In [3]:
from collections import OrderedDict
import csv
from dateutil.parser import parse
import datetime,time
class DataFrame(object):
@classmethod
def from_csv(cls, file_path, delimiting_character=',', quote_character='"'):
with open(file_path, 'rU') as infile:
reader = csv.reader(infile, delimiter=delimiting_character, quotechar=quote_character)
data = []
for row in reader:
data.append(row)
return cls(list_of_lists=data)
def __init__(self, list_of_lists, header=True):
if header:
self.header = list_of_lists[0]
self.data = list_of_lists[1:]
else:
self.header = ['column' + str(index + 1) for index, column in enumerate(list_of_lists[0])]
self.data = list_of_lists
############# HW2 task 1 #############
if len(self.header) != len(set(self.header)):
raise Exception('There are duplicates!!!')
############# end task 1 #############
############# HW2 task 2 #############
self.data=[[s.strip() for s in row] for row in self.data]
############# end task 2 #############
self.data = [OrderedDict(zip(self.header, row)) for row in self.data]
def __getitem__(self, item):
# this is for rows only
if isinstance(item, (int, slice)):
return self.data[item]
# this is for columns only
elif isinstance(item, str):
return [row[item] for row in self.data]
# this is for rows and columns
elif isinstance(item, tuple):
if isinstance(item[0], list) or isinstance(item[1], list):
if isinstance(item[0], list):
rowz = [row for index, row in enumerate(self.data) if index in item[0]]
else:
rowz = self.data[item[0]]
if isinstance(item[1], list):
if all([isinstance(thing, int) for thing in item[1]]):
return [[column_value for index, column_value in enumerate([value for value in row.itervalues()]) if index in item[1]] for row in rowz]
elif all([isinstance(thing,str) for thing in item[1]]):
return [[row[column_name] for column_name in item[1]] for row in rowz]
else:
raise TypeError('What the hell is this?')
else:
return [[value for value in row.itervalues()][item[1]] for row in rowz]
else:
if isinstance(item[1], (int, slice)):
return [[value for value in row.itervalues()][item[1]] for row in self.data[item[0]]]
elif isinstance(item[1],str):
return [row[item[1]] for row in self.data[item[0]]]
else:
raise TypeError('I don\'t know how to handle this...')
# only for lists of column names
elif isinstance(item, list):
return [[row[column_name] for column_name in item] for row in self.data]
def get_rows_where_column_has_value(self, column_name, value, index_only=False):
if index_only:
return [index for index, row_value in enumerate(self[column_name]) if row_value==value]
else:
return [row for row in self.data if row[column_name]==value]
############# HW2 task 3 #############
def min(self,col_name):
try:
nums = [float(row[col_name].replace(',','')) for row in self.data]
print min(nums)
except ValueError:
try:
nums=[datetime.datetime.strptime(row[col_name], '%m/%d/%y %H:%M') for row in self.data]
nums=[time.mktime(num.timetuple()) for num in nums]
print time.strftime('%m-%d-%y %H:%M',time.localtime(min(nums)))
except:
print ('Cannot be calculated')
def max(self,col_name):
try:
nums = [float(row[col_name].replace(',','')) for row in self.data]
print max(nums)
except ValueError:
try:
nums=[datetime.datetime.strptime(row[col_name], '%m/%d/%y %H:%M') for row in self.data]
nums=[time.mktime(num.timetuple()) for num in nums]
print time.strftime('%m-%d-%y %H:%M',time.localtime(max(nums)))
