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data.replace(to_replace={'column':0}, value=pd.np.nan, inplace=True)

FelixChop

from pandas_profiling import ProfileReport
profile = ProfileReport(data, progress_bar=True)
profile.to_notebook_iframe()

FelixChop

import pandas as pd
data = pd.read_csv('path/dataset.csv') # pd.read_excel or pd.read_sas, depending on your file type

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import os
import ast
import time
import string
from tqdm import tqdm_notebook
tqdm_notebook()

def files():
    return [t for t in os.listdir(os.curdir) if t.endswith('.txt')]
    

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python spotify_scraper.py a &
python spotify_scraper.py b &
python spotify_scraper.py c &
python spotify_scraper.py d &
python spotify_scraper.py e &
python spotify_scraper.py f &
python spotify_scraper.py g &
python spotify_scraper.py h &
python spotify_scraper.py i &
python spotify_scraper.py j &

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import sys
from selenium import webdriver
import string
import time
import os

def main(letter1):
	browser = webdriver.Chrome(executable_path='/usr/local/bin/chromedriver')
	browser.get(url="https://accounts.spotify.com/fr/login")
	time.sleep(3)

FelixChop

%matplotlib inline
import pyLDAvis
import pyLDAvis.gensim
vis = pyLDAvis.gensim.prepare(topic_model=lda_model, corpus=corpus, dictionary=dictionary_LDA)
pyLDAvis.enable_notebook()
pyLDAvis.display(vis)

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new_text = 'How many species of animals are there in Russia and in the US, and where are the biggest oceans?'
tokens = [stemmer.stem(token) for token in tokenizer.tokenize(new_text.lower()) if token not in stopwords_fr]
lda_model[dictionary_LDA.doc2bow(tokens)]