Made "nfts.dataset" more userfriendly

datasets/nfts/nfts/dataset.py

@@ -2,7 +2,7 @@
 Functions to access various data in the NFTs dataset.
 """
 import sqlite3
-from typing import Union
+from typing import Dict
 
 import pandas as pd
 
@@ -92,27 +92,44 @@ def explain() -> None:
 The Moonstream NFTs dataset
 ===========================
 
+To load the NFTs dataset from a SQLite file, run:
+>>> ds = nfts.dataset.FromSQLite(<path to sqlite database>)
+
 This dataset consists of the following dataframes:"""
 
     print(preamble)
     for name, explanation in AVAILABLE_DATAFRAMES.items():
         print(f"\nDataframe: {name}")
         print(
-            f"Load using:\n\t{name}_df = nfts.dataset.load_dataframe(<sqlite connection or path to sqlite db>, {name})"
+            f'Load using:\n\t{name}_df = ds.load_dataframe(<sqlite connection or path to sqlite db>, "{name}")'
         )
         print("")
         print(explanation)
         print("- - -")
 
 
-def load_dataframe(db: Union[str, sqlite3.Connection], name: str) -> pd.DataFrame:
+class FromSQLite:
-    """
+    def __init__(self, datafile: str) -> None:
-    Loads one of the available dataframes. To learn more about the available dataframes, run:
+        """
-    >>> nfts.dataset.explain()
+        Initialize an NFTs dataset instance by connecting it to a SQLite database containing the data.
-    """
+        """
-    if name not in AVAILABLE_DATAFRAMES:
+        self.conn = sqlite3.connect(datafile)
-        raise ValueError(
+
-            f"Invalid dataframe: {name}. Please choose from one of the available dataframes: {','.join(AVAILABLE_DATAFRAMES)}."
+    def load_dataframe(self, name: str) -> pd.DataFrame:
-        )
+        """
-    df = pd.read_sql_table(name, db)
+        Loads one of the available dataframes. To learn more about the available dataframes, run:
-    return df
+        >>> nfts.dataset.explain()
+        """
+        if name not in AVAILABLE_DATAFRAMES:
+            raise ValueError(
+                f"Invalid dataframe: {name}. Please choose from one of the available dataframes: {','.join(AVAILABLE_DATAFRAMES)}."
+            )
+        df = pd.read_sql_query(f"SELECT * FROM {name};", self.conn)
+        return df
+
+    def load_all(self) -> Dict[str, pd.DataFrame]:
+        """
+        Load all the datasets and return them in a dictionary with the keys being the dataframe names.
+        """
+        dfs = {f"{name}_df": self.load_dataframe(name) for name in AVAILABLE_DATAFRAMES}
+        return dfs