feat(pandas) add problem 1193

Author: IndexSeek

README.md

@@ -35,7 +35,7 @@ Fiddling around with DataFusion, pandas, and PyArrow.
 | 1075 | [Project Employees I](https://leetcode.com/problems/project-employees-i) | Easy | ❌ | ✅ | ✅ |
 | 1633 | [Percentage of Users Attended a Contest](https://leetcode.com/problems/percentage-of-users-attended-a-contest) | Easy | ❌ | ✅ | ✅ |
 | 1211 | [Queries Quality and Percentage](https://leetcode.com/problems/queries-quality-and-percentage) | Easy | ❌ | ✅ | ✅ |
-| 1193 | [Monthly Transactions I](https://leetcode.com/problems/monthly-transactions-i) | Medium | ✅ | ❌ | ❌ |
+| 1193 | [Monthly Transactions I](https://leetcode.com/problems/monthly-transactions-i) | Medium | ✅ | ✅ | ❌ |
 | 1174 | [Immediate Food Delivery II](https://leetcode.com/problems/immediate-food-delivery-ii) | Medium | ❌ | ❌ | ✅ |
 | 550  | [Game Play Analysis IV](https://leetcode.com/problems/game-play-analysis-iv) | Medium | ❌ | ❌ | ✅ |
 

problems/pandas.py

@@ -230,6 +230,43 @@ def problem_1075(project: pd.DataFrame, employee: pd.DataFrame) -> pd.DataFrame:
     return joined
 
 
+def problem_1193(transactions: pd.DataFrame) -> pd.DataFrame:
+    """Find monthly, country-wise transaction counts, totals, approved counts and sums.
+
+    Find for each month and country, the number of transactions and their total amount,
+    the number of approved transactions and their total amount.
+
+    Return the result table in any order.
+
+    Parameters
+    ----------
+    transactions : pd.DataFrame
+        The table has information about incoming transactions.
+
+    Returns
+    -------
+    pd.DataFrame
+
+    """
+    transactions["month"] = transactions["trans_date"].dt.strftime("%Y-%m")
+    transactions["is_approved"] = transactions["state"] == "approved"
+    transactions["approved_amount"] = transactions["is_approved"].case_when(
+        [
+            (transactions["is_approved"] == True, transactions["amount"]),  # noqa E712
+            (transactions["is_approved"] == False, 0),  # noqa E712
+        ]
+    )
+
+    return transactions.groupby(
+        ["month", "country"], as_index=False, dropna=False
+    ).aggregate(
+        trans_count=pd.NamedAgg("id", "count"),
+        approved_count=pd.NamedAgg("is_approved", "sum"),
+        trans_total_amount=pd.NamedAgg("amount", "sum"),
+        approved_total_amount=pd.NamedAgg("approved_amount", "sum"),
+    )
+
+
 def problem_1148(views: pd.DataFrame) -> pd.DataFrame:
     """Find all the authors that viewed at least one of their own articles.
 

ruff.toml

@@ -51,7 +51,7 @@ dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
 
 [lint.per-file-ignores]
 "problems/datasets.py" = ["D103"]
-"tests/*" = ["D"]
+"tests/*" = ["D", "F403", "F405"]
 
 [format]
 # Like Black, use double quotes for strings.

tests/test_datafusion.py

@@ -3,21 +3,7 @@
 import pyarrow as pa
 import pytest
 
-from problems.datafusion import (
-    problem_176,
-    problem_180,
-    problem_584,
-    problem_595,
-    problem_620,
-    problem_1068,
-    problem_1148,
-    problem_1321,
-    problem_1378,
-    problem_1484,
-    problem_1517,
-    problem_1683,
-    problem_1757,
-)
+from problems.datafusion import *
 
