task_id
int64 1.59k
65.3k
| prompt
stringlengths 15
46
| suffix
stringlengths 0
23
| canonical_solution
stringlengths 5
244
| test_start
stringlengths 22
167
| test
sequence | entry_point
stringlengths 6
7
| intent
stringlengths 5
84
| library
sequence |
|---|---|---|---|---|---|---|---|---|
37,146 | def f_37146(arr):
return | arr[:, 0] |
import numpy as np
def check(candidate): | [
"\n arr = np.array([[1,2],[3,4]])\n assert all(candidate(arr) == np.array([1,3]))\n",
"\n arr = np.array([[3,4,5]])\n assert all(candidate(arr) == np.array([3]))\n"
] | f_37146 | 2次元配列`arr`の要素となっている1次元配列から先頭の値のみを抜き出す | [
"numpy"
] |
|
25,263 | def f_25263(df):
return | df.to_dict() |
import pandas as pd
def check(candidate): | [
"\n df = pd.DataFrame([[1,2,3], [4,5,6], [6,5,4], [2,1,0]], columns=[\"AA\", \"b\", \"3\"])\n assert candidate(df) == {'AA': {0: 1, 1: 4, 2: 6, 3: 2},\n 'b': {0: 2, 1: 5, 2: 5, 3: 1},\n '3': {0: 3, 1: 6, 2: 4, 3: 0}}\n",
"\n df = pd.DataFrame([[1,2,3], [4,5,6], [6,5,4], [2,1,0]])\n assert candidate(df) == {0: {0: 1, 1: 4, 2: 6, 3: 2},\n 1: {0: 2, 1: 5, 2: 5, 3: 1},\n 2: {0: 3, 1: 6, 2: 4, 3: 0}}\n"
] | f_25263 | データフレームを辞書型オブジェクトに変換する | [
"pandas"
] |
|
28,178 | def f_28178(soup):
return | soup.find('tbody').find_all('tr') |
from bs4 import BeautifulSoup
def check(candidate): | [
"\n soup = BeautifulSoup(\"<td><b>Address:</b></td><tbody><tr>My home address</tr></tbody>\")\n result = candidate(soup)\n assert len(result) == 1\n assert result[0].contents == ['My home address']\n"
] | f_28178 | HTMLテーブルから各行を取得する | [
"bs4"
] |
|
8,656 | def f_8656():
|
return handler | class handler(http.server.BaseHTTPRequestHandler):
def do_POST(self):
os.environ['REQUEST_METHOD'] = 'POST'
form = cgi.FieldStorage(self.rfile, self.headers) |
import cgi
import http.server
def check(candidate): | [
"\n try:\n handler = candidate()\n srvr = http.server.HTTPServer(('127.0.0.1', 8889), handler)\n except:\n assert False\n"
] | f_8656 | POSTデータをcgi.FieldStrageで受け取る | [
"cgi",
"http"
] |
9,836 | def f_9836(li):
return | random.choice(li) |
import random
def check(candidate): | [
"\n assert candidate([1,2,3]) in [1,2,3]\n"
] | f_9836 | リスト`li`の中からランダムに一つの要素を選択する | [
"random"
] |
|
1,589 | def f_1589(d):
|
return | X = np.array(d, dtype='float32')
X.tofile('binaryVec.bin') |
import numpy as np
def check(candidate): | [
"\n f = open('binaryVec.bin', 'w')\n f.close()\n \n d = np.array([1., 2., 3.])\n candidate(d)\n d1 = np.fromfile('binaryVec.bin', dtype='float32')\n assert np.all(d == d1)\n"
] | f_1589 | 要素が数値のリスト型データ`d`をバイナリデータ`binaryVrc.bin`として保存する | [
"numpy"
] |
38,532 | def f_38532(f):
|
return | f.close() |
def check(candidate): | [
"\n f = open('tmp.txt', 'w')\n candidate(f)\n assert f.closed\n"
] | f_38532 | 開いているファイル'f'を閉じる | [] |
37,696 | def f_37696(files, url, data):
return | requests.post(url, files=files, data=data) |
import requests
from unittest.mock import Mock
def check(candidate): | [
"\n r = requests.Response()\n r.status_code = 200\n requests.post = Mock(return_value = r)\n file_path = 'a.txt'\n with open (file_path, 'w') as f:\n f.write('abc')\n files = {'file': open(file_path, 'rb')}\n assert candidate(files, 'https://def.xyz', {'key':'value'}).status_code == 200\n"
] | f_37696 | multipartのリクエストで複数のデータ`files`, `data`を`url'にPOSTする | [
"requests"
] |
|
29,368 | def f_29368(X, y):
|
return sss | sss = StratifiedShuffleSplit()
sss.get_n_splits(X, y) |
import numpy as np
import sklearn
from sklearn.model_selection import StratifiedShuffleSplit
def check(candidate): | [
"\n X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])\n y = np.array([0, 0, 0, 1, 1, 1])\n assert candidate(X, y).__class__ == sklearn.model_selection._split.StratifiedShuffleSplit\n"
] | f_29368 | クラス数の比率を保ったままデータを分割する | [
"numpy",
"sklearn"
] |
40,699 | def f_40699(low, high):
return | plt.yticks(range(low,high)) |
import matplotlib.pyplot as plt
def check(candidate): | [
"\n assert len(candidate(20, 50)[0]) == 30\n",
"\n assert len(candidate(0, 10)[0]) == 10\n"
] | f_40699 | y軸のプロットの範囲を下限`low`、上限`high`に設定する | [
"matplotlib"
] |
|
11,011 | def f_11011(fig, onclick):
|
return | fig.canvas.mpl_connect('pick_event', onclick) |
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def check(candidate): | [
"\n def onclick(event):\n pass\n fig = plt.figure()\n X = [[1,2,3,4,5],[1,2,3,4,5],[1,2,3,4,5]]\n Y = [[1,1,1,1,1],[2,2,2,2,2],[3,3,3,3,3]]\n Z = [[10,11,13,14,16],[5,8,7,7,7,],[0,0,0,9,8]]\n ax = Axes3D(fig)\n ax.scatter3D(np.ravel(X),np.ravel(Y),np.ravel(Z))\n try:\n candidate(fig, onclick)\n except:\n assert False\n"
