TEAM EDA

1. 절대경로 찾기 import os os.getcwd() '/home/ubuntu/Upstage AI : 수식인식기' 2. 압축 풀기 from zipfile import ZipFile dataset = ZipFile('/home/ubuntu/Upstage AI : 수식인식기/train_dataset.zip') dataset.extractall('/home/ubuntu/Upstage AI : 수식인식기/') dataset.close() '/home/ubuntu/Upstage AI : 수식인식기/train_daset' 폴더가 생성됨

list(map(list, zip(*mat)))

import numpy as np x = [[1, 2, 3], [4, 5, 6]] # 방법1. np.concatenate(x) np.concatenate(x) # 방법2. flatten() np.array(x).flatten() # 방법3. chain from itertools import chain list(chain(*x)) # 방법4. List Comprehension [j for i in x for j in i] # 방법5. reduce 사용 방법 list(reduce(lambda x, y: x+y, myMatrix))

원문 링크 ; http://www.somanet.xyz/2017/06/blog-post_21.html?showComment=1609072753155#c2471006822219488770 Google Drive path Make Google Drive Path Linkable Linkable Image path Save to Clipboard Image Tag Save to Clipboard Preview image

파이썬에서 데이터 프레임의 메모리를 줄여주는 코드 def reduce_mem_usage(df): """ iterate through all the columns of a dataframe and modify the data type to reduce memory usage. """ start_mem = df.memory_usage().sum() / 1024**2 # print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) cols = [c for c in df.columns if c not in ['log_date', 'date', 'etc_str2']] for col in cols: #..

파이썬 및 파이토치의 시드를 고정해주는 명령어 # Update 코드 # 링크 : https://www.kaggle.com/reighns/complete-and-reusable-pytorch-pipeline def seed_all(seed: int = 1930): print("Using Seed Number {}".format(seed)) os.environ["PYTHONHASHSEED"] = str( seed) # set PYTHONHASHSEED env var at fixed value torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.cuda.manual_seed(seed) # pytorch (both CPU and CUDA) np.ran..

파이썬에서 발생하는 경고문을 무시해주는 명령어 import warnings warnings.filterwarnings('ignore')