title: How to split data?
body: <p>Lets say I've 1010 number of rows in my data frame. Now I want to split them using <code>train_test_split</code> so that first 1000 rows comes to train data and  next 10 rows comes to test data.</p>
  
  <pre><code># Natural Language Processing
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
  newset=pd.read_csv('Test.tsv',delimiter='\t',quoting=3)
  frames=[dataset,newset]
  res=pd.concat(frames,ignore_index=True)
  # Cleaning the texts
  import re
  import nltk
  nltk.download('stopwords')
  from nltk.corpus import stopwords
  from nltk.stem.porter import PorterStemmer
  corpus = []
  for i in range(0, 1010):
      review = re.sub('[^a-zA-Z]', ' ', res['Review'][i])
      review = review.lower()
      review = review.split()
      ps = PorterStemmer()
      review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
      review = ' '.join(review)
      corpus.append(review)
  from sklearn.feature_extraction.text import CountVectorizer
  cv=CountVectorizer(max_features=1500)
  #X=cv.fit_transform(corpus).toarray()
  X=corpus
  y=res.iloc[:,1].values
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.cross_validation import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.01, random_state = 0)
  </code></pre>