How to do text pre-processing using spaCy?

This may help:

import spacy #load spacy
nlp = spacy.load("en", disable=['parser', 'tagger', 'ner'])
stops = stopwords.words("english")

def normalize(comment, lowercase, remove_stopwords):
    if lowercase:
        comment = comment.lower()
    comment = nlp(comment)
    lemmatized = list()
    for word in comment:
        lemma = word.lemma_.strip()
        if lemma:
            if not remove_stopwords or (remove_stopwords and lemma not in stops):
                lemmatized.append(lemma)
    return " ".join(lemmatized)


Data['Text_After_Clean'] = Data['Text'].apply(normalize, lowercase=True, remove_stopwords=True)

It can easily be done via a few commands. Also note that spacy doesn't support stemming. You can refer this to this thread

import spacy
nlp = spacy.load('en')

# sample text
text = """Lorem Ipsum is simply dummy text of the printing and typesetting industry. \
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown \
printer took a galley of type and scrambled it to make a type specimen book. It has survived not \
only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. \
It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, \
and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.\
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration \
in some form, by injected humour, or randomised words which don't look even slightly believable. If you are \
going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the \
middle of text. All the Lorem Ipsum generators on the Internet tend to repeat predefined chunks as necessary, \
making this the first true generator on the Internet. It uses a dictionary of over 200 Latin words, combined \
with a handful of model sentence structures, to generate Lorem Ipsum which looks reasonable. The generated \
Lorem Ipsum is therefore always free from repetition, injected humour, or non-characteristic words etc."""

# convert the text to a spacy document
document = nlp(text) # all spacy documents are tokenized. You can access them using document[i]
document[0:10] # = Lorem Ipsum is simply dummy text of the printing and

#the good thing about spacy is a lot of things like lemmatization etc are done when you convert them to a spacy document `using nlp(text)`. You can access sentences using document.sents
list(document.sents)[0]

# lemmatized words can be accessed using document[i].lemma_ and you can check 
# if a word is a stopword by checking the `.is_stop` attribute of the word.
# here I am extracting the lemmatized form of each word provided they are not a stop word
lemmas = [token.lemma_ for token in document if not token.is_stop]

The best pipeline I have encountered so far is from Maksym Balatsko's Medium article Text preprocessing steps and universal reusable pipeline. The best part is that we can use it as part of scikit-learn transformer pipeline and supports multiprocess

I have modified Maksym and kept packages to a minimum and used generators instead of lists to avoid loading data to memory:

import numpy as np
import multiprocessing as mp

import string
import spacy 
from sklearn.base import TransformerMixin, BaseEstimator


nlp = spacy.load("en_core_web_sm")

class TextPreprocessor(BaseEstimator, TransformerMixin):
    def __init__(self,
                 nlp = nlp,
                 n_jobs=1):
        """
        Text preprocessing transformer includes steps:
            1. Punctuation removal
            2. Stop words removal
            3. Lemmatization

        nlp  - spacy model
        n_jobs - parallel jobs to run
        """
        self.nlp = nlp
        self.n_jobs = n_jobs

    def fit(self, X, y=None):
        return self

    def transform(self, X, *_):
        X_copy = X.copy()

        partitions = 1
        cores = mp.cpu_count()
        if self.n_jobs <= -1:
            partitions = cores
        elif self.n_jobs <= 0:
            return X_copy.apply(self._preprocess_text)
        else:
            partitions = min(self.n_jobs, cores)

        data_split = np.array_split(X_copy, partitions)
        pool = mp.Pool(cores)
        data = pd.concat(pool.map(self._preprocess_part, data_split))
        pool.close()
        pool.join()

        return data

    def _preprocess_part(self, part):
        return part.apply(self._preprocess_text)

    def _preprocess_text(self, text):
        doc = self.nlp(text)
        removed_punct = self._remove_punct(doc)
        removed_stop_words = self._remove_stop_words(removed_punct)
        return self._lemmatize(removed_stop_words)

    def _remove_punct(self, doc):
        return (t for t in doc if t.text not in string.punctuation)

    def _remove_stop_words(self, doc):
        return (t for t in doc if not t.is_stop)

    def _lemmatize(self, doc):
        return ' '.join(t.lemma_ for t in doc)

You can use it as:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import  LogisticRegressionCV
from sklearn.pipeline import Pipeline

# ... assuming data split X_train, X_test ...

clf  = Pipeline(steps=[
        ('normalize': TextPreprocessor(n_jobs=-1)), 
        ('features', TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True)),
        ('classifier', LogisticRegressionCV(cv=5,solver='saga',scoring='accuracy', n_jobs=-1, verbose=1))
    ])

clf.fit(X_train, y_train)
clf.predict(X_test)

X_train is data that will pass through TextPreprocessing, then we extract features, then pass to a classifier.