Support csv with single column

examples/utils.py

@@ -27,11 +27,10 @@ def pretty_print_df(
         df: DataFrame,
         *,
         sample_size: Optional[int] = 10,
-        sort_key: Optional[str] = "id",
         inferred_schema: Optional[ShmessySchema] = None
 ) -> None:
     df = df[:sample_size]
-    df = df.sort_values(by=[sort_key])
+    df = df.sort_values(df.columns[0])
     df = df.rename(columns=add_data_types_to_column_names(df, inferred_schema))
     print(tabulate(df, headers="keys", tablefmt="rounded_outline", showindex=False))
 

src/shmessy/__init__.py

@@ -92,27 +92,29 @@ def read_csv(
         fix_column_names: Optional[bool] = False,
     ) -> DataFrame:
         try:
+            dialect = None
+
             if use_sniffer:
-                dialect = csv.Sniffer().sniff(
-                    sample=_get_sample_from_csv(
-                        filepath_or_buffer=filepath_or_buffer,
-                        sample_size=self.__sample_size,
-                        encoding=self.__reader_encoding,
-                    ),
-                    delimiters="".join([",", "\t", ";", " ", ":"]),
-                )
-                df = pd.read_csv(
-                    filepath_or_buffer=filepath_or_buffer,
-                    dialect=dialect(),
-                    low_memory=False,
-                    encoding=self.__reader_encoding,
-                )
-            else:
-                df = pd.read_csv(
-                    filepath_or_buffer=filepath_or_buffer,
-                    low_memory=False,
-                    encoding=self.__reader_encoding,
-                )
+                try:
+                    dialect = csv.Sniffer().sniff(
+                        sample=_get_sample_from_csv(
+                            filepath_or_buffer=filepath_or_buffer,
+                            sample_size=self.__sample_size,
+                            encoding=self.__reader_encoding,
+                        ),
+                        delimiters="".join([",", "\t", ";", " ", ":"]),
+                    )
+                except Exception as e:  # noqa
+                    logger.debug(
+                        f"Could not use python sniffer to infer csv schema, Using pandas default settings: {e}"
+                    )
+
+            df = pd.read_csv(
+                filepath_or_buffer=filepath_or_buffer,
+                dialect=dialect() if dialect else None,
+                low_memory=False,
+                encoding=self.__reader_encoding,
+            )
 
             if fixed_schema is None:
                 fixed_schema = self.infer_schema(df)

src/shmessy/exceptions.py

@@ -4,9 +4,6 @@
 def exception_router(exception: Exception):
     error_message = str(exception)
 
-    if "Could not determine delimiter" in error_message:
-        raise CouldNotDetermineDelimiterException()
-
     match = re.match(
         r"(.*)'(.*)' codec can't decode byte (.*) in position (.*):(.*)", error_message
     )
@@ -64,11 +61,3 @@ def __init__(self, expected_encoding: str):
         super().__init__(
             f"The given file cannot be read using {expected_encoding} encoding."
         )
-
-
-class CouldNotDetermineDelimiterException(ShmessyException):
-    def __init__(self):
-        super().__init__(
-            "Could not determine delimiter. "
-            "Make sure a delimiter is shown the same number of times on every row in the file."
-        )

tests/data/data_7.csv

@@ -0,0 +1,9 @@
+header_name
+this
+is
+csv
+file
+with
+single
+column
+only

tests/intg/test_read_csv.py

@@ -1,55 +1,62 @@
-from numpy import dtypes
+import numpy as np
 
 from shmessy import Shmessy
 
 
 def test_read_csv(files_folder):
     df = Shmessy().read_csv(files_folder.as_posix() + "/data_1.csv")
 
-    assert isinstance(df["created_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["modified_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["deleted_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["celebrated_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["joined_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["laughed_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["loled_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["fooled_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["emerged_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["processed_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["isolated_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["scheduled_at"].dtype, dtypes.DateTime64DType)
-    assert isinstance(df["unixed_at"].dtype, dtypes.DateTime64DType)
+    assert df["created_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["modified_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["deleted_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["celebrated_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["joined_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["laughed_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["loled_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["fooled_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["emerged_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["processed_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["isolated_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["scheduled_at"].dtype == np.dtype("datetime64[ns]")
+    assert df["unixed_at"].dtype == np.dtype("datetime64[ns]")
 
 
 def test_read_csv_colon_as_delimiter(files_folder):
     df = Shmessy().read_csv(files_folder.as_posix() + "/data_3.csv")
-    assert isinstance(df["id"].dtype, dtypes.Int64DType)
-    assert isinstance(df["name"].dtype, dtypes.ObjectDType)
-    assert isinstance(df["value"].dtype, dtypes.Int64DType)
+    assert df["id"].dtype == np.dtype("int64")
+    assert df["name"].dtype == np.dtype("O")
+    assert df["value"].dtype == np.dtype("int64")
 
 
 def test_read_csv_semicolon_as_delimiter(files_folder):
     df = Shmessy().read_csv(files_folder.as_posix() + "/data_4.csv")
-    assert isinstance(df["id"].dtype, dtypes.Int64DType)
-    assert isinstance(df["name"].dtype, dtypes.ObjectDType)
-    assert isinstance(df["value"].dtype, dtypes.Int64DType)
+    assert df["id"].dtype == np.dtype("int64")
+    assert df["name"].dtype == np.dtype("O")
+    assert df["value"].dtype == np.dtype("int64")
 
 
 def test_buffer_as_read_csv_input(files_folder):
     path = files_folder.as_posix() + "/data_4.csv"
     with open(path, mode="rt") as file_input:
         df = Shmessy().read_csv(file_input)
 
-    assert isinstance(df["id"].dtype, dtypes.Int64DType)
-    assert isinstance(df["name"].dtype, dtypes.ObjectDType)
-    assert isinstance(df["value"].dtype, dtypes.Int64DType)
+    assert df["id"].dtype == np.dtype("int64")
+    assert df["name"].dtype == np.dtype("O")
+    assert df["value"].dtype == np.dtype("int64")
 
 
 def test_binary_buffer_as_read_csv_input(files_folder):
     path = files_folder.as_posix() + "/data_4.csv"
     with open(path, mode="rb") as file_input:
         df = Shmessy().read_csv(file_input)
 
-    assert isinstance(df["id"].dtype, dtypes.Int64DType)
-    assert isinstance(df["name"].dtype, dtypes.ObjectDType)
-    assert isinstance(df["value"].dtype, dtypes.Int64DType)
+    assert df["id"].dtype == np.dtype("int64")
+    assert df["name"].dtype == np.dtype("O")
+    assert df["value"].dtype == np.dtype("int64")
+
+
+def test_read_csv_file_with_single_column(files_folder):
+    path = files_folder.as_posix() + "/data_7.csv"
+    with open(path, mode="rb") as file_input:
+        df = Shmessy().read_csv(file_input)
+    assert df["header_name"].dtype == np.dtype("O")