diff --git a/offsets_db_api/routers/charts.py b/offsets_db_api/routers/charts.py
index c5262cf..ea8ab8e 100644
--- a/offsets_db_api/routers/charts.py
+++ b/offsets_db_api/routers/charts.py
@@ -30,16 +30,7 @@ def filter_valid_projects(df: pd.DataFrame, categories: list | None = None) -> p
         return df
     # Filter the dataframe to include only rows with the specified categories
     valid_projects = df[df['category'].isin(categories)]
-
-    # Group by project and filter out projects that have any categories outside the given list
-    def all_categories_valid(group):
-        return all(category in categories for category in group['category'].unique())
-
-    valid_project_ids = (
-        valid_projects.groupby('project_id').filter(all_categories_valid).project_id.unique()
-    )
-
-    return df[df['project_id'].isin(valid_project_ids)]
+    return valid_projects
 
 
 def projects_by_category(
@@ -85,7 +76,7 @@ def generate_date_bins(
     min_value,
     max_value,
     freq: typing.Literal['D', 'W', 'M', 'Y'] | None = None,
-    num_bins: int = None,
+    num_bins: int | None = None,
 ):
     """
     Generate date bins with the specified frequency.
@@ -509,6 +500,7 @@ def get_credits_by_transaction_date(
 
     df = pd.read_sql_query(query.statement, engine).explode('category')
     logger.info(f'Sample of the dataframe with size: {df.shape}\n{df.head()}')
+    df.to_csv('/tmp/testing.csv')
     # fix the data types
     df = df.astype({'transaction_date': 'datetime64[ns]'})
     results = credits_by_transaction_date(df=df, freq=freq, categories=category)
diff --git a/tests/test_charts.py b/tests/test_charts.py
index 98f5047..92b236c 100644
--- a/tests/test_charts.py
+++ b/tests/test_charts.py
@@ -1,5 +1,71 @@
+import pandas as pd
 import pytest
 
+from offsets_db_api.routers.charts import filter_valid_projects, projects_by_category
+
+
+@pytest.fixture
+def sample_projects():
+    data = pd.DataFrame(
+        [
+            {'category': 'ghg-management', 'project_id': 'ACR123', 'issued': 100, 'retired': 50},
+            {'category': 'renewable-energy', 'project_id': 'ACR456', 'issued': 200, 'retired': 150},
+            {'category': 'biodiversity', 'project_id': 'VER789', 'issued': 300, 'retired': 250},
+            {
+                'category': 'water-management',
+                'project_id': 'CDM1011',
+                'issued': 400,
+                'retired': 350,
+            },
+            {'category': 'ghg-management', 'project_id': 'ACR111', 'issued': 500, 'retired': 450},
+        ]
+    )
+
+    return data
+
+
+@pytest.mark.parametrize(
+    'categories, expected',
+    [
+        (None, ['ghg-management', 'renewable-energy', 'biodiversity', 'water-management']),
+        (['renewable-energy'], ['renewable-energy']),
+    ],
+)
+def test_filter_valid_projects(sample_projects, categories, expected):
+    result = filter_valid_projects(sample_projects, categories=categories)
+    assert list(result['category'].unique()) == expected
+
+
+@pytest.mark.parametrize(
+    'categories,expected',
+    [
+        (
+            None,
+            [
+                {'category': 'ghg-management', 'value': 2},
+                {'category': 'renewable-energy', 'value': 1},
+                {'category': 'biodiversity', 'value': 1},
+                {'category': 'water-management', 'value': 1},
+            ],
+        ),
+        (['ghg-management'], [{'category': 'ghg-management', 'value': 2}]),
+        (
+            ['renewable-energy', 'biodiversity'],
+            [
+                {'category': 'renewable-energy', 'value': 1},
+                {'category': 'biodiversity', 'value': 1},
+            ],
+        ),
+        (['non-existent-category'], []),
+        ([], []),
+    ],
+)
+def test_projects_by_category(categories, expected, sample_projects):
+    result = projects_by_category(df=sample_projects, categories=categories)
+    sorted_result = sorted(result, key=lambda x: x['category'])
+    sorted_expected = sorted(expected, key=lambda x: x['category'])
+    assert sorted_result == sorted_expected
+
 
 @pytest.mark.parametrize('freq', ['D', 'M', 'Y', 'W'])
 @pytest.mark.parametrize('registry', ['american-carbon-registry', 'climate-action-reserve'])