Update import logic to use polars instead of pyarrow, added loggi…

…ng (#63)

With pola-rs/polars#5687 closed, I've updated all import logic for pdstools to use polars over pyarrow. Since we make heavy use of ndjson, this issue was a blocker. All imports should now be much faster.

Additionally, I've added some basic logging to the file import logic.


* Fill SnapshotTime with nan if missing

* Changed all import logic to Polars from pyarrow

* Updated ValueFinder to use correct keyword for polars instead of pyarrow

Co-authored-by: Stijn Kas <[email protected]>

python/pdstools/adm/ADMDatamart.py

@@ -485,7 +485,8 @@ def _set_types(df: pd.DataFrame, verbose=True) -> pd.DataFrame:
 
         for col in {"Performance"} & set(df.columns):
             df[col] = df[col].astype(float)
-
+        if 'SnapshotTime' not in df.columns:
+            df['SnapshotTime'] = np.nan
         try:
             df["SnapshotTime"] = pd.to_datetime(df["SnapshotTime"])
         except Exception:

python/pdstools/utils/cdh_utils.py

@@ -22,25 +22,28 @@
 
 
 def readDSExport(
-    filename: Union[pd.DataFrame, str],
+    filename: Union[pd.DataFrame, pl.DataFrame, str],
     path: str = ".",
     verbose: bool = True,
     **kwargs,
-) -> pd.DataFrame:
+) -> Union[pd.DataFrame, pl.DataFrame]:
     """Read a Pega dataset export file.
     Can accept either a Pandas DataFrame or one of the following formats:
     - .csv
     - .json
     - .zip (zipped json or CSV)
+    - .feather
+    - .ipc
+    - .parquet
 
     It automatically infers the default file names for both model data as well as predictor data.
     If you supply either 'modelData' or 'predictorData' as the 'file' argument, it will search for them.
     If you supply the full name of the file in the 'path' directory, it will import that instead.
 
     Parameters
     ----------
-    filename : [pd.DataFrame, str]
-        Either a Pandas DataFrame with the source data (for compatibility),
+    filename : [pd.DataFrame, pl.DataFrame, str]
+        Either a Pandas/Polars DataFrame with the source data (for compatibility),
         or a string, in which case it can either be:
         - The name of the file (if a custom name) or
         - Whether we want to look for 'modelData' or 'predictorData' in the path folder.
@@ -49,12 +52,17 @@ def readDSExport(
     verbose : bool, default = True
         Whether to print out which file will be imported
 
-    Keyword arguments:
-        Any arguments to plug into the read csv or json function, from either PyArrow or Pandas.
+    Keyword arguments
+    -----------------
+    return_pl: bool
+        Whether to return polars dataframe
+        if False, transforms to Pandas
+    Any:
+        Any arguments to plug into the read_* function from Polars.
 
     Returns
     -------
-    pd.DataFrame
+    pd.DataFrame | pl.DataFrame
         The read data from the given file
 
     Examples:
@@ -67,35 +75,41 @@ def readDSExport(
     """
 
     # If a dataframe is supplied directly, we can just return it
-    if isinstance(filename, pd.DataFrame):
+    if isinstance(filename, pd.DataFrame) | isinstance(filename, pl.DataFrame):
+        logging.debug("Dataframe returned directly")
         return filename
 
     # If the data is a BytesIO object, such as an uploaded file
     # in certain webapps, then we can simply return the object
     # as is, while extracting the extension as well.
     if isinstance(filename, BytesIO):
+        logging.debug("Filename is of type BytesIO, importing that directly")
         name, extension = os.path.splitext(filename.name)
         return import_file(filename, extension, **kwargs)
 
     # If the filename is simply a string, then we first
     # extract the extension of the file, then look for
     # the file in the user's directory.
     if os.path.isfile(os.path.join(path, filename)):
+        logging.debug("File found in directory")
         file = os.path.join(path, filename)
     else:
+        logging.debug("File not found in directory, scanning for latest file")
         file = get_latest_file(path, filename)
 
