pandas extension for typed Series and DataFrame creation
Project description
pandas-dataclasses
pandas extension for typed Series and DataFrame creation
Overview
pandas-dataclass makes it easy to create pandas Series and DataFrame objects that are "typed" (i.e. fixed data types, attributes, and names) using dataclass:
Click to see all imports
from dataclasses import dataclass
from pandas_dataclasses import AsDataFrame, Data, Index
@dataclass
class Weather(AsDataFrame):
"""Weather information."""
year: Index[int]
month: Index[int]
temp: Data[float]
humid: Data[float]
df = Weather.new(
[2020, 2020, 2021, 2021, 2022],
[1, 7, 1, 7, 1],
[7.1, 24.3, 5.4, 25.9, 4.9],
[65, 89, 57, 83, 52],
)
temp humid
year month
2020 1 7.1 65.0
7 24.3 89.0
2021 1 5.4 57.0
7 25.9 83.0
2022 1 4.9 52.0
Features
- Type casting to NumPy and pandas data types
- Easy hierarchial indexing (
MultiIndex
) - Metadata storing in attributes (
attrs
) - Support for dataclass features (
field
,__post_init__
, ...) - Support for static type check (Pylance, Pyright, ...)
Installation
pip install pandas-dataclasses
How it works
pandas-dataclasses provides you the following features:
- Type hints for dataclass fields (
Attr
,Data
,Index
,Name
) for specifying field types and data types - Mix-in classes for dataclasses (
AsDataFrame
,AsSeries
) for creating a Series or DataFrame object via a classmethod (new
)
When you call new
, it will first create a dataclass object and then create a Series or DataFrame object from the dataclass object according the type hints and values in it.
In the example above, df = Weather.new(...)
is thus equivalent to:
obj = Weather([2020, ...], [1, ...], [7.1, ...], [65, ...])
df = asdataframe(obj)
where asdataframe
is a conversion function.
pandas-dataclasses does not touch the dataclass object creation itself; this allows you to fully customize your dataclass before conversion using the dataclass features (field
, __post_init__
, ...).
Basic usage
DataFrame creation
As shown in the example above, a dataclass that has the AsDataFrame
mix-in will create DataFrame objects:
Click to see all imports
from dataclasses import dataclass
from pandas_dataclasses import AsDataFrame, Data, Index
@dataclass
class Weather(AsDataFrame):
"""Weather information."""
year: Index[int]
month: Index[int]
temp: Data[float]
humid: Data[float]
df = Weather.new(...)
where fields typed by Index
are "index fields", each value of which will become an index or a part of a hierarchial index of a DataFrame object.
Fields typed by Data
are "data fields", each value of which will become a data column of a DataFrame object.
Fields typed by other types are just ignored in DataFrame creation.
Each data or index will be cast to the data type specified in the type hint like Index[int]
.
Use Any
or None
if you do not want type casting.
See data typing rules for more examples.
By default, field name (i.e. argument name) is used for the name of data or index. See custom data/index naming if you want customization.
Series creation
A dataclass that has the AsSeries
mix-in will create Series objects:
Click to see all imports
from dataclasses import dataclass
from pandas_dataclasses import AsSeries, Data, Index
@dataclass
class Temperature(AsSeries):
"""Temperature information."""
year: Index[int]
month: Index[int]
temp: Data[float]
ser = Temperature.new(...)
Unlike AsDataFrame
, the second and subsequent data fields are ignored in Series creation.
Other rules are the same as for the DataFrame creation.
