가져오기 오류: dateutil.parser라는 모듈이 없습니다.

가져오기 오류: dateutil.parser라는 모듈이 없습니다.

가져올 때 다음 오류가 발생합니다.pandas순식간에Python프로그램.

monas-mbp:book mona$ sudo pip install python-dateutil
Requirement already satisfied (use --upgrade to upgrade): python-dateutil in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python
Cleaning up...
monas-mbp:book mona$ python t1.py
No module named dateutil.parser
Traceback (most recent call last):
  File "t1.py", line 4, in <module>
    import pandas as pd
  File "/Library/Python/2.7/site-packages/pandas/__init__.py", line 6, in <module>
    from . import hashtable, tslib, lib
  File "tslib.pyx", line 31, in init pandas.tslib (pandas/tslib.c:48782)
ImportError: No module named dateutil.parser

다음은 프로그램입니다.

import codecs 
from math import sqrt
import numpy as np
import pandas as pd

users = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0,
                      "Norah Jones": 4.5, "Phoenix": 5.0,
                      "Slightly Stoopid": 1.5,
                      "The Strokes": 2.5, "Vampire Weekend": 2.0},

         "Bill":{"Blues Traveler": 2.0, "Broken Bells": 3.5,
                 "Deadmau5": 4.0, "Phoenix": 2.0,
                 "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0},

         "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0,
                  "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5,
                  "Slightly Stoopid": 1.0},

         "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0,
                 "Deadmau5": 4.5, "Phoenix": 3.0,
                 "Slightly Stoopid": 4.5, "The Strokes": 4.0,
                 "Vampire Weekend": 2.0},

         "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0,
                    "Norah Jones": 4.0, "The Strokes": 4.0,
                    "Vampire Weekend": 1.0},

         "Jordyn":  {"Broken Bells": 4.5, "Deadmau5": 4.0,
                     "Norah Jones": 5.0, "Phoenix": 5.0,
                     "Slightly Stoopid": 4.5, "The Strokes": 4.0,
                     "Vampire Weekend": 4.0},

         "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0,
                 "Norah Jones": 3.0, "Phoenix": 5.0,
                 "Slightly Stoopid": 4.0, "The Strokes": 5.0},

         "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0,
                      "Phoenix": 4.0, "Slightly Stoopid": 2.5,
                      "The Strokes": 3.0}
        }



class recommender:

    def __init__(self, data, k=1, metric='pearson', n=5):
        """ initialize recommender
        currently, if data is dictionary the recommender is initialized
        to it.
        For all other data types of data, no initialization occurs
        k is the k value for k nearest neighbor
        metric is which distance formula to use
        n is the maximum number of recommendations to make"""
        self.k = k
        self.n = n
        self.username2id = {}
        self.userid2name = {}
        self.productid2name = {}
        # for some reason I want to save the name of the metric
        self.metric = metric
        if self.metric == 'pearson':
            self.fn = self.pearson
        #
        # if data is dictionary set recommender data to it
        #
        if type(data).__name__ == 'dict':
            self.data = data

    def convertProductID2name(self, id):
        """Given product id number return product name"""
        if id in self.productid2name:
            return self.productid2name[id]
        else:
            return id


    def userRatings(self, id, n):
        """Return n top ratings for user with id"""
        print ("Ratings for " + self.userid2name[id])
        ratings = self.data[id]
        print(len(ratings))
        ratings = list(ratings.items())
        ratings = [(self.convertProductID2name(k), v)
                   for (k, v) in ratings]
        # finally sort and return
        ratings.sort(key=lambda artistTuple: artistTuple[1],
                     reverse = True)
        ratings = ratings[:n]
        for rating in ratings:
            print("%s\t%i" % (rating[0], rating[1]))




