spyder-notebook | Jupyter notebook integration with Spyder | Code Editor library
kandi X-RAY | spyder-notebook Summary
kandi X-RAY | spyder-notebook Summary
Jupyter notebook integration with Spyder
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Top functions reviewed by kandi - BETA
- Setup the menu
- Add token to url
- Shuts down the kernel
- Closes a notebook
- Refreshes the data
- Select process edit
- Runs lpm install
- Run a command
- Close all open notebooks
- Shutdown all server servers
- Check if the server has timeout
- Show kernel error page
- Handle selection selection
- Updates the actions in the current tabwidget
- Get the version number
- Show loading
- Set the software rendering
- Open an IPython console
- Example demo
- Open a notebook
- Show blank page
- Sets the value of an input selector
- Handle server started
- Handle switch mode
spyder-notebook Key Features
spyder-notebook Examples and Code Snippets
Community Discussions
Trending Discussions on spyder-notebook
QUESTION
I am using Python 3.9.0 and Spyder 4.2.0 on Windows 10 (x64) machine. Via official repo, I installed the spyder-terminal
plugin using pip
. It installed successfully. After installation, when I open the Spyder IDE, I can't see the terminal. I tried digging into View>Panes
and also under Preferences
, but couldn't see any hints towards enabling/checking the spyder-terminal?
Did someone come across the same issue and has a workaround to suggest? Am I missing some dependencies?
Here is the output of pip list
:
ANSWER
Answered 2020-Dec-20 at 20:18Click on View => Pane => IPython Console. Ipython console should open up at the bottom right corner
QUESTION
I have a "seed" GeoDataFrame (GDF)(RED) which contains a 0.5 arc minutes global grid ((180*2)*(360*2) = 259200). Each cell contains an absolute population estimate. In addition, I have a "leech" GDF (GREEN) with roughly 8250 adjoining non-regular shapes of various sizes (watersheds).
I wrote a script to allocate the population estimates to the geometries in the leech GDF based on the overlapping area between grid cells (seed GDF) and the geometries in the leech GDF. The script works perfectly fine for my sample data (see below). However, once I run it on my actual data, it is very slow. I ran it overnight and the next morning only 27% of the calculations had been performed. I will have to run this script many times and waiting for two days each time, is simply not an option.
After doing a bit of literature research, I already replaced (?) for loops with for index i in df.iterrows()
(or is this the same as "conventional" python for loops) but it didn't bring about the performance imporvement I had hoped for.
Any suggestion son how I can speed up my code? In twelve hours, my script only processed only ~30000 rows out of ~200000.
My expected output is the column leech_df['leeched_values']
.
ANSWER
Answered 2020-Feb-27 at 18:33It might be worthy to profile your code in details to get precise insights of what is your bottleneck.
Bellow some advises to already improve your script performance:
- Avoid
list.append(1)
to count occurrences, usecollection.Counter
instead; - Avoid
pandas.DataFrame.iterrows
, usepandas.DataFrame.itertuples
instead; - Avoid extra assignation that are not needed, use
pandas.DataFrame.fillna
instead:
Eg. this line:
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