pandera.dtypes.PandasDtype¶

class pandera.dtypes.PandasDtype(value)[source]¶

Bases: enum.Enum

Enumerate all valid pandas data types.

pandera follows the numpy data types subscribed to by pandas and by default supports using the numpy data type string aliases to validate DataFrame or Series dtypes.

This class simply enumerates the valid numpy dtypes for pandas arrays. For convenience PandasDtype enums can all be accessed in the top-level pandera name space via the same enum name.

Examples

>>> import pandas as pd
>>> import pandera as pa
>>>
>>>
>>> pa.SeriesSchema(pa.Int).validate(pd.Series([1, 2, 3]))
0    1
1    2
2    3
dtype: int64
>>> pa.SeriesSchema(pa.Float).validate(pd.Series([1.1, 2.3, 3.4]))
0    1.1
1    2.3
2    3.4
dtype: float64
>>> pa.SeriesSchema(pa.String).validate(pd.Series(["a", "b", "c"]))
    0    a
1    b
2    c
dtype: object

Alternatively, you can use built-in python scalar types for integers, floats, booleans, and strings:

>>> pa.SeriesSchema(int).validate(pd.Series([1, 2, 3]))
0    1
1    2
2    3
dtype: int64

You can also use the pandas string aliases in the schema definition:

>>> pa.SeriesSchema("int").validate(pd.Series([1, 2, 3]))
0    1
1    2
2    3
dtype: int64

Note

pandera also offers limited support for pandas extension types, however since the release of pandas 1.0.0 there are backwards incompatible extension types like the Integer array. The extension types, e.g. pd.IntDtype64() and their string alias should work when supplied to the pandas_dtype argument, unless otherwise specified below, but this functionality is only tested for pandas >= 1.0.0. Extension types in earlier versions are not guaranteed to work as the pandas_dtype argument in schemas or schema components.

Attributes

`Bool`	`"bool"` numpy dtype
`Category`	pandas `"categorical"` datatype
`Complex`	`"complex"` numpy dtype
`Complex128`	`"complex"` numpy dtype
`Complex256`	`"complex"` numpy dtype
`Complex64`	`"complex"` numpy dtype
`DateTime`	`"datetime64[ns]"` numpy dtype
`Float`	`"float"` numpy dtype
`Float16`	`"float16"` numpy dtype
`Float32`	`"float32"` numpy dtype
`Float64`	`"float64"` numpy dtype
`INT16`	`"Int16"` pandas dtype: pandas 0.24.0+
`INT32`	`"Int32"` pandas dtype: pandas 0.24.0+
`INT64`	`"Int64"` pandas dtype: pandas 0.24.0+
`INT8`	`"Int8"` pandas dtype:: pandas 0.24.0+
`Int`	`"int"` numpy dtype
`Int16`	`"int16"` numpy dtype
`Int32`	`"int32"` numpy dtype
`Int64`	`"int64"` numpy dtype
`Int8`	`"int8"` numpy dtype
`Object`	`"object"` numpy dtype
`STRING`	`"string"` pandas dtypes: pandas 1.0.0+.
`String`	`"str"` numpy dtype
`Timedelta`	`"timedelta64[ns]"` numpy dtype
`UINT16`	`"UInt16"` pandas dtype: pandas 0.24.0+
`UINT32`	`"UInt32"` pandas dtype: pandas 0.24.0+
`UINT64`	`"UInt64"` pandas dtype: pandas 0.24.0+
`UINT8`	`"UInt8"` pandas dtype: pandas 0.24.0+
`UInt16`	`"uint16"` numpy dtype
`UInt32`	`"uint32"` numpy dtype
`UInt64`	`"uint64"` numpy dtype
`UInt8`	`"uint8"` numpy dtype

str_alias¶: Get datatype string alias.

classmethod from_str_alias(str_alias)[source]¶

Get PandasDtype from string alias.

Param: pandas dtype string alias from https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-dtypes
Return type: PandasDtype
Returns: pandas dtype

classmethod from_pandas_api_type(pandas_api_type)[source]¶

Get PandasDtype enum from pandas api type.

Parameters: pandas_api_type (str) – string output from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.types.infer_dtype.html
Return type: PandasDtype
Returns: pandas dtype