instalar - pip install pandas python 3

ImportError: Ningún módulo llamado dateutil.parser

Question

Recibo el siguiente error al importar pandas en un programa de Python

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

También aquí está el programa:

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

En Ubuntu, es posible que tengas que instalar primero el administrador de paquetes pip :

sudo apt-get install python-pip

Luego instale el paquete python-dateutil con:

sudo pip install python-dateutil

Answer 2

Ninguna de las soluciones funcionó para mí. Si está usando PIP, haga lo siguiente:

pip install pycrypto==2.6.1

Answer 3

Puede encontrar el paquete dateutil en https://pypi.python.org/pypi/python-dateutil . Extraelo en algún lugar y ejecuta el comando:

python setup.py install

¡Funcionó para mí!

Answer 4

Si está usando Pipenv , puede necesitar agregar esto a su Pipfile :

[packages]
python-dateutil = "*"

Answer 5

Si está utilizando un virtualenv , asegúrese de ejecutar pip desde dentro del virtualenv .

$ 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