except:
print ('Cannot be calculated')
def median(self,col_name):
try:
nums = [float(row[col_name].replace(',','')) for row in self.data]
nums = sorted(nums)
center = int(len(nums) / 2)
if len(nums) % 2 == 0:
print sum(nums[center - 1:center + 1]) / 2.0
else:
print nums[center]
except ValueError:
try:
nums=[datetime.datetime.strptime(row[col_name], '%m/%d/%y %H:%M') for row in self.data]
nums=[time.mktime(num.timetuple()) for num in nums]
nums = sorted(nums)
center = int(len(nums) / 2)
if len(nums) % 2 == 0:
print time.strftime('%m-%d-%y %H:%M',time.localtime(sum(nums[center - 1:center + 1]) / 2.0))
else:
print time.strftime('%m-%d-%y %H:%M',time.localtime(nums[center]))
except:
print ('Cannot be calculated')
def mean(self,col_name):
try:
nums = [float(row[col_name].replace(',','')) for row in self.data]
count = len(nums)
print sum(nums) / count
except ValueError:
try:
nums=[datetime.datetime.strptime(row[col_name], '%m/%d/%y %H:%M') for row in self.data]
nums=[time.mktime(num.timetuple()) for num in nums]
count = len(nums)
print time.strftime('%m-%d-%y %H:%M',time.localtime(sum(nums) / count))
except:
print ('Cannot be calculated')
def sum(self,col_name):
try:
nums = [float(row[col_name].replace(',','')) for row in self.data]
print sum(nums)
except ValueError:
print ('Cannot be calculated')
def std(self,col_name):
try:
nums = [float(row[col_name].replace(',','')) for row in self.data]
mean = sum(nums)/len(nums)
print sum((x-mean)**2/len(nums) for x in nums)**0.5
except ValueError:
print ('Cannot be calculated')
############# end task 3 #############
############# HW2 task 4 #############
def add_rows(self, list_of_lists):
for new_list in list_of_lists:
if len(new_list) == len(self.header):
new_row = [OrderedDict(zip(self.header, row)) for row in list_of_lists]
self.data = self.data + new_row
return self
else:
print ('Wrong number of len')
############# end task 4 #############
############# HW2 task 5 #############
def add_column(self, list_of_lists, col_name):
if len(list_of_lists) == len(self.data):
self.header=self.header+col_name
new_col_dict = [OrderedDict(zip(col_name,row)) for row in list_of_lists]
for l in range(len(self.data)):
for rowz in self.data:
rowz = self.data[l].update(new_col_dict[l])
return self
else:
print ("Wrong number of column")
############# end task 5 #############
In [413]:
infile = open('SalesJan2009.csv')
In [414]:
lines = infile.readlines()
In [415]:
lines = lines[0].split('\r')
In [416]:
data = [l.split(',') for l in lines]
In [423]:
len(data[0])
Out[423]:
12
In [438]:
for index,row in enumerate(data):
if len(data[index])!=12:
print index
559
In [439]:
data[559]
Out[439]:
['1/28/09 18:00',
'Product1',
'"13',
'000"',
'Visa',
'sandhya',
'Centennial ',
'CO',
'United States',
'12/2/06 23:24',
'2/7/09 15:18',
'39.57917',
'-104.87639']
In [450]:
things= lines[559].split('"')
things
Out[450]:
['1/28/09 18:00,Product1,',
'13,000',
',Visa,sandhya,Centennial ,CO,United States,12/2/06 23:24,2/7/09 15:18,39.57917,-104.87639']
In [458]:
things[0].split(',')[:-1]
Out[458]:
['1/28/09 18:00', 'Product1']
In [459]:
things[1]
Out[459]:
'13,000'
In [462]:
things[2].split(',')[1:]
Out[462]:
['Visa',
'sandhya',
'Centennial ',
'CO',
'United States',
'12/2/06 23:24',
'2/7/09 15:18',
'39.57917',
'-104.87639']
In [463]:
data[559]=things[0].split(',')[:-1]+[things[1]]+things[2].split(',')[1:]
