blob_id stringlengths 40 40 | language stringclasses 1
value | repo_name stringlengths 5 133 | path stringlengths 2 333 | src_encoding stringclasses 30
values | length_bytes int64 18 5.47M | score float64 2.52 5.81 | int_score int64 3 5 | detected_licenses listlengths 0 67 | license_type stringclasses 2
values | text stringlengths 12 5.47M | download_success bool 1
class |
|---|---|---|---|---|---|---|---|---|---|---|---|
3bbcaf26cf7f9b12f454f1eab576bc1d55e025d1 | Python | bipinkh/ai-search-algorithms | /node.py | UTF-8 | 852 | 3.21875 | 3 | [] | no_license | class Node:
def __init__(self, parent_node = None, move=None, state=None, depth=0, g=0):
self.parent_node = parent_node # the anccestor of this node
self.move = move # Up, Down, Left, Right
self.state = state # 2D Array of state
self.depth = depth
self.__map()
... | true |
a3c3b8de50facef60137e568588ca7513889b555 | Python | aggo/jku-tcml | /tcml_a3_e5/ex5.py | UTF-8 | 3,261 | 3.625 | 4 | [] | no_license | #!/usr/bin/python
# Author: Amalia Ioana Goia
# Matr. Nr.: k1557854
# Exercise 2
from pprint import pprint
import math
def read_data(filename):
import csv, numpy as np
instances = []
with open(filename, 'r') as csvfile:
dataset = csv.reader(csvfile, delimiter=',')
for row in dataset:
... | true |
a1f7090200078ace0ebc88c38dea9352923b8c7e | Python | Anseik/algorithm | /study/정올/Language_Coder/j_102_형성평가2.py | UTF-8 | 108 | 3.15625 | 3 | [] | no_license | text1 = 'hometown'
text2 = 'Flowering'
print('My {}'.format(text1), '{} mountain'.format(text2), sep='\n')
| true |
2aa59c82fcf8e1c3a079ff7f3c0bfa50568d6e20 | Python | blueshed/chat | /chat/websocket.py | UTF-8 | 914 | 2.640625 | 3 | [] | no_license | """ our websocket handler """
import logging
from tornado.websocket import WebSocketHandler
log = logging.getLogger(__name__)
class Websocket(WebSocketHandler):
""" a websocket handler that broadcasts to all clients """
clients = []
def check_origin(self, origin):
""" in development allow ws fr... | true |
3385bfa22f98c30ec746287a3dfb700265a5daca | Python | keshavjha018/Web_Automation | /test2.py | UTF-8 | 682 | 2.953125 | 3 | [] | no_license | from selenium import webdriver
PATH = 'C:\Program Files (x86)\chromedriver.exe'
driver = webdriver.Chrome(PATH)
import time
#from selenium import webdriver
from selenium.webdriver.common.keys import Keys
# If you have Chromedriver In Same folder then you dont need to pass executable path...
#driver = webdriver.Chro... | true |
cedb3a8a6d329a28c0abc23a6caa40dec3ce508d | Python | quangxdu/cs425 | /MP2/main.py | UTF-8 | 1,495 | 2.84375 | 3 | [] | no_license | import nodes, coordinator, threading, sys
continue_looping = 1
#Check if the user wants writeback
writeback = 0
if len(sys.argv) > 2:
if (sys.argv[1] == "-g"):
outputfile = sys.argv[2]
writeback = 1
f = open(outputfile, 'w')
#Create new coordinator
coordinator = coordinator.Coordinator()
Node0 ... | true |
33287f4872a2994f668586c1a4ee3f7782862d2d | Python | matanboaz/SPC | /change_in_var/prog10.py | UTF-8 | 2,737 | 2.828125 | 3 | [] | no_license | import numpy as np
import pandas as pd
from scipy.stats import gamma
"""
This is a program for detecting a 2-sided change in the variance with known baseline s.d.,
unknown baseline mean, by a mixture of discretized conditional Gamma.
input:
======
:x: data
:sigma: s.d.
:alpha1:
:beta1:
... | true |
aa5fa2df17add420aa420e8cdf0eb043d032edf3 | Python | pranav-maddali/playlister | /app.py | UTF-8 | 4,290 | 2.71875 | 3 | [] | no_license | from flask import Flask, request, jsonify, render_template
import spotipy.util as util
from spotipy.oauth2 import SpotifyClientCredentials
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans, DBSCAN
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
app = Fl... | true |
126c18c6b07f4c8879495eab2058d802378632a8 | Python | tlananthu/python-learning | /00_simple_examples/07_float_int.py | UTF-8 | 112 | 2.9375 | 3 | [
"Apache-2.0"
] | permissive | f=float(8.11)
print(f)
i=int(f)
print(i)
j=7
print(type(j))
j=f
print(j)
print(type(j))
print(int(8.6))
| true |
298fd47ef063564f220bf7491ecb427e1127c349 | Python | RengarAndKhz/QuoraInterview | /InorderPreoder.py | UTF-8 | 1,300 | 3.46875 | 3 | [] | no_license | # Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
#O(n^2) every node in postorder need index of in inorder
class Solution(object):
'''def buildTree(self, inorder, postorder):
"""
:... | true |
d55ccfcc528e3a67b9d180b1d739df8b431807f7 | Python | Windman/AlgorithminThinking | /GraphTheory/GraphTheory.py | UTF-8 | 1,912 | 3.71875 | 4 | [] | no_license | """Graph theory p1 m1"""
EX_GRAPH0 = {0: set([1,2]), 1: set([]), 2: set([])};
EX_GRAPH1 = {0: set([1,4,5]), 1: set([2,6]), 2: set([3]), 3: set([0]), 4: set([1]), 5: set([2]), 6: set([])};
EX_GRAPH2 = {0: set([1,4,5]), 1: set([2,6]), 2: set([3,7]), 3: set([7]), 4: set([1]), 5: set([2]), 6: set([]), 7: set([3]), 8: set(... | true |
76e3b65f764f43bd105495eae79ac6da7bd359c5 | Python | chae1108/wheel-of-jeopardy | /classes/Board.py | UTF-8 | 1,305 | 3.84375 | 4 | [] | no_license | from classes.Category import Category
class Board:
def __init__(self, categories, round):
self.__categories = self.__createCategories(categories, round)
# Returns a list of category objects from the list provided by the game
def __createCategories(self, categories, round):
categoryObjects... | true |
