Show HN: Python 3 TSP solver without dependencies: https://github.com/dimitrovskif/elkai
GitHub
filipArena/elkai
Python 3 TSP solver based on LKH (cross platform). Contribute to filipArena/elkai development by creating an account on GitHub.
Python: Batteries Included, but They're Leaking: http://pyfound.blogspot.com/2019/05/amber-brown-batteries-included-but.html
Python Software Foundation Blog
Amber Brown: Batteries Included, But They're Leaking
Amber Brown of the Twisted project shared her criticisms of the Python standard library. This proved to be the day’s most controversial ta...
How to apply large python model to pyspark-dataframe?: https://stackoverflow.com/questions/56153303/how-to-apply-large-python-model-to-pyspark-dataframe
Stack Overflow
How to apply large python model to pyspark-dataframe?
I have:
Large dataframe (parquet format, 100.000.000 rows, 4.5TB size) that contains some data (features)
Several huge ML models (each one takes 5-15GB of RAM)
Spark cluster (AWS EMR), typical node
Large dataframe (parquet format, 100.000.000 rows, 4.5TB size) that contains some data (features)
Several huge ML models (each one takes 5-15GB of RAM)
Spark cluster (AWS EMR), typical node
How to destroy Python objects and free up memory: https://stackoverflow.com/questions/56126062/how-to-destroy-python-objects-and-free-up-memory
Stack Overflow
How to destroy Python objects and free up memory
I am trying to iterate over 100,000 images and capture some image features and store the resulting dataFrame on disk as a pickle file.
Unfortunately due to RAM constraints, i am forced to split the
Unfortunately due to RAM constraints, i am forced to split the
Has the Python GIL Been Slain? Subinterpreters in Python 3.8: https://hackernoon.com/has-the-python-gil-been-slain-9440d28fa93d
Hackernoon
Has the Python GIL been slain? | HackerNoon
In early 2003, Intel launched the new Pentium 4 “HT” processor. This processor was clocked at 3 GHz and had “Hyper-Threading” Technology.
Plot k-Nearest-Neighbor graph with 8 features?: https://stackoverflow.com/questions/56153726/plot-k-nearest-neighbor-graph-with-8-features
Stack Overflow
Plot k-Nearest-Neighbor graph with 8 features?
I'm new to machine learning and would like to setup a little sample using the k-nearest-Neighbor-method with the Python library Scikit.
Transforming and fitting the data works fine but I can't fig...
Transforming and fitting the data works fine but I can't fig...
Selenium Chromedriver not navigating to url: https://stackoverflow.com/questions/56164489/selenium-chromedriver-not-navigating-to-url
Stack Overflow
Selenium Chromedriver not navigating to url
What I have now is:
chrome_options = Options()
chrome_options.add_extension(r"C:\Users\x\OneDrive\Desktop\pp\crxSolver.crx")
driver = webdriver.Chrome(r'C:\Users\x\OneDrive\Desktop\chromedriver.ex...
chrome_options = Options()
chrome_options.add_extension(r"C:\Users\x\OneDrive\Desktop\pp\crxSolver.crx")
driver = webdriver.Chrome(r'C:\Users\x\OneDrive\Desktop\chromedriver.ex...
Theoretical vs actual time-complexity for algorithm calculating 2^n: https://stackoverflow.com/questions/55509854/theoretical-vs-actual-time-complexity-for-algorithm-calculating-2n
Stack Overflow
Theoretical vs actual time-complexity for algorithm calculating 2^n
I am trying to compute the time-complexity and compare it with the actual computation times.
If I am not mistaken, the time-complexity is O(log(n)), but looking at the actual computation times it ...
If I am not mistaken, the time-complexity is O(log(n)), but looking at the actual computation times it ...
I wrote a series of tutorials on numerical simulations using Scipy!: https://www.reddit.com/r/Python/comments/bqe1t1/i_wrote_a_series_of_tutorials_on_numerical/
reddit
r/Python - I wrote a series of tutorials on numerical simulations using Scipy!
0 votes and 0 comments so far on Reddit
Watch Now - Scapy: Python for Hackers ft. Raspberry Pi: https://www.reddit.com/r/Python/comments/bqgwzb/watch_now_scapy_python_for_hackers_ft_raspberry_pi/
reddit
Watch Now - Scapy: Python for Hackers ft. Raspberry Pi
Posted in r/Python by u/PNPtutorials • 270 points and 9 comments
How I replaced Excel with Python. (Not in the way you would expect, though.): https://www.reddit.com/r/Python/comments/bqhis2/how_i_replaced_excel_with_python_not_in_the_way/
reddit
r/Python - How I replaced Excel with Python. (Not in the way you would expect, though.)
0 votes and 0 comments so far on Reddit
Using SciPy curve_fit to predict post final score: https://stackoverflow.com/questions/56172822/using-scipy-curve-fit-to-predict-post-final-score
Stack Overflow
Using SciPy curve_fit to predict post final score
I have a post, and I need to predict the final score as close as I can.
Apparently using curve_fit should do the trick, although I am not really understanding how I should use it.
I have two known
Apparently using curve_fit should do the trick, although I am not really understanding how I should use it.
I have two known
Plotly chart is not displayed in PyCharm: https://stackoverflow.com/questions/50250010/plotly-chart-is-not-displayed-in-pycharm
Stack Overflow
Plotly chart is not displayed in PyCharm
How can I display interactive plotly graphs in Pycharm? I run the following code:
import plotly.offline as py
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
data = [go.Bar(...
import plotly.offline as py
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
data = [go.Bar(...
How to combine integration equation in Python?: https://stackoverflow.com/questions/56163690/how-to-combine-integration-equation-in-python
Stack Overflow
How to combine integration equation in Python?
I'm calculating key rate (R^Rate-wise) by integrating R(eta) over all possible eta from 0 to 1, with a probability distribution (PDTC) which is a log-normal distribution.
The equation of log-normal
The equation of log-normal
How to use multiprocessing for accelerate the following function?: https://stackoverflow.com/questions/56157485/how-to-use-multiprocessing-for-accelerate-the-following-function
Stack Overflow
How to use multiprocessing for accelerate the following function?
I have the following for loop:
for j in range(len(a_nested_list_of_ints)):
arr_1_, arr_2_, arr_3_ = foo(a_nested_list_of_ints[j])
arr_1[j,:] = arr_1_.data.numpy()
arr_2[j,:] = arr_2_.d...
for j in range(len(a_nested_list_of_ints)):
arr_1_, arr_2_, arr_3_ = foo(a_nested_list_of_ints[j])
arr_1[j,:] = arr_1_.data.numpy()
arr_2[j,:] = arr_2_.d...