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DS-1000 / Tasks

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default / test
I have the following DataFrame:
Answer published by the source
Identifiers
upstream_id
0
task_id
2aab3812-8950-56e7-b202-17dd7299b404
task_key
default--test--0
default / test
I have the following DataFrame:
Answer published by the source
Identifiers
upstream_id
1
task_id
1c7b3ca6-9a28-5f6c-9f48-d9a19678f17a
task_key
default--test--1
default / test
Let's say I have a pandas DataFrame containing names like so:
Answer published by the source
Identifiers
upstream_id
106
task_id
200cdb83-28e7-541f-9ade-f2fc6b991d05
task_key
default--test--106
default / test
I have been struggling with removing the time zone info from a column in a…
Answer published by the source
Identifiers
upstream_id
11
task_id
151490fe-f438-52e6-a605-e4d83bbc4aa2
task_key
default--test--11
default / test
I am trying to extract rows from a Pandas dataframe using a list of row names,…
Answer published by the source
Identifiers
upstream_id
118
task_id
373762a8-40a2-5332-b2ab-c42a2e81996c
task_key
default--test--118
default / test
I have a set of objects and their positions over time. I would like to get the…
Answer published by the source
Identifiers
upstream_id
121
task_id
0c407fd5-5176-5f83-bb21-064dd7617a0b
task_key
default--test--121
default / test
My sample df has four columns with NaN values. The goal is to concatenate all…
Answer published by the source
Identifiers
upstream_id
126
task_id
15fd64da-d0bd-5a21-ae07-ce83e0f97f05
task_key
default--test--126
default / test
I am trying to find duplicates rows in a pandas dataframe.
Answer published by the source
Identifiers
upstream_id
130
task_id
09394135-aed9-52d3-8060-a224ca5139f1
task_key
default--test--130
default / test
I have been struggling with removing the time zone info from a column in a…
Answer published by the source
Identifiers
upstream_id
14
task_id
40b4e1d7-1ef9-5232-b7e4-9a0b8dfbeb33
task_key
default--test--14
default / test
I am performing a query on a DataFrame:
Answer published by the source
Identifiers
upstream_id
140
task_id
03f572c6-afbb-56c0-bb3c-6e9323352ed3
task_key
default--test--140
default / test
I have a Pandas DataFrame that looks something like:
Answer published by the source
Identifiers
upstream_id
142
task_id
4033f573-d847-5bad-bb50-1502630c9768
task_key
default--test--142
default / test
I have
Answer published by the source
Identifiers
upstream_id
143
task_id
337d8017-5394-567a-b0c9-4ff9d4530354
task_key
default--test--143
default / test
Let's say I have 5 columns.
Answer published by the source
Identifiers
upstream_id
152
task_id
06b7ad79-72be-5477-bb86-d7f2e1395c9d
task_key
default--test--152
default / test
Let's say I have 5 columns.
Answer published by the source
Identifiers
upstream_id
153
task_id
0afc5877-0eb8-5ce3-aba4-480f370ac62b
task_key
default--test--153
default / test
Having a pandas data frame as follow:
Answer published by the source
Identifiers
upstream_id
167
task_id
03222e1e-d9ba-58fd-b179-3926847956d1
task_key
default--test--167
default / test
How do I find all rows in a pandas DataFrame which have the max value for count…
Answer published by the source
Identifiers
upstream_id
177
task_id
31286d99-71dd-5bae-b538-224f1b352596
task_key
default--test--177
default / test
How do I find all rows in a pandas DataFrame which have the max value for count…
Answer published by the source
Identifiers
upstream_id
180
task_id
1fdbc5f5-17e5-5e5f-88ae-20c23ad3709d
task_key
default--test--180
default / test
I'm looking to map the value in a dict to one column in a DataFrame where the…
Answer published by the source
Identifiers
upstream_id
181
task_id
3f11b63b-1058-557b-b017-f4d05614ba59
task_key
default--test--181
default / test
I'm looking to map the value in a dict to one column in a DataFrame where the…
Answer published by the source
Identifiers
upstream_id
184
task_id
1f087136-d4bb-5e94-83ff-1e39a1ca27c6
task_key
default--test--184
default / test
I am trying to groupby counts of dates per month and year in a specific output.…
Answer published by the source
Identifiers
upstream_id
186
task_id
3606b76e-2d86-5a71-855c-1cf0f228df83
task_key
default--test--186
default / test
I have a dataframe, e.g:
Answer published by the source
Identifiers
upstream_id
189
task_id
10d85889-60f6-5cf8-aa56-327298fee31c
task_key
default--test--189
default / test
I have a dataframe that looks like this:
Answer published by the source
Identifiers
upstream_id
19
task_id
116f625d-8e6a-5ed2-87e2-91bcf744f5e4
task_key
default--test--19
default / test
Was trying to generate a pivot table with multiple "values" columns. I know I…
Answer published by the source
Identifiers
upstream_id
190
task_id
4056d979-53e0-590a-876f-5bd8c70b6092
task_key
default--test--190
default / test
Was trying to generate a pivot table with multiple "values" columns. I know I…
Answer published by the source
Identifiers
upstream_id
193
task_id
0fffa319-c87d-58cc-a0ee-1ab825fd759b
task_key
default--test--193
default / test
