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Inverting an array with pytorch I’ve been working with pytorch more and more lately for NLP, and I’d like to write down some things I want to keep in mind. github The file in jupyter notebook format is here google colaboratory To run it in google colaboratory here Author’s environment The author’s OS is macOS, and the options are different from Linux and Unix commands.
matplotlib template for a graph for a paper Here is my personal template for writing graphs for papers in matplotlib. github The file in jupyter notebook format on github is here . google colaboratory If you want to run it in google colaboratory here Author’s environment sw_vers ProductName: Mac OS X ProductVersion: 10.14.6 BuildVersion: 18G9323 Python -V Python 3.8.5 %matplotlib inline %config InlineBackend.figure_format = 'svg' import time import json import numpy as np import pandas as pd import matplotlib.
Unpack and assign dictionary type arguments When assigning a large number of arguments at once, it is very convenient to unpack and assign dictionary type arguments. I’ll write it down so I don’t forget. github The file in jupyter notebook format on github is here . google colaboratory If you want to run it on google colaboratory here Author’s environment sw_vers ProductName: Mac OS X ProductVersion: 10.
Python Tips I’ll leave some personal notes on useful notations when using python. github The file in jupyter notebook format on github is here . google colaboratory To run it in google colaboratory here Author’s environment sw_vers ProductName: Mac OS X ProductName: Mac OS X ProductVersion: 10.14.6 BuildVersion: 18G103 Python -V Python 3.8.5 %matplotlib inline %config InlineBackend.figure_format = 'svg' import time import json import matplotlib.
Python Tips This is my personal memo about useful notations in python. I have not touched on the basics. It is limited to what I find useful. github The jupyter notebook format file on github is here google colaboratory To run it on google colaboratory here Author’s environment ! sw_vers ProductName: Mac OS X ProductVersion: 10.14.6 BuildVersion: 18G95 Python -V Python 3.5.5 :: Anaconda, Inc.
Python Tips When I was doing data analysis, I had a chance to use Venn diagrams to visualize the data, so I’m writing this down so I don’t forget. The Venn diagram is a handy tool for visualizing the relationships between datasets, such as duplicates, and is useful in the EDA stage. github The jupyter notebook format file on github is here . google colaboratory If you want to run it on google colaboratory here Author’s environment sw_vers ProductName: Mac OS X ProductVersion: 10.
Unable to load jupyter notebook kernel You can easily change the virtual environment in jupyter notebook, but I couldn’t switch the environment well this time, so I’ll write it down. github The file in jupyter notebook format on github is here . google colaboratory If you want to run it on google colaboratory here 015/015_nb.ipynb) Author’s environment !sw_vers ProductName: Mac OS X ProductVersion: 10.14.6 BuildVersion: 18G9323 !
Python Tips A personal note on some useful notations for using python. github The jupyter notebook format file on github is here . google colaboratory If you want to run it in google colaboratory here 010/010_nb.ipynb) Author’s environment sw_vers ProductName: Mac OS X ProductName: Mac OS X ProductVersion: 10.14.6 BuildVersion: 18G95 Python -V Python 3.5.5 :: Anaconda, Inc. Fast aggregate retrieval after groupby in pandas A pandas specialist once told me about a fast way to get the results (DataFrame type) after a groupby.
GC (garbage collection) and reference counters in Python This is a personal note on some useful notations for using python. I’m not going to cover the basics, and I’m limiting this to things I’ve found useful. github The jupyter notebook format file on github is here . google colaboratory If you want to run it on google colaboratory here 009/009_nb.ipynb) Author’s environment sw_vers ProductName: Mac OS X ProductName: Mac OS X ProductVersion: 10.
LightGBM template LightGBM is often used to analyze table data, and since I’ve been using it a lot recently, I’ll put it together as a template. (Update: 2022/2/5: A good DataScientist taught me how to analyze features using Shap, so I’ve added it to the template) Getting the data Creating a Model Cross Validation Evaluation of TEST Analysis by Shap github Files in jupyter notebook format on github are here google colaboratory To run it in google colaboratory here Author’s environment !