data[559]
Out[463]:
['1/28/09 18:00',
'Product1',
'13,000',
'Visa',
'sandhya',
'Centennial ',
'CO',
'United States',
'12/2/06 23:24',
'2/7/09 15:18',
'39.57917',
'-104.87639']
In [465]:
data
Out[465]:
[['Transaction_date',
'Product',
'Price',
'Payment_Type',
'Name',
'City',
'State',
'Country',
'Account_Created',
'Last_Login',
'Latitude',
'Longitude'],
['1/2/09 6:17',
'Product1',
'1200',
'Mastercard',
'carolina',
'Basildon',
'England',
'United Kingdom',
'1/2/09 6:00',
'1/2/09 6:08',
'51.5',
'-1.1166667'],
['1/2/09 4:53',
'Product1',
'1200',
'Visa',
'Betina',
'Parkville ',
'MO',
'United States',
'1/2/09 4:42',
'1/2/09 7:49',
'39.195',
'-94.68194'],
['1/2/09 13:08',
'Product1',
'1200',
'Mastercard',
'Federica e Andrea',
'Astoria ',
'OR',
'United States',
'1/1/09 16:21',
'1/3/09 12:32',
'46.18806',
'-123.83'],
['1/3/09 14:44',
'Product1',
'1200',
'Visa',
'Gouya',
'Echuca',
'Victoria',
'Australia',
'9/25/05 21:13',
'1/3/09 14:22',
'-36.1333333',
'144.75'],
['1/4/09 12:56',
'Product2',
'3600',
'Visa',
'Gerd W ',
'Cahaba Heights ',
'AL',
'United States',
'11/15/08 15:47',
'1/4/09 12:45',
'33.52056',
'-86.8025'],
['1/4/09 13:19',
'Product1',
'1200',
'Visa',
'LAURENCE',
'Mickleton ',
'NJ',
'United States',
'9/24/08 15:19',
'1/4/09 13:04',
'39.79',
'-75.23806'],
['1/4/09 20:11',
'Product1',
'1200',
'Mastercard',
'Fleur',
'Peoria ',
'IL',
'United States',
'1/3/09 9:38',
'1/4/09 19:45',
'40.69361',
'-89.58889'],
['1/2/09 20:09',
'Product1',
'1200',
'Mastercard',
'adam',
'Martin ',
'TN',
'United States',
'1/2/09 17:43',
'1/4/09 20:01',
'36.34333',
'-88.85028'],
['1/4/09 13:17',
'Product1',
'1200',
'Mastercard',
'Renee Elisabeth',
'Tel Aviv',
'Tel Aviv',
'Israel',
'1/4/09 13:03',
'1/4/09 22:10',
'32.0666667',
'34.7666667'],
['1/4/09 14:11',
'Product1',
'1200',
'Visa',
'Aidan',
'Chatou',
'Ile-de-France',
'France',
'6/3/08 4:22',
'1/5/09 1:17',
'48.8833333',
'2.15'],
['1/5/09 2:42',
'Product1',
'1200',
'Diners',
'Stacy',
'New York ',
'NY',
'United States',
'1/5/09 2:23',
'1/5/09 4:59',
'40.71417',
'-74.00639'],
['1/5/09 5:39',
'Product1',
'1200',
'Amex',
'Heidi',
'Eindhoven',
'Noord-Brabant',
'Netherlands',
'1/5/09 4:55',
'1/5/09 8:15',
'51.45',
'5.4666667'],
['1/2/09 9:16',
'Product1',
'1200',
'Mastercard',
'Sean ',
'Shavano Park ',
'TX',
'United States',
'1/2/09 8:32',
'1/5/09 9:05',
'29.42389',
'-98.49333'],
['1/5/09 10:08',
'Product1',
'1200',
'Visa',
'Georgia',
'Eagle ',
'ID',
'United States',
'11/11/08 15:53',
'1/5/09 10:05',
'43.69556',
'-116.35306'],
['1/2/09 14:18',
'Product1',
'1200',
'Visa',
'Richard',
'Riverside ',
'NJ',
'United States',
'12/9/08 12:07',
'1/5/09 11:01',
'40.03222',
'-74.95778'],
['1/4/09 1:05',
'Product1',
'1200',
'Diners',
'Leanne',
'Julianstown',
'Meath',
'Ireland',
'1/4/09 0:00',
'1/5/09 13:36',
'53.6772222',
'-6.3191667'],
['1/5/09 11:37',
'Product1',
'1200',
'Visa',
'Janet',
'Ottawa',
'Ontario',
'Canada',
'1/5/09 9:35',
'1/5/09 19:24',
'45.4166667',
'-75.7'],
['1/6/09 5:02',
'Product1',
'1200',
'Diners',
'barbara',
'Hyderabad',
'Andhra Pradesh',
'India',
'1/6/09 2:41',
'1/6/09 7:52',
'17.3833333',
'78.4666667'],
['1/6/09 7:45',
'Product2',
'3600',
'Visa',
'Sabine',
'London',
'England',
'United Kingdom',
'1/6/09 7:00',
'1/6/09 9:17',
'51.52721',
'0.14559'],
['1/2/09 7:35',
'Product1',
'1200',
'Diners',
'Hani',
'Salt Lake City ',
'UT',
'United States',
'12/30/08 5:44',
'1/6/09 10:52',
