ImportError: No module named dateutil.parser

I am receiving the following error when importing pandas in a Python program

    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

Also here's the program:

    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')

On Ubuntu you may need to install the package manager pip first:

    sudo apt-get install python-pip

Then install the python-dateutil package with:

    sudo pip install python-dateutil

From: stackoverflow.com/q/20853474

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