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Description:

ProText 0.0.4

ProText makes your Text Preprocessing & Feature extraction tasks easy for NLP applications
A Helper pip package for text prepocessing & feature extraction.
This pip package helps you execute text cleaning like

General cleaning (Removal of URL, @username, addtional whitespaces, Hashtags, punctuations..etc)
Stopword removal
Adding more stop words to "stopwords.words" library list
Stemming,
lemmatiing,
Removal of single or two character word,
Converting to lower case,
Removal of digits
Spell correction
Creating of Wordcloud & line graph

All of these can be executed in a single line of command sequentially in any prefered order or can be executed in multiline.
Also feature extrations like CountVectorizer & TfidfVectorizer

Package can be executed on both panda DataFrames and single line of text

This tool can be really helpful and No one neither needs to rememebr various syntax associated with processing nor packages.
This packages reuses functions from nltk, Textblob and wordcloud for the above operations.
Assumptions:

Assuming python is installed on your system.
nltk, Textblob and wordcloud installed on your system

Install ProText on your system using :
pip install ProText

Text Preprocessing user guide on df or text

Importing library
from ProText import *

or
from ProText import gen, low, dig,stopw, lemma, stem, spell, clean_len, WCloud, countvec, tfidf

Execution of single preprocessing property on dataframe
dfcleaned['tweets'] = dfcleaned['tweets'].apply(gen)

Adding more stop words to "stopwords.words" library list
stopadd = ['sample', 'much', 'thank']

df['tweet']= df['tweet'].apply(stopw, args=(stopadd,))
or
df['tweet']= df['tweet'].apply(stopw, args=(['sample', 'much', 'thank'],))

If there are multiple lines, better convert to Dataframe

Sequential operation in a single line of command
dfcleaned['tweets'] = dfcleaned['tweets'].apply(gen).apply(low).apply(low).apply(stopw).apply(lemma)

Finally WordCloud
WCloud(dfcleaned.tweets)

Feature extraction user guide on df or list

For CountVectorizer

cv_vect, cv_feature, cvdf = countvec(dfcleaned)

For TfidfVectorizer

tfidf_vect, tfidf_feature, tfidfdf = tfidf(dfcleaned)