completed function calls

dataloader/loader.py

@@ -185,22 +185,34 @@ def fields(cls, source: str) -> List[str]:
     @classmethod
     def all(cls, source: str) -> pd.DataFrame:
         """Fetch entire table."""
-        raise NotImplementedError
+        query = f"SELECT * FROM {source}"
+        df = cls.client.query(query)
+        return cls._format_dataframe(df)
 
+    # added columns here
     @classmethod
-    def columns(cls, source: str) -> pd.DataFrame:
+    def columns(cls, source: str, columns: List[str]) -> pd.DataFrame:
         """Select specific columns from source."""
-        raise NotImplementedError
+        query = f"SELECT {', '.join(columns)} FROM {source}"
+        df = cls.client.query(query)
+        return cls._format_dataframe(df)
 
+    # QUESTION
+    # order by date here?
     @classmethod
     def head(cls, source: str, n: int = 10) -> pd.DataFrame:
         """Get first N rows."""
-        raise NotImplementedError
+        query = f"SELECT * FROM {source} LIMIT {n}"
+        df = cls.client.query(query)
+        return cls._format_dataframe(df)
 
+    # Same question as above
     @classmethod
     def paginate(cls, source: str, limit: int, offset: int) -> pd.DataFrame:
         """Get paginated results."""
-        raise NotImplementedError
+        query = f"SELECT * From {source} LIMIT {limit} OFFSET {offset}"
+        df = cls.client.query(query)
+        return cls._format_dataframe(df)
 
     @classmethod
     def filter(cls, source: str, **kwargs) -> pd.DataFrame:
@@ -210,50 +222,95 @@ def filter(cls, source: str, **kwargs) -> pd.DataFrame:
         Example:
             DataLoader.filter('equities', symbol='AAPL', date_start='2024-01-01')
         """
-        raise NotImplementedError
+        symbol = kwargs.pop("symbol", None)
+        date_start = kwargs.pop("date_start", None)
+        date_end = kwargs.pop("date_end", None)
+
+        filters = {}
+        if symbol:
+            filters["symbol"] = symbol
+        if date_start:
+            filters["start"] = date_start
+        if date_end:
+            filters["end"] = date_end
+        filters.update(kwargs)
+
+        query = cls._build_query(source, filters=filters)
+        df = cls.client.query(query, params=filters)
+        return cls._format_dataframe(df)
 
     @classmethod
     def match_pattern(cls, source: str, pattern: str) -> List[str]:
         """Get columns matching a pattern."""
-        raise NotImplementedError
+        query = f"FROM {source} SELECT COLUMNS('{pattern}')"
+        df = cls.client.query(query)
+        return df["columns"].tolist()
 
     @classmethod
     def select_pattern(cls, source: str, pattern: str, **filters) -> pd.DataFrame:
         """Select columns matching a pattern with optional filters."""
-        raise NotImplementedError
+        columns = cls.match_pattern(source, pattern)
+        query = cls._build_query(source, columns_list=columns, filters=filters)
+        df = cls.client.query(query, params=filters)
+        return cls._format_dataframe(df)
 
     @classmethod
     def date_range(
         cls, source: str, start_date: str, end_date: str, **additional_filters
     ) -> pd.DataFrame:
         """Get data between two dates (YYYY-MM-DD format)."""
-        raise NotImplementedError
+        filters = {
+            "start": start_date,
+            "end": end_date,
+        }
+        filters.update(additional_filters)
+
+        query = cls._build_query(source, filters=filters)
+        df = cls.client.query(query, params=filters)
+        return cls._format_dataframe(df)
 
     @classmethod
     def first_date(cls, source: str) -> pd.Timestamp:
         """Return the earliest date in the table."""
-        raise NotImplementedError
+        query = f"SELECT MIN(date) AS first_date FROM {source}"
+        df = cls.client.query(query)
+        return pd.to_datetime(df["first_date"].iloc[0])
 
     @classmethod
     def last_date(cls, source: str) -> pd.Timestamp:
         """Return the latest date in the table."""
-        raise NotImplementedError
+        query = f"SELECT MAX(date) AS last_date FROM {source}"
+        df = cls.client.query(query)
+        return pd.to_datetime(df["last_date"].iloc[0])
 
+    # want me to add an optional ticker parameter here?
     @classmethod
     def latest(cls, source: str, n: int = 1) -> pd.DataFrame:
         """Return the last N rows per symbol or table."""
-        raise NotImplementedError
+        query = f"""
+        SELECT * FROM {source}
+        ORDER BY date DESC
+        LIMIT {n}
+        """
+        df = cls.client.query(query)
+        return cls._format_dataframe(df)
 
     @classmethod
     def describe(cls, source: str) -> pd.DataFrame:
         """Return column types, non-null counts, basic stats."""
-        raise NotImplementedError
+        query = f"DESCRIBE TABLE {source}"
+        df = cls.client.query(query)
+        return df
 
     @classmethod
     def column_types(cls, source: str) -> Dict[str, str]:
         """Return data types for each column in a table."""
-        raise NotImplementedError
+        query = f"SHOW COLUMNS FROM {source}"
+        # will return a dataframe with 'field' and 'type' columns
+        df = cls.client.query(query)
+        return dict(zip(df["field"], df["type"]))
 
+    # order here too?
     @classmethod
     def stream(cls, source: str, batch_size: int = 10000):
         """
@@ -262,7 +319,14 @@ def stream(cls, source: str, batch_size: int = 10000):
             for df_chunk in DataLoader.stream('equities', 5000):
                 process(df_chunk)
         """
-        raise NotImplementedError
+        offset = 0 # for pagination
+        while True:
+            query = f"SELECT * FROM {source} ORDER BY date LIMIT {batch_size} OFFSET {offset}"
+            df = cls.client.query(query)
+            if df.empty:
+                break
+            yield cls._format_dataframe(df)
+            offset += batch_size
 
     @classmethod
     def iter_chunks(cls, source: str, chunk_size: int = 10000):
@@ -272,4 +336,10 @@ def iter_chunks(cls, source: str, chunk_size: int = 10000):
     @classmethod
     def batch_query(cls, sources: List[str], filters: Optional[Dict[str, Any]] = None):
         """Query multiple tables or symbols in a single call."""
-        raise NotImplementedError
+        combined_df = pd.DataFrame()
+        for source in sources:
+            query = cls._build_query(source, filters=filters)
+            df = cls.client.query(query, params=filters)
+            formatted_df = cls._format_dataframe(df)
+            combined_df = pd.concat([combined_df, formatted_df], axis=1)
+        return combined_df