    def loadBookDB(self, path=''):
        """loads the BX book dataset. Path is where the BX files are
        located"""
        self.data = {}
        i = 0
        #
        # First load book ratings into self.data
        #
        f = codecs.open(path + "BX-Book-Ratings.csv", 'r', 'utf8')
        for line in f:
            i += 1
            #separate line into fields
            fields = line.split(';')
            user = fields[0].strip('"')
            book = fields[1].strip('"')
            rating = int(fields[2].strip().strip('"'))
            if user in self.data:
                currentRatings = self.data[user]
            else:
                currentRatings = {}
            currentRatings[book] = rating
            self.data[user] = currentRatings
        f.close()
        #
        # Now load books into self.productid2name
        # Books contains isbn, title, and author among other fields
        #
        f = codecs.open(path + "BX-Books.csv", 'r', 'utf8')
        for line in f:
            i += 1
            #separate line into fields
            fields = line.split(';')
            isbn = fields[0].strip('"')
            title = fields[1].strip('"')
            author = fields[2].strip().strip('"')
            title = title + ' by ' + author
            self.productid2name[isbn] = title
        f.close()
        #
        #  Now load user info into both self.userid2name and
        #  self.username2id
        #
        f = codecs.open(path + "BX-Users.csv", 'r', 'utf8')
        for line in f:
            i += 1
            #print(line)
            #separate line into fields
            fields = line.split(';')
            userid = fields[0].strip('"')
            location = fields[1].strip('"')
            if len(fields) > 3:
                age = fields[2].strip().strip('"')
            else:
                age = 'NULL'
            if age != 'NULL':
                value = location + '  (age: ' + age + ')'
            else:
                value = location
            self.userid2name[userid] = value
            self.username2id[location] = userid
        f.close()
        print(i)


    def pearson(self, rating1, rating2):
        sum_xy = 0
        sum_x = 0
        sum_y = 0
        sum_x2 = 0
        sum_y2 = 0
        n = 0
        for key in rating1:
            if key in rating2:
                n += 1
                x = rating1[key]
                y = rating2[key]
                sum_xy += x * y
                sum_x += x
                sum_y += y
                sum_x2 += pow(x, 2)
                sum_y2 += pow(y, 2)
        if n == 0:
            return 0
        # now compute denominator
        denominator = (sqrt(sum_x2 - pow(sum_x, 2) / n)
                       * sqrt(sum_y2 - pow(sum_y, 2) / n))
        if denominator == 0:
            return 0
        else:
            return (sum_xy - (sum_x * sum_y) / n) / denominator


    def computeNearestNeighbor(self, username):
        """creates a sorted list of users based on their distance to
        username"""
        distances = []
        for instance in self.data:
            if instance != username:
                distance = self.fn(self.data[username],
                                   self.data[instance])
                distances.append((instance, distance))
        # sort based on distance -- closest first
        distances.sort(key=lambda artistTuple: artistTuple[1],
                       reverse=True)
        return distances

    def recommend(self, user):
       """Give list of recommendations"""
       recommendations = {}
       # first get list of users  ordered by nearness
       nearest = self.computeNearestNeighbor(user)
       #
       # now get the ratings for the user
       #
       userRatings = self.data[user]
       #
       # determine the total distance
       totalDistance = 0.0
       for i in range(self.k):
          totalDistance += nearest[i][1]
       # now iterate through the k nearest neighbors
       # accumulating their ratings
       for i in range(self.k):
          # compute slice of pie 
          weight = nearest[i][1] / totalDistance
          # get the name of the person
          name = nearest[i][0]
          # get the ratings for this person
          neighborRatings = self.data[name]
          # get the name of the person
          # now find bands neighbor rated that user didn't
          for artist in neighborRatings:
             if not artist in userRatings:
                if artist not in recommendations:
                   recommendations[artist] = (neighborRatings[artist]
                                              * weight)
                else:
                   recommendations[artist] = (recommendations[artist]
                                              + neighborRatings[artist]
                                              * weight)
       # now make list from dictionary
       recommendations = list(recommendations.items())
       recommendations = [(self.convertProductID2name(k), v)
                          for (k, v) in recommendations]
       # finally sort and return
       recommendations.sort(key=lambda artistTuple: artistTuple[1],
                            reverse = True)
       # Return the first n items
       return recommendations[:self.n]