] | f_11011 | グラフ上で選択されたデータの座標を表示する | [
"matplotlib",
"mpl_toolkits",
"numpy"
] |
42,344 | def f_42344():
return | re.compile('[ぁ-んァ-ン一-龥]+') |
import re
def check(candidate): | [
"\n pattern = candidate()\n words = ['あいうえお', '546', 'たぬき', '饅頭', 'abdf', '#%&', ' ']\n ja_words = [pattern.findall(w) for w in words]\n ja_words = [a for jw in ja_words for a in jw]\n assert ja_words == ['あいうえお', 'たぬき', '饅頭']\n"
] | f_42344 | 日本語（ひらがな、カタカナ、漢字）の判別をする正規表現を得る | [
"re"
] |
|
17,145 | def f_17145(br):
return | br.submit().read() |
import mechanize
import urllib.request
from unittest.mock import Mock
def check(candidate): | [
"\n br = mechanize.Browser()\n x = urllib.request.urlopen('https://www.wikipedia.org')\n br.submit = Mock(return_value = x)\n assert b'Wikipedia' in candidate(br)\n"
] | f_17145 | ブラウザオブジェクト`br`からsubmitした際の返り値を読みこむ | [
"mechanize",
"urllib"
] |
|
38,824 | def f_38824(data):
return | [print(*i) for i in data] |
import sys
from io import StringIO
def check(candidate): | [
"\n stdout = sys.stdout\n s = StringIO()\n sys.stdout = s\n candidate([[1],[2],[3],[4],[5],[6]])\n sys.stdout = stdout \n s.seek(0)\n assert len(s.read()) == 12\n"
] | f_38824 | タプル`data`を空白区切りで表示する | [
"io",
"sys"
] |
|
38,824 | def f_38824(data):
|
return | for i in data:
print(' '.join(str(j) for j in i)) |
import sys
from io import StringIO
def check(candidate): | [
"\n stdout = sys.stdout\n s = StringIO()\n sys.stdout = s\n candidate([[1],[2],[3],[4],[5],[6]])\n sys.stdout = stdout \n s.seek(0)\n assert len(s.read()) == 12\n"
] | f_38824 | タプル`data`を空白区切りで表示する | [
"io",
"sys"
] |
38,824 | def f_38824(data):
|
return | for i in data:
print(' '.join(map(str, i))) |
import sys
from io import StringIO
def check(candidate): | [
"\n stdout = sys.stdout\n s = StringIO()\n sys.stdout = s\n candidate([[1],[2],[3],[4],[5],[6]])\n sys.stdout = stdout \n s.seek(0)\n assert len(s.read()) == 12\n"
] | f_38824 | タプル`data`を空白区切りで表示する | [
"io",
"sys"
] |
35,299 | def f_35299(n):
|
return result |
t = 5
z = tf.constant(0, shape=[n, n], dtype=tf.int32)
abs = tf.maximum(t, z)
result = tf.reduce_sum(abs)
|
import tensorflow as tf
def check(candidate): | [
"\n assert str(type(candidate(4))).split(' ')[1] == \"'tensorflow.python.framework.ops.EagerTensor'>\"\n"
] | f_35299 | `n`×`n`のテンソルの要素のうち0以上の値の和を計算する | [
"tensorflow"
] |
38,328 | def f_38328(df, pat):
return | df.x.str.extract(pat) |
import pandas as pd
def check(candidate): | [
"\n df = pd.DataFrame({'x': ['車5(0.8km)', '5', '車27(8.6km)']}, index=[1, 2, 3])\n pat = r'車(\\d*)'\n ref = df.x.str.extract(pat)\n assert candidate(df, pat).count()[0] == 2\n"
] | f_38328 | データフレーム`df`の列ラベル`x`の各行のデータに対して正規表現`pat`を適用する | [
"pandas"
] |
|
37,418 | def f_37418(file):
return | open(file, 'w') |
def check(candidate): | [
"\n f = candidate('test.txt')\n assert f.name == 'test.txt'\n assert f.mode == 'w'\n"
] | f_37418 | ファイル`file`を上書きモードで開く | [] |
|
41,200 | def f_41200(x_list, y_list):
return | plt.plot(x_list, y_list) |
import matplotlib.pyplot as plt
def check(candidate): | [
"\n assert isinstance(candidate([1, 3, 5], [2, 4, 6]), list)\n"
] | f_41200 | データ`x_list`、`y_list`からなるグラフを描画する指定する | [
"matplotlib"
] |
|
43,369 | def f_43369(a, b):
return | pd.DataFrame([a, b]) |
import numpy as np
import pandas as pd
def check(candidate): | [
"\n assert candidate([1,1,1], [2,2,2]).equals(pd.DataFrame([[1,1,1], [2,2,2]]))\n",
"\n assert candidate([1,2,1], [2,3,4]).equals(pd.DataFrame([[1,2,1], [2,3,4]]))\n",
"\n assert candidate([0], [1]).equals(pd.DataFrame([[0], [1]]))\n"
] | f_43369 | 2つのデータフレーム`a`と`b`を行方向に結合する | [
"numpy",
"pandas"
] |
|
24,438 | def f_24438(file):
return | codecs.open(file, 'r', 'utf-8') |
import codecs
def check(candidate): | [
"\n with open('test.txt', 'w') as fw:\n fw.write('hello world!')\n fr = candidate('test.txt')\n assert fr.name == 'test.txt'\n"
] | f_24438 | 文字コードをutf-8に指定してファイル`file`を開く | [
"codecs"
] |
|
10,215 | def f_10215(file):
|
return data | with open(file, 'rb') as f:
data = f.read() |
def check(candidate): | [
"\n with open('tmp.pkl', 'wb') as fw:\n fw.write(b\"hello world!\")\n assert candidate('tmp.pkl') == b\"hello world!\"\n"
] | f_10215 | ファイル`file`をバイナリデータとして開く | [] |
18,992 | def f_18992(x):
return | pickle.dump(x, open('hoge.pkl', 'wb')) |
import pickle
def check(candidate): | [
"\n x = [100, 435, 56, 2, 99]\n candidate(x)\n with open('hoge.pkl', 'rb') as fr:\n data = pickle.load(fr)\n assert data == x\n"
