Unable to resolve Type error using Tokenizer.tokenize from NLTK Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsComplex Chunking with NLTKHow to train NLTK Sequence labeling algorithm for using custom labels/Train set?Unable to load NLTK in spark using PySparkI need to measure Performance : AUC for this code of NLTK and skLearnNeed help in improving accuracy of text classification using Naive Bayes in nltk for movie reviewsHow to extract Question/s from document with NLTK?How to extract a relation from a Named entity recognition model using NLTK in pythonUnable to generate error bars with seabornWhere to know the list of NLTK tagset?Installing NLTK using WHL file -
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Unable to resolve Type error using Tokenizer.tokenize from NLTK
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsComplex Chunking with NLTKHow to train NLTK Sequence labeling algorithm for using custom labels/Train set?Unable to load NLTK in spark using PySparkI need to measure Performance : AUC for this code of NLTK and skLearnNeed help in improving accuracy of text classification using Naive Bayes in nltk for movie reviewsHow to extract Question/s from document with NLTK?How to extract a relation from a Named entity recognition model using NLTK in pythonUnable to generate error bars with seabornWhere to know the list of NLTK tagset?Installing NLTK using WHL file -
$begingroup$
I want to tokenize text data and am unable to proceed due to a type error, am unable to know how to proceed to rectify the error, To give some context - all the columns - Resolution code','Resolution Note','Description','Shortdescription' are text data in English- here is the code that I have written :
#Removal of Stop words:
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
My error output is attached below:
python nltk tokenization
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
$endgroup$
add a comment |
$begingroup$
I want to tokenize text data and am unable to proceed due to a type error, am unable to know how to proceed to rectify the error, To give some context - all the columns - Resolution code','Resolution Note','Description','Shortdescription' are text data in English- here is the code that I have written :
#Removal of Stop words:
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
My error output is attached below:
python nltk tokenization
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
$endgroup$
$begingroup$
Where do you expect that the data would come from? You initialize a dataframe, but I fail to spot where you load in data.
$endgroup$
– S van Balen
Apr 1 at 22:33
$begingroup$
I have loaded the data at line 15, the dataframe dfimpnetc has already been loaded with data from a csv file earlier.
$endgroup$
– Vivek Rmk
Apr 2 at 14:19
add a comment |
$begingroup$
I want to tokenize text data and am unable to proceed due to a type error, am unable to know how to proceed to rectify the error, To give some context - all the columns - Resolution code','Resolution Note','Description','Shortdescription' are text data in English- here is the code that I have written :
#Removal of Stop words:
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
My error output is attached below:
python nltk tokenization
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
$endgroup$
I want to tokenize text data and am unable to proceed due to a type error, am unable to know how to proceed to rectify the error, To give some context - all the columns - Resolution code','Resolution Note','Description','Shortdescription' are text data in English- here is the code that I have written :
#Removal of Stop words:
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
My error output is attached below:
python nltk tokenization
python nltk tokenization
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
edited Apr 2 at 14:17
Vivek Rmk
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
asked Apr 1 at 21:48
Vivek RmkVivek Rmk
12
12
$begingroup$
Where do you expect that the data would come from? You initialize a dataframe, but I fail to spot where you load in data.
$endgroup$
– S van Balen
Apr 1 at 22:33
$begingroup$
I have loaded the data at line 15, the dataframe dfimpnetc has already been loaded with data from a csv file earlier.
$endgroup$
– Vivek Rmk
Apr 2 at 14:19
add a comment |
$begingroup$
Where do you expect that the data would come from? You initialize a dataframe, but I fail to spot where you load in data.
$endgroup$
– S van Balen
Apr 1 at 22:33
$begingroup$
I have loaded the data at line 15, the dataframe dfimpnetc has already been loaded with data from a csv file earlier.
$endgroup$
– Vivek Rmk
Apr 2 at 14:19
$begingroup$
Where do you expect that the data would come from? You initialize a dataframe, but I fail to spot where you load in data.
$endgroup$
– S van Balen
Apr 1 at 22:33
$begingroup$
Where do you expect that the data would come from? You initialize a dataframe, but I fail to spot where you load in data.