eda2ba42a6e803ab4a5255648286b4842fdab86f | Python | jamesaduke/toy-problems-new | /codewars/python-problems/anti_vowel.py | UTF-8 | 140 | 3.296875 | 3 | [] | no_license | def anti_vowel(text):
vowels="aeiouAEIOIU"
string=""
for char in text:
if char not in vowels:
string += char
return string | true |
8fd3dcf13f5dbfae9ee509ebff2c964b51b365ec | Python | carlesanton/visuall-hull-extractor | /utils/fundamental_matrix.py | UTF-8 | 3,129 | 2.625 | 3 | [] | no_license | from sift import compute_sift_descriptor_of_image, compute_sift_matches
import os
import cv2
import random
from matplotlib import pyplot as plt
import numpy as np
def compute_fundamental_matrix():
calibration_images_folder_path = os.path.join(os.getcwd(),'visuall_hull_extractor/calibration_images/v3labs')
... | true |
c154487ce52e90b01243c30ea60403f7d47fb948 | Python | hed-standard/hed-python | /tests/tools/util/test_data_util.py | UTF-8 | 9,670 | 2.546875 | 3 | [
"MIT"
] | permissive | import os
import unittest
import numpy as np
from pandas import DataFrame
from hed.errors.exceptions import HedFileError
from hed.tools.util.data_util import add_columns, check_match, delete_columns, delete_rows_by_column, \
get_key_hash, get_new_dataframe, get_row_hash, get_value_dict, \
make_info_dataf... | true |
50670815d03c919c665b55bb569d7d59a1074110 | Python | ModelSEED/PlantSEED | /Scripts/PlantSEED_v3/Template/Fix_Reaction_Curation.py | UTF-8 | 586 | 2.75 | 3 | [] | no_license | #!/usr/bin/env python
from urllib.request import urlopen
import sys
import json
import string
with open("../PlantSEED_Roles.json") as subsystem_file:
roles_list = json.load(subsystem_file)
reactions_list=list()
for entry in roles_list:
for rxn in entry['reactions']:
if(rxn not in reactions_list):
... | true |
bbe321005807a4bd950595eeb59f589a1cb7744c | Python | wadayamada/Tensorflow-ImageClassification | /data_preparation/keep_2_gakushu.py | SHIFT_JIS | 1,358 | 2.8125 | 3 | [] | no_license | from PIL import Image
import os
#keep_2_gakushu.py
#摜̖
#data_keep_other_gazou=1101
#data_keep_latte_gazou=901
#data_keep̉摜(28,28)ɃTCYāAdataɈړ
def keep_2_other(data_keep_other_gazou,name):
for a in range(data_keep_other_gazou):
b=str(a+1)
#pXw肵ĉ摜ǂݍ
image=Image.open("./dat... | true |
fcfff80359a6a7596a050c81a9fc9877a666f348 | Python | jmiths/PE | /Problem23.py | UTF-8 | 1,428 | 3.25 | 3 | [] | no_license | #!/usr/bin/python
# Primes by default are not abundant
import math, itertools
from sets import Set
#primes = [1]*28125
#primes[0] = 0
#primes[1] = 0
#for num in range(0,len(primes)):
# if primes[num] == 0:
# continue
# else:
# for bad_val in range(num*num,len(primes),num):
# primes[bad_val] = 0
#abun = []
#for... | true |
f5cdde2a396d0330bfa9e86c19d972e91786cb40 | Python | Mat4wrk/Parallel-Programming-with-Dask-in-Python-Datacamp | /3.Working with Dask DataFrames/Building a pipeline of delayed tasks.py | UTF-8 | 382 | 2.71875 | 3 | [] | no_license | # Read from 'WDI.csv': df
df = dd.read_csv('WDI.csv')
# Boolean series where 'Indicator Code' is 'EN.ATM.PM25.MC.ZS': toxins
toxins = df['Indicator Code'] == 'EN.ATM.PM25.MC.ZS'
# Boolean series where 'Region' is 'East Asia & Pacific': region
region = df['Region'] == 'East Asia & Pacific'
# Filter the DataFrame using... | true |
ed48cef2f6b31a439d0c974932e66974d6c5392d | Python | gplatono/lexical_resources | /pos_split.py | UTF-8 | 655 | 2.578125 | 3 | [] | no_license | #Uncomment the lines below to download and install wordnet corpus for nltk
#import nltk
#nltk.download('wordnet')
import nltk
#nltk.download('averaged_perceptron_tagger')
#nltk.download('brown')
from nltk.corpus import wordnet as wn
from nltk.corpus import brown
from nltk.tag import pos_tag
brown_news_tagged = brown.... | true |
90a5cd6e797608f3033109bb32dab01302c85795 | Python | SeanDavis268/Mine-Sweeper | /main.py | UTF-8 | 9,363 | 3.265625 | 3 | [] | no_license | from tkinter import *
from math import *
from random import randrange
#print("yeet")
class startUp():
'''This is the first window to pop up and it asks for the players
prefered difficulty and board size. '''
def __init__(self):
self.frame=Tk()
self.size = 0
Label(self.frame,text... | true |
70b86fb5719508e92e72ec5e281d457472a00214 | Python | dbreen/connectfo | /game/scenes/main.py | UTF-8 | 6,701 | 3.046875 | 3 | [
"MIT"
] | permissive | import pygame
from game import gamestate, utils
from game.constants import *
from game.media import media
from game.scene import Scene
class MainScene(Scene):
def load(self):
# state variables
self.set_state('running', True)
self.drop_info = None
# resources
... | true |
ce29ecd821ffbdbde6f09ac8a847691e3a79cfa5 | Python | szheng15/Computer-Vision | /pa1/assignment1.py | UTF-8 | 713 | 2.96875 | 3 | [] | no_license | import numpy as np
from scipy.ndimage.filters import convolve
import skimage
from skimage import color
def energy_image(im):
input_color_image = skimage.img_as_float(color.rgb2gray(im))
gradient_x = convolve(input_color_image, np.array([[1,-1]]), mode = "wrap")
gradient_y = convolve(input_color_image, np.a... | true |
8d2855c8652f3db74ad194ff17c3e0d507ad8838 | Python | francisco-igor/ifpi-ads-algoritmos2020 | /G - Fabio 2a e 2b - Condicionais/G - Fabio 2a - Condicionais/G_Fabio_2a_q25_senha.py | UTF-8 | 633 | 3.8125 | 4 | [] | no_license | '''Verifique a validade de uma senha fornecida pelo usuário. A senha é 1234. O algoritmo deve escrever
uma mensagem de permissão de acesso ou não.'''
