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))