$endgroup$
– S van Balen
Apr 1 at 22:33
$begingroup$
I have loaded the data at line 15, the dataframe dfimpnetc has already been loaded with data from a csv file earlier.
$endgroup$
– Vivek Rmk
Apr 2 at 14:19
$begingroup$
I have loaded the data at line 15, the dataframe dfimpnetc has already been loaded with data from a csv file earlier.
$endgroup$
– Vivek Rmk
Apr 2 at 14:19
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
w_tokenizer = nltk.tokenize.WhitespaceTokenizer()
dfimpnetc[column] = dfimpnetc[column].apply(lambda x: [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(x)])
Try the above code.
Mark as correct if this helps ;)
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
$endgroup$
$begingroup$
Am thankful for the suggestion @William Scott however am still getting the same error...
$endgroup$
– Vivek Rmk
Apr 2 at 20:41
add a comment |
$begingroup$
I agree with S van Balen in that it's not clear where and whether you actually load the data. Even if you loaded it earlier, initializing a new DataFrame object might erase it from memory if you're using the same variable name to store it.
Anyway, assuming the DataFrame 'dfclean_imp_netc''s rows and columns have indeed been filled with values, then I think the issue is that you initialize the frame as dfclean_imp_netc but then you apply the tokenizer on a different variable, dfimpnetc. I think you need to move the assignment of values to dfimpnetc before the for loops, as shown in the code snippet below.
Please note also that the two for loops are not assigning values to the same variable: the first loop updates dfimpnetc but the second loop updates dfclean_imp_netc. I get the sense you may want to be updating the same variable in both cases.
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
$endgroup$
add a comment |
Your Answer
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
w_tokenizer = nltk.tokenize.WhitespaceTokenizer()
dfimpnetc[column] = dfimpnetc[column].apply(lambda x: [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(x)])
Try the above code.
Mark as correct if this helps ;)
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
$endgroup$
$begingroup$
Am thankful for the suggestion @William Scott however am still getting the same error...
$endgroup$
– Vivek Rmk
Apr 2 at 20:41
add a comment |
$begingroup$
w_tokenizer = nltk.tokenize.WhitespaceTokenizer()
dfimpnetc[column] = dfimpnetc[column].apply(lambda x: [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(x)])
Try the above code.
Mark as correct if this helps ;)
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
$endgroup$
$begingroup$
Am thankful for the suggestion @William Scott however am still getting the same error...
$endgroup$
– Vivek Rmk
Apr 2 at 20:41
add a comment |
$begingroup$
w_tokenizer = nltk.tokenize.WhitespaceTokenizer()
dfimpnetc[column] = dfimpnetc[column].apply(lambda x: [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(x)])
Try the above code.
Mark as correct if this helps ;)
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
$endgroup$
w_tokenizer = nltk.tokenize.WhitespaceTokenizer()
dfimpnetc[column] = dfimpnetc[column].apply(lambda x: [lemmatizer.lemmatize(w) for w in w_tokenizer.tokenize(x)])
Try the above code.
Mark as correct if this helps ;)
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
answered Apr 1 at 23:52
William ScottWilliam Scott
1063
1063
$begingroup$
Am thankful for the suggestion @William Scott however am still getting the same error...
$endgroup$
– Vivek Rmk
Apr 2 at 20:41
add a comment |
$begingroup$
Am thankful for the suggestion @William Scott however am still getting the same error...
$endgroup$
– Vivek Rmk
Apr 2 at 20:41
$begingroup$
Am thankful for the suggestion @William Scott however am still getting the same error...
$endgroup$
– Vivek Rmk
Apr 2 at 20:41
$begingroup$
Am thankful for the suggestion @William Scott however am still getting the same error...
$endgroup$
– Vivek Rmk
Apr 2 at 20:41
add a comment |
$begingroup$
I agree with S van Balen in that it's not clear where and whether you actually load the data. Even if you loaded it earlier, initializing a new DataFrame object might erase it from memory if you're using the same variable name to store it.