] | f_18992 | オブジェクト`x`をファイル`hoge.pkl`に保存する | [
"pickle"
] |
|
38,400 | def f_38400(df, change_dict):
return | df.replace(change_dict) |
import pandas as pd
def check(candidate): | [
"\n df = pd.DataFrame({'kai': ['2', 'B1', '23-49', 'M2']}, index=[1, 2, 3, 4])\n change_dict = {'2': '22', 'B1': 'B2'}\n assert candidate(df, change_dict).equals(df.replace(change_dict))\n"
] | f_38400 | データフレーム`df`の複数の異なる要素を辞書型オブジェクト`change_dict`のキーと要素のペアに従って置き換える | [
"pandas"
] |
|
35,793 | def f_35793():
|
return data | data = []
i = 0
while(i<100):
data.append(pd.read_csv('file_%d.csv'%i))
i+=1 |
import pandas as pd
def check(candidate): | [
"\n for i in range(0, 100):\n with open ('file_'+str(i)+'.csv', 'w') as f:\n f.write(str(i))\n \n assert len(candidate()) == 100\n"
] | f_35793 | 連番になっている100個のCSVファイル'file_%d'をリストに取り込む | [
"pandas"
] |
20,549 | def f_20549(vectorized):
|
return | numpy.save('my_vector.npy', vectorized.toarray()) |
import os
import numpy
from sklearn.feature_extraction import DictVectorizer
def check(candidate): | [
"\n measurements = [\n {'city': 'Dubai', 'temperature': 33.},\n {'city': 'London', 'temperature': 12.},\n {'city': 'San Francisco', 'temperature': 18.},\n ]\n vec = DictVectorizer()\n candidate(vec.fit_transform(measurements))\n assert os.path.exists('my_vector.npy')\n"
] | f_20549 | 学習データのベクトル`vectorized`をファイル'my_vector.npy`に保存する | [
"numpy",
"os",
"sklearn"
] |
9,518 | def f_9518(li):
|
return | for i, name in enumerate(li):
print(i, name) |
import sys
def check(candidate): | [
"\n file_name = 'output.txt'\n f = open(file_name, 'w')\n sys.stdout = f\n candidate([1, 3])\n f.close()\n with open (file_name, 'r') as f:\n lines = f.readlines()\n assert lines[0] == '0 1\\n'\n assert lines[1] == '1 3\\n'\n \n f = open(file_name, 'w')\n sys.stdout = f\n candidate(['abc', 'def'])\n f.close()\n with open (file_name, 'r') as f:\n lines = f.readlines()\n assert lines[0] == '0 abc\\n'\n assert lines[1] == '1 def\\n'\n"
] | f_9518 | リスト'li'のインデックスと要素に繰り返し処理を行って表示する | [
"sys"
] |
38,760 | def f_38760(arr, n):
return | arr[arr > n].sum(), numpy.sum(arr > n) |
import numpy
import numpy as np
def check(candidate): | [
"\n assert candidate(np.array([1,2,3,4]), 3) == (4, 1)\n"
] | f_38760 | numpy配列`arr`に対して数値`n`より大きい要素の合計及び個数を求めて表示する | [
"numpy"
] |
|
35,102 | def f_35102(data):
|
return results | results = {}
for item in data:
results[item.find('areacode').text] = item.find('prefecture').text |
import xml.etree.ElementTree as ET
def check(candidate): | [
"\n data_temp = [\n '<?xml version=\"1.0\" encoding=\"UTF-8\" ?><root><areacode>area1</areacode><prefecture>prefecture1</prefecture></root>', \n '<?xml version=\"1.0\" encoding=\"UTF-8\" ?><root><areacode>area2</areacode><prefecture>prefecture2</prefecture></root>'\n ]\n data = []\n for tr in data_temp:\n data.append(ET.ElementTree(ET.fromstring(tr)))\n \n res = candidate(data)\n assert \"area1\" in res\n assert \"area2\" in res\n assert \"prefecture1\" == res[\"area1\"]\n assert \"prefecture2\" == res[\"area2\"]\n"
] | f_35102 | イテラブルオブジェクト`data`の要素から文字列`area_code`と`prefecture`を探し、それぞれキーと要素に持つ辞書`results`を作る | [
"xml"
] |
41,440 | def f_41440(arr_list):
return | np.stack(arr_list) |
import numpy as np
def check(candidate): | [
"\n arr_list = [np.array([1,2]), np.array([3,4]), np.array([5,6])]\n assert candidate(arr_list).tolist() == [[1, 2], [3, 4], [5, 6]]\n"
] | f_41440 | 要素がNumPy配列のリスト`arr_list`を2次元のNumPy配列に変換する | [
"numpy"
] |
|
35,741 | def f_35741(soup):
return | soup.find_all('p') |
from bs4 import BeautifulSoup
def check(candidate): | [
"\n soup = BeautifulSoup('<p>text</p>')\n assert candidate(soup)[0].contents == ['text']\n"
] | f_35741 | HTMLをパースしたオブジェクト`soup`からタグ`p`をすべて見つける | [
"bs4"
] |
|
40,444 | def f_40444(df, c_label):
return | df.groupby([c_label]).last() |
import pandas as pd
def check(candidate): | [
"\n df = pd.DataFrame({'利用者ID': [1, 2], 'コンテンツID': ['a', 'b'], '値': [170, 45]})\n c_label = ['利用者ID', 'コンテンツID']\n assert candidate(df, c_label).equals(df.groupby([c_label]).last())\n"
] | f_40444 | データフレーム`df`の列`c_label`をGroupbyでまとめたデータの最後の行を取り出す | [
"pandas"
] |
|
39,240 | def f_39240(a_list, b_list):
return | [i for i in b_list if i in a_list] |
def check(candidate): | [
"\n assert candidate([1,2,3], [4,1,2]) == [1,2]\n",
"\n assert sorted(candidate([1,2,3,4,5], [4,1,2])) == [1,2,4]\n",
"\n assert candidate([1,2,3], []) == []\n"
] | f_39240 | リスト`a_list`の要素の中のリスト`b_list`の要素と一致するものを表示する | [] |
|
39,375 | def f_39375(dt_s):