'40.76083',
'-111.89028'],
['1/6/09 12:56',
'Product1',
'1200',
'Visa',
'Jeremy',
'Manchester',
'England',
'United Kingdom',
'1/6/09 10:58',
'1/6/09 13:31',
'53.5',
'-2.2166667'],
['1/1/09 11:05',
'Product1',
'1200',
'Diners',
'Janis',
'Ballynora',
'Cork',
'Ireland',
'12/10/07 12:37',
'1/7/09 1:52',
'51.8630556',
'-8.58'],
['1/5/09 4:10',
'Product1',
'1200',
'Mastercard',
'Nicola',
'Roodepoort',
'Gauteng',
'South Africa',
'1/5/09 2:33',
'1/7/09 5:13',
'-26.1666667',
'27.8666667'],
['1/6/09 7:18',
'Product1',
'1200',
'Visa',
'asuman',
'Chula Vista ',
'CA',
'United States',
'1/6/09 7:07',
'1/7/09 7:08',
'32.64',
'-117.08333'],
['1/2/09 1:11',
'Product1',
'1200',
'Mastercard',
'Lena',
'Kuopio',
'Ita-Suomen Laani',
'Finland',
'12/31/08 2:48',
'1/7/09 10:20',
'62.9',
'27.6833333'],
['1/1/09 2:24',
'Product1',
'1200',
'Visa',
'Lisa',
'Sugar Land ',
'TX',
'United States',
'1/1/09 1:56',
'1/7/09 10:52',
'29.61944',
'-95.63472'],
['1/7/09 8:08',
'Product1',
'1200',
'Diners',
'Bryan Kerrene',
'New York ',
'NY',
'United States',
'1/7/09 7:39',
'1/7/09 12:38',
'40.71417',
'-74.00639'],
['1/2/09 2:57',
'Product1',
'1200',
'Visa',
'chris',
'London',
'England',
'United Kingdom',
'1/3/08 7:23',
'1/7/09 13:14',
'51.52721',
'0.14559'],
['1/1/09 20:21',
'Product1',
'1200',
'Visa',
'Maxine',
'Morton ',
'IL',
'United States',
'10/24/08 6:48',
'1/7/09 20:49',
'40.61278',
'-89.45917'],
['1/8/09 0:42',
'Product1',
'1200',
'Visa',
'Family',
'Los Gatos ',
'CA',
'United States',
'1/8/09 0:28',
'1/8/09 3:39',
'37.22667',
'-121.97361'],
['1/8/09 3:56',
'Product1',
'1200',
'Mastercard',
'Katherine',
'New York ',
'NY',
'United States',
'1/8/09 3:33',
'1/8/09 6:19',
'40.71417',
'-74.00639'],
['1/8/09 3:16',
'Product1',
'1200',
'Mastercard',
'Linda',
'Miami ',
'FL',
'United States',
'1/8/09 3:06',
'1/8/09 6:34',
'25.77389',
'-80.19389'],
['1/8/09 1:59',
'Product1',
'1200',
'Mastercard',
'SYLVIA',
'Vesenaz',
'Geneve',
'Switzerland',
'11/28/07 11:56',
'1/8/09 7:20',
'46.2333333',
'6.2'],
['1/3/09 9:03',
'Product1',
'1200',
'Diners',
'Sheila',
'Brooklyn ',
'NY',
'United States',
'1/3/09 8:47',
'1/8/09 10:38',
'40.65',
'-73.95'],
['1/5/09 13:17',
'Product1',
'1200',
'Mastercard',
'Stephanie',
'Badhoevedorp',
'Noord-Holland',
'Netherlands',
'1/5/09 12:45',
'1/8/09 11:45',
'52.3333333',
'4.7833333'],
['1/6/09 7:46',
'Product1',
'1200',
'Amex',
'Kelly',
'Reston ',
'VA',
'United States',
'1/6/09 7:30',
'1/8/09 12:40',
'38.96861',
'-77.34139'],
['1/5/09 20:00',
'Product2',
'3600',
'Visa',
'James',
'Burpengary',
'Queensland',
'Australia',
'12/10/08 19:53',
'1/8/09 17:58',
'-27.1666667',
'152.95'],
['1/8/09 16:24',
'Product1',
'1200',
'Visa',
'jennifer',
'Phoenix ',
'AZ',
'United States',
'1/8/09 15:57',
'1/8/09 18:30',
'33.44833',
'-112.07333'],
['1/9/09 6:39',
'Product1',
'1200',
'Mastercard',
'Anneli',
'Houston ',
'TX',
'United States',
'1/9/09 5:09',
'1/9/09 7:11',
'29.76306',
'-95.36306'],
['1/6/09 22:19',
'Product2',
'3600',
'Amex',
'Ritz',
'Pittsfield ',
'VT',
'United States',
'1/6/09 12:00',
'1/9/09 10:05',
'43.77222',
'-72.81333'],
['1/6/09 23:00',
'Product2',
'3600',
'Amex',
'Sylvia',
'Pittsfield ',
'VT',
'United States',
'1/6/09 12:00',
'1/9/09 10:05',
'43.77222',
'-72.81333'],
['1/7/09 7:44',
'Product1',
'1200',
'Mastercard',
'Marie',
'Ball Ground ',
'GA',
'United States',
'1/7/09 5:35',
'1/9/09 10:52',
'34.33806',
'-84.37667'],