     # If we can't find the file locally, we can try
     # if the file's a URL. If it is, we need to wrap
     # the file in a BytesIO object, and read the file
     # fully to disk for pyarrow to read it.
     if file == "Target not found" or file == None:
+        logging.debug("Could not find file in directory, checking if URL")
         import requests
 
         try:
             response = requests.get(f"{path}/{filename}")
             logging.info(f"Response: {response}")
             if response.status_code == 200:
+                logging.debug("File found online, importing and parsing to BytesIO")
                 file = f"{path}/{filename}"
                 file = BytesIO(urllib.request.urlopen(file).read())
                 name, extension = os.path.splitext(filename)
@@ -111,44 +125,72 @@ def readDSExport(
         name, extension = os.path.splitext(file)
 
     # Now we should either have a full path to a file, or a
-    # BytesIO wrapper around the file. Pyarrow can read those both.
+    # BytesIO wrapper around the file. Polars can read those both.
     return import_file(file, extension, **kwargs)
 
 
-def import_file(file, extension, **kwargs):
-    import pyarrow
+def import_file(
+    file: str, extension: str, **kwargs
+) -> Union[pl.DataFrame, pd.DataFrame]:
+    """Imports a file using Polars
+
+    By default, exports to Pandas
+
+    Parameters
+    ----------
+    File: str
+        The path to the file, passed directly to the read functions
+    extension: str
+        The extension of the file, used to determine which function to use
+
+    Keyword arguments
+    -----------------
+    return_pl: bool, default = False
+        Whether to return Polars or Pandas
+        If return_pl is True, then keeps the Polars dataframe
+
+    Returns
+    -------
+    Union[pd.DataFrame | pl.DataFrame]
+        A Pandas or Polars dataframe, depending on `return_pl`
+    """
 
     if extension == ".zip":
-        file = readZippedFile(file)
-    elif extension == ".csv":
-        from pyarrow import csv
+        logging.debug("Zip file found, extracting data.json to BytesIO.")
+        file, extension = readZippedFile(file)
 
-        file = csv.read_csv(
+    if extension == ".csv":
+        file = pl.read_csv(
             file,
-            parse_options=pyarrow.csv.ParseOptions(delimiter=kwargs.get("sep", ",")),
+            sep=kwargs.get("sep", ","),
         )
+
     elif extension == ".json":
-        from pyarrow import json
+        try:
+            file = pl.read_ndjson(file)
+        except:
+            file = pl.read_json(file)
 
-        file = json.read_json(file, **kwargs)
     elif extension == ".parquet":
-        from pyarrow import parquet
+        file = pl.read_parquet(file)
+
+    elif extension == ".feather" | extension.casefold() == ".ipc":
+        file = pl.read_ipc(file)
 
-        file = parquet.read_table(file)
     else:
         raise ValueError(f"Could not import file: {file}, with extension {extension}")
 
-    if not kwargs.pop("return_pa", False) and isinstance(file, pyarrow.Table):
-        return file.to_pandas()
-    else:
+    if kwargs.pop("return_pl", False):
         return file
+    else:
+        return file.to_pandas()
 
 
-def readZippedFile(file: str, verbose: bool = False, **kwargs) -> pd.DataFrame:
-    """Read a zipped file.
+def readZippedFile(file: str, verbose: bool = False, **kwargs) -> BytesIO:
+    """Read a zipped NDJSON file.
     Reads a dataset export file as exported and downloaded from Pega. The export
-    file is formatted as a zipped multi-line JSON file or CSV file
-    and the data is read into a pandas dataframe.
+    file is formatted as a zipped multi-line JSON file. It reads the file,
+    and then returns the file as a BytesIO object.
 
