def show_tweets_by_month(): ''' 앞서 작성한 함수를 이용해 월별 트윗 개수를 보여주는 그래프를 출력합니다. ''' months = range(1, 13) num_tweets = [len(filter_by_month(trump_tweets, month)) for month in months] plt.bar(months, num_tweets, align='center') plt.xticks(months, months) plt.savefig('graph.png') elice_utils = EliceUtils() elice_utils.send_image('graph.png')
from elice_utils import EliceUtils import urllib.request from bs4 import BeautifulSoup elice_utils = EliceUtils() def main(): print("href 출력해보기") list_href = [] url = "http://sports.donga.com/Enter" soup = BeautifulSoup(urllib.request.urlopen(url).read(), "html.parser") # 반복문을 사용해 원하는 정보 range(3,23)까지 find("a")["href"] 를 사용해서 # href 모두 수집하여 list_href에 저장 for i in range(3, 23): list_href.append( soup.find_all("span", class_="tit")[i].find("a")["href"]) # source = soup.find_all("span", class_="tit"): # for i in ragne(3, 23): # link = source[i].find("a")["href"] # list_href.append(link) print(list_href) if __name__ == "__main__": main()
# Task 1. K-means clustering from scratch import numpy as np import pandas as pd from sklearn.datasets import make_blobs from matplotlib import pyplot as plt from copy import deepcopy from elice_utils import EliceUtils eu = EliceUtils() # Check versions print('numpy version: ', np.__version__) print('pandas version: ', pd.__version__) np.random.seed(12345) # Q1. Create a dataset (X and y) with 3 clusters usng sklearn.datasets.make_blobs X, y = make_blobs(n_samples=800, n_features=2, centers=3, random_state=12345) #print(X.shape,y.shape) # Q2: define a function to calculate Euclidean distance def dist(a, b, ax=1): """ :param a: 1-D input array :param b: 1-D input array :param axis: an integer for the axis of a and b along which to compute the vector norms :return: Eucleadian distance (float) """ return np.linalg.norm(a - b, axis=ax) # axis = 1 행연산
# I am very sorry for # * not obeying PEP8, # * abusing & spamming array comprehension instead of for-loop, # * using dynamic import although it is not dynamic, # * using __setitem__ to avoid variable-assignment limit in lambda, # (:= operator was possible, but that is implemented in Python 3.8) # * creating useless lambda function for each loop, # * finally, last but not least, using not-documented properties (in cs1 libraries) # But I hope you TA guys enjoy this one-lined code. # If you enjoyed this, please push 'Like' and 'Subscribe' button in git.nenw.dev (GitHub @HelloWorld017) """ # --- Your code ends here --- ######################################## download_message = '⬇ Download the result image file from the link below ⬇' print('#' * len(download_message)) print(download_message) sleep(0.1) # Save your image object as a file. image.save_as('./result.png') # Show a download link for your result image file. utils = EliceUtils() utils.send_file('./result.png') sleep(0.1) print('#' * len(download_message))