['1/3/09 13:24',
'Product1',
'1200',
'Visa',
'Mehmet Fatih',
'Helsingor',
'Frederiksborg',
'Denmark',
'1/3/09 12:47',
'1/9/09 11:14',
'56.0333333',
'12.6166667'],
['1/7/09 15:12',
'Product2',
'3600',
'Visa',
'Anabela',
'Flossmoor ',
'IL',
'United States',
'1/5/09 19:55',
'1/9/09 16:03',
'41.54278',
'-87.68472'],
['1/7/09 20:15',
'Product1',
'1200',
'Amex',
'Nicole',
'Houston ',
'TX',
'United States',
'1/7/09 17:17',
'1/9/09 20:02',
'29.76306',
'-95.36306'],
['1/3/09 10:11',
'Product2',
'3600',
'Visa',
'Christiane ',
'Delray Beach ',
'FL',
'United States',
'1/3/09 9:27',
'1/10/09 9:46',
'26.46111',
'-80.07306'],
['1/9/09 15:58',
'Product1',
'1200',
'Mastercard',
'Sari',
'Newbury',
'England',
'United Kingdom',
'1/9/09 14:53',
'1/10/09 13:16',
'51.4',
'-1.3166667'],
['1/3/09 13:11',
'Product1',
'1200',
'Mastercard',
'simone',
'Kobenhavn',
'Kobenhavn',
'Denmark',
'10/31/08 0:31',
'1/10/09 13:24',
'55.6666667',
'12.5833333'],
['1/10/09 12:57',
'Product1',
'1200',
'Amex',
'Vanessa',
'Sandy Springs ',
'GA',
'United States',
'2/7/07 20:16',
'1/10/09 14:09',
'33.92417',
'-84.37861'],
['1/10/09 14:43',
'Product1',
'1200',
'Diners',
'Anupam',
'Kinsaley',
'Dublin',
'Ireland',
'1/9/09 11:38',
'1/10/09 14:37',
'53.4247222',
'-6.1758333'],
['1/10/09 12:05',
'Product1',
'1200',
'Visa',
'Karina',
'Fort Lauderdale ',
'FL',
'United States',
'7/1/08 12:53',
'1/10/09 16:33',
'26.12194',
'-80.14361'],
['1/6/09 1:20',
'Product1',
'1200',
'Mastercard',
'Frank',
'Melbourne',
'Victoria',
'Australia',
'1/2/09 17:51',
'1/10/09 18:27',
'-37.8166667',
'144.9666667'],
['1/10/09 14:56',
'Product1',
'1200',
'Visa',
'Angela',
'Ankeny ',
'IA',
'United States',
'1/8/09 3:06',
'1/10/09 19:41',
'41.72972',
'-93.60556'],
['1/7/09 10:01',
'Product1',
'1200',
'Visa',
'Darren',
'Pittsboro ',
'NC',
'United States',
'1/7/09 9:04',
'1/10/09 20:02',
'35.72',
'-79.1775'],
['1/1/09 1:26',
'Product1',
'1200',
'Mastercard',
'Nikki',
'New Rochelle ',
'NY',
'United States',
'1/1/09 0:58',
'1/10/09 21:31',
'40.91139',
'-73.78278'],
['1/11/09 2:04',
'Product1',
'1200',
'Visa',
'chris',
'Gold Coast',
'Queensland',
'Australia',
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['1/5/09 7:55',
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['1/25/09 14:40',
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'2/27/09 1:59',
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['1/23/09 5:11',
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'7/15/08 7:20',
'2/27/09 4:00',
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'2/27/09 14:35',
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['1/12/09 20:31',
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['1/24/09 12:00',
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'12/30/08 15:19',
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['1/28/09 11:19',
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'3/3/08 17:38',
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['1/7/09 19:48',
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['1/26/09 11:19',
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'1/4/09 5:25',
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['1/5/09 13:23',
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['1/26/09 13:41',
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['1/20/09 10:42',
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['1/22/09 14:25',