return | datetime.strptime(dt_s,'%d%b%Y') |
from datetime import datetime
def check(candidate): | [
"\n assert candidate('10OCT2017') == datetime.strptime('10OCT2017','%d%b%Y')\n"
] | f_39375 | 英名の月を含む日付フォーマット'%d%b%Y'の文字列`dt_s`をdatetime型に変換する | [
"datetime"
] |
|
38,960 | def f_38960(n):
return | [int(c) for c in n] |
def check(candidate): | [
"\n assert candidate('12345') == [1,2,3,4,5]\n",
"\n assert candidate('') == []\n",
"\n assert candidate('0') == [0]\n"
] | f_38960 | 数値`n`を分割してリストに格納する | [] |
|
33,908 | def f_33908():
|
return profile | profile = webdriver.FirefoxProfile()
profile.DEFAULT_PREFERENCES['frozen']['javascript.enabled'] = False
profile.set_preference("app.update.auto", False)
profile.set_preference("app.update.enabled", False)
profile.update_preferences() |
import selenium
from selenium import webdriver
from selenium.webdriver.firefox.options import Options
def check(candidate): | [
"\n profile = candidate()\n assert profile.__class__ == selenium.webdriver.firefox.firefox_profile.FirefoxProfile\n"
] | f_33908 | seleniumtでFirefox仕様時にjavascriptを無効にする | [
"selenium"
] |
33,908 | def f_33908():
|
return options |
options = Options()
options.set_preference('javascript.enabled', False)
|
import selenium
from selenium import webdriver
from selenium.webdriver.firefox.options import Options
def check(candidate): | [
"\n options = candidate()\n assert options.preferences == {'javascript.enabled': False}\n"
] | f_33908 | seleniumtでFirefox仕様時にjavascriptを無効にする | [
"selenium"
] |
19,770 | def f_19770(s):
return | s.isnumeric() |
def check(candidate): | [
"\n assert candidate('1') == True\n",
"\n assert candidate('a') == False\n"
] | f_19770 | 文字列`s`が数を表す文字かどうか判定する | [] |
|
29,614 | def f_29614():
return | socket.socket() |
import socket
def check(candidate): | [
"\n assert candidate().__class__ == socket.socket\n"
] | f_29614 | ソケット情報を保存する | [
"socket"
] |
|
41,032 | def f_41032(dir):
return | os.listdir(dir) |
import os
def check(candidate): | [
"\n assert candidate('.') == os.listdir('.')\n"
] | f_41032 | ディレクトリ`dir`内にあるファイルのリストを取得する | [
"os"
] |
|
37,709 | def f_37709(img):
return | img is None |
import cv2
import numpy as np
def check(candidate): | [
"\n assert candidate(None) == True\n blank_image = np.zeros((10,5,3), np.uint8)\n assert candidate(blank_image) == False\n"
] | f_37709 | 画像`img`が空かどうかを判定する | [
"cv2",
"numpy"
] |
|
33,677 | def f_33677(f):
|
return coeffs |
p = Poly(f, x)
coeffs = p.coeffs()
|
from sympy import Poly, var
def check(candidate): | [
"\n var('x a b')\n f = a*(2*x**2 - 1) + 4*x**3 + x*(b - 3)\n co = candidate(f)\n assert co == [4, 2*a, b - 3, -a]\n"
] | f_33677 | `x`に関する多項式`f`の各次数の係数を求めてリストにする `coeffs` | [
"sympy"
] |
37,449 | def f_37449(a, b):
return | a & b |
def check(candidate): | [
"\n assert candidate(22, 56678) == 6\n",
"\n assert candidate(0, -1) == 0\n",
"\n assert candidate(1000, 1) == 0\n",
"\n assert candidate(479, 234) == 202\n"
] | f_37449 | 変数`a`と`b`のビット演算 | [] |
|
42,442 | def f_42442():
return | globals() |
def check(candidate): | [
"\n assert candidate() == globals()\n"
] | f_42442 | グローバル変数の一覧を得る | [] |
|
38,030 | def f_38030(word_list):
return | Counter(word_list) |
from collections import Counter
def check(candidate): | [
"\n assert candidate(['this', 'is', 'a', 'word', 'List']) == Counter({'List': 1, 'a': 1, 'is': 1, 'this': 1, 'word': 1})\n",
"\n assert candidate(['List']) == Counter({'List': 1})\n",
"\n assert candidate(['this', 'this', 'this', 'this', 'this']) == Counter({'this': 5})\n",
"\n assert candidate([]) == Counter({})\n"
] | f_38030 | リスト`word_list'内に出現する単語を数える | [
"collections"
] |
|
38,724 | def f_38724(f, g):
|
return add_functions | def add_functions(f, g):
return lambda x: f(x) + g(x) |
def check(candidate): | [
"\n def f(x): return x\n def g(y): return 1\n assert candidate(f,g)(f,g)(3) == 4\n"
] | f_38724 | 関数`f`と`g`を受け取って関数同士の和を計算する関数`add_functions`を定義する | [] |
22,439 | def f_22439(obj):
return | type(obj) |
def check(candidate): | [
"\n assert candidate('this is a string') == str\n",
"\n assert candidate(123.4) == float\n",
"\n assert candidate(400) == int\n",
"\n assert candidate({}) == dict \n",
"\n assert candidate([{}]) == list\n"
] | f_22439 | オブジェクト`obj`のクラスを得る | [] |
|
22,439 | def f_22439(obj):
return | obj.__class__ |
def check(candidate): | [
"\n assert candidate('this is a string') == str\n",
"\n assert candidate(123.4) == float\n",
"\n assert candidate(400) == int\n",
"\n assert candidate({}) == dict \n",
"\n assert candidate([{}]) == list\n"