# ENTRADA
def main():
senha = int(input('Senha do usuário: '))
verificar(senha)
# PROCESSAMENTO
def verificar(senha):
digito1 = senha // 1000
... | true |
8659b1ac62975a7052a111f1a4529a63be538623 | Python | sanjibkd/py_stringmatching | /py_stringmatching/tests/test_simfunctions.py | UTF-8 | 31,476 | 2.6875 | 3 | [
"BSD-3-Clause"
] | permissive | from __future__ import unicode_literals
import math
import unittest
from nose.tools import *
# sequence based similarity measures
from py_stringmatching.simfunctions import levenshtein, jaro, jaro_winkler, hamming_distance, needleman_wunsch, \
smith_waterman, affine, editex, bag_distance, soundex
# token based ... | true |
3bc30d6861e5cb0df68b40b3cfd0103d3791b270 | Python | matiboy/test_contact_app | /contact/views.py | UTF-8 | 1,703 | 2.5625 | 3 | [] | no_license | from flask import render_template, request, redirect, url_for, flash
from models import MyContact
def reg_views(app):
db_session = app.config['DATABASE']
@app.route('/')
def index():
contacts = MyContact.query.all()
return render_template('index.html', contacts=contacts)
@app.route('... | true |
10f4d48450d0e8809143fee66ff77d61a8825d9f | Python | Shusuke-Irikuchi/X-means | /xmeans.py | UTF-8 | 4,073 | 2.9375 | 3 | [] | no_license | """=====================================================
import
====================================================="""
import pandas as pd
from sklearn.cluster import KMeans
import scipy
import numpy as np
from numpy.random import *
"""=====================================================
... | true |
849e123943e45bf161ec1aea47d7c0c5e2785406 | Python | Ojisama/breeding | /Mapping.py | UTF-8 | 10,239 | 3.171875 | 3 | [] | no_license | from math import *
from collections import deque, namedtuple
DIRECTIONS = namedtuple('DIRECTIONS',
['Up', 'Down', 'Left', 'Right'])(0, 1, 2, 3)
BOARD_LENGTH=32
#renvoie un tableau de taille 34*34*4 correspondant aux inputs du rĂŠseau de neurones Ă partir du board
#parcours de la grille dans le sens... | true |
72635d8b1ee94439e34dd44d1f76c321a6f5fb4a | Python | LightXEthan/ServerNetwork | /socket_client.py | UTF-8 | 4,818 | 2.875 | 3 | [] | no_license | """
CITS3002 Computer Networks Project
Written by Ethan Chin 22248878 and Daphne Yu 22531975
@brief: socket client for basic_server.c
extended version of the given example code
"""
import socket,sched,time
from time import sleep
# Create a TCP/IP socket
sock = socket.socket(socket.AF_INET, socket.SOCK_... | true |
f2d3680ecff51f18ac623bb7f7835d752afe21bf | Python | Feipeng-Yang/python-challenge | /PyPoll/main.py | UTF-8 | 2,974 | 3.34375 | 3 | [] | no_license | # Election result analysis for the data in "election_data.csv"
# import modules
import os
import csv
# read data from "election_data.csv"
election_data = os.path.join("Resources", "election_data.csv")
# initialize variables
candidate_list = []
total_votes = 0
with open(election_data) as VotingResult:
votes = csv... | true |
65da1233d7e4464e33186b55844b91b9b19caccc | Python | AleksasVaitulevicius/masters | /3d_object_recognition_from_images/model.py | UTF-8 | 3,125 | 2.78125 | 3 | [] | no_license | from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Activation, Dense
# args -------------------------------------------------------------------------------------------------
epochs = 9
batch_size = 100
image_widt... | true |
87e19fc2f2dc65c1f1b2aa1932120dec9e9e01b1 | Python | dennlinger/Dagger | /nodagger.py | UTF-8 | 7,361 | 2.609375 | 3 | [] | no_license | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 11 10:42:45 2019
@author: dennis
"""
import random
import sys
import os
import numpy as np
import scipy.sparse as spa
from sklearn.linear_model import SGDClassifier
from sklearn.neighbors import DistanceMetric
from sklearn.metrics import classi... | true |
5aa9241315ca7de6d55078c5f16089a0e8e19c5f | Python | celtic108/Generative-MNIST-Digits | /src/test_file.py | UTF-8 | 249 | 2.546875 | 3 | [] | no_license | from network import sigmoid
from network import sigmoid_prime
import numpy as np
test = np.array([-6, -4, -2, 0, 2, 4, 6])
vsigmoid = np.vectorize(sigmoid)
vsigmoid_prime = np.vectorize(sigmoid_prime)
print vsigmoid(test)
print vsigmoid_prime(test) | true |
b5d87aaffc923686eb8053e1c91a9f69ec0b5a61 | Python | zoejane/uband_python | /worldcup/comments.py | UTF-8 | 1,094 | 3.46875 | 3 | [] | no_license | import json
# 读取文件
with open('response.txt', 'r', encoding='utf-8') as f:
contents = ''
for line in f.readlines():
contents += line.strip()
# print(contents)
comments = json.loads(contents) # 这是一个 dictionary
# print(comments)
# print(type(comments))
# 初始化计算器
users = dict()
# 循坏遍历
for comment in comm... | true |
af30ace529f65ebe2549b3172bd0587b09b0a07e | Python | TheMiles/aoc | /12.py | UTF-8 | 3,330 | 3.03125 | 3 | [] | no_license | #!/usr/bin/python3
import argparse
import numpy as np
import re
from datetime import datetime
def getArguments():
parser = argparse.ArgumentParser(description='Advent of code')
parser.add_argument('input', metavar='file', type=argparse.FileType('r'))
parser.add_argument('-i','--iterations', type=int, def... | true |
51b2bf3ea8a97f0c85da5153fa14b5d4669e6434 | Python | weltonvaz/Coursera | /Python3/1a. parte/5semana/vogal.py | UTF-8 | 292 | 3.71875 | 4 | [] | no_license | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
""" Escreva a função vogal que recebe um único caractere como parâmetro e devolve True
se ele for uma vogal e False se for uma consoante."""
