On 1/21/2024 7:37 AM, marc nicole via Python-list 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

1. Why do you want a Pandas-specific approach? Many people would rather
keep code independent of special libraries if possible;

2. How big can these collections of target cells be, roughly speaking?
The size could make a big difference in picking a design;

3. You really should work on formatting code for this list. Your code
below is very complex and would take a lot of work to reformat to the
point where it is readable, especially with the nearly impenetrable
arguments in some places. Probably all that is needed is to replace all
tabs by (say) three spaces, and to make sure you intentionally break
lines well before they might get word-wrapped. Here is one example I
have reformatted (I hope I got this right):

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))
]))

4. As an aside, it doesn't look like you need to convert all those
sequences and iterators to lists all over the place;

(but rather using an all in one approach):
for now I have written this example which I think is not Pandas specific:

[snip]

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)

On 1/21/2024 11:54 AM, marc nicole wrote:

Thanks for the reply,

I think using a Pandas (or a Numpy) approach would optimize the
execution of the program.

Target cells could be up to 10% the size of the dataset, a good example
to start with would have from 10 to 100 values.

Thanks for the reformatted code. It's much easier to read and think about.

For say 100 points, it doesn't seem that "optimization" would be much of
an issue. On my laptop machine and Python 3.12, your example takes
around 5 seconds to run and print(). OTOH if you think you will go to
much larger datasets, certainly execution time could become a factor.

I would think that NumPy arrays and/or matrices would have good potential.

Is this some kind of a cellular automaton, or an image filtering process?

Let me know your thoughts, here's a reproducible example which I formatted:

from numpy import random
import pandas as pd
import numpy as np
import operator
import math
from collections import deque
from queue import *
from queue import Queue
from itertools import product

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_cells

def 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 create_zipf_distribution():
    zipf_dist = random.zipf(2, size=(50, 5)).reshape((50, 5))

    zipf_distribution_dataset = pd.DataFrame(zipf_dist).round(3)

    return zipf_distribution_dataset

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()

    list_tuples_idx_cells_all_datasets = list(
        filter(
            lambda x: x,
            [list(tuples) for tuples in list(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

def main():
    zipf_dataset = create_zipf_distribution()

    target_cells = select_target_values(zipf_dataset, 5)
    print(target_cells)
    contour_cells = select_contours(target_cells)
    print(contour_cells)
    target_cells_with_contour = apply_contours(target_cells, contour_cells)
    datasets = create_possible_datasets(zipf_dataset, target_cells_with_contour)
    print(datasets)

main()

Le dim. 21 janv. 2024 à 16:33, Thomas Passin via Python-list <python-list@python.org <mailto:python-list@python.org>> a écrit :

On 1/21/2024 7:37 AM, marc nicole via Python-list 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

1. Why do you want a Pandas-specific approach?  Many people would
rather
keep code independent of special libraries if possible;

2. How big can these collections of target cells be, roughly speaking?
The size could make a big difference in picking a design;

3. You really should work on formatting code for this list.  Your code
below is very complex and would take a lot of work to reformat to the
point where it is readable, especially with the nearly impenetrable
arguments in some places.  Probably all that is needed is to replace
all
tabs by (say) three spaces, and to make sure you intentionally break
lines well before they might get word-wrapped.  Here is one example I
have reformatted (I hope I got this right):

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))
    ]))

4. As an aside, it doesn't look like you need to convert all those
sequences and iterators to lists all over the place;

> (but rather using an all in one approach):
> for now I have written this example which I think is not Pandas
specific:
[snip]

--
https://mail.python.org/mailman/listinfo/python-list
<https://mail.python.org/mailman/listinfo/python-list>

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)

On 1/21/2024 1:25 PM, marc nicole wrote:

It is part of a larger project aiming at processing data according to a
given algorithm
Do you have any comments or any enhancing recommendations on the code?

I'm not knowledgeable enough about either pandas or numpy, I'm afraid,
just very basic usage. Someone else will probably pitch in.

Thanks.

Le dim. 21 janv. 2024 à 18:28, Thomas Passin via Python-list <python-list@python.org <mailto:python-list@python.org>> a écrit :

On 1/21/2024 11:54 AM, marc nicole wrote:
> Thanks for the reply,
>
> I think using a Pandas (or a Numpy) approach would optimize the
> execution of the program.
>
> Target cells could be up to 10% the size of the dataset, a good
example
> to start with would have from 10 to 100 values.

Thanks for the reformatted code.  It's much easier to read and think
about.

For say 100 points, it doesn't seem that "optimization" would be
much of
an issue.  On my laptop machine and Python 3.12, your example takes
around 5 seconds to run and print().  OTOH if you think you will go to
much larger datasets, certainly execution time could become a factor.

I would think that NumPy arrays and/or matrices would have good
potential.

Is this some kind of a cellular automaton, or an image filtering
process?

> Let me know your thoughts, here's a reproducible example which I
formatted:
>
>
>
> from numpy import random
> import pandas as pd
> import numpy as np
> import operator
> import math
> from collections import deque
> from queue import *
> from queue import Queue
> from itertools import product
>
>
> 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_cells
>
>
> def 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 create_zipf_distribution():
>      zipf_dist = random.zipf(2, size=(50, 5)).reshape((50, 5))
>
>      zipf_distribution_dataset = pd.DataFrame(zipf_dist).round(3)
>
>      return zipf_distribution_dataset
>
>
> 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()
>
>      list_tuples_idx_cells_all_datasets = list(
>          filter(
>              lambda x: x,
>              [list(tuples) for tuples in
> list(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
>
>
> def main():
>      zipf_dataset = create_zipf_distribution()
>
>      target_cells = select_target_values(zipf_dataset, 5)
>      print(target_cells)
>      contour_cells = select_contours(target_cells)
>      print(contour_cells)
>      target_cells_with_contour = apply_contours(target_cells,
contour_cells)
>      datasets = create_possible_datasets(zipf_dataset,
> target_cells_with_contour)
>      print(datasets)
>
>
> main()
>
> Le dim. 21 janv. 2024 à 16:33, Thomas Passin via Python-list
> <python-list@python.org <mailto:python-list@python.org>
<mailto:python-list@python.org <mailto:python-list@python.org>>> a
écrit :
>
>     On 1/21/2024 7:37 AM, marc nicole via Python-list 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
>
>     1. Why do you want a Pandas-specific approach?  Many people would
>     rather
>     keep code independent of special libraries if possible;
>
>     2. How big can these collections of target cells be, roughly
speaking?
>     The size could make a big difference in picking a design;
>
>     3. You really should work on formatting code for this list.
Your code
>     below is very complex and would take a lot of work to
reformat to the
>     point where it is readable, especially with the nearly
impenetrable
>     arguments in some places.  Probably all that is needed is to
replace
>     all
>     tabs by (say) three spaces, and to make sure you
intentionally break
>     lines well before they might get word-wrapped.  Here is one
example I
>     have reformatted (I hope I got this right):
>
>     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))
>          ]))
>
>     4. As an aside, it doesn't look like you need to convert all
those
>     sequences and iterators to lists all over the place;
>
>
>      > (but rather using an all in one approach):
>      > for now I have written this example which I think is not
Pandas
>     specific:
>     [snip]
>
>     --
> https://mail.python.org/mailman/listinfo/python-list
<https://mail.python.org/mailman/listinfo/python-list>
>     <https://mail.python.org/mailman/listinfo/python-list
<https://mail.python.org/mailman/listinfo/python-list>>
>

--
https://mail.python.org/mailman/listinfo/python-list
<https://mail.python.org/mailman/listinfo/python-list>

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)