] | f_22439 | オブジェクト`obj`のクラスを得る | [] |
|
39,340 | def f_39340(url):
return | urllib.request.urlopen(url).read() |
import urllib
def check(candidate): | [
"\n url = \"http://www.google.com\"\n text = b\"google\"\n assert text in candidate(url)\n"
] | f_39340 | 指定したURL`url`の内容を表示する | [
"urllib"
] |
|
39,589 | def f_39589(foldername, filename):
return | os.path.join(foldername, filename) |
import os
def check(candidate): | [
"\n assert candidate('folder', 'file') == 'folder/file'\n",
"\n assert candidate('', 'file') == 'file'\n",
"\n assert candidate('.', 'file') == './file'\n"
] | f_39589 | フォルダ名`foldername'とファイル名`filename`を結合したパスを得る | [
"os"
] |
|
23,577 | def f_23577(ax, l, h):
return | ax.set_xlim(l, h) |
import matplotlib.pyplot as plt
def check(candidate): | [
"\n fig, ax = plt.subplots()\n assert candidate(ax, 10, 100) == (10.0, 100.0)\n"
] | f_23577 | X軸の範囲を下限`l`と上限`h`に指定する | [
"matplotlib"
] |
|
41,087 | def f_41087(src, range):
return | int(math.ceil(src/float(range)) * range) |
import math
def check(candidate): | [
"\n assert candidate(22, 50) == 50\n",
"\n assert candidate(100, 23) == 115\n",
"\n assert candidate(0, 13) == 0\n",
"\n assert candidate(12, 1) == 12\n",
"\n assert candidate(34, 23) == 46\n"
] | f_41087 | 整数`src`を特定の範囲`range`の倍数で切り上げる | [
"math"
] |
|
41,087 | def f_41087(src, range):
return | src if src % range == 0 else src + range - src % range |
def check(candidate): | [
"\n assert candidate(22, 50) == 50\n",
"\n assert candidate(100, 23) == 115\n",
"\n assert candidate(0, 13) == 0\n",
"\n assert candidate(12, 1) == 12\n",
"\n assert candidate(34, 23) == 46\n"
] | f_41087 | 整数`src`を特定の範囲`range`の倍数で切り上げる | [] |
|
40,711 | def f_40711(n, N):
|
return answer | random_numbers = np.random.rand(n)
answer = N * random_numbers / np.sum(random_numbers) |
import numpy as np
def check(candidate): | [
"\n answer = candidate(20, 5)\n assert answer.shape == (20,)\n assert max(answer) < 5\n"
] | f_40711 | 要素数の総和が`N`となる制約の下、`n`次元のランダムベクトル`answer`を生成する | [
"numpy"
] |
40,711 | def f_40711(n, N):
|
return answer | answer = np.random.dirichlet(np.ones(n)) * N |
import numpy as np
def check(candidate): | [
"\n answer = candidate(20, 5)\n assert answer.shape == (20,)\n"
] | f_40711 | 要素数の総和が`N`となる制約の下、`n`次元のランダムベクトル`answer`を生成する | [
"numpy"
] |
31,924 | def f_31924(li):
|
return li | random.shuffle(li) |
import random
def check(candidate): | [
"\n li_a = [i for i in range(10)]\n li_a = candidate(li_a)\n assert sorted(li_a) == [i for i in range(10)]\n"
] | f_31924 | リスト`li`をランダムに並び替える | [
"random"
] |
40,343 | def f_40343(a, b):
return | itertools.product(a,b) |
import itertools
def check(candidate): | [
"\n assert list(candidate([1,2],[3,4])) == [(1, 3), (1, 4), (2, 3), (2, 4)]\n"
] | f_40343 | 複数のリスト`a`と`b`の直積（デカルト積）を生成し、要素の組み合わせの結果を得る | [
"itertools"
] |
|
36,217 | def f_36217(df, reg):
return | df['a'].str.extract(reg, expand=True) |
import pandas as pd
def check(candidate): | [
"\n df = pd.DataFrame([['abc def'],['123 567'], ['qqq eee']], columns=['a'])\n reg = r'(.{3})$'\n assert candidate(df, reg).equals(df['a'].str.extract(reg, expand=True))\n"
] | f_36217 | データフレーム`df`の列`a`を正規表現`reg'で抽出する | [
"pandas"
] |
|
27,871 | def f_27871(factories, shops, costs):
return | {f+s : cost for ((f,s), cost) in zip(product(factories,shops), costs)} |
from itertools import product
def check(candidate): | [
"\n assert candidate(['A', 'B'], ['1', '2'], [8, 10, 12, 16]) == {'A1': 8, 'A2': 10, 'B1': 12, 'B2': 16}\n"
] | f_27871 | 2つのリスト`factories'と`shops`の要素の組み合わせをキーとし、タプル`costs`各要素を要素等する辞書型オブジェクトを作る | [
"itertools"
] |
|
27,871 | def f_27871(factories, shops, cost):
|
return d | root = [''.join((x, y)) for x, y in itertools.product(factories, shops)]
d = dict(zip(root, cost)) |
import itertools
def check(candidate): | [
"\n factories = ['A', 'B', 'C', 'D']\n shops = ['1', '2', '3', '4', '5']\n costs = ( 8, 10, 12, 16, 20,\n 12, 8, 6, 10, 16,\n 18, 7, 4, 3, 4,\n 12, 10, 12, 16, 20 ) \n res_dict = candidate(factories, shops, costs)\n assert list(res_dict.items()) == [\n ('A1', 8), ('A2', 10), ('A3', 12), ('A4', 16), ('A5', 20), \n ('B1', 12), ('B2', 8), ('B3', 6), ('B4', 10), ('B5', 16), \n ('C1', 18), ('C2', 7), ('C3', 4), ('C4', 3), ('C5', 4), \n ('D1', 12), ('D2', 10), ('D3', 12), ('D4', 16), ('D5', 20), \n ] \n"
] | f_27871 | 2つのリスト`factories'と`shops`の要素の組み合わせをキーとし、タプル`costs`各要素を要素等する辞書型オブジェクトを作る | [
"itertools"
] |
40,676 | def f_40676(soup):
return | soup.find_all(attrs={"data-locate": "address"}) |
