小编mar*_*rin的帖子

我正在尝试从 NLTK 停用词列表中添加和删除单词：

from nltk.corpus import stopwords

stop_words = set(stopwords.words('french'))

#add words that aren't in the NLTK stopwords list
new_stopwords = ['cette', 'les', 'cet']
new_stopwords_list = set(stop_words.extend(new_stopwords))

#remove words that are in NLTK stopwords list
not_stopwords = {'n', 'pas', 'ne'} 
final_stop_words = set([word for word in new_stopwords_list if word not in not_stopwords])

print(final_stop_words)

输出：

Traceback (most recent call last):
  File "test_stop.py", line 10, in <module>
new_stopwords_list = set(stop_words.extend(new_stopwords))
AttributeError: 'set' object has no attribute 'extend'

我正在尝试删除所有不属于法语的短语.我尝试使用langdetect库(不幸的是没有pandas)

CSV文件

message
Je suis fatiguée
The book is on the table
Il fait chaud aujourd'hui!
They are sicks
La vie est belle

脚本:

import csv
from langdetect import detect

with open('ddd.csv', 'r') as file:
    fichier = csv.reader(file)

    for line in fichier:
        if line[0] != '':
            message = line[0]

            def detecteur_FR(message):
                #We need to turn the column into a list of lists.
                message_list = [comments for comments in message.split('\n')]
                for text in message_list:
                    if detect(text) == 'fr':
                        message_FR = text …

我正在尝试找到具有最大编号的月份(列'月')(在DepDelay列中)

数据

flightID         Month  ArrTime ActualElapsedTime  DepDelay   ArrDelay
BBYYEUVY67527        1   1514.0               58.0       NA      64.0   
MUPXAQFN40227        1     37.0              120.0       13      52.0   
LQLYUIMN79169        1    916.0              166.0       NA     -25.0   
KTAMHIFO10843        1      NaN                NaN        5       NaN   
BOOXJTEY23623        1      NaN                NaN        4       NaN  
BBYYEUVY67527        2   1514.0               58.0       NA      64.0   
MUPXAQFN40227        2     37.0              120.0       NA      52.0   
LQLYUIMN79169        2    916.0              166.0       NA     -25.0   
KTAMHIFO10843        2      NaN                NaN       15       NaN   
BOOXJTEY23623        2      NaN                NaN        4       NaN  

我试过了:

data = pd.read_csv('data.csv', sep='\t')

dep_delay = all_data.groupby(["Month"].DepDelay.count().max())

print(dep_delay)

错误:

AttributeError                            Traceback …

我正在尝试根据另一列的信息填充一个空列

我的数据框

   A        B                                    C
0  F    House                     Are you at home?
1  E    House    description: to deliver tomorrow
2  F    Apt                 Here is some exemples 
3  F    House          description: a brown table
4  E    Apt               description: in the bus
5  F    House                 Hello, how are you?
6  E    Apt                     description: keys

所以,我创建一个D列,如果列C以'description'开头,我填写'fuzzy',如果没有'buzzy'.

new_column['D'] = ''

我试着填补它们

def fill_column(delete_column):
    if new_column['D'].loc[new_column['D'].str.startswith('description:'):
        new_column['D'] == 'fuzzy'
    else:
        new_column['D'] == 'buzzy'

    return new_column

我的输出:

  File "<ipython-input-41-ec3c1407168c>", line 6
    else:
       ^
SyntaxError: invalid syntax …

我正在尝试使用 word2vec 和 Kmeans 进行聚类，但它不起作用。

这是我的部分数据：

demain fera chaud à paris pas marseille
mauvais exemple ce n est pas un cliché mais il faut comprendre pourquoi aussi
il y a plus de travail à Paris c est d ailleurs pour cette raison qu autant de gens",
mais s il y a plus de travail, il y a aussi plus de concurrence
s agglutinent autour de la capitale

脚本：

import nltk
import pandas
import pprint
import numpy as np
import pandas …

我正在尝试进行分类，其中一个文件完全是培训，另一个文件完全是测试。这是可能的？我试过了：

import pandas
import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn import cross_validation
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer, CountVectorizer, TfidfTransformer
from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score

#csv file from train
df = pd.read_csv('data_train.csv', sep = ',')

#csv file from test
df_test = pd.read_csv('data_test.csv', sep = …

我正在尝试进行聚类。我正在使用熊猫和 sklearn。

import pandas
import pprint
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score
from sklearn.feature_extraction.text import TfidfVectorizer

dataset = pandas.read_csv('text.csv', encoding='utf-8')

dataset_list = dataset.values.tolist()


vectors = TfidfVectorizer()
X = vectors.fit_transform(dataset_list)

clusters_number = 20

model = KMeans(n_clusters = clusters_number, init = 'k-means++', max_iter = 300, n_init = 1)

model.fit(X)

centers = model.cluster_centers_
labels = model.labels_

clusters = {}
for comment, label in zip(dataset_list, labels):
    print ('Comment:', comment)
    print ('Label:', label)

try:
    clusters[str(label)].append(comment)
except:
    clusters[str(label)] = [comment] …

我正在尝试使文本分类

import pandas as pd
import pandas
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsOneClassifier
from sklearn.svm import SVC
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix

dataset = pd.read_csv('data.csv', encoding = 'utf-8')
data = dataset['text']
labels = dataset['label']

X_train, X_test, y_train, y_test = train_test_split (data, labels, test_size = 0.2, random_state = 0)

count_vector = CountVectorizer()
tfidf = TfidfTransformer() …