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- import numpy as np
- from pandas._typing import ArrayLike
- from pandas import (
- DataFrame,
- Index,
- )
- from pandas.core.internals.api import _make_block
- from pandas.core.internals.managers import BlockManager as _BlockManager
- def create_dataframe_from_blocks(
- blocks: list[tuple[ArrayLike, np.ndarray]], index: Index, columns: Index
- ) -> DataFrame:
- """
- Low-level function to create a DataFrame from arrays as they are
- representing the block structure of the resulting DataFrame.
- Attention: this is an advanced, low-level function that should only be
- used if you know that the below-mentioned assumptions are guaranteed.
- If passing data that do not follow those assumptions, subsequent
- subsequent operations on the resulting DataFrame might lead to strange
- errors.
- For almost all use cases, you should use the standard pd.DataFrame(..)
- constructor instead. If you are planning to use this function, let us
- know by opening an issue at https://github.com/pandas-dev/pandas/issues.
- Assumptions:
- - The block arrays are either a 2D numpy array or a pandas ExtensionArray
- - In case of a numpy array, it is assumed to already be in the expected
- shape for Blocks (2D, (cols, rows), i.e. transposed compared to the
- DataFrame columns).
- - All arrays are taken as is (no type inference) and expected to have the
- correct size.
- - The placement arrays have the correct length (equalling the number of
- columns that its equivalent block array represents), and all placement
- arrays together form a complete set of 0 to n_columns - 1.
- Parameters
- ----------
- blocks : list of tuples of (block_array, block_placement)
- This should be a list of tuples existing of (block_array, block_placement),
- where:
- - block_array is a 2D numpy array or a 1D ExtensionArray, following the
- requirements listed above.
- - block_placement is a 1D integer numpy array
- index : Index
- The Index object for the `index` of the resulting DataFrame.
- columns : Index
- The Index object for the `columns` of the resulting DataFrame.
- Returns
- -------
- DataFrame
- """
- block_objs = [_make_block(*block) for block in blocks]
- axes = [columns, index]
- mgr = _BlockManager(block_objs, axes)
- return DataFrame._from_mgr(mgr, mgr.axes)
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