1

I have a numpy.ndarrays: x,y:

>>> x = np.ndarray(shape=(10,), buffer=np.array([0.9902, 0.9394, 0.839, 0.8574, 0.9174, 0.8742, 0.8955, 0.9196, 0.9388, 0.9602]), dtype=float) [0.9902 0.9394 0.839 0.8574 0.9174 0.8742 0.8955 0.9196 0.9388 0.9602] >>> y = np.ndarray(shape=(10,), buffer=np.array([0.956, 0.884, 0.875, 0.880, 0.865, 0.870, 0.861, 0.817, 0.771, 0.727]), dtype=float) [0.956, 0.884, 0.875, 0.880, 0.865, 0.870, 0.861, 0.817, 0.771, 0.727]

and series edge_or_not:

>>> d = {'2020-03-17 04:39:00+03:00': 0, '2020-03-17 04:40:00+03:00': 1, '2020-03-17 04:41:00+03:00': 0, '2020-03-17 04:42:00+03:00': -1, '2020-03-17 04:43:00+03:00': 0, '2020-03-17 04:44:00+03:00': 0, '2020-03-17 04:45:00+03:00': 1, '2020-03-17 04:46:00+03:00': -1, '2020-03-17 04:47:00+03:00': -1, '2020-03-17 04:48:00+03:00': -1} >>> edge_or_not = pd.Series(data=d) 2020-03-17 04:39:00+03:00 0 2020-03-17 04:40:00+03:00 1 2020-03-17 04:41:00+03:00 0 2020-03-17 04:42:00+03:00 -1 2020-03-17 04:43:00+03:00 0 2020-03-17 04:44:00+03:00 0 2020-03-17 04:45:00+03:00 1 2020-03-17 04:46:00+03:00 -1 2020-03-17 04:47:00+03:00 -1 2020-03-17 04:48:00+03:00 -1 dtype: int64

And I'm getting up_edge_x, up_edge_y, down_edge_x, down_edge_y like this:

>>> up_edge_x = x[edge_or_not > 0] array([0.9394, 0.8955]) >>> up_edge_y = y[edge_or_not > 0] array([0.884, 0.861]) >>> down_edge_x = x[edge_or_not < 0] array([0.8574, 0.9196, 0.9388, 0.9602]) >>> down_edge_y = y[edge_or_not < 0] array([0.88 , 0.817, 0.771, 0.727])

And all_edges_x, all_edges_y:

>>> all_edges_x = x[edge_or_not != 0] array([0.9394, 0.8574, 0.8955, 0.9196, 0.9388, 0.9602]) >>> all_edges_y = y[edge_or_not != 0] array([0.884, 0.88 , 0.861, 0.817, 0.771, 0.727])

And then creating DataFrames:

>>> up_edge = pd.DataFrame({'y':up_edge_y}, index=up_edge_x) y (pos) 0.9394 0.884 0 0.8955 0.861 1 >>> down_edge = pd.DataFrame({'y':down_edge_y}, index=down_edge_x) y (pos) 0.8574 0.880 0 0.9196 0.817 1 0.9388 0.771 2 0.9602 0.727 3

All I need is creating all_edges DataFrame where will be 3 columns: 'y', 'edge', 'pos'

>>> all_edges = pd.DataFrame({'y':all_edges_y, 'edge':edge_or_not[edge_or_not != 0].to_numpy(), 'pos':???}, index=all_edges_x)

So that after all all_edges DataFrame must look like this:

 y edge pos 0.9394 0.884 1 0 0.8574 0.880 -1 0 0.8955 0.861 1 1 0.9196 0.817 -1 1 0.9388 0.771 -1 2 0.9602 0.727 -1 3

How to calculate 3rd column pos, that I can links to all_edges from up_edge and down_edge DataFrames like in below stupid example:

>>> down_x1 = 0.9602 >>> loc = down_edge.index.get_loc(down_x1) >>> edges = all_edges.loc[all_edges['pos']==loc]['edge'] >>> print(edges) 0.9602 -1 Name: edge, dtype: int64

And I've got a second question: How to get array of locations another DataFrame? Like this:

>>> locations = down_edge.index.get_loc(#mb all indexes) [0, 1, 2, 3]
Improve this question
2
  • 1
    stackoverflow.com/a/52173171/4687565 Commented Jun 29, 2021 at 13:04
  • @Dimitry, Я обновил пост. И пока я это делал - осознал, чтобы посчитать третий столбец можно вообще не привязываться к первым двум датафреймам. А также понял, что сразу не уточнил, что было бы неплохо понимать как можно получить массив из позиций pandas.DataFrame? Что собственно я и указал как второй вопрос. By the way, спасбо за комментарий. Commented Jul 1, 2021 at 19:53

2 Answers 2

Reset to default
1

Use:

up_edge_x = x[edge_or_not > 0] up_edge_y = y[edge_or_not > 0] down_edge_x = x[edge_or_not < 0] down_edge_y = y[edge_or_not < 0] all_edges_x = x[edge_or_not != 0] all_edges_y = y[edge_or_not != 0]

Create Series by ranges with index by up_edge_x, down_edge_x first:

up_edge = pd.Series(range(len(up_edge_x)), index=up_edge_x, name='pos') down_edge = pd.Series(range(len(down_edge_x)), index=down_edge_x, name='pos') print (up_edge) 0.9394 0 0.8955 1 Name: pos, dtype: int64 print (down_edge) 0.8574 0 0.9196 1 0.9388 2 0.9602 3 Name: pos, dtype: int64

Then join together:

pos = pd.concat([up_edge, down_edge]) print (pos) 0.9394 0 0.8955 1 0.8574 0 0.9196 1 0.9388 2 0.9602 3 Name: pos, dtype: int64

And last map new column:

all_edges = pd.DataFrame({'y':all_edges_y, 'edge':edge_or_not[edge_or_not != 0].to_numpy(), 'pos': pd.Index(all_edges_x).map(pos)}, index=all_edges_x) print (all_edges) y edge pos 0.9394 0.884 1 0 0.8574 0.880 -1 0 0.8955 0.861 1 1 0.9196 0.817 -1 1 0.9388 0.771 -1 2 0.9602 0.727 -1 3
Improve this answer
Sign up to request clarification or add additional context in comments.

2 Comments

0) I edited the post. 1) If using np.where so I have 2 conditions edge_or_not > 0 and edge_or_not < 0. 2) I don't know how to calculate positionS_in_up_edge and positionS_in_down_edge, so I pointed it as 2nd question. Thank you for your answer.
@RenNingen - Can you check now?
0

I thought I might not get attached to up_edge and down_edge and just proceed as follows:

>>> all_edges['pos'] = all_edges.groupby(all_edges['edge']).cumcount()

Pre-creating a DataFrame all_edges like this:

>>> all_edges = pd.DataFrame({'y':all_edges_y, 'edge':edge_or_not[edge_or_not != 0].to_numpy()}, index=all_edges_x)
Improve this answer

Comments

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.