Data Conversions Between Excel and Python

xlSlim always tries to determine the appropriate conversions when passing data between Excel and Python. Without type hints xlSlim will handle numbers, strings, bools and datetimes. Adding type hints allows xlSlim to handle Python dictionaries and instances of custom classes. Lists of simple data types are also better handled when type hints are provided.

See Passing simple data types, Passing Python objects, Passing Python dictionaries, Passing lists, numpy Arrays, pandas Series and pandas DataFrames for worked examples.

Function Inputs

Python functions without type hints are handled well enough for many use cases.

Data Type Conversions Without Type Hints

Type in Excel	Type passed to Python
integer	float
double	float
string	string
bool	bool
datetime	float
Range	Wrapped .Net array of objects.

For more advanced use cases, Python type hints open up the full potential of xlSlim.

Data Type Conversions With Type Hints

Type in Excel	Type passed to Python	Supported Type Hints
integer	integer	int Optional[int] Union[int, None]
double	float	float Optional[float] Union[float, None]
string	string	str Optional[str] Union[str, None]
bool	bool	bool Optional[bool] Union[bool, None]
datetime	datetime	datetime.datetime Optional[datetime.datetime] Union[datetime.datetime, None] datetime.date Optional[datetime.date] Union[datetime.date, None]
cached object handle	Python object	Any Python class Dict np.ndarray pd.Series pd.DataFrame
Range of ints	Wrapped .Net array of objects.	List[int] Iterable[int] Optional[List[int]] Optional[Iterable[int]] Union[List[int], None] Union[Iterable[int], None]
Range of doubles	Wrapped .Net array of doubles.	List[float] Iterable[float] Optional[List[float]] Optional[Iterable[float]] Union[List[float], None] Union[Iterable[float], None]
Range of strings	Wrapped .Net array of objects.	List[str] Iterable[str] Optional[List[str]] Optional[Iterable[str]] Union[List[str], None] Union[Iterable[str], None]
Range of datetimes	Wrapped .Net array of objects.	List[datetime.datetime] List[datetime.date] Iterable[datetime.datetime] Iterable[datetime.date] Optional[List[datetime.datetime]] Optional[List[datetime.date]] Optional[Iterable[datetime.datetime]] Optional[Iterable[datetime.date]] Union[List[datetime.datetime], None] Union[List[datetime.date], None] Union[Iterable[datetime.datetime], None] Union[Iterable[datetime.date], None]
Range	np.ndarray	np.ndarray
Range first column is the index second column is the data	pd.Series	pd.Series
Range first column is the index first row is the column names the top left cell is ignored	pd.DataFrame	pd.DataFrame
Range of cached object handles	List of Python objects	List[Any Python class] Iterable[Any Python class]
Range of cached object handles	List of np.ndarray	List[np.ndarray] Iterable[np.ndarray]
Range of cached object handles	List of pd.Series	List[pd.Series] Iterable[pd.Series]
Range of cached object handles	List of pd.DataFrame	List[pd.DataFrame] Iterable[pd.DataFrame]

Note

The Python wrapped .Net arrays are not converted to native Python lists for performance reasons. The wrapped arrays behave exactly the same as native Python lists with one important difference - they are immutable. Additionally, type checking is only done when a function expects a list of floats, in other cases lists of objects are passed and the objects may be any Excel type.

Function Outputs

The results of Python functions are handled equally well with or without type hints.

Data Type Conversions

Type in Python	Result type in Excel
integer	double
float	double
bool	bool
datetime	float (this is correct as all Excel datetimes are floats)
string	string
dict	cached object handle
Python object (an instance of a custom class)	cached object handle
np.ndarray	cached object handle
pd.Series	cached object handle
pd.DataFrame	cached object handle
list of integers	Range of doubles
list of floats	Range of doubles
list of bool	Range of bool
list of strings	Range of strings
list of strings, ints, floats and bools	Range of strings, ints, floats and bools