first iteration of pandas fdataframe.py

environment.yml

@@ -10,5 +10,4 @@ dependencies:
       - pyspark~=3.3.0
       - networkx~=2.8.7
       - numpy~=1.23.4
-      - pandas~=1.5.1
       - pyarrow~=8.0.0

fsspark/fs/fdataframe.py

@@ -50,27 +50,25 @@ def __init__(
         :param parse_features: Coerce all features to float.
         """
 
-        self.__df = self._convert_psdf_to_sdf(df)
         self.__sample_col = sample_col
         self.__label_col = label_col
-        self.__row_index_name = row_index_col
+        self.__row_index_col = row_index_col
+        self.__df = df
 
         # check input dataframe
         self._check_df()
 
         # replace dots in column names, if any.
         if parse_col_names:
-            #  TODO: Dots in column names are prone to errors, since dots are used to access attributes from DataFrame.
-            #        Should we make this replacement optional? Or print out a warning?
             self.__df = self.__df.toDF(*(c.replace('.', '_') for c in self.__df.columns))
 
         # If the specified row index column name does not exist, add row index to the dataframe
-        if self.__row_index_name not in self.__df.columns:
-            self.__df = self._add_row_index(index_name=self.__row_index_name)
+        if self.__row_index_col not in self.__df.columns:
+            self.__df = self._add_row_index(index_name=self.__row_index_col)
 
         if parse_features:
             # coerce all features to float
-            non_features_cols = [self.__sample_col, self.__label_col, self.__row_index_name]
+            non_features_cols = [self.__sample_col, self.__label_col, self.__row_index_col]
             feature_cols = [c for c in self.__df.columns if c not in non_features_cols]
             self.__df = self.__df.withColumns({c: self.__df[c].cast('float') for c in feature_cols})
 
@@ -87,7 +85,7 @@ def _check_df(self):
             raise ValueError(f"Column sample name {self.__sample_col} not found...")
         elif self.__label_col not in col_names:
             raise ValueError(f"Column label name {self.__label_col} not found...")
-        elif not isinstance(self.__row_index_name, str):
+        elif not isinstance(self.__row_index_col, str):
             raise ValueError("Row index column name must be a valid string...")
         else:
             pass
@@ -97,21 +95,24 @@ def _set_indexed_cols(self) -> Series:
         Create a distributed indexed Series representing features.
         :return: Pandas on  (PoS) Series
         """
-        non_features_cols = [self.__sample_col, self.__label_col, self.__row_index_name]
+        non_features_cols = [self.__sample_col, self.__label_col, self.__row_index_col]
         features = [f for f in self.__df.columns if f not in non_features_cols]
         return Series(features)
 
-    def _set_indexed_rows(self) -> Series:
+    def _set_indexed_rows(self) -> pd.Series:
         """
-        Create a distributed indexed Series representing samples labels.
-        It will use existing row indices, if any.
+        Create an indexed Series representing sample labels.
+        It will use existing row indices from the DataFrame.
 
-        :return: Pandas on  (PoS) Series
+        :return: Pandas Series
         """
-        # TODO: Check for equivalent to pandas distributed Series in .
-        label = self.__df.select(self.__label_col).collect()
-        row_index = self.__df.select(self.__row_index_name).collect()
-        return Series(label, index=row_index)
+
+        # Extract the label and row index columns from the DataFrame
+        labels = self.__df[self.__label_col]
+        row_indices = self.__df[self.__row_index_col]
+
+        # Create a Pandas Series with row_indices as index and labels as values
+        return pd.Series(data=labels.values, index=row_indices.values)
 
     def get_features_indexed(self) -> Series:
         """
@@ -223,7 +224,7 @@ def get_row_index_name(self) -> str:
 
         :return: Row id column name.
         """
-        return self.__row_index_name
+        return self.__row_index_col
 
     def _add_row_index(self, index_name: str = '_row_index') -> pd.DataFrame:
         """
@@ -276,12 +277,12 @@ def filter_features(self, features: List[str], keep: bool = True) -> 'FSDataFram
             sdf = sdf.select(
                 self.__sample_col,
                 self.__label_col,
-                self.__row_index_name,
+                self.__row_index_col,
                 *features)
         else:
             sdf = sdf.drop(*features)
 
-        fsdf_filtered = self.update(sdf, self.__sample_col, self.__label_col, self.__row_index_name)
+        fsdf_filtered = self.update(sdf, self.__sample_col, self.__label_col, self.__row_index_col)
         count_b = fsdf_filtered.count_features()
 
         logger.info(f"{count_b} features out of {count_a} remain after applying this filter...")

fsspark/tests/test_fsdataframe.py

@@ -0,0 +1,27 @@
+import pytest
+import pandas as pd
+from fsspark.fs.fdataframe import FSDataFrame
+
+def test_initializes_fsdataframe():
+
+    # Create a sample DataFrame
+    data = {
+        'sample_id': [1, 2, 3],
+        'label': ['A', 'B', 'C'],
+        'feature1': [0.1, 0.2, 0.3],
+        'feature2': [1.1, 1.2, 1.3]
+    }
+    df = pd.DataFrame(data)
+
+    # Initialize FSDataFrame
+    fs_df = FSDataFrame(
+        df=df,
+        sample_col='sample_id',
+        label_col='label',
+        row_index_col='_row_index',
+        parse_col_names=False,
+        parse_features=False
+    )
+
+    # Assertions to check if the initialization is correct
+    assert (fs_df.get_sdf(), df)