A little bit of Twitter text analysis

Now that you have your DataFrame of tweets set up, you're going to do a bit of text analysis to count how many tweets contain the words 'clinton', 'trump', 'sanders' and 'cruz'. In the pre-exercise code, we have defined the following function word_in_text(), which will tell you whether the first argument (a word) occurs within the 2nd argument (a tweet).

import re

def word_in_text(word, tweet):
    word = word.lower()
    text = tweet.lower()
    match = re.search(word, tweet)

    if match:
        return True
    return False

You're going to iterate over the rows of the DataFrame and calculate how many tweets contain each of our keywords!

This exercise is part of the course

Importing Data in Python

View Course

Exercise instructions

Initialize the list [clinton, trump, sanders, cruz] so that all values are 0.
Within the for loop for index, row in df.iterrows():, the code currently increases the value of clinton by 1 each time a tweet mentioning 'Clinton' is encountered; complete the code so that the same happens for trump, sanders and cruz.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Initialize list to store tweet counts
[clinton, trump, sanders, cruz] = ____

# Iterate through df, counting the number of tweets in which
# each candidate is mentioned
for index, row in df.iterrows():
    clinton += word_in_text('clinton', row['text'])
    trump += word_in_text(____, ____)
    sanders += word_in_text(____, ____)
    cruz += word_in_text(____, ____)

Edit and Run Code