Well first off, I used issues = jira.get_project_issuekey_all("project_name") to obtain all the issues in my project but I am only getting 50 results back. How would I go about getting all of the results?
Secondly, once this is figured out, I am wondering how I would be able to check if an issue would have a certain label. As far as I come is figuring out jira.issue_field_value('issue key', 'labels').
I want to combine these two conditions so that it will return all the issues under a certain project with a specific label. Any thoughts?
Related
i am currently working with HeatMapWithTime, it is working fine. But i want more. I am working with geopoints, one per timestamp. After pass over a specific timestamp with the slider, i want that the heatmap stays updated without only showing the current point. So that all points passed also showed up on the map. Is This possible? Is there mabye any tool in python that work better then Folium?
Actually i did not find something that comes close to this, would appreciate any help.
I'm currently trying to learn more about Deep learning/CNN's/Keras through what I thought would be a quite simple project of just training a CNN to detect a single specific sound. It's been a lot more of a headache than I expected.
I'm currently reading through this ignoring the second section about gpu usage, the first part definitely seems like exactly what I'm needing. But when I go to run the script, (my script is pretty much totally lifted from the section in the link above that says "Putting the pieces together, you may end up with something like this:"), it gives me this error:
AttributeError: 'DataFrame' object has no attribute 'file_path'
I can't find anything in the pandas documentation about a DataFrame.file_path function. So I'm confused as to what that part of the code is attempting to do.
My CSV file contains two columns, one with the paths and then a second column denoting the file paths as either positive or negative.
Sidenote: I'm also aware that this entire guide just may not be the thing I'm looking for. I'm having a very hard time finding any material that is useful for the specific project I'm trying to do and if anyone has any links that would be better I'd be very appreciative.
The statement df.file_path denotes that you want access the file_path column in your dataframe table. It seams that you dataframe object does not contain this column. With df.head() you can check if you dataframe object contains the needed fields.
I am trying to run a python script so that I can create a household count based on the residential address column and residential city column. Both columns just contain strings.
The script that I have tried can be seen below:
dataset['id'] =dataset.groupby(['RESIDENTIAL_ADDRESS1','RESIDENTIAL_CITY']).ngroup()
dataset['household_count'] = dataset.groupby(['id'])['id'].transform('count')
Yet, it gives me this error after 20,000 rows:
DataSource.Error: ADO.NET: A problem occurred while processing your Python script. Here are the technical details: [DataFormat.Error] We couldn't convert to Number. Details:DataSourceKind=Python DataSourcePath=Python Message=A problem occurred while processing your Python script. Here are the technical details: [DataFormat.Error] We couldn't convert to Number. ErrorCode=-2147467259.
Is there any way to fix this? This code works in python every single time and the error code make absolutely no sense in Power BI and I would greatly appreciate any advice on how to do this with DAX.
I have not been able to reproduce your error, but I strongly suspect the source of the error to be the datatypes. In the Power Query Editor, try transforming your grouping variables to text. The fact that your query fails for a dataset larger than 20000 rows should have absolutely nothing to do with the problem. Unless, of course, the data content somehow changes after row 20000.
If you could describe your datasource and show the applied steps in the Power Query Editor that would be of great help for anyone trying to assist you. You could also try to apply your code one step at a time, meaning making one table using dataset['id'] =dataset.groupby(['RESIDENTIAL_ADDRESS1','RESIDENTIAL_CITY']).ngroup() and yet another table using dataset['household_count'] = dataset.groupby(['id'])['id'].transform('count')
I might as well show you how to do just that, and maybe at the same time cement my suspicion that the error lies in the datatypes and hopefully rule out other error sources.
I'm using numpy along with a few random city and street names to build a dataset that I hope represents the structure and datatypes of your real world dataset:
Snippet 1:
import numpy as np
import pandas as pd
np.random.seed(123)
strt=['Broadway', 'Bowery', 'Houston Street', 'Canal Street', 'Madison', 'Maiden Lane']
city=['New York', 'Chicago', 'Baltimore', 'Victory Boulevard', 'Love Lane', 'Utopia Parkway']
RESIDENTIAL_CITY=np.random.choice(strt,21000).tolist()
RESIDENTIAL_ADDRESS1=np.random.choice(strt,21000).tolist()
sample_dataset=pd.DataFrame({'RESIDENTIAL_CITY':RESIDENTIAL_CITY,
'RESIDENTIAL_ADDRESS1':RESIDENTIAL_ADDRESS1})
Copy that snippet, go to PowerBI Desktop > Power Query Editor > Transform > Run Python Script and run it to get this:
Then do the same thing with this snippet:
dataset['id'] =dataset.groupby(['RESIDENTIAL_ADDRESS1','RESIDENTIAL_CITY']).ngroup()
Now you should have this:
So far, your last step is called Changed Type 2. Right above is a step called dataset. If you click that you'll see that the datatype of ID there is a string ABC and that it changes to number 123 in the next step. With my settings, Power BI inserts the step Changed Type 2 automatically. Maybe that is not the case with you? It cerainly can be a potential error source.
Next, insert your last line as a step of it's own:
dataset['household_count'] = dataset.groupby(['id'])['id'].transform('count')
Now you should have the dataset like below, along with the same steps under Applied Steps:
With this setup, everything seems to be working fine. So, what do we know for sure by now?
The size of the dataset is not the problem
Your code itself is not the problem
Python should handle this perfectly in Power BI
And what do we suspect?
Your data is the problem - either missing values or wrong type
I hope this helps you out somehow. If not, then don't hesitate to let me know.
Been studying Python on my own for some months. I'm about to ventur into the field of databases. I am currently aiming to create a very small scale application which would feature retrieving data based on a simple search (keyword) and reflect static data linked to it. Some numbers to put it in perspective:
About 150-200 "key" values
About 5-10 values to be displayed per "key" value
Editable (although the info tends to remain same most of time - maybe 1 or 2 amendments/month in total on all data stored)
An example would be the cards you see when you do a simple google search. For example you search for an actor (key value) and your query generates a "card" with values (age, length, wage, brothers,...).
As it would be the first attempt on creating something which works with such a (for me personally) larger amount of data, I am a bit puzzled with the options available to me. I have been reading up on different database models (relational,..). Ultimately I came to below 3 possibilities:
XML
database
hardcode
I intend on making the data amendable by 1 privileged user. This alone makes hardcoding it not really an option. Even though, as I am a novice, I could be interpreting this wrong here.
I'd be happy if you could point me in the right direction and if you'd go for a database, which one you'd recommend going for (MySQL,..).
Many thanks in advance. If I made any error with the post (as it is my initial post), do not hesitate to point this out.
I have some pretty strange data i'm working with, as can be seen in the image. Now I can't seem to find any source data for the numbers these graphs are presenting.
Furthermore if I search for the source it only points to an empty cell for each graph.
Ideally I want to be able to retrieve the highlighted labels in each case using python, and it seems finding the source is the only way to do this, so if you know of a python module that can do that i'd be happy to use it. Otherwise if you can help me find the source data that would be even perfecter :P
So far i've tried the XLDR module for python as well as manually showing all hidden cells, but neither work.
Here's a link to the file: Here
EDIT I ended up just converting the xlsx to a pdf using cloudconvert.com API
Then using pdftotext to convert the data to a .txt which just analyses everything including the numbers on the edge of the chart which can then be searched using an algorithm.
If a hopeless internet wanderer comes upon this thread with the same problem, you can PM me for more details :P