r = recommender(users)
# The author implementation
r.loadBookDB('/Users/mona/Downloads/BX-Dump/')

ratings = pd.read_csv('/Users/danialt/BX-CSV-Dump/BX-Book-Ratings.csv', sep=";", quotechar="\"", escapechar="\\")
books = pd.read_csv('/Users/danialt/BX-CSV-Dump/BX-Books.csv', sep=";", quotechar="\"", escapechar="\\")
users = pd.read_csv('/Users/danialt/BX-CSV-Dump/BX-Users.csv', sep=";", quotechar="\"", escapechar="\\")



pivot_rating = ratings.pivot(index='User-ID', columns='ISBN', values='Book-Rating')

Ubuntu에서 패키지 관리자를 설치해야 할 수 있습니다.pip첫 번째:

sudo apt-get install python-pip

그런 다음 을 설치합니다.python-dateutil패키지 포함:

sudo pip install python-dateutil

Python 3의 경우:

pip3 install python-dateutil

dateutil 패키지는 https://pypi.python.org/pypi/python-dateutil 에서 찾을 수 있습니다.어딘가에 압축을 풀고 다음 명령을 실행합니다.

python setup.py install

저한테 효과가 있었어요!

MacOS에도 동일한 문제가 있으며 이 문제를 해결하기 위해 노력하고 있습니다.

pip3 install python-dateutil

Ubuntu에서

sudo apt-get install python-dateutil

가상 환경을 사용하는 경우 가상 환경 내에서 pip을 실행하고 있는지 확인합니다.

$ which pip
/Library/Frameworks/Python.framework/Versions/Current/bin/pip
$ find . -name pip -print
./flask/bin/pip
./flask/lib/python2.7/site-packages/pip
$ ./flask/bin/pip install python-dateutil

어떤 해결책도 저에게 효과가 없었습니다.PIP를 사용하는 경우 다음을 수행합니다.

pip install pycrypto==2.6.1

Ubuntu 18.04에서 Python2:

sudo apt-get install python-dateutil

저는 Panda 설치에 문제가 있었습니다.이것이 모든 사람에게 해결책은 아닐 수 있지만, 특히 다음과 같은 문제가 있는 경우에는 더욱 그렇습니다.

File "/home/cc/.local/lib/python3.8/site-packages/pandas/core/dtypes/cast.py", line 23, in <module>
    from dateutil.parser import ParserError
ImportError: cannot import name 'ParserError' from 'dateutil.parser' (/usr/lib/python3/dist-packages/dateutil/parser/__init__.py)

또는 그와 같은 방식으로, 이것이 해결책이 방법은 다음과 같습니다.

pip3 uninstall pandas
pip3 install pandas

이것은 Python 3을 가정한 것입니다.

저도 비슷한 문제가 있었습니다.다음은 스택 추적입니다.

Traceback (most recent call last):
File "/usr/local/bin/aws", line 19, in <module> import awscli.clidriver
File "/usr/local/lib/python2.7/dist-packages/awscli/clidriver.py", line 17, in <module> import botocore.session
File "/usr/local/lib/python2.7/dist-packages/botocore/session.py", line 30, in <module> import botocore.credentials
File "/usr/local/lib/python2.7/dist-packages/botocore/credentials.py", line 27, in <module> from dateutil.parser import parse
ImportError: No module named dateutil.parser

설치(재설치)를 시도했습니다.dateutil.parser온갖 수단을 다성공하지 못했습니다.

로 해결했습니다.

pip3 uninstall awscli
pip3 install awscli

sudo python3 -m pip install PyPDF2 passlib babel werkzeug lxml decorator polib pillow psycopg2 idna python-dateutil psutil requests jinja2

사용 중인 경우Pipenv당신은 이것을 당신의 것에 추가할 필요가 있을지도 모릅니다.Pipfile:

[packages]
python-dateutil = "*"

언급URL : https://stackoverflow.com/questions/20853474/importerror-no-module-named-dateutil-parser