evals: add pandas-focussed multi shot prompt

evals/notebook_states.py

@@ -83,8 +83,8 @@
         'excel_transactions': pd.DataFrame({'Transaction ID': [12975, 16889, 57686, 53403, 42699], 'Share Quantity': [20, 25, 24, 22, 40]}), 
         'excel_transactions': pd.DataFrame({'Transaction ID': [12975, 16889, 57686, 53403, 42699], 'Share Quantity': [20, 25, 24, 22, 0]})},
     cell_contents=["""import pandas as pd
-excel_transactions = pd.read_excel('evals/data/simple_recon/transactions_excel.csv')
-eagle_transactions = pd.read_excel('evals/data/simple_recon/transactions_eagle.csv')
+excel_transactions = pd.read_csv('evals/data/simple_recon/transactions_excel.csv')
+eagle_transactions = pd.read_csv('evals/data/simple_recon/transactions_eagle.csv')
 """, '']
 )
 

evals/prompts/multi_shot_pandas_focussed_prompt.py

@@ -0,0 +1,97 @@
+from evals.eval_types import NotebookState, PromptGenerator
+
+__all__ = ['multi_shot_pandas_focussed_prompt']
+
+class _MultiShotPandasFocussedPrompt(PromptGenerator):
+    prompt_name = "multi_shot_pandas_focussed_prompt"
+
+    def get_prompt(self, user_input: str, notebook_state: NotebookState) -> str:
+
+        return f"""You are an expert python programmer writing a script in a Jupyter notebook. You are given a set of variables, existing code, and a task.
+
+Respond with the updated active code cell and a short explanation of the changes you made.
+
+When responding:
+- Do not use the word "I"
+- Do not recreate variables that already exist
+- Keep as much of the original code as possible
+
+<Example 1>
+
+Defined Variables:
+{{
+    'loan_multiplier': 1.5,
+    'sales_df': pd.DataFrame({{
+        'transaction_date': ['2024-01-02', '2024-01-02', '2024-01-02', '2024-01-02', '2024-01-03'],
+        'price_per_unit': [10, 9.99, 13.99, 21.00, 100],
+        'units_sold': [1, 2, 1, 4, 5],
+        'total_price': [10, 19.98, 13.99, 84.00, 500]
+    }})
+}}
+
+Code in the active code cell:
+```python
+import pandas as pd
+sales_df = pd.read_csv('./sales.csv')
+```
+
+Your task: convert the transaction_date column to datetime and then multiply the total_price column by the sales_multiplier.
+
+Output:
+
+```python
+import pandas as pd
+sales_df = pd.read_csv('./sales.csv')
+sales_df['transaction_date'] = pd.to_datetime(sales_df['transaction_date'])
+sales_df['total_price'] = sales_df['total_price'] * sales_multiplier
+```
+
+Converted the `transaction_date` column to datetime using the built-in pd.to_datetime function and multiplied the `total_price` column by the `sales_multiplier` variable.
+</Example 1>
+
+<Example 2>
+Defined Variables:
+{{
+    'df': pd.DataFrame({{
+        'id': ['id-49830', 'id-39301', 'id-85011', 'id-51892', 'id-99111'],
+        'name': ['Tamir', 'Aaron', 'Grace', 'Nawaz', 'Julia'],
+        'age': [29, 31, 26, 21, 30],
+        'dob': ['1994-06-15', '1992-03-27', '1997-04-11', '2002-07-05', '1993-08-22'],
+        'city': ['San Francisco', 'New York', 'Los Angeles', 'Chicago', 'Houston'],
+        'state': ['CA', 'NY', 'CA', 'IL', 'TX'],
+        'zip': ['94103', '10001', '90038', '60611', '77002'],
+        'start_date': ['2024-01-01', '2024-01-01', '2024-01-01', '2024-01-01', '2024-01-01'],
+        'department': ['Engineering', 'Sales', 'Marketing', 'Operations', 'Finance'],
+        'salary': ['$100,000', '$50,000', '$60,000', '$55,000', '$70,000']
+    }})
+}}
+
+Code in the active code cell:
+```python
+
+```
+
+Your task: Calculate the weekly salary for each employee.
+
+Output:
+
+```python
+df['salary'] = df['salary'].str[1:].replace(',', '', regex=True).astype('float')
+df['weekly_salary'] = df['salary'] / 52
+```
+
+Remove the `$` and `,` from the `salary` in order to convert it to a float. Then, divide the salary by 52 to get the weekly salary.
+</Example 2>
+
+Defined Variables:
+{notebook_state.global_vars}
+
+Code in the active code cell:
+
+```python
+{notebook_state.cell_contents[-1] if len(notebook_state.cell_contents) > 0 else ""}
+```
+
+Your task: ${user_input}"""
+
+multi_shot_pandas_focussed_prompt = _MultiShotPandasFocussedPrompt()