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Get the latest version using pip/PyPi
165 |pip install dataclassframe
166 | A container data-type for dataclasses…
172 |from dataclasses import dataclass
173 | from dataclassframe import DataClassFrame
174 |
175 | @dataclass
176 | class ExampleDC:
177 | field1: str
178 | field2: int
179 |
180 | records = [
181 | ExampleDC('a', 1),
182 | ExampleDC('b', 2),
183 | ExampleDC('c', 3),
184 | ]
185 |
186 | dcf = DataClassFrame(
187 | record_class=ExampleDC,
188 | data=records,
189 | index=['field1', 'field2']
190 | )
191 | Which acts like a ordered dictionary with multi-indexing…
194 |# Obtain record `ExampleDC('b', 2)`
195 | row_idx = dcf.iat[1] # Using positional index
196 | row_f1 = dcf.at['b'] # Using index of `field1`
197 | row_f2 = dcf.at[:, 2] # Using index of `field2`
198 | assert row_idx == row_f1 == row_f2
199 | With bulk operations on the columns..
202 |assert dcf.cols.field2.sum() == 6
203 | Works nicely with Python 3 type hints…
206 |dcf: DataClassFrame[ExampleDC]
207 | dcf.iat[1]: ExampleDC
208 | Contents:
157 |dataclassframe| 165 | d | ||
| 168 | |
169 | dataclassframe | 170 | |
A dataclass container with multi-indexing and bulk operations. Provides the typed benefits and ergonomics of dataclasses while having the efficiency of Pandas dataframes.
159 |The container is based on data-oriented design by optimising the memory layout of the stored data, providing fast bulk operations and a smaller memory footprint for large collections. Bulk operations are enabled using Pandas which has a rich set of vectorised methods for both numerical and string data types.
160 |Multi-indexing provides the ability to use multiple fields as keys to index the records. This is suitable for bidirectional and inverse dictionary keys.
161 |A DataClassFrame provides good ergonomics for production code as columns are immutable and columns/data types are well defined by the dataclasses. This makes it easier for users to understand the “shape” of the data in large projects and refactor when necessary.
162 |