jenny | Command line Jenkinsfile runner written in groovy | Continous Integration library
kandi X-RAY | jenny Summary
kandi X-RAY | jenny Summary
Command line Jenkinsfile runner written in groovy. Does not need a Jenkins installation to run the Jenkinsfile. This does not require Jenkins to run, and will run everything locally. It’s great to debug and quickly iterate over Jenkinsfile creation, before pushing them for the real Jenkins. In order to do that, a lot of effort is concentrated on execution paths. It allows skipping sections of Jenkinsfiles, and starting from different parts.
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QUESTION
I am trying to use Clojure Spec to define a data structure containing a java.time.LocalDate element:
...ANSWER
Answered 2022-Apr-10 at 20:46You're probably looking for instance?
- and your example fails, because in:
QUESTION
I'm trying to remove single word strings from a dataframe (ou
) and move it to another dataframe (removedSet
and allowedSet
), and then to a csv (names.csv
and removed.csv
). I am able to filter out specific strings, but I am having trouble removing single words from the dataframe I just made allowedSet
.
So I need to use the two dataframes I just made and check them for the single word strings. I want to append the single words to the dataframe with removed strings removedSet
and remove the single words from the other dataframe with only full names allowedSet
.
This is my desired output COMPnames.csv
:
ANSWER
Answered 2022-Mar-28 at 14:31IIUC, use a regex with word boundaries and groupby
to save your files:
QUESTION
I have next table :
...ANSWER
Answered 2022-Mar-21 at 21:18Try:
QUESTION
Below I am creating 3 dataframes. df2
and df3
are both nested dataframes of df1
. I am then trying to use .apply()
on all the nested dataframes, and ultimately add a new column to the outer dataframe that is essentially a revised version of the nested dataframes.
I would like to apply the function below to all of the elements (dataframes) that could be found in the 'df_name'
column of df1
. I also need to pass other column values from df1
into the .apply()
function that are on the same row - ie. the value 'sp'
needs to be known when running on the .apply()
function to df2
.
In the attempt below, I would grateful for some insight on:
-how to access the nested dataframes with the .apply()
function and refer to values from the same row/different column of df1
.
-is there a way to approach this using vectorization?
ANSWER
Answered 2022-Mar-15 at 00:15Try changing your cmpngs
function to take a single parameter - row
, and call apply
on the whole dataframe instead of just the df_name
column, and with axis=1
:
QUESTION
Say I have some data in table, which has the names and ages of customers who came into a store each day (the id functions as a unique day identifier).
...ANSWER
Answered 2022-Mar-04 at 22:28Assuming your data is actually json you can manipulate it:
QUESTION
I have this csv file, file1.csv:
...ANSWER
Answered 2022-Mar-01 at 12:56Since you're not using the header (header=False
), you can check if dept
is in the list of words that needs to be written to CORP
file. Then, for the CORP
file, you can use to_csv
with argument mode='a'
, which makes the data being written to be inserted at the end, after any preexisting data (of the CORP
category).
QUESTION
I'm trying to create python bindings for some legacy C++ code which implemented its own 'extended' string class aString
which it uses extensively. This works fine for .def(py::init())
, but once I start trying to wrap overloaded methods pybind11 does not appear to automatically cast std::string
to aString
. MWE adapted from the pybind11 docs:
src/example.cpp
:
ANSWER
Answered 2022-Feb-15 at 14:01You can enable an implicit conversion between types by adding the following statement:
QUESTION
I have this csv file:
...ANSWER
Answered 2022-Feb-11 at 17:48It sounds like the "OU" column should be split into two columns on the :
character. You can do this with df['OU'].str.split(':')
. Save the output to new columns and then you can use the same filter technique on the column created from the left of :
QUESTION
Let's say I have file1.csv:
...ANSWER
Answered 2022-Feb-05 at 13:05You could read both files into dataframes, loop through the values in the 'OU' column of file2.csv, filter the data from file1.csv and save as individual CSV files.
QUESTION
I want to parse through a contacts list CSV file that looks like this:
...ANSWER
Answered 2022-Jan-28 at 23:42To filter you DataFrame, you could do something like this:
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