[Pandas-dev] How to replace a cell value with each of its contour cells and yield the corresponding datasets seperately in a list according to a Pandas-way?

[Richard Shadrach]

Hi Marc,

This email list is for developers and contributors to the pandas package
itself, rather than pandas users. I would recommend posting your question
to Stack Overflow. If you don't get any satisfactory answers there, then
you're welcome to open up a Usage Question on the pandas issue tracker at
https://github.com/pandas-dev/pandas/issues.

Best,
Richard

On Sat, Jan 20, 2024 at 2:22 PM marc nicole <mk1853387 at gmail.com> wrote:

> Hello,
>
> I have an initial dataframe with a random list of target cells (each cell
> being identified with a couple (x,y)).
> I want to yield four different dataframes each containing the value of one
> of the contour (surrounding) cells of each specified target cell.
>
> the surrounding cells to consider for a specific target cell are :
> (x-1,y), (x,y-1),(x+1,y);(x,y+1), specifically I randomly choose 1 to 4
> cells from these and consider for replacement to the target cell.
>
> I want to do that through a pandas-specific approach without having to
> define the contour cells separately and then apply the changes on the
> dataframe (but rather using an all in one approach):
> for now I have written this example which I think is not Pandas specific:
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> *def select_target_values(dataframe, number_of_target_values):
> target_cells = []    for _ in range(number_of_target_values):        row_x
> = random.randint(0, len(dataframe.columns) - 1)        col_y =
> random.randint(0, len(dataframe) - 1)        target_cells.append((row_x,
> col_y))    return target_cellsdef select_contours(target_cells):
> contour_coordinates = [(0, 1), (1, 0), (0, -1), (-1, 0)]    contour_cells =
> []    for target_cell in target_cells:        # random contour count for
> each cell        contour_cells_count = random.randint(1, 4)        try:
>         contour_cells.append(                [tuple(map(lambda i, j: i + j,
> (target_cell[0], target_cell[1]), contour_coordinates[iteration_]))
>          for iteration_ in range(contour_cells_count)])        except
> IndexError:            continue    return contour_cells def
> apply_contours(target_cells, contour_cells):    target_cells_with_contour =
> []    # create one single list of cells    for idx, target_cell in
> enumerate(target_cells):        target_cell_with_contour = [target_cell]
>     target_cell_with_contour.extend(contour_cells[idx])
> target_cells_with_contour.append(target_cell_with_contour) return
> target_cells_with_contour def create_possible_datasets(dataframe,
> target_cells_with_contour):    all_datasets_final = []
> dataframe_original = dataframe.copy()     #check for nans
> list_tuples_idx_cells_all_datasets = list(filter(lambda x:
> utils_tuple_list_not_contain_nan(x),
>                 [list(tuples) for tuples in list(itertools.product(
>
> *target_cells_with_contour))]))    target_original_cells_coordinates =
> list(map(lambda x: x[0],
> [target_and_contour_cell for target_and_contour_cell in
>                                  target_cells_with_contour]))    for
> dataset_index_values in list_tuples_idx_cells_all_datasets:
> all_datasets = []        for idx_cell in range(len(dataset_index_values)):
>           dataframe_cpy = dataframe.copy()            dataframe_cpy.iat[
>             target_original_cells_coordinates[idx_cell][1],
> target_original_cells_coordinates[idx_cell][                    0]] =
> dataframe_original.iloc[dataset_index_values[idx_cell][1],
> dataset_index_values[idx_cell][0]]
> all_datasets.append(dataframe_cpy)
> all_datasets_final.append(all_datasets)    return all_datasets_final*
>
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> If you have a better Pandas approach (unifying all these methods into one
> that make use of dataframe methods only) please let me know.
>
> thanks!
>
>
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