Extracting @mentions from tweets using findall python (Giving incorrect results)

Question

I have a csv file something like this

text
RT @CritCareMed: New Article: Male-Predominant Plasma Transfusion Strategy for Preventing Transfusion-Related Acute Lung Injury... htp://…
#CRISPR Inversion of CTCF Sites Alters Genome Topology & Enhancer/Promoter Function in @CellCellPress htp://.co/HrjDwbm7NN
RT @gvwilson: Where's the theory for software engineering? Behind a paywall, that's where. htp://.co/1t3TymiF3M #semat #fail
RT @sciencemagazine: What’s killing off the sea stars? htp://.co/J19FnigwM9 #ecology
RT @MHendr1cks: Eve Marder describes a horror that is familiar to worm connectome gazers. htp://.co/AEqc7NOWoR via @nucAmbiguous htp://…

I want to extract all the mentions (starting with '@') from the tweet text. So far I have done this

import pandas as pd
import re

mydata = pd.read_csv("C:/Users/file.csv")
X = mydata.ix[:,:]
X=X.iloc[:,:1] #I have multiple columns so I'm selecting the first column only that is 'text'

for i in range(X.shape[0]):
result = re.findall("(^|[^@\w])@(\w{1,25})", str(X.iloc[:i,:]))

print(result);

There are two problems here: First: at str(X.iloc[:1,:]) it gives me ['CritCareMed'] which is not ok as it should give me ['CellCellPress'] , and at str(X.iloc[:2,:]) it again gives me ['CritCareMed'] which is of course not fine again. The final result I'm getting is

[(' ', 'CritCareMed'), (' ', 'gvwilson'), (' ', 'sciencemagazine')]

It doesn't include the mentions in 2nd row and both two mentions in last row. What I want should look something like this:

How can I achieve these results? this is just a sample data my original data has lots of tweets so is the approach ok?

Answer 1

You can use str.findall method to avoid the for loop, use negative look behind to replace (^|[^@\\w]) which forms another capture group you don't need in your regex:

df['mention'] = df.text.str.findall(r'(?<![@\w])@(\w{1,25})').apply(','.join)
df
#                                                text   mention
#0  RT @CritCareMed: New Article: Male-Predominant...   CritCareMed
#1  #CRISPR Inversion of CTCF Sites Alters Genome ...   CellCellPress
#2  RT @gvwilson: Where's the theory for software ...   gvwilson
#3  RT @sciencemagazine: What’s killing off the se...   sciencemagazine
#4  RT @MHendr1cks: Eve Marder describes a horror ...   MHendr1cks,nucAmbiguous

---

Also X.iloc[:i,:] gives back a data frame, so str(X.iloc[:i,:]) gives you the string representation of a data frame, which is very different from the element in the cell, to extract the actual string from the text column, you can use X.text.iloc[0] , or a better way to iterate through a column, use iteritems :

import re
for index, s in df.text.iteritems():
    result = re.findall("(?<![@\w])@(\w{1,25})", s)
    print(','.join(result))

#CritCareMed
#CellCellPress
#gvwilson
#sciencemagazine
#MHendr1cks,nucAmbiguous

Answer 2

While you already have your answer, you could even try to optimize the whole import process like so:

import re, pandas as pd

rx = re.compile(r'@([^:\s]+)')

with open("test.txt") as fp:
    dft = ([line, ",".join(rx.findall(line))] for line in fp.readlines())

    df = pd.DataFrame(dft, columns = ['text', 'mention'])
    print(df)

---

Which yields:

  text mention 0 RT @CritCareMed: New Article: Male-Predominant... CritCareMed 1 #CRISPR Inversion of CTCF Sites Alters Genome ... CellCellPress 2 RT @gvwilson: Where's the theory for software ... gvwilson 3 RT @sciencemagazine: What's killing off the se... sciencemagazine 4 RT @MHendr1cks: Eve Marder describes a horror ... MHendr1cks,nucAmbiguous 

This might be a bit faster as you don't need to change the df once it's already constructed.

Answer 3

mydata['text'].str.findall(r'(?:(?<=\s)|(?<=^))@.*?(?=\s|$)')

Same as this: Extract hashtags from columns of a pandas dataframe , but for mentions.

• @.*? carries out a non-greedy match for a word starting with a hashtag
• (?=\\s|$) look-ahead for the end of the word or end of the sentence
• (?:(?<=\\s)|(?<=^)) look-behind to ensure there are no false positives if a @ is used in the middle of a word

The regex lookbehind asserts that either a space or the start of the sentence must precede a @ character.