Question :
I am trying to run through a function with my million lines in a datasets.
- I read the data from CSV in a dataframe
- I use drop list to drop data i don’t need
- I pass it through a NLTK function in a for loop.
code:
def nlkt(val):
val=repr(val)
clean_txt = [word for word in val.split() if word.lower() not in stopwords.words('english')]
nopunc = [char for char in str(clean_txt) if char not in string.punctuation]
nonum = [char for char in nopunc if not char.isdigit()]
words_string = ''.join(nonum)
return words_string
Now i am calling the above function using a for loop to run through by million records. Even though i am on a heavy weight server with 24 core cpu and 88 GB Ram i see the loop is taking too much time and not using the computational power that is there
I am calling the above function like this
data = pd.read_excel(scrPath + "UserData_Full.xlsx", encoding='utf-8')
droplist = ['Submitter', 'Environment']
data.drop(droplist,axis=1,inplace=True)
#Merging the columns company and detailed description
data['Anylize_Text']= data['Company'].astype(str) + ' ' + data['Detailed_Description'].astype(str)
finallist =[]
for eachlist in data['Anylize_Text']:
z = nlkt(eachlist)
finallist.append(z)
The above code works perfectly OK just too slow when we have few million record. It is just a sample record in excel but actual data will be in DB which will run in few hundred millions. Is there any way I can speed up the operation to pass the data through the function faster – use more computational power instead?
Answer #1:
Your original nlkt() loops through each row 3 times.
def nlkt(val):
val=repr(val)
clean_txt = [word for word in val.split() if word.lower() not in stopwords.words('english')]
nopunc = [char for char in str(clean_txt) if char not in string.punctuation]
nonum = [char for char in nopunc if not char.isdigit()]
words_string = ''.join(nonum)
return words_string
Also, each time you’re calling nlkt(), you’re re-initializing these again and again.
stopwords.words('english')string.punctuation
These should be global.
stoplist = stopwords.words('english') + list(string.punctuation)
Going through things line by line:
val=repr(val)
I’m not sure why you need to do this. But you could easy cast a column to a str type. This should be done outside of your preprocessing function.
Hopefully this is self-explanatory:
>>> import pandas as pd
>>> df = pd.DataFrame([[0, 1, 2], [2, 'xyz', 4], [5, 'abc', 'def']])
>>> df
0 1 2
0 0 1 2
1 2 xyz 4
2 5 abc def
>>> df[1]
0 1
1 xyz
2 abc
Name: 1, dtype: object
>>> df[1].astype(str)
0 1
1 xyz
2 abc
Name: 1, dtype: object
>>> list(df[1])
[1, 'xyz', 'abc']
>>> list(df[1].astype(str))
['1', 'xyz', 'abc']
Now going to the next line:
clean_txt = [word for word in val.split() if word.lower() not in stopwords.words('english')]
Using str.split() is awkward, you should use a proper tokenizer. Otherwise, your punctuations might be stuck with the preceding word, e.g.
>>> from nltk.corpus import stopwords
>>> from nltk import word_tokenize
>>> import string
>>> stoplist = stopwords.words('english') + list(string.punctuation)
>>> stoplist = set(stoplist)
>>> text = 'This is foo, bar and doh.'
>>> [word for word in text.split() if word.lower() not in stoplist]
['foo,', 'bar', 'doh.']
>>> [word for word in word_tokenize(text) if word.lower() not in stoplist]
['foo', 'bar', 'doh']
Also checking for .isdigit() should be checked together:
>>> text = 'This is foo, bar, 234, 567 and doh.'
>>> [word for word in word_tokenize(text) if word.lower() not in stoplist and not word.isdigit()]
['foo', 'bar', 'doh']
Putting it all together your nlkt() should look like this:
def preprocess(text):
return [word for word in word_tokenize(text) if word.lower() not in stoplist and not word.isdigit()]
And you can use the DataFrame.apply:
data['Anylize_Text'].apply(preprocess)