def vogal(s):
if s.lower() in 'aeiou':
return True
else:
return False
| true |
a7cd879c743fb08a8801c8766381f06808fdfe8f | Python | q-hwang/tacotron2 | /chinese_process.py | UTF-8 | 2,785 | 2.890625 | 3 | [
"BSD-3-Clause"
] | permissive | import numpy as np
import re
from pypinyin import pinyin, Style
num=['零','一','二','三','四','五','六','七','八','九']
kin=['十','百','千','万','零']
def sadd(x):
x.reverse()
if len(x) >= 2:
x.insert(1,kin[0])
if len(x) >= 4:
x.insert(3,kin[1])
if len(x) >= 6:
x.inser... | true |
ca5371ab7a1975d0f07a0fe54f12b5f81014752d | Python | MDYLL/EPAM | /EPAM_hw11.py | UTF-8 | 760 | 2.890625 | 3 | [] | no_license | import pytest
from homework4_2 import count_words
def test_count_words_positive():
assert count_words(['epam-pam-pam', 'param-pam-bam'], 'pam') == 4
def test_count_words_zero():
assert count_words(['intel', 'mera', 'microsoft'], 'epam') == 0
def test_count_words_bad_input():
with pytest.raises(TypeErro... | true |
495ba20a6c80ff229cf17ac8ae2d59c87d9a5710 | Python | johnmerm/bioinfo | /src/main/java/bioinfo/yeast/quiz.py | UTF-8 | 2,004 | 2.96875 | 3 | [] | no_license | '''
Created on May 26, 2015
@author: grmsjac6
'''
from math import sqrt
import numpy
def pointDist(a,b):
return sum([(a[i]-b[i])**2 for i in range(len(a))])
def test_maxDist():
points = [(2, 6), (4, 9), (5, 7), (6, 5), (8, 3) ]
centers = [ (4, 5), (7, 4)]
dists = [[sqrt(pointDist(p, c)) for c in... | true |
a5ce3c9292dcbf4eb1785810f5963dd2b0089821 | Python | lruczu/ml_utils | /ml_utils/optimization/pyomo/linear programming/basic_example1.py | UTF-8 | 509 | 2.65625 | 3 | [] | no_license | from pyomo.environ import ConcreteModel, Constraint, Objective, Var, NonNegativeReals
"""
min x1 + 2 * x2
s.t.
3 * x1 + 4 * x2 >= 1
2 * x1 + 5 * x2 >= 2
x1, x2 >= 0
"""
model = ConcreteModel()
model.x_1 = Var(within=NonNegativeReals)
model.x_2 = Var(within=NonNegativeReals)
model.obj = Objective(expr=model.x_1 + 2 * ... | true |
b56a8c2b677028639a6781af19c6114e68744343 | Python | satyam-seth-learnings/python_learning | /Geeky Shows/Core Python/91.Pass_Or_Call_By_Object_Reference_Example-3[124].py | UTF-8 | 163 | 2.703125 | 3 | [] | no_license | def val(lst):
print("IFBA",lst,id(lst))
lst=[11,22,33]
print("IFAA",lst,id(lst))
lst=[1,2,3]
print("BCF",lst,id(lst))
val(lst)
print("ACF",lst,id(lst)) | true |
21b7ccf3dda56703ecdcf68001e8fec6d5bd1868 | Python | surenderthakran/test_suite | /keras_runner/trainers/wine_quality/wine_quality_classification.py | UTF-8 | 2,048 | 2.84375 | 3 | [] | no_license | import os
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
dir_path = os.path.dirname(os.path.realpath(__file__))
# Read in red wine data
red = pd.read_csv(dir... | true |
dc49048442f175b7a5c8c6d47e9371639dc70e91 | Python | ijnji/sandbox | /python/epi/interconverting_string_integer_test.py | UTF-8 | 704 | 2.953125 | 3 | [] | no_license | #!/usr/bin/env python3
import pytest
from epi.interconverting_string_integer import int_to_string
from epi.interconverting_string_integer import string_to_int
def test_small():
assert(int_to_string(0) == '0')
assert(int_to_string(-1) == '-1')
assert(int_to_string(1) == '1')
assert(int_to_string(1000)... | true |
82e695190257703b73e06e0b998dc82b8489f388 | Python | lukaswals/busqueda-soluciones | /busqueda.py | UTF-8 | 4,426 | 3.296875 | 3 | [] | no_license | # -*- coding: utf-8 -*-
import time
from tkinter import *
class Busqueda(object):
def __init__(self, inicio):
self.abiertos = []
self.cerrados = []
self.tiempo_total = 0
self.inicio = inicio
def buscar(self):
encontro = False
self.abiertos.append(self.inicio)
... | true |
d78bf2e22c0b7e7d13eb8def9f83ff64a7c5c658 | Python | RailanderJoseSantos/Python-Learnning | /ex017.py | UTF-8 | 177 | 3.375 | 3 | [] | no_license | from math import hypot
kop = float(input('Informe o cateto oposto:'))
kad = float(input('Informe o cateto adjacente:'))
print('A hipotenusa é: {}'.format(hypot(kop, kad)))
| true |
fa2351c67ccfc917229d91d6682e651f7228971e | Python | sebaF96/C2_practicas | /walkie_talkie/bob.py | UTF-8 | 1,330 | 3.296875 | 3 | [] | no_license | #!/usr/bin/python
import socket
import sys
import getopt
def stablish_connection():
try:
opt, arg = getopt.getopt(sys.argv[1:], 'a:')
alice_address = opt[0][1] if opt else ''
bob_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
bob_socket.connect((alice_address, 8080))
... | true |
2c3541620cbc642360c1f4cace0051c6afd19919 | Python | gabrielasigolo/exerciseGeometricForms | /formasVolume.py | UTF-8 | 1,344 | 4.21875 | 4 | [] | no_license | #Author: GabrielaSigolo
from math import pi
print("Choose a geometric shape to know the volume: ")
print("[1] Sphere ")
print("[2] Cub")
print("[3] Parallelepiped")
print("[4] Pyramid ")
answer = int(input("Answer: "))
## answer check
if answer == 1:
rad = float(input("Type the rad: "))
elif answer == 2:
sid... | true |
bd72d1274fbad0f68f7dcb08db60efa75e4678df | Python | ptracton/experimental | /python3/stats/stats.py | UTF-8 | 1,025 | 2.953125 | 3 | [] | no_license | #! /usr/bin/env python3
import datetime
import random
import statistics
import pandas
#import pandas_datareader
import pandas_datareader.data as web
import matplotlib.pyplot as plt
if __name__ == "__main__":
print ("Stats")
data = [random.randint(0,100) for x in range(100)]
print (data)
print (statis... | true |
642a0323423d92e414d046a3892f4d65a8885bfd | Python | BenBlueAlvi/Algorithms | /hw2/inversions.py | UTF-8 | 1,083 | 3.421875 | 3 | [] | no_license | import math
#how many pairs in the opposite order
#if sorted, 0
#reverse sorted, n(n-1)/2
#3, 1, 5, 2, 4 = 4
#should be nlogn
#equal to number of sort swaps
#if left pointer less than right, no inversions
#broken
#sorted array + n inverssions
def _num_inversions(arr, ninv):
if (len(arr) > 1):
#get midpoint
mid =... | true |
8112e7d856979095b58cb0855d64eaf240510fd8 | Python | rboulton/adventofcode | /2017/day4b.py | UTF-8 | 501 | 3.296875 | 3 | [
"MIT"
] | permissive | import sys
def has_duplicates(phrase):
words = phrase.split()
sorted_words = [''.join(sorted(word)) for word in words]
already_seen = set()
for word in sorted_words:
if word in already_seen:
return True
already_seen.add(word)
return False
def phrase_is_valid(phrase):
... | true |
f951df26678a6845ac67647a896b25ca4bb4764a | Python | HatemSaadallah/speedmedia | /speedmedia.py | UTF-8 | 1,869 | 2.625 | 3 | [] | no_license | from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.options import Options
import time
import os
import sys
options = Options()
options.add_argument('--headless')
options.add_argument('--disable-gpu')
driver = webdriver.Chrome('chromedriver', options=options... | true |
1d8f51ed2c8674e26fdc9f9d1b1f5df10547f5cf | Python | kadglass/SHELS_metallicity | /SHELS_gal_flux.py | UTF-8 | 2,444 | 2.75 | 3 | [
"BSD-3-Clause"
] | permissive | '''Loads galaxy .fits file, coverts flux data to table'''
################################################################################
#
# LOAD LIBRARIES
#
################################################################################
from os.path import isfile
from astropy.io import fits
f... | true |
c64087486482bd6d8ffc605cf887057fe3383d83 | Python | akshshu/space_invader_game | /space_invad.py | UTF-8 | 4,465 | 3.109375 | 3 | [] | no_license | import pygame
import random
import math
from pygame import mixer # needed to handle music
class player_begin:
def __init__(self, x, y, change):
self.x = x
self.y = y
self.change = change
class enemy_begin:
def __init__(self, index, x, y, changeX):
self.index = index
... | true |
cfee59ee77584670b859bad56392003a09c5308f | Python | ByronPhung/combinations-using-stacks | /stack.py | UTF-8 | 3,347 | 3.890625 | 4 | [
"MIT"
] | permissive | #===============================================================================
