Let's break down each snippet: ### 1. `if 'case_id_1' and 'legal_nm'...

Let's break down each snippet:

1. if 'case_id_1' and 'legal_nm' and 'trade_nm' and 'naic_cd' in df.columns:

• How Python evaluates this: The and operator checks for the truthiness of each value from left to right. Non-empty strings evaluate to True, so the first three strings, 'case_id_1', 'legal_nm', and 'trade_nm', will always return True. Then, the expression 'naic_cd' in df.columns is evaluated.
• The overall meaning: This checks whether 'naic_cd' is in df.columns. The other strings are effectively ignored as they do not directly contribute to the evaluation.
• Outcome: This is misleading code since it implies checking for multiple columns in df.columns but only actually checks for 'naic_cd'.

2. if ('case_id_1' and 'legal_nm' and 'trade_nm' and 'naic_cd') in df.columns:

• How Python evaluates this: Inside the parentheses, 'case_id_1' and 'legal_nm' and 'trade_nm' and 'naic_cd' is first evaluated. Since all the strings are non-empty, Python evaluates 'case_id_1' and 'legal_nm' and 'trade_nm' and 'naic_cd' to the last truthy value, which is 'naic_cd'. Then the final expression becomes 'naic_cd' in df.columns.
• The overall meaning: This also only checks whether 'naic_cd' is in df.columns. Like the previous snippet, it does not perform the desired check for all column names.
• Outcome: Similarly, this is misleading code, as only the presence of 'naic_cd' in df.columns is being evaluated.

3. if ('case_id_1' in df.columns and 'legal_nm' in df.columns and 'trade_nm' in df.columns and 'naic_cd' in df.columns):

• How Python evaluates this: This explicitly checks each column's presence in df.columns individually. Using the and operator ensures that all four column names ('case_id_1', 'legal_nm', 'trade_nm', 'naic_cd') are present in df.columns for the condition to evaluate to True.
• The overall meaning: This performs the correct check to ensure that all four column names exist in df.columns.
• Outcome: This is the correct approach if the intent is to check for the presence of all specified column names.

Summary

• The first two versions only check if 'naic_cd' exists in df.columns and are misleading in intent.
• The third version properly checks for the presence of all specified column names and is the correct implementation.