from bs4 import BeautifulSoup
def check(candidate): | [
"\n soup = BeautifulSoup('<div data-locate=\"address\">foo!</div>')\n res = candidate(soup)\n assert len(res) == 1\n assert res[0].attrs == {'data-locate': 'address'} \n assert res[0].text == \"foo!\"\n"
] | f_40676 | キーワード引数として用いる事ができないHTML5のdata-属性、例えば`data-locel`が`address`に一致するものをオブジェクト`soup`から検索する | [
"bs4"
] |
|
42,256 | def f_42256(li):
|
return s | s = ''.join(i[0] for i in li) |
def check(candidate): | [
"\n assert candidate(['sda', 'dahkdja', 'uehjkw', 'ebhjda']) == 'sdue'\n",
"\n assert candidate(['happy', 'apple', 'pear', 'pie', 'yummy']) == 'happy'\n",
"\n assert candidate(['a', 'b', 'c', 'd']) == 'abcd'\n",
"\n assert candidate([str(i) for i in range(10)]) == '0123456789'\n"
] | f_42256 | 文字列を要素に持つリスト`li`の頭文字を結合した文字列`s`を得る | [] |
42,256 | def f_42256(li):
|
return s |
s = ''
for line in li:
s += line[0]
|
def check(candidate): | [
"\n assert candidate(['sda', 'dahkdja', 'uehjkw', 'ebhjda']) == 'sdue'\n",
"\n assert candidate(['happy', 'apple', 'pear', 'pie', 'yummy']) == 'happy'\n",
"\n assert candidate(['a', 'b', 'c', 'd']) == 'abcd'\n",
"\n assert candidate([str(i) for i in range(10)]) == '0123456789'\n"
] | f_42256 | 文字列を要素に持つリスト`li`の頭文字を結合した文字列`s`を得る | [] |
18,967 | def f_18967(li, i):
return | i not in li |
def check(candidate): | [
"\n assert candidate(['sda', 'dahkdja', 'uehjkw'], \"sda\") == False\n",
"\n assert candidate(['happy', 'apple', 'pear', 'pie', 'yummy'], \"dog\") == True\n",
"\n assert candidate([str(i) for i in range(10)], 10) == True\n"
] | f_18967 | リスト`li`の中に要素`i`が含まれていない条件分岐を行う | [] |
|
37,648 | def f_37648(req_data):
return | json.dumps(req_data).encode('utf-8') |
import json
def check(candidate): | [
"\n assert candidate({'test': 'just a test'}) == b'{\"test\": \"just a test\"}'\n"
] | f_37648 | サーバーに送信するデータ`req_data`をUTF-8で符号化する | [
"json"
] |
|
39,502 | def f_39502(str):
return | re.sub('([あ-んア-ン一-鿐ー])\s+((?=[あ-んア-ン一-鿐ー]))',r'\1\2', str) |
import re
def check(candidate): | [
"\n assert candidate('日 本 語 で 挟 ま れ た 空 白 を 削 除 す る') == '日本語で挟まれた空白を削除する'\n"
] | f_39502 | 文字列`str`内の、日本語で挟まれた空白を削除する | [
"re"
] |
|
16,805 | def f_16805(s, n):
return | u'{0}{1}'.format(s, n) |
def check(candidate): | [
"\n assert candidate('abd', 35) == 'abd35'\n",
"\n assert candidate('', 12.34) == '12.34'\n",
"\n assert candidate([1,2,3], 'string') == '[1, 2, 3]string'\n"
] | f_16805 | 文字列の変数`s`と`n`をUTF-8に変換して結合する | [] |
|
40,978 | def f_40978(M, N):
return | [x+1 for x in range(M) for y in range(N)] |
def check(candidate): | [
"\n assert candidate(2, 3) == [1,1,1,2,2,2]\n",
"\n assert candidate(2, 1) == [1,2]\n"
] | f_40978 | 1が`N`個, 2が`N`個, ..., `M`が`N`個並ぶリストを生成する | [] |
|
40,978 | def f_40978(M, N):
return | [i // N + 1 for i in range(N * M)] |
def check(candidate): | [
"\n assert candidate(2, 3) == [1,1,1,2,2,2]\n",
"\n assert candidate(2, 1) == [1,2]\n"
] | f_40978 | 1が`N`個, 2が`N`個, ..., `M`が`N`個並ぶリストを生成する | [] |
|
39,379 | def f_39379(x):
return | [h.get_height() for h in sns.distplot(x).patches] |
import seaborn as sns
import numpy as np
sns.set()
np.random.seed(0)
def check(candidate): | [
"\n x = np.random.rand(100)\n res = candidate(x)\n assert res == [\n 1.2707405677074517,\n 0.8132739633327691,\n 1.0674220768742593,\n 1.0674220768742597,\n 0.8641035860410673\n ]\n"
] | f_39379 | distplotで表示したデータ`x`に関するヒストグラム上のピンの高さをリストとして得る | [
"numpy",
"seaborn"
] |
|
38,415 | def f_38415():
|
return ax | ax=plt.subplot(aspect='equal') |
import matplotlib.pyplot as plt
def check(candidate): | [
"\n res_ax = candidate()\n assert res_ax.get_xlim() == res_ax.get_ylim()\n"
] | f_38415 | グラフの描画範囲`ax`を正方形にする | [
"matplotlib"
] |
37,757 | def f_37757(string):
return | eval(string) |
def check(candidate): | [
"\n assert candidate(\"[1,1,1,2,2,2]\") == [1,1,1,2,2,2]\n",
"\n assert candidate(\"[1,2]\") == [1,2]\n"
] | f_37757 | 文字列型変数`string`の値を数値型のインスタンス変数として評価する | [] |
|
34,422 | def f_34422(s_json):
|
return d | d = json.loads(s_json) |
import json
def check(candidate): | [
"\n assert candidate('{\"a\":123,\"b\":45.6}') == {'a':123, 'b':45.6}\n"
] | f_34422 | JSONを表す文字列`s_json`から辞書型オブジェクト`d`を得る | [
"json"
] |
27,686 | def f_27686(soup):
return | soup.get('a_id') |
from bs4 import BeautifulSoup
def check(candidate): | [
"\n soup = BeautifulSoup('<p>riginsf</p>')\n soup['a_id'] = 'some value'\n assert candidate(soup) == 'some value'\n"
] | f_27686 | HTMLパースオブジェクト`soup`の中でHTMLタグの`a_id`の属性値を取得する | [
"bs4"
] |
|
41,054 | def f_41054():