# File : stack.py
# Project : Combinations Using Stacks
# Description : Simulate stacks.
# Company : Cal Poly Pomona
# Engineer : Byron Phung
# Gregory Lynch
#==========================================... | true |
2e6e717c5f5b4d02b62eab63157e0850bad671d3 | Python | bassil/codefights | /arcade/python/lurking_in_lists/remove_tasks.py | UTF-8 | 422 | 3.046875 | 3 | [] | no_license | def removeTasks(k, toDo):
"""
remove each kth task from input list toDo
----------
parameters
----------
k: integer
toDo: list of integers
----------
>>> removeTasks(3, [1237, 2847, 27485, 2947, 1, 247, 374827, 22])
[1237, 2847, 2947, 1, 374827, 22]
"""
del toDo[k-... | true |
c8e6065ac061f5e6d25cc11ac5aa55d2177cee5a | Python | stefansilverio/holbertonschool-higher_level_programming | /0x0A-python-inheritance/class_behavior/2-is_same_class.py | UTF-8 | 249 | 3.515625 | 4 | [] | no_license | #!/usr/bin/python3
"""
Determine if obj is instance of class
Return:
Status
"""
def is_same_class(obj, a_class):
"""Return class status
examine object
"""
if type(obj) == a_class:
return True
else:
return False
| true |
80c42755c8234b05f25316dad363aae56b24875e | Python | xeaser/aws-auto-remediation | /remediation-functions/rds_instance/rdsinstance_copytagstosnapshot.py | UTF-8 | 2,137 | 2.65625 | 3 | [
"MIT"
] | permissive | '''
Enable Copy Tags to snapshot feature for AWS RDS database instance
'''
from botocore.exceptions import ClientError
def run_remediation(rds, RDSInstanceName):
print("Executing RDS instance remediation")
copytags=''
try:
response = rds.describe_db_instances(DBInstanceIdentifier=RDSInstanceName... | true |
55adaadf7420293a29a6e44889777eba70d121b4 | Python | WoosukYang-MEG/ros2-study | /src/my_first_ros_rclpy_pkg/my_first_ros_rclpy_pkg/helloworld_publisher.py | UTF-8 | 1,094 | 2.75 | 3 | [] | no_license | import rclpy
from rclpy.node import Node
from rclpy.qos import QoSProfile
from std_msgs.msg import String
class HelloworldPublisher(Node):
def __init__(self):
super().__init__('helloworld_publisher')
qos_profile = QoSProfile(depth=10)
self.helloworld_publisher = self.create_publisher(
... | true |
8b825d62c93846dbfe8fcab4787473701c3f3c01 | Python | slonoten/deep_nlp | /word_casing.py | UTF-8 | 610 | 3.671875 | 4 | [] | no_license | """Извлекаем признаки из регистра и наличия цифр в слове"""
def get_casing(word):
num_digits = sum(int(ch.isdigit()) for ch in word)
digit_fraction = num_digits / float(len(word))
casing = [
float(word.isdigit()),
float(digit_fraction > 0.5), # В основном цифры
float(num_digits ... | true |
5c905d6408852a8376eab1bb323d04aa07023967 | Python | Angeliz/text-index | /knn_predict.py | UTF-8 | 1,367 | 3.015625 | 3 | [] | no_license | # encoding=utf-8
from sklearn.neighbors import KNeighborsClassifier
from utils import read_bunch_obj
from config_local import space_path, experiment_space_path
def predict_result(space_path, experiment_space_path):
train_set = read_bunch_obj(space_path)
test_set = read_bunch_obj(experiment_space_path)
... | true |
1b3ac1b61ae0707a19fc4530f9cf9c88f97c4b8b | Python | luoye2333/ann | /rbinary/prepare_binary.py | UTF-8 | 1,010 | 3.0625 | 3 | [] | no_license | #生成一串01代码
#长度为20,要求其中1出现的次数
import pandas as pd
import random
import os
path=os.path.dirname(__file__)
len=20
#含1个数从0~len,每组n个,共(len+1)*n
sample_num=10000
si=[]
labels=[]
n1=-1
for _ in range(len+1):
n1=n1+1
for _ in range(sample_num//(len+1)):#//整除
s=[]
for i in range(len):
if i<... | true |
ca691ca19a1fb5995077f3923e020871f011757d | Python | GuglielmoS/ProjectEuler | /euler22.py | UTF-8 | 959 | 4 | 4 | [] | no_license | #!/usr/bin/ python