Anyway, assuming the DataFrame 'dfclean_imp_netc''s rows and columns have indeed been filled with values, then I think the issue is that you initialize the frame as dfclean_imp_netc but then you apply the tokenizer on a different variable, dfimpnetc. I think you need to move the assignment of values to dfimpnetc before the for loops, as shown in the code snippet below.
Please note also that the two for loops are not assigning values to the same variable: the first loop updates dfimpnetc but the second loop updates dfclean_imp_netc. I get the sense you may want to be updating the same variable in both cases.
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
$endgroup$
add a comment |
$begingroup$
I agree with S van Balen in that it's not clear where and whether you actually load the data. Even if you loaded it earlier, initializing a new DataFrame object might erase it from memory if you're using the same variable name to store it.
Anyway, assuming the DataFrame 'dfclean_imp_netc''s rows and columns have indeed been filled with values, then I think the issue is that you initialize the frame as dfclean_imp_netc but then you apply the tokenizer on a different variable, dfimpnetc. I think you need to move the assignment of values to dfimpnetc before the for loops, as shown in the code snippet below.
Please note also that the two for loops are not assigning values to the same variable: the first loop updates dfimpnetc but the second loop updates dfclean_imp_netc. I get the sense you may want to be updating the same variable in both cases.
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
$endgroup$
add a comment |
$begingroup$
I agree with S van Balen in that it's not clear where and whether you actually load the data. Even if you loaded it earlier, initializing a new DataFrame object might erase it from memory if you're using the same variable name to store it.
Anyway, assuming the DataFrame 'dfclean_imp_netc''s rows and columns have indeed been filled with values, then I think the issue is that you initialize the frame as dfclean_imp_netc but then you apply the tokenizer on a different variable, dfimpnetc. I think you need to move the assignment of values to dfimpnetc before the for loops, as shown in the code snippet below.
Please note also that the two for loops are not assigning values to the same variable: the first loop updates dfimpnetc but the second loop updates dfclean_imp_netc. I get the sense you may want to be updating the same variable in both cases.
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
$endgroup$
I agree with S van Balen in that it's not clear where and whether you actually load the data. Even if you loaded it earlier, initializing a new DataFrame object might erase it from memory if you're using the same variable name to store it.
Anyway, assuming the DataFrame 'dfclean_imp_netc''s rows and columns have indeed been filled with values, then I think the issue is that you initialize the frame as dfclean_imp_netc but then you apply the tokenizer on a different variable, dfimpnetc. I think you need to move the assignment of values to dfimpnetc before the for loops, as shown in the code snippet below.
Please note also that the two for loops are not assigning values to the same variable: the first loop updates dfimpnetc but the second loop updates dfclean_imp_netc. I get the sense you may want to be updating the same variable in both cases.
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'w+')
stop_words = set(stopwords.words('english'))
tokenizer = RegexpTokenizer(r'w+')
dfclean_imp_netc=pd.DataFrame()
dfimpnetc['Resolution Note'] = dfclean_imp_netc['Resolution Note']
dfimpnetc['Description'] = dfclean_imp_netc['Description']
dfimpnetc['Short description'] = dfclean_imp_netc['Short description']
dfimpnetc['Resolution code'] = dfclean_imp_netc['Resolution code']
for column in ['Resolution code','Resolution Note','Description','Shortdescription']:
dfimpnetc[column] = dfimpnetc[column].apply(tokenizer.tokenize)
for column in ['Resolution code','Resolution Note','Description','Short description']:
dfclean_imp_netc[column] = dfimpnetc[column].apply(lambda vec: [word for word in vec if word not in stop_words])
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
answered Apr 3 at 5:25
JordiCarreraJordiCarrera
713
713
add a comment |
add a comment |
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Where do you expect that the data would come from? You initialize a dataframe, but I fail to spot where you load in data.
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– S van Balen
Apr 1 at 22:33
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I have loaded the data at line 15, the dataframe dfimpnetc has already been loaded with data from a csv file earlier.
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– Vivek Rmk
Apr 2 at 14:19