return | [os.rename(f, f.replace('.dat', '.gui')) for f in os.listdir('.') if not f.startswith('.')] |
import os
def check(candidate): | [
"\n assert all([((item is None) or item.endswiths('.gui')) for item in candidate()])\n"
] | f_41054 | カレントディレクトリにある特定の拡張子`.dat`をもつファイルの拡張子を`.gui`にすべて書き換える | [
"os"
] |
|
43,303 | def f_43303(df):
|
return df2 | df2 = df.reset_index(drop=True) |
import pandas as pd
def check(candidate): | [
"\n df = pd.DataFrame(data={'col1':[0,1,2,3], 'col2': pd.Series([2,3], index=[2,3])}, index=[0,2,1,3])\n assert candidate(df).equals(pd.DataFrame(data={'col1': [0,1,2,3], 'col2': pd.Series([2,3], index=[1,3])}, index=[0,1,2,3]))\n"
] | f_43303 | データフレーム`df`のインデックスをリセットした新たなデータフレーム`df2`を得る | [
"pandas"
] |
43,322 | def f_43322(df):
return | df.resample('1min').ffill() |
import io
import pandas as pd
def check(candidate): | [
"\n data = (\n \"年月日時,気温(℃),降水量(mm),風速(m/s),日射量(MJ/㎡)\\n\"\n \"2017-01-01 00:00:00,5.8,0.0,1.5,0.0\\n\"\n \"2017-01-01 01:00:00,4.9,0.0,0.8,0.0\\n\"\n \"2017-01-01 02:00:00,4.9,0.0,1.5,0.0\\n\"\n \"2017-01-01 03:00:00,4.2,0.0,0.8,0.0\\n\"\n \"2017-01-01 04:00:00,4.4,0.0,1.0,0.0\\n\"\n )\n df = pd.read_csv(io.StringIO(data), parse_dates=['年月日時'], index_col='年月日時')\n res = candidate(df)\n assert len(res) == 241\n"
] | f_43322 | 時系列データの入ったデータフレーム`df`を1分ごとにリサンプルし、間の値は直前の値で補完する | [
"io",
"pandas"
] |
|
35,683 | def f_35683(z):
return | z.real |
def check(candidate): | [
"\n assert candidate(1.23-0j) == 1.23\n",
"\n assert candidate(1.23+0j) == 1.23\n",
"\n assert candidate(0.0-1j) == 0.0\n"
] | f_35683 | 複素数`z`の実数部のみを得る | [] |
|
41,058 | def f_41058(iter, r):
return | list(itertools.combinations(iter, r)) |
import itertools
def check(candidate): | [
"\n assert candidate([1,2,3], 2) == [(1, 2), (1, 3), (2, 3)]\n",
"\n assert candidate([1], 2) == []\n",
"\n assert candidate([1], 1) == [(1, )]\n"
] | f_41058 | イテラブルオブジェクト`iter`の`r`個の要素の組み合わせをリストとして得る | [
"itertools"
] |
|
42,573 | def f_42573(sheet, row, col):
return | sheet.cell_value(row, col) |
import xlrd
from xlwt import Workbook
def check(candidate): | [
"\n file_location = \"test.xlsx\"\n\n book = Workbook()\n sheet1 = book.add_sheet('Sheet 1')\n sheet1.write(0, 0, 'A1')\n sheet1.write(0, 1, 'B1')\n sheet1.write(8, 5, \"Hello, world!\")\n book.save(file_location)\n\n workbook = xlrd.open_workbook(file_location)\n sheet = workbook.sheet_by_index(0)\n assert candidate(sheet, 0, 0) == \"A1\"\n assert candidate(sheet, 0, 1) == \"B1\"\n assert candidate(sheet, 8, 5) == \"Hello, world!\"\n"
] | f_42573 | Excelシートオブジェクト`sheet`内の行`row`、列`col`のセルの値を得る | [
"xlrd",
"xlwt"
] |
|
40,361 | def f_40361(func, args):
return | func(*args) |
def check(candidate): | [
"\n def func1(x, y, z): return x + y + z \n assert candidate(func1, [1,2,3]) == 6\n",
"\n def func2(a): return 0.8\n assert candidate(func2, ['random']) == 0.8\n"
] | f_40361 | 引数`args`をアンパックして関数`func`に渡す | [] |
|
43,333 | def f_43333(r, l):
return | pd.DataFrame(data={'range': r, 'result': l}) |
import pandas as pd
def check(candidate): | [
"\n r, l = [1,2,3], [4,5,6]\n assert candidate(r, l).equals(pd.DataFrame(data={'range': r, 'result': l}))\n"
] | f_43333 | 列名`range`の要素をリスト`r`、列名`result`の要素をリスト`l`としてデータフレームを作る | [
"pandas"
] |
|
11,582 | def f_11582():
return | open('C:\\Users\\Documents\\python programs', 'r', encoding='utf-8') |
import builtins
from unittest.mock import Mock
def check(candidate): | [
"\n with open('a.txt', 'w') as f:\n f.write('t')\n f1 = open('a.txt')\n builtins.open = Mock(return_value = f1)\n assert candidate() == f1\n"
] | f_11582 | ファイル`C:\Users\Documents\python programs`を開く | [
"builtins"
] |
|
12,174 | def f_12174():
return | sys.path |
import sys
def check(candidate): | [
"\n assert candidate() == sys.path\n"
] | f_12174 | PYTHONPATHを表示する | [
"sys"
] |
|
6,225 | def f_6225():
return | sum(1 for line in open('myfile.txt')) |
def check(candidate): | [
"\n with open('myfile.txt', 'w') as fw:\n for i in range(10): fw.write(f\"{i}\\n\")\n assert candidate() == 10\n",
"\n with open('myfile.txt', 'w') as fw:\n for i in range(88): fw.write(f\"{i}\\n\")\n assert candidate() == 88\n"
] | f_6225 | テキストファイル`myfile.txt`の行数を取得する | [] |
|
6,225 | def f_6225():
return | len(open('myfile.txt').readlines()) |
def check(candidate): | [
"\n with open('myfile.txt', 'w') as fw:\n for i in range(10): fw.write(f\"{i}\\n\")\n assert candidate() == 10\n",
"\n with open('myfile.txt', 'w') as fw:\n for i in range(88): fw.write(f\"{i}\\n\")\n assert candidate() == 88\n"
] | f_6225 | テキストファイル`myfile.txt`の行数を取得する | [] |