# Using names.txt (http://projecteuler.net/project/names.txt), a 46K text file
# containing over five-thousand first names, begin by sorting it into
# alphabetical order.
# Then working out the alphabetical value for each name, multiply this value
# by its alphabetical position in the list to obt... | true |
33e3f23b0968f6a50fe7c4e2af347ebf07a51007 | Python | narinn-star/Python | /Review/Chapter08/8-09.py | UTF-8 | 478 | 3.875 | 4 | [] | no_license | class Queue:
def __init__(self):
self.q = []
def isEmpty(self):
return (len(self.q) == 0)
def enqueue(self, item):
return self.q.append(item)
def dequeue(self):
'''큐 맨 앞의 항목을 제거하고 반환
만약 큐가 비어있으면 KeyboardInterrupt 예외가 발생'''
if len(self)==0:
... | true |
7f00a6385288322c6837a016e486b704ff53ec26 | Python | JessicaJang/cracking | /Chapter 4/q_4_2.py | UTF-8 | 1,174 | 3.4375 | 3 | [] | no_license | import unittest
import random
from random import seed
from random import randint
class Node():
def __init__(self, item, left=None, right=None):
self.item = item
self.left = None
self.right = None
def disp(self, nesting=0):
indent = " " * nesting * 2
output = f"{self.item}\n"
if self.left is ... | true |
2b73e6ad71cfe966c9b8dcd7db7d4be7d4e448af | Python | trishu99/Microsoft-WIT-hackathon | /api/startp.py | UTF-8 | 5,418 | 2.671875 | 3 | [] | no_license | import subprocess
import shlex
import psutil
import signal
import os
def startprocess(cmd):
try:
p = subprocess.run(cmd, stdout=False, stderr=False, shell=True, check=True)
if p.returncode != 0:
return cmd + ' : wrong command to run program or no such program exist'
else:
return cmd + ' : program started ... | true |
3b62492860ec030a1e50b6964393406b945f4ab8 | Python | wuluchaoren/py_learning_projects | /data_visualization/dice_visual.py | GB18030 | 721 | 3.46875 | 3 | [] | no_license | # -*- coding: utf-8 -*-
import pygal
from die import Die
# D6ɫ
die_1=Die()
die_2=Die()
results=[]
# ɫӲ洢һб
for roll_num in range(1000):
result=die_1.roll()+die_2.roll()
results.append(result)
#
frequencies=[]
max_result=die_1.num_sides+die_2.num_sides
for value in range(2,max_result+1):
frequency=results.... | true |
c54086f79eb3a164e1b685e1903ce39f997f6482 | Python | quesmues/curso-python-basico | /exercicio4.py | UTF-8 | 321 | 3.796875 | 4 | [] | no_license | def maiorNumero(colecao):
colecao = max(colecao)
print('O maior número é: ',colecao)
def menorNumero(colecao):
colecao = min(colecao)
print('O menor número é: ',colecao)
colecao = []
for i in range(5):
colecao.append(int(input('Informe um numero: ')))
maiorNumero(colecao)
menorNumero(colecao) | true |
2316a295baf695b2250b1e4bc525ec4cdca24c5a | Python | hyunjun/practice | /Problems/hacker_rank/Algorithm/Strings/20150316_Make_it_Anagram/solution2.py | UTF-8 | 305 | 3.625 | 4 | [] | no_license | from collections import Counter
def number_of_deletion(s1, s2):
total, d1, d2 = 0, Counter(s1), Counter(s2)
for i in range(97, 97 + 26):
c = chr(i)
total += abs(d1[c] - d2[c])
return total
if __name__ == '__main__':
s1 = raw_input()
s2 = raw_input()
print number_of_deletion(s1, s2)
| true |
e17fb5682564b8cce516ce16a40c688f2c83e7ee | Python | Racso/puzzles | /AdventOfCode2017/3.py | UTF-8 | 363 | 3.296875 | 3 | [] | no_license | import math
def distanceToCenter(x):
if x==1: return 0
circle = math.ceil(x**0.5)//2
circleSize = circle*2+1
corner = circleSize**2
distToCorner = (corner-x)%(circleSize-1)
distToMidPoint = abs(distToCorner-circleSize//2)
return circle+distToMidPoint
def partOne():
print(distanceToCent... | true |
a9bb93b7d54367fa398a7abe9510e8c317e6c216 | Python | code-roamer/IF-Defense | /baselines/dataset/ModelNet40.py | UTF-8 | 5,709 | 2.625 | 3 | [
"MIT"
] | permissive | import numpy as np
from torch.utils.data import Dataset
from util.pointnet_utils import normalize_points_np, random_sample_points_np
from util.augmentation import rotate_point_cloud, jitter_point_cloud
def load_data(data_root, partition='train'):
npz = np.load(data_root, allow_pickle=True)
if partition == '... | true |
de42c6737ab2027b03d3a5133e5023142c58eb94 | Python | loganrane/zipcodes-in | /zipcode_in/zipcode.py | UTF-8 | 3,600 | 3.75 | 4 | [
"MIT"
] | permissive | """
Zipcode India
-----------------------------
Zipcode class to validate and fetch details of India zipcodes
"""
import json
import os
import sys
import warnings
import random
class Zipcode():
"""Zipcode Class"""
def __init__(self):
"""Constructor to initialze the path, data and valid length."""
... | true |
36001e6449053606573ff687dd3323d7ec93ade1 | Python | ToucanToco/toucan-data-sdk | /tests/utils/generic/test_date_requester.py | UTF-8 | 4,401 | 2.703125 | 3 | [
"BSD-3-Clause"
] | permissive | import pandas as pd
from toucan_data_sdk.utils.generic import date_requester_generator
fixtures_base_dir = "tests/fixtures"
df = pd.DataFrame(
{
"date": ["2018-01-01", "2018-01-05", "2018-01-04", "2018-01-03", "2018-01-02"],
"my_kpi": [1, 2, 3, 4, 5],
}
)
df_2 = pd.DataFrame(
{
... | true |
ff48f0a10d3409189299d59003c6100c59a4b5e0 | Python | zww520/segmentation0 | /segmentation/Component.py | UTF-8 | 2,009 | 2.859375 | 3 | [
"MIT"
] | permissive | import numpy as np
import random as rand
import matplotlib.pyplot as plt
class component:
def __init__(self,num_node):
self.num_node = num_node
self.parent = [i for i in range(num_node)]
self.weight = [0 for i in range(num_node)]
self.size = [1 for i in range(num_node)]
def find(... | true |
1792cf28b0e58a58ea2eb363869c3e6ef8753200 | Python | katapenzes/W1DKME.beadando | /W1DKME_EX04.py | UTF-8 | 249 | 3.3125 | 3 | [] | no_license | def EX4(str):
t = ''
str = str.lower()
for i in str.split(' '):
for j in range(1,len(i)):
t += i[j]
t += i[0]
t += 'ay'
t += ' '
return t.capitalize()
print(EX4('The quick brown fox'))
| true |
6dc3b12e8129e3f5e364a61bc5c30ac2e9390f77 | Python | qmnguyenw/python_py4e | /geeksforgeeks/python/python_all/125_8.py | UTF-8 | 2,834 | 3.96875 | 4 | [] | no_license | Python | Dictionary key combinations
Sometimes, while working with Python dictionaries, we can have a problem in
which we need to get all the possible pair combinations of dictionary pairs.
