feat(pandas): add problem 1757

Author: IndexSeek

README.md

@@ -6,7 +6,7 @@ Fiddling around with DataFusion, pandas, and PyArrow.
 
 | Problem # | Title | Difficulty | PyArrow | DataFusion | pandas |
 |----------:|:------|:-----------|:-------:|:----------:|:------:|
-| 1757      | [Recyclable and Low Fat Products](https://leetcode.com/problems/recyclable-and-low-fat-products) | Easy | ✅ | ❌ | ❌ |
+| 1757      | [Recyclable and Low Fat Products](https://leetcode.com/problems/recyclable-and-low-fat-products) | Easy | ✅ | ❌ | ✅ |
 | 584       | [Find Customer Referee](https://leetcode.com/problems/find-customer-referee) | Easy | ✅ | ❌ | ❌ |
 | 595       | [Big Countries](https://leetcode.com/problems/big-countries) | Easy | ✅ | ❌ | ❌ |
 | 1148      | [Article Views I](https://leetcode.com/problems/article-views-i) | Easy | ✅ | ❌ | ❌ |

problems/pandas.py

@@ -59,3 +59,25 @@ def problem_1321(customer: pd.DataFrame) -> pd.DataFrame:
         .loc[6:]
     )
     return grouped.assign(average_amount=(grouped["amount"] / 7).round(2))
+
+
+def problem_1757(products: pd.DataFrame) -> pd.DataFrame:
+    """Find the ids of products that are both low fat and recyclable.
+
+    Return the result table in any order.
+
+    Parameters
+    ----------
+    products : pa.Table
+        Table stores products with low_fats and recyclable, keyed by product_id.
+
+    Returns
+    -------
+    pa.Table
+
+    """
+    return pd.DataFrame(
+        products[(products["low_fats"] == "Y") & (products["recyclable"] == "Y")][
+            "product_id"
+        ]
+    )

tests/test_pandas.py

@@ -3,7 +3,7 @@
 import pandas as pd
 import pytest
 
-from problems.pandas import problem_176, problem_1321
+from problems.pandas import problem_176, problem_1321, problem_1757
 
 
 @pytest.mark.parametrize(
@@ -142,3 +142,34 @@ def test_problem_1321(input_data, expected_data):
         assert result[col].equals(expected_table[col]), f"Mismatch in column '{col}'"
 
     assert result.equals(expected_table)
+
+
+@pytest.mark.parametrize(
+    "input_data, expected_data",
+    [
+        (
+            {
+                "product_id": [0, 1, 2, 3, 4],
+                "low_fats": ["Y", "Y", "N", "Y", "N"],
+                "recyclable": ["N", "Y", "Y", "Y", "N"],
+            },
+            {"product_id": [1, 3]},
+        ),
+        (
+            {
+                "product_id": [0, 1, 2, 3, 4],
+                "low_fats": ["Y", "Y", "Y", "Y", "Y"],
+                "recyclable": ["Y", "Y", "Y", "Y", "Y"],
+            },
+            {"product_id": [0, 1, 2, 3, 4]},
+        )
+    ],
+    ids=[
+        "happy_path_mixed_values", "all_ys"
+    ],
+)
+def test_problem_1757(input_data, expected_data):
+    table = pd.DataFrame(input_data)
+    expected_table = pd.DataFrame(expected_data)
+    result = problem_1757(table).reset_index(drop=True)
+    assert result.equals(expected_table)

tests/test_pyarrow.py

@@ -2153,10 +2153,18 @@ def test_problem_1731(input_data, expected_data):
                 "recyclable": ["N", "Y", "Y", "Y", "N"],
             },
             {"product_id": [1, 3]},
+        ),
+        (
+            {
+                "product_id": [0, 1, 2, 3, 4],
+                "low_fats": ["Y", "Y", "Y", "Y", "Y"],
+                "recyclable": ["Y", "Y", "Y", "Y", "Y"],
+            },
+            {"product_id": [0, 1, 2, 3, 4]},
         )
     ],
     ids=[
-        "happy_path_mixed_values",
+        "happy_path_mixed_values", "all_ys"
     ],
 )
 def test_problem_1757(input_data, expected_data):