 
 @pytest.mark.parametrize(

tests/test_pandas.py

@@ -4,32 +4,7 @@
 import pytest
 from pandas.testing import assert_frame_equal
 
-from problems.pandas import (
-    problem_176,
-    problem_180,
-    problem_197,
-    problem_570,
-    problem_577,
-    problem_584,
-    problem_595,
-    problem_620,
-    problem_1068,
-    problem_1075,
-    problem_1148,
-    problem_1211,
-    problem_1251,
-    problem_1280,
-    problem_1321,
-    problem_1327,
-    problem_1378,
-    problem_1517,
-    problem_1581,
-    problem_1633,
-    problem_1661,
-    problem_1683,
-    problem_1757,
-    problem_1934,
-)
+from problems.pandas import *
 
 
 @pytest.mark.parametrize(
@@ -517,6 +492,78 @@ def test_problem_1148(input_data, expected_data):
     )
 
 
+@pytest.mark.parametrize(
+    "input_data, expected_data",
+    [
+        pytest.param(
+            {
+                "trans_date": [
+                    datetime(2023, 1, 1),
+                    datetime(2023, 1, 15),
+                    datetime(2023, 2, 1),
+                ],
+                "state": ["approved", "pending", "approved"],
+                "amount": [100, 200, 300],
+                "country": ["US", "US", "CA"],
+                "id": [1, 2, 3],
+            },
+            {
+                "month": ["2023-01", "2023-02"],
+                "country": ["US", "CA"],
+                "trans_count": [2, 1],
+                "approved_count": [1, 1],
+                "trans_total_amount": [300, 300],
+                "approved_total_amount": [100, 300],
+            },
+            id="happy_path_1",
+        ),
+        pytest.param(
+            {
+                "trans_date": [datetime(2023, 3, 1)],
+                "state": ["approved"],
+                "amount": [500],
+                "country": [None],
+                "id": [4],
+            },
+            {
+                "month": ["2023-03"],
+                "country": [float("nan")],
+                "trans_count": [1],
+                "approved_count": [1],
+                "trans_total_amount": [500],
+                "approved_total_amount": [500],
+            },
+            id="happy_path_null_country",
+        ),
+        pytest.param(
+            {
+                "trans_date": [datetime(2023, 4, 1), datetime(2023, 4, 2)],
+                "state": ["pending", "rejected"],
+                "amount": [150, 250],
+                "country": ["FR", "FR"],
+                "id": [5, 6],
+            },
+            {
+                "month": ["2023-04"],
+                "country": ["FR"],
+                "trans_count": [2],
+                "approved_count": [0],
+                "trans_total_amount": [400],
+                "approved_total_amount": [0],
+            },
+            id="edge_case_all_unapproved",
+        ),
+    ],
+)
+def test_problem_1193(input_data, expected_data):
+    table = pd.DataFrame(input_data)
+    expected_table = pd.DataFrame(expected_data)
+    result = problem_1193(table).reset_index(drop=True)
+    assert_frame_equal(
+        result, expected_table, check_dtype=False, check_index_type=False
+    )
+
+
 @pytest.mark.parametrize(
     "input_data, expected_data",
     [

tests/test_pyarrow.py

@@ -3,56 +3,7 @@
 import pyarrow as pa
 import pytest
 
-from problems.pyarrow import (
-    problem_176,
-    problem_180,
-    problem_185,
-    problem_196,
-    problem_197,
-    problem_550,
-    problem_570,
-    problem_577,
-    problem_584,
-    problem_585,
-    problem_595,
-    problem_596,
-    problem_602,
-    problem_610,
-    problem_619,
-    problem_620,
-    problem_626,
-    problem_1045,
-    problem_1068,
-    problem_1070,
-    problem_1075,
-    problem_1141,
-    problem_1148,
-    problem_1164,
-    problem_1174,
-    problem_1193,
-    problem_1204,
-    problem_1211,
-    problem_1251,
-    problem_1280,
-    problem_1327,
-    problem_1341,
-    problem_1378,
-    problem_1517,
-    problem_1527,
-    problem_1581,
-    problem_1633,
-    problem_1661,
-    problem_1667,
-    problem_1683,
-    problem_1729,
-    problem_1731,
-    problem_1757,
-    problem_1789,
-    problem_1907,
-    problem_1934,
-    problem_1978,
-    problem_2356,
-)
+from problems.pyarrow import *
 
 
 @pytest.mark.parametrize(