This kind of applications can occur in data science domain. Let’s discuss
certain ways in which this task can be perform... | true |
fcafd0485182ed4b28b146407a239968b7bc6dbb | Python | Cica013/Exercicios-Python-CursoEmVideo | /Pacote_Python/Ex_python_CEV/exe042.py | UTF-8 | 801 | 4.1875 | 4 | [] | no_license | # Refaça o desafio 35 dos triângulos, acrescentando o recurso de mostrar que tipo de triângulo será formado:
# -Equilatero: Todos os lados iguais -isóceles: dois lados iguais - escaleno: todos os lados diferentes
n1 = int(input('Digite um valor: '))
n2 = int(input('Digite outro valor: '))
n3 = int(input('Digite mais um... | true |
423f6e287d6613dce2b09d4a6d5fbda5790c106b | Python | mkuczynski11/Leetcode-solutions | /1334.py | UTF-8 | 1,511 | 3.515625 | 4 | [] | no_license | #https://leetcode.com/problems/find-the-city-with-the-smallest-number-of-neighbors-at-a-threshold-distance/
import heapq
class Solution(object):
def findTheCity(self, n, edges, distanceThreshold):
"""
:type n: int
:type edges: List[List[int]]
:type distanceThreshold: int
:rty... | true |
e79babc7ecca1546f6177dff6cec09053702270d | Python | KePcA/LVR-sat | /SAT_implementation/algorithm_utilities.py | UTF-8 | 10,936 | 3.078125 | 3 | [] | no_license | """
Utility methods used in DPLL algorithm.
"""
__author__ = 'Grega'
import SAT_implementation.bool_formulas as bf
import itertools
def cnf_nnf(p):
"""
Converts the specified p formula to a NNF form and then to a CNF form.
"""
nnf_p = nnf(p)
return cnf(nnf_p)
def cnf(p):
"""
Converts the specified p formula... | true |
3174dd5a8edd2bd6a244fd87aca13462b6cb6db1 | Python | sudh4444/Gear_image_processing | /Pooja/flank_seperation.py | UTF-8 | 6,926 | 2.71875 | 3 | [] | no_license | import numpy as np
import time
import os
import math
import cv2
from envision import crop
from envision.convolution import convolve_sobel
ind = 0
def showimage(name,image):
# return
# cv2.imwrite("output/" + str(ind) + str(name) +".jpg", image)
cv2.imshow(str(name),image)
cv2.waitKey(0)
cv2.destro... | true |
88cbea9957231274ab980cb2300feec6109ad50b | Python | sprout42/cgm | /cgm/types/string.py | UTF-8 | 988 | 2.65625 | 3 | [] | no_license | from cgm.utils import word
from .base import CGMBaseType, CGMLengthType
class _SF(CGMLengthType):
def extract_item(self):
data = self.fp.read(self.param_len)
self.value = data.decode('latin-1')
class _S(CGMBaseType):
def extract(self):
# Variable length string that is null terminated... | true |
ad4ab4532e143f004fb57e8dc922b45112c4fd74 | Python | Jay-gupta/Data-Analysis | /ZomatodataAnalysis.py | UTF-8 | 900 | 2.890625 | 3 | [] | no_license | #!/usr/bin/env python
# coding: utf-8
# In[2]:
import numpy as np
# In[1]:
import pandas as pd
# In[3]:
import matplotlib.pyplot as plt
# In[5]:
data = pd.read_csv('C:/Users/hkc03/Desktop/JAY/project2/zomato.csv')
data
# In[12]:
data.describe()
# In[9]:
data['Country Code'].value_counts()
# In[... | true |
4671a5307493bf19cf66348dee8c7c84a6d573ed | Python | manojsudharsan/Python | /RemoveExtraSpace.py | UTF-8 | 51 | 2.734375 | 3 | [] | no_license | X=list(map(str,input().split()))
print(*X,end=" ")
| true |
894248939d84fdfbcf420143ca7a8f6297b7fe4d | Python | gorlins/PyMVPA | /mvpa/clfs/ridge.py | UTF-8 | 3,201 | 3.03125 | 3 | [
"MIT"
] | permissive | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ###... | true |
57246aaaade483c4ed190d947cc93c9969774ed6 | Python | manifoldco/aggregate-ml-logs | /ml_logs/training/keras_job.py | UTF-8 | 1,045 | 2.59375 | 3 | [] | no_license | from keras.models import Sequential
from keras.layers import Dense, Dropout
from ml_logs.data import read_traces, scale_traces
from ml_logs import env
from ml_logs.logs import logger
def train_nn(x_train, y_train):
logger.info('Training Simple Keras NN')
model = Sequential()
model.add(Dense(1, kernel_in... | true |
c459f967a4a8445e08f4a267cac8887963f306ff | Python | gkchai/planus | /bin/testnlu.py | UTF-8 | 2,238 | 2.75 | 3 | [] | no_license | #!/usr/bin/python
# -*- coding: utf-8 -*-
from optparse import OptionParser
import sys, pdb, traceback
from math import log10
import numpy as np, re
from src import ds
# Example test file for using an object of type ds()
def main():
for i, line in enumerate(open('data/sentences.txt', 'r')):
testobj = ds.ds() #... | true |
0ea37acaee2181a6c15c4ad9cfa1969c31903bd3 | Python | tom-f-oconnell/scripts | /change_git_auth.py | UTF-8 | 1,853 | 2.921875 | 3 | [] | no_license | #!/usr/bin/env python3
"""
Takes piped output of git remote -v and returns one line with remote changed
to desired authentication type.