     Parameters
     ----------
@@ -159,35 +201,17 @@ def readZippedFile(file: str, verbose: bool = False, **kwargs) -> pd.DataFrame:
 
     Returns
     -------
-    pd.DataFrame
-        A pandas dataframe with the contents.
+    os.BytesIO
+        A Polars dataframe with the contents.
     """
-
     with zipfile.ZipFile(file, mode="r") as z:
+        logging.debug("Opened zip file.")
         files = z.namelist()
-        if verbose:
-            print(files)  # pragma: no cover
+        logging.debug(f"Files found: {files}")
         if "data.json" in files:
+            logging.debug("data.json found.")
             with z.open("data.json") as zippedfile:
-                try:
-                    from pyarrow import json
-
-                    return json.read_json(zippedfile)
-                except ImportError:  # pragma: no cover
-                    try:
-                        dataset = pd.read_json(zippedfile, lines=True)
-                        return dataset
-                    except ValueError:
-                        dataset = pd.read_json(zippedfile)
-                        return dataset
-        if "csv.json" in files:  # pragma: no cover
-            with z.open("data.csv") as zippedfile:
-                try:
-                    from pyarrow import csv
-
-                    return csv.read_json(zippedfile).to_pandas()
-                except ImportError:
-                    return pd.read_csv(zippedfile)
+                return (BytesIO(zippedfile.read()), ".json")
         else:  # pragma: no cover
             raise FileNotFoundError("Cannot find a 'data' file in the zip folder.")
 
@@ -217,7 +241,7 @@ def get_latest_file(path: str, target: str, verbose: bool = False) -> str:
     if target not in {"modelData", "predictorData", "ValueFinder"}:
         return f"Target not found"
 
-    supported = [".json", ".csv", ".zip", ".parquet"]
+    supported = [".json", ".csv", ".zip", ".parquet", ".feather", ".ipc"]
 
     files_dir = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
     files_dir = [f for f in files_dir if os.path.splitext(f)[-1].lower() in supported]
@@ -279,33 +303,43 @@ def getMatches(files_dir, target):
 
 
 def cache_to_file(
-    df: pd.DataFrame, path: os.PathLike, name: str, cache_type="parquet"
+    df: pd.DataFrame,
+    path: os.PathLike,
+    name: str,
+    cache_type: str = "ipc",
+    compression: str = "uncompressed",
 ) -> os.PathLike:
     """Very simple convenience function to cache data.
     Caches in parquet format for very fast reading.
 
     Parameters
     ----------
-    df: pd.DataFrame
+    df: pd.DataFrame | pl.DataFrame
         The dataframe to cache
     path: os.PathLike
         The location to cache the data
     name: str
         The name to give to the file
     cache_type: str
         The type of file to export.
-        Currently, only Parquet is supported,
-        will support Arrow/Feather/IPC soon.
+        Default is IPC, also supports parquet
+    compression: str
+        The compression to apply, default is uncompressed
 
     Returns
     -------
     os.PathLike:
         The filepath to the cached file
     """
     outpath = f"{path}/{name}"
+    if isinstance(df, pd.DataFrame):
+        df = pl.DataFrame(df)
+    if cache_type == "ipc":
+        outpath = f"{outpath}.arrow"
+        df.write_ipc(outpath, compression=compression)
     if cache_type == "parquet":
         outpath = f"{outpath}.parquet"
-        df.to_parquet(outpath)
+        df.write_parquet(outpath, compression=compression)
     return outpath
 
 

python/pdstools/valuefinder/ValueFinder.py

@@ -73,10 +73,8 @@ def __init__(
             start = time.time()
             filename = kwargs.pop("filename", "ValueFinder")
             self.df = cdh_utils.readDSExport(
-                filename, path, return_pa=True, verbose=verbose
+                filename, path, return_pl=True, verbose=verbose
             )
-            if kwargs.get("subset", True):
-                self.df = self.df.select(keep_cols)
             if verbose:
                 print(f"Data import took {round(time.time() - start,2)} seconds")