I have a data frame with one (string) column and I'd like to split it into two…
Answer published by the source
Identifiers
upstream_id
199
task_id
33c5eaff-a523-5c5b-944e-e82488a54ae8
task_key
default--test--199
default / test
Given a pandas DataFrame, how does one convert several binary columns (where 1…
Answer published by the source
Identifiers
upstream_id
22
task_id
28485d64-e970-54e1-b86e-f8265d9b0b89
task_key
default--test--22
default / test
So I have a dataframe that looks like this:
Answer published by the source
Identifiers
upstream_id
26
task_id
190c3b1e-093a-5a8b-a460-5a0b4a1a3533
task_key
default--test--26
default / test
So I have a dataframe that looks like this:
Answer published by the source
Identifiers
upstream_id
27
task_id
256d3640-17e4-5b90-a832-26c5afd42a6a
task_key
default--test--27
default / test
So I have a dataframe that looks like this:
Answer published by the source
Identifiers
upstream_id
29
task_id
17fb5dc3-e9df-5e4e-b685-4485f50bc65a
task_key
default--test--29
default / test
I have pandas df with say, 100 rows, 10 columns, (actual data is huge). I also…
Answer published by the source
Identifiers
upstream_id
37
task_id
03dd3bb1-75a5-53b1-a00e-e5737484b0e5
task_key
default--test--37
default / test
I have a dataframe with numerous columns (≈30) from an external source (csv…
Answer published by the source
Identifiers
upstream_id
40
task_id
0ccf21ab-0b2a-5be9-929b-bc7b4ee353fa
task_key
default--test--40
default / test
I am trying to clean up a Excel file for some further research. Problem that I…
Answer published by the source
Identifiers
upstream_id
43
task_id
3f7346ba-7f13-5573-be25-df7b04cce1b7
task_key
default--test--43
default / test
I have following pandas dataframe :
Answer published by the source
Identifiers
upstream_id
5
task_id
026622ef-beb1-57f4-b330-3a3fd6a11ee8
task_key
default--test--5
default / test
Sample dataframe:
Answer published by the source
Identifiers
upstream_id
53
task_id
0ad217ca-7c2d-5dcb-b7b0-37455282c29a
task_key
default--test--53
default / test
The title might not be intuitive--let me provide an example. Say I have df,…
Answer published by the source
Identifiers
upstream_id
54
task_id
3b60bfdb-e4dc-5891-98e5-4ee2d7be6140
task_key
default--test--54
default / test
The title might not be intuitive--let me provide an example. Say I have df,…
Answer published by the source
Identifiers
upstream_id
55
task_id
30af8612-0229-52e1-96b8-f50951037439
task_key
default--test--55
default / test
I've a data frame that looks like the following
Answer published by the source
Identifiers
upstream_id
59
task_id
40054c8d-14dd-5088-96fd-f9dc948408e1
task_key
default--test--59
default / test
I have following pandas dataframe :
Answer published by the source
Identifiers
upstream_id
6
task_id
23b41876-ef75-52d7-882e-56cb73677f0e
task_key
default--test--6
default / test
I am using Pandas to get a dataframe like this:
Answer published by the source
Identifiers
upstream_id
62
task_id
1624bab7-c800-5813-baf8-e6674dd0a729
task_key
default--test--62
default / test
I have a table like this.
Answer published by the source
Identifiers
upstream_id
67
task_id
302713a8-e18f-5a17-aa2c-5aef9c400edd
task_key
default--test--67
default / test
I have a dataset :
Answer published by the source
Identifiers
upstream_id
7
task_id
3a6fffa5-fec9-52b8-821a-919bdf7f1d6a
task_key
default--test--7
default / test
I'm wondering if there is a simpler, memory efficient way to select a subset of…
Answer published by the source
Identifiers
upstream_id
70
task_id
225b6fba-d6ae-5a3d-b864-c91da40d41d9
task_key
default--test--70
default / test
I have a pandas dataframe that looks like the following:
Answer published by the source
Identifiers
upstream_id
75
task_id
3a03d2a0-6a45-5bbc-8e52-4b3927f94301
task_key
default--test--75
default / test
I have a simple dataframe which I would like to bin for every 3 rows.
Answer published by the source
Identifiers
upstream_id
76
task_id
36c213c0-589f-5d85-9515-43cf54e1a1fd
task_key
default--test--76
default / test
I have the following dataframe:
Answer published by the source
Identifiers
upstream_id
82
task_id
17d465a5-d718-596d-96cd-4da6dd80a414
task_key
default--test--82
default / test
This is my data frame
Answer published by the source
Identifiers
upstream_id
85
task_id
1ceab0dd-fd2f-50a7-a497-072820bfb1eb
task_key
default--test--85
default / test
I have a dataset :
Answer published by the source
Identifiers
upstream_id
9
task_id
188f5ed7-1f9c-53c5-9048-0330832270e4
task_key
default--test--9
default / test
I have multi-index df as follows
Answer published by the source
Identifiers
upstream_id
91
task_id
15892217-bc19-5361-9761-ec3d10f2fc90
task_key
default--test--91
default / test
I have a data frame like below
Answer published by the source
Identifiers
upstream_id
98
task_id
04f1b7bc-8731-51fa-bb00-b4ea9473e685
task_key
default--test--98
default / test
I have a data frame like below
Answer published by the source
Identifiers
upstream_id
99
task_id
0fb198f5-b830-5fe0-a055-2347f91d649c
task_key
default--test--99