"""
import sys
if __name__ == '__main__':
valid_auth_types = ('g','h','s')
if len(sys.argv) != 2:
raise ValueError('one required argument (g/h/s) to specify auth typ... | true |
db698a16a165aac9b4c5ae29d8c4b189b3726b33 | Python | HilaGast/FT | /network_analysis/nodes_network_properties.py | UTF-8 | 2,398 | 2.875 | 3 | [
"Apache-2.0"
] | permissive |
import numpy as np
import networkx as nx
def merge_dict(dict1, dict2):
''' Merge dictionaries and keep values of common keys in list'''
import more_itertools
dict3 = {**dict1, **dict2}
for key, value in dict3.items():
if key in dict1 and key in dict2:
dict3[key] = [value , dict1[key... | true |
4d0d1c6fdb282d93d6fe97e25adba1b832a04464 | Python | Menooker/dgl | /tests/graph_index/test_hetero.py.bak | UTF-8 | 14,648 | 2.546875 | 3 | [
"Apache-2.0"
] | permissive | import numpy as np
import dgl
import dgl.ndarray as nd
import dgl.graph_index as dgl_gidx
import dgl.heterograph_index as dgl_hgidx
from dgl.utils import toindex
import backend as F
"""
Test with a heterograph of three ntypes and three etypes
meta graph:
0 -> 1
1 -> 2
2 -> 1
Num nodes per ntype:
0 : 5
1 : 2
2 : 3
re... | true |
907836a00eb2fea6f4925d1ad2e143a99a4a4d26 | Python | TMKangwantas/TicTacToe | /Tic.Tac.Toe.py | UTF-8 | 15,228 | 3.78125 | 4 | [] | no_license | #Thanathip Mark Kangwantas
#################################################
#Triplets
#################################################
triplets = []
x = 0
while x < 1:
for col in range (3):
rowWin = []
for row in range(3):
rowWin.append([row,col])
triplets.append(rowWin)
x... | true |
a25aa6de4b06c7d4ec48d693d3a4f71a3c9ded03 | Python | AwuorMarvin/Bank-Account | /bank_account.py | UTF-8 | 566 | 3.65625 | 4 | [] | no_license | class BankAccount(object):
def __init__(self, account_balance = 500):
self.account_balance = account_balance
def deposit(self, amount):
self.amount = amount
self.account_balance += amount
return self.account_balance
def withdraw(self, amount):
if self.account_balanc... | true |
22b155e7456d8bb8ea402d5a64c630f636354944 | Python | Bubbet/MyAnimeList-PlanToWatchComparison | /main.py | UTF-8 | 1,245 | 3.234375 | 3 | [] | no_license | from requests import get
from json import loads
import concurrent.futures
from time import time
from bs4 import BeautifulSoup
def parse_url(user, any_bool=False):
url = f'https://myanimelist.net/animelist/{user}'
page = get(url)
soup = BeautifulSoup(page.content, 'html.parser')
results = soup.find('ta... | true |
3f4ca4360cd8988cf61436066f23287dd44c8ce5 | Python | caionms/python | /Reaprendendo/l1q9.py | UTF-8 | 219 | 3.578125 | 4 | [] | no_license | print('Iremos calcular o valor a ser pago em um aluguel de carro.')
t = int(input('Digite a qtd de dias: '))
d = int(input('Digite a qtd de KM rodados: '))
print(f'O valor a ser pago será de {(60*t)+(d*0.15)} reais.')
| true |
6642ec02f3d8fca17d237d3565ad041cf83dfa15 | Python | kramax8/gthome1Part2 | /tz20/tz20.py | UTF-8 | 806 | 3.21875 | 3 | [] | no_license | g_countries = {1: 'google_kazakstan.txt',
2: 'google_paris.txt',
3: 'google_uar.txt',
4: 'google_kyrgystan.txt',
5: 'google_san_francisco.txt',
6: 'google_germany.txt',
7: 'google_moscow.txt',
8: 'google_sweden.txt'}
for key in g_countries:
a = str(key)
... | true |
fc4f9604d8f9ecb274900fd268092444ebf7178e | Python | LukaP-BB/PyDiscord | /coroPack/geo.py | UTF-8 | 8,175 | 2.515625 | 3 | [] | no_license | #!/usr/bin/env python3
#-*- coding:utf-8 -*-
import requests as req
import pandas as pd
import numpy as np
from scipy import stats
import geopandas as gpd
import matplotlib.pyplot as plt
import datetime as dtt
from coroPack.interface import depFromCode
from coroPack.analyse import timeFrame, loadData
from colors imp... | true |
6018ddb4cd390b7e4056079c971f98357aecf787 | Python | realbadbytes/POP | /pipeline.py | UTF-8 | 3,017 | 3.15625 | 3 | [] | no_license | #!/usr/bin/python3
import numpy as np
from parser import Operation
registers = ['.gp0', '.gp1', '.gp2', '.gp3', '.gp4', '.gp5', '.gp6', '.stack', '.next']
class Pipeline():
def __init__(self, program):
self.program = program
print ('[+] initializing pipeline for')
for op in program.oper... | true |
598d57c3b0d6b4dc7e13e901d6d8f5e8c7b339db | Python | Polaricicle/practical02 | /q09_find_smallest.py | UTF-8 | 422 | 4.21875 | 4 | [] | no_license | #Filename: q09_find_smallest.py
#Author: Tan Di Sheng
#Created: 20130130
#Modified: 20130130
#Description: This program uses a while loop to find the smallest integer n
#such that n^2 is greater than 12,000.
print("""This program finds the smallest integer n such that n^2
is greater than 12,000.""")
sInteger = 1
whi... | true |
853a69b125b11d270fc34d010883d45f8792e247 | Python | Madndev/myfirstproject | /checkandcopy.py | UTF-8 | 538 | 3.015625 | 3 | [] | no_license | import os
fh = open("developer.txt","w")
n=int(input("how many developing languages do u want : "))
i=0
while(i<n):
dev=input("enter the developing programas : ")
fh.write(dev+"\n")
i+=1
fh.close()
fh1 = open("web.txt","w")
n=int(input("how many web languages do u want : "))
i=0
while(i<n):
dev=input... | true |
3995c73a708e75564b2f54093f433e19666ed2a0 | Python | SilasA/CIS-HW | /CIS-365/uninformed-search/search.py | UTF-8 | 1,779 | 4.21875 | 4 | [] | no_license | #!/usr/bin python3
# Maze represented by dictionary with key: Node; value: Array of children
# The way I implemented the maze turns out to make the graph a binary tree.
graph = {
"Start" : ["A", "B"],
"A": ["C", "D"],
"B": [],
"C": [],
"D": ["E", "F"],
"E": ["G", "H"],
... | true |
1fd1b585c646513b89268de86b9b6c6cc3ce36e2 | Python | ksm0207/Python_study | /chapter6_python_function/forward_return.py | UTF-8 | 1,016 | 3.890625 | 4 | [] | no_license | # 전달값 and 반환값
def open_account():
print("새로운 계좌가 생성되었습니다.")
def deposit(balance, money): # 입금
print("입금이 완료되었습니다.{0}원".format(balance + money))
return balance + money
def withdraw(balance, money): # 출금
if balance > money:
print("출금이 완료되었습니다. 잔액은 {0}원 남았습니다.".format(balance ... | true |
71decca796e3a6ad3a3941d56d7e9cfb242eeb0a | Python | Thien223/Sentiment-Analysis | /source/sentiment_models.py | UTF-8 | 8,298 | 2.71875 | 3 | [] | no_license | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Dense, Embedding, Input, Bidirectional
from keras.layers import LSTM, Dropout
from keras.models import Model
from keras.preprocessing import text, seq... | true |