Advanced usage
Metadata storing
Fields typed by Attr
are "attribute fields", each value of which will become an item of attributes (attrs
) of a DataFrame of Series object:
Click to see all imports
from dataclasses import dataclass
from pandas_dataclasses import AsDataFrame, Attr, Data, Index
@dataclass
class Weather(AsDataFrame):
"""Weather information."""
year: Index[int]
month: Index[int]
temp: Data[float]
humid: Data[float]
loc: Attr[str] = "Tokyo"
lon: Attr[float] = 139.69167
lat: Attr[float] = 35.68944
In this example, Weather.new(...).attrs
will become like:
{"loc": "Tokyo", "lon": 139.69167, "lat": 35.68944}
Custom naming
The name of data, index, or attribute can be explicitly specified by adding an annotation to the corresponding type:
Click to see all imports
from dataclasses import dataclass
from typing import Annotated as Ann
from pandas_dataclasses import AsDataFrame, Attr, Data, Index
@dataclass
class Weather(AsDataFrame):
"""Weather information."""
year: Ann[Index[int], "Year"]
month: Ann[Index[int], "Month"]
temp: Ann[Data[float], "Temperature (deg C)"]
humid: Ann[Data[float], "Humidity (%)"]
loc: Ann[Attr[str], "Location"] = "Tokyo"
lon: Ann[Attr[float], "Longitude (deg)"] = 139.69167
lat: Ann[Attr[float], "Latitude (deg)"] = 35.68944
In this example, Weather.new(...)
and its attributes will become like:
Temperature (deg C) Humidity (%)
Year Month
2020 1 7.1 65.0
7 24.3 89.0
2021 1 5.4 57.0
7 25.9 83.0
2022 1 4.9 52.0
{"Location": "Tokyo", "Longitude (deg)": 139.69167, "Latitude (deg)": 35.68944}
For the Series creation, a field typed by Name
is a "name field", whose value will become the name of a Series object.
This is useful for dynamic naming.
See also naming rules for more details and examples.
Custom pandas factory
A custom class can be used as a factory of Series or DataFrame creation by As
, the generic version of the mix-in classes:
Click to see all imports
import pandas as pd
from dataclasses import dataclass
from pandas_dataclasses import As, Data, Index
class CustomSeries(pd.Series):
"""Custom pandas Series."""
pass
@dataclass
class Temperature(As[CustomSeries]):
"""Temperature information."""
year: Index[int]
month: Index[int]
temp: Data[float]
ser = Temperature.new(...)
isinstance(ser, CustomSeries) # True
Appendix
Data typing rules
The data type (dtype) of data/index is inferred from the first Data
/Index
type of the corresponding field.
The following table shows how the data type is inferred:
Click to see all imports
from typing import Any
from typing import Annotated as Ann
from typing import Literal as L
from pandas_dataclasses import Data
Type hint | Inferred data type |
---|---|
Data[Any] |
None (no type casting) |
Data[None] |
None (no type casting) |
Data[int] |
numpy.dtype("i8") |
Data[numpy.int32] |
numpy.dtype("i4") |
Data[L["datetime64[ns]"]] |
numpy.dtype("<M8[ns]") |
Data[L["category"]] |
pandas.CategoricalDtype() |
Data[int] | str |
numpy.dtype("i8") |
Data[int] | Data[float] |
numpy.dtype("i8") |
Ann[Data[int], "spam"] |
numpy.dtype("i8") |
Data[Ann[int, "spam"]] |
numpy.dtype("i8") |
Naming rules
The name of data/index is determined by the following rules:
- If a name field exists, its value will be preferentially used (Series creation only)
- If a data/index field is annotated, the first hashable annotation in the first
Data
/Index
type will be used - Otherwise, the field name (i.e. argument name) will be used
The following table shows how the name is inferred in the case of 2 and 3:
Click to see all imports
from typing import Any
from typing import Annotated as Ann
from pandas_dataclasses import Data
Type hint | Inferred name |
---|---|
Data[Any] |
(field name) |
Ann[Data[Any], {}] |
(field name) |
Ann[Data[Any], "spam"] |
"spam" |
Ann[Data[Any], "spam"] |
"spam" |
Ann[Data[Any], "spam", "ham"] |
"spam" |
Ann[Data[Any], {}, "spam"] |
"spam" |
Ann[Data[Any], "spam"] | Ann[str, "ham"] |
"spam" |
Ann[Data[Any], "spam"] | Ann[Data[float], "ham"] |
"spam" |
Development roadmap
Release version | Features |
---|---|
v0.4.0 | Support for hierarchial column |
v1.0.0 | Initial major release (freezing public features until v2.0.0) |
Project details
Release history Release notifications | RSS feed
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