|
47,199 | def f_47199(a):
return | a is not None |
def check(candidate): | [
"\n assert candidate(None) == False\n",
"\n assert candidate(0) == True\n",
"\n assert candidate(0.00) == True\n",
"\n assert candidate([]) == True\n",
"\n assert candidate(102) == True\n"
] | f_47199 | 変数`a`がNoneでない場合に変数を表示する | [] |
|
23,332 | def f_23332(data):
|
return list |
list = []
for r in data:
list.append(', '.join(r))
|
def check(candidate): | [
"\n data = [['a','b','c','x','y','z'],\n ['f', 'g', 'h', 'i', 'j', 'k']]\n assert candidate(data) == [\"a, b, c, x, y, z\", \"f, g, h, i, j, k\"]\n"
] | f_23332 | 二次元リスト`list`の中身を全て | [] |
34,431 | def f_34431():
|
return f | f = open('all_names.csv', 'w', encoding='UTF-8') |
def check(candidate): | [
"\n f = candidate()\n assert f.name == 'all_names.csv'\n assert f.mode == 'w'\n assert f.encoding == 'UTF-8'\n"
] | f_34431 | 文字コード | [] |
33,700 | def f_33700(list):
|
return newlist |
newlist = []
for s in list:
if s.endswith('string'):
newlist.append(s)
|
def check(candidate): | [
"\n assert candidate(['abcstring', 'bbbb', 'fhstringyjn', '1326546']) == ['abcstring']\n"
] | f_33700 | リスト`list`から条件となる文字列`string`と部分一致する要素を取り出して新しいリスト`newlist`を作る | [] |
33,700 | def f_33700(list):
|
return newlist | newlist = []
for s in list:
if 'string' in s:
newlist.append(s) |
def check(candidate): | [
"\n assert candidate(['abcstring', 'bbbb', 'fhstringyjn', '1326546']) == ['abcstring', 'fhstringyjn']\n"
] | f_33700 | リスト`list`から条件となる文字列`string`と部分一致する要素を取り出して新しいリスト`newlist`を作る | [] |
27,556 | def f_27556():
return | plt.figure() |
import matplotlib
import matplotlib.pyplot as plt
def check(candidate): | [
"\n assert isinstance(candidate(), matplotlib.figure.Figure)\n"
] | f_27556 | グラフを表示する | [
"matplotlib"
] |
|
37,060 | def f_37060(data_frame):
return | display(data_frame) |
import pandas as pd
from IPython.display import display
def check(candidate): | [
"\n df = pd.DataFrame([1, 2, 3])\n try:\n candidate(df)\n except:\n assert False\n"
] | f_37060 | データフレーム`data_frame`を表示する | [
"IPython",
"pandas"
] |
|
12,310 | def f_12310():
|
return table | table = Texttable()
print(table.draw()) |
from texttable import Texttable
def check(candidate): | [
"\n try:\n candidate()\n except:\n assert False\n"
] | f_12310 | 表`table`を画面に表示する（texttable) | [
"texttable"
] |
19,311 | def f_19311():
return | pd.read_csv('arena.txt', header=None, delim_whitespace=True, decimal=',') |
import pandas as pd
def check(candidate): | [
"\n file_name = 'arena.txt'\n with open(file_name, 'w') as f:\n f.write('1 0,000000 4,219309 4,219309 8,988674 8,988674 10,848450\\n')\n f.write('2 4,219309 7,414822 7,414822 12,430150 12,430150 14,198310\\n')\n f.write('3 8,000000 10,478795 10,478795 15,417747 15,417747 17,297929\\n')\n f.write('1 11,000000 14,257995 14,257995 19,009302 19,009302 20,873072\\n')\n df = candidate()\n assert df.shape[0] == 4\n assert df.shape[1] == 7\n"
] | f_19311 | 少数点にコンマが使われているファイル`arena.txt`を読み込む | [
"pandas"
] |
|
42,268 | def f_42268(json_data):
|
return json_str | json_str = json.dumps(json_data) |
import json
def check(candidate): | [
"\n assert candidate({'a': 134, 'bvgdfbh': 46.7576}) == '{\"a\": 134, \"bvgdfbh\": 46.7576}'\n",
"\n assert candidate(['foo', {'bar': ('baz', None, 1.0, 2)}]) == '[\"foo\", {\"bar\": [\"baz\", null, 1.0, 2]}]'\n"
] | f_42268 | オブジェクト`json_data`をJSON文字列`json_str`に変換する | [
"json"
] |
51,387 | def f_51387():
|
return data | data = np.genfromtxt('file.csv') |
import numpy as np
from unittest.mock import Mock
def check(candidate): | [
"\n np.genfromtxt = Mock(return_value = np.array([[2, 3, 5], [1, 5, 6]]))\n assert candidate().shape == (2, 3)\n",
"\n np.genfromtxt = Mock(return_value = np.array([['abc'], ['lkm']]))\n assert candidate().shape == (2, 1)\n"
] | f_51387 | CSVファイル`file.csv`を`data`に読み込む | [
"numpy"
] |
38,598 | def f_38598(df):
|
return | df.drop(df.index[df.row == "condition"], inplace=True) |
import pandas as pd
def check(candidate): | [
"\n df = pd.DataFrame({'row': ['同意します', 'condition', '同意します', '同意しません',]},\n index=[1, 2, 3, 4, ])\n candidate(df)\n assert df.shape == (3, 1)\n"
] | f_38598 | 条件`condition`を満たす行を削除する | [
"pandas"
] |
20,094 | def f_20094(list, x, y):
return | list[x][y] |
import json
def check(candidate): | [
"\n mat = [[i+j for i in range(2)] for j in range(3)]\n assert candidate(mat, 0, 0) == 0\n",
"\n mat = [[i+j for i in range(2)] for j in range(3)]\n assert candidate(mat, 2, 1) == 3\n"
] | f_20094 | 二次元リスト`list`内の要素 | [
"json"
] |