| string = '11100100’ | |
| c = string[0] | |
| lista = [] | |
| lista.append(c) | |
| for x in string[1:]: | |
| if x == c: | |
| lista.append(x) | |
| else: | |
| print(lista) | |
| c = x |
| #on yours computer | |
| import pickle | |
| from xgboost import XGBClassifier | |
| from xgboost import Booster | |
| clf = XGBClassifier() | |
| booster = Booster() | |
| booster.load_model('xgbsave_test.model') | |
| clf._Booster = booster |
| import pickle | |
| pkl_filename = "pickle_model.pkl" | |
| #on server | |
| with open(pkl_filename, 'rb') as file: | |
| clf = pickle.load(file) |
| from sklearn.model_selection import train_test_split | |
| from xboost import XGBClassifier | |
| X = #yours data | |
| y = #labels | |
| train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=100) | |
| from collections import Counter | |
| vacancy_names = df.name | |
| cloud = Counter(vacancy_names) | |
| from wordcloud import WordCloud, STOPWORDS | |
| stopwords = set(STOPWORDS) | |
| cloud = '' | |
| for x in list(vacancy_names): | |
| cloud+=x+' ' | |
| wordcloud = WordCloud(width = 800, height = 800, |
| import ast # run code from string for example ast.literal_eval("1+1") | |
| salaries = df.salary.dropna() # remove all NA's from dataframe | |
| currencies = [ast.literal_eval(salaries.iloc[i])['currency'] for i in range(len(salaries))] | |
| curr = set(currencies) #{'EUR', 'RUR', 'USD'} | |
| #divide dataframe salararies by currency | |
| rur = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='RUR'] | |
| eur = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='EUR'] | |
| usd = [ast.literal_eval(salaries.iloc[i]) for i in range(len(salaries)) if ast.literal_eval(salaries.iloc[i])['currency']=='USD'] |
| import numpy as np | |
| import cv2 | |
| import imutils | |
| import os | |
| import pytesseract | |
| from PIL import Image | |
| from pytesseract import image_to_string | |
| import numpy as np | |
| def mean(data, x): | |
| temp = data[::x] | |
| return np.mean(temp) | |
The git command-line utility has plenty of inconsistencies http://steveko.wordpress.com/2012/02/24/10-things-i-hate-about-git/
A GUI like http://sourcetreeapp.com is often helpful, but staying on the command line usually quicker. This is a list of the commands I use most frequently, listed by functional category:
git status list which (unstaged) files have changed