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['1/28/09 5:36',
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['1/1/09 4:24',
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['1/8/09 11:55',
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'julie',
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['1/12/09 21:30',
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'Madison ',
'WI',
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'-89.40111']]
In [4]:
df = DataFrame.from_csv('SalesJan2009.csv')
In [472]:
value=df['Price']
value= [l.replace(',','') for l in value]
In [475]:
[int(l) for l in value]
Out[475]:
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In [519]:
header = ['a','b','c']
new_row = [[1,2,3],[4,5,6]]
new_data=[OrderedDict(zip(header,l))for l in new_row]
data
Out[519]:
[OrderedDict([('a', 1), ('b', 2), ('c', 3)]),
OrderedDict([('a', 4), ('b', 5), ('c', 6)])]
In [520]:
header = ['a','b','c']
new_row = [1,2,3]
yy=OrderedDict(zip(header,new_row))
yy
Out[520]:
OrderedDict([('a', 1), ('b', 2), ('c', 3)])
In [522]:
data=[OrderedDict([('a', 1), ('b', 2), ('c', 3)]),
OrderedDict([('a', 4), ('b', 5), ('c', 6)])]
data=data +new_data
data
Out[522]:
[OrderedDict([('a', 1), ('b', 2), ('c', 3)]),
OrderedDict([('a', 4), ('b', 5), ('c', 6)]),
OrderedDict([('a', 1), ('b', 2), ('c', 3)]),
OrderedDict([('a', 4), ('b', 5), ('c', 6)])]
In [542]:
new_col=[['d','e'],[1,1],[2,2],[3,3],[4,4]]
new_col_head=new_col[0]
new_col_data=new_col[1:]
print new_col_head
print new_col_data
['d', 'e']
[[1, 1], [2, 2], [3, 3], [4, 4]]
In [562]:
len(data)== len(new_col_data)
len(data)
Out[562]:
4
In [598]:
header = ['a','b','c']
header_new = header+new_col_head
header_new
In [591]:
new_col_dict = [OrderedDict(zip(new_col_head,row)) for row in new_col_data]
new_col_dict
Out[591]:
[OrderedDict([('d', 1), ('e', 1)]),
OrderedDict([('d', 2), ('e', 2)]),
OrderedDict([('d', 3), ('e', 3)]),
OrderedDict([('d', 4), ('e', 4)])]
In [596]:
for l in range(len(data)):
for rowz in data:
rowz = data[l].update(new_col_dict[l])
data
Out[596]:
[OrderedDict([('a', 1), ('b', 2), ('c', 3), ('d', 1), ('e', 1)]),
OrderedDict([('a', 4), ('b', 5), ('c', 6), ('d', 2), ('e', 2)]),
OrderedDict([('a', 1), ('b', 2), ('c', 3), ('d', 3), ('e', 3)]),
OrderedDict([('a', 4), ('b', 5), ('c', 6), ('d', 4), ('e', 4)])]
In [587]:
data[3]
Out[587]:
OrderedDict([('a', 4), ('b', 5), ('c', 6)])
In [589]:
new_col_dict[1]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-589-f582518922c6> in <module>()
----> 1 new_col_dict(1)
TypeError: 'OrderedDict' object is not callable
In [18]:
dt = DataFrame.from_csv('test_hw2_task5.csv')
In [19]:
dt[:]
Out[19]:
[OrderedDict([('A', '1'), ('B', '4'), ('C', '7')]),
OrderedDict([('A', '2'), ('B', '5'), ('C', '8')]),
OrderedDict([('A', '3'), ('B', '6'), ('C', '9')])]
In [20]:
add=[['D','E'],[1,2,3],[4,5,6],[7,8,9]]
add
Out[20]:
[['D', 'E'], [1, 2, 3], [4, 5, 6], [7, 8, 9]]
In [21]:
new=dt.add_column(add[1:],add[0])
print new
<__main__.DataFrame object at 0x7f73d05b5890>
In [22]:
dt[:]
Out[22]:
[OrderedDict([('A', '1'), ('B', '4'), ('C', '7'), ('D', 1), ('E', 2)]),
OrderedDict([('A', '2'), ('B', '5'), ('C', '8'), ('D', 4), ('E', 5)]),
OrderedDict([('A', '3'), ('B', '6'), ('C', '9'), ('D', 7), ('E', 8)])]
In [ ]:
Content source: ZeroZeng/RongZeng
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