Cleaning Twitter data pandas python

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Trying to clean twitter data as a panda data frame. I seem to be missing a step. After I process all the the tweets, I think I am missing overwriting the new ones over old tweets? When I save the file, I see no changes in the tweets. What am I missing?

import pandas as pd
import re
import emoji
import nltk
nltk.download('words')
words = set(nltk.corpus.words.words())

trump_df = pd.read_csv('new_Trump.csv')
for tweet in trump_df['tweet']:
    tweet = re.sub("@[A-Za-z0-9]+","",tweet) #Remove @ sign
    tweet = re.sub(r"(?:\@|http?\://|https?\://|www)\S+", "", tweet) #Remove http links
    tweet = " ".join(tweet.split())
    tweet = ''.join(c for c in tweet if c not in emoji.UNICODE_EMOJI) #Remove Emojis
    tweet = tweet.replace("#", "").replace("_", " ") #Remove hashtag sign but keep the text
    tweet = " ".join(w for w in nltk.wordpunct_tokenize(tweet) \
         if w.lower() in words or not w.isalpha()) #Remove non-english tweets (not 100% success)
    print(tweet)
trump_df.to_csv('new_Trump.csv')
pandas python-3.x python
1 Answers

As you very well said, you are never storing the data back, let's create a function that does all the work and then pass it to the dataframe using map. It's more efficient than looping through each value in the dataframe and storing it into a list (option B).

def cleaner(tweet):
    tweet = re.sub("@[A-Za-z0-9]+","",tweet) #Remove @ sign
    tweet = re.sub(r"(?:\@|http?\://|https?\://|www)\S+", "", tweet) #Remove http links
    tweet = " ".join(tweet.split())
    tweet = ''.join(c for c in tweet if c not in emoji.UNICODE_EMOJI) #Remove Emojis
    tweet = tweet.replace("#", "").replace("_", " ") #Remove hashtag sign but keep the text
    tweet = " ".join(w for w in nltk.wordpunct_tokenize(tweet) \
         if w.lower() in words or not w.isalpha())
    return tweet
trump_df['tweet'] = trump_df['tweet'].map(lambda x: cleaner(x))
trump_df.to_csv('') #specify location

This will overwrite the tweet column with the modifications.

Option B:

As stated, this will prove to be a bit more inefficient I'm thinking but it's as easy as creating a list previous to the for loop, filling it with each clean tweet.

clean_tweets = []
for tweet in trump_df['tweet']:
    tweet = re.sub("@[A-Za-z0-9]+","",tweet) #Remove @ sign
    ##Here's where all the cleaning takes place
    clean_tweets.append(tweet)
trump_df['tweet'] = clean_tweets
trump_df.to_csv('') #Specify location
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