
       'knew_feelings', 'knew_experiences', 'easily_identify',
       'understand_perspective', 'found_interesting', 'found_memorable',
       'reminded_of_myself', 'reminded_of_someone', 'anyone_trustworthy',
       'who_trustworthy', 'why_not_trustworthy', 'competent', 'warm',
       'authentic', 'fake', 'arrogant', 'likeable', 'approachable',
       'authoritative', 'trustworthy', 'anyone_share_values',
       'who_share_values', 'how_share_values', 'seemed_scientist',
       'who_seemed_scientist', 'why_not_scientist', 'interest_full_film',
       'geographic_place', 'self_geographic_place',
       'self_geographic_place_other', 'science_proximity', 'ses_class',
       'self_working_class', 'RESPONDENT_ID', 'chapter', 'id', 'age',
       'born_usa', 'car_ownership', 'children', 'country', 'division',
       'economic_political_ideology', 'education', 'employment', 'ethnicity',
       'familial_origin', 'gender', 'hispanic', 'hispanic_acculturation',
       'home', 'income', 'industry_personal', 'job_title', 'licensed_driver',
       'marital', 'military_status', 'msa', 'neighborhood', 'pets',
       'political_ideology', 'political_party_preference',
       'purchase_decisions', 'region', 'religiosity', 'religious_identity',
       'sexual_orientation', 'social_media_accounts', 'social_media_used',
       'social_political_ideology', 'state', 'vehicle_types',
       'voter_registration_usa', 'yob', 'yob_and_gender_of_children', 'zip'],
      dtype='object')

Index(['Start Date', 'End Date', 'Progress', 'Duration (in seconds)',
       'Finished', 'Recorded Date', 'Response ID', 'consent', 'stood_out',
       'character_impressions', 'identify_closed', 'identified_with',
       'why_not_identify', 'involved_experiences', 'knew_feelings',
       'knew_experiences', 'easily_identify', 'understand_perspective',
       'found_interesting', 'found_memorable', 'reminded_of_myself',
       'reminded_of_someone', 'anyone_trustworthy', 'who_trustworthy',
       'why_not_trustworthy', 'competent', 'warm', 'authentic', 'fake',
       'arrogant', 'likeable', 'approachable', 'authoritative', 'trustworthy',
       'anyone_share_values', 'who_share_values', 'how_share_values',
       'seemed_scientist', 'who_seemed_scientist', 'why_not_scientist',
       'interest_full_film', 'geographic_place', 'self_geographic_place',
       'self_geographic_place_other', 'science_proximity', 'ses_class',
       'self_working_class', 'RESPONDENT_ID', 'chapter', 'age', 'born_usa',
       'car_ownership', 'children', 'division', 'economic_political_ideology',
       'education', 'employment', 'ethnicity', 'familial_origin', 'gender',
       'hispanic', 'hispanic_acculturation', 'home', 'income',
       'industry_personal', 'job_title', 'licensed_driver', 'marital',
       'military_status', 'msa', 'neighborhood', 'pets', 'political_ideology',
       'political_party_preference', 'purchase_decisions', 'region',
       'religiosity', 'religious_identity', 'sexual_orientation',
       'social_media_accounts', 'social_media_used',
       'social_political_ideology', 'state', 'vehicle_types',
       'voter_registration_usa', 'yob', 'yob_and_gender_of_children', 'zip'],
      dtype='object')

8. Fix the zip code column and remove unnecessary columns.

Load the dataset

The `zip` column contains zip codes, which must be 5-digit integers. Identify all the rows in `zip` that have 4-digit integers and add a leading 0 to the value so that it is a 5-digit integer.

Show me the value of `zip` for the first 100 rows in the dataset.

Remove the column called FL_11 - Block Randomizer - Display Order.

Remove the following columns: `Response Type` , `id`, `country`

save the dataset as a new excel file called chapter_merged_finished_cleaned_2

Load the dataset

The zip column contains zip codes, which must be 5-digit integers. Identify all the rows in zip that have 4-digit integers and add a leading 0 to the value so that it is a 5-digit integer.

Show me the value of zip for the first 100 rows in the dataset.

Remove the column called FL_11 - Block Randomizer - Display Order.

Remove the following columns: Response Type , id, country

save the dataset as a new excel file called chapter_merged_finished_cleaned_2

The `zip` column contains zip codes, which must be 5-digit integers. Identify all the rows in `zip` that have 4-digit integers and add a leading 0 to the value so that it is a 5-digit integer.

Show me the value of `zip` for the first 100 rows in the dataset.

Remove the following columns: `Response Type` , `id`, `country`