ImportError: No module named dateutil.parser

Question

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') 

Answer 1

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 

Answer 2

For Python 3:

pip3 install python-dateutil