I trying to refuse importing lines with date lesser than already imported.
timelimit = Operation.objects.filter(account = 3).aggregate(Max('date'))
for row in csv.reader(reencode(f), delimiter=';', quotechar='"')
if row != []:
if row[0]>timelimit:
operation.date=row[0]
row looks like:
2012-01-12,something,0,something2
Of course comparison row[0]>timelimit is wrong - but what is correct?
#this will convert your string("2012-01-12") to a datetime object
from datetime import datetime
>>> x = datetime.strptime(row[0], "%Y-%m-%d")
>>> x
>>> datetime.datetime(2012, 1, 12, 0, 0)
And then you can convert timelimit in a datetime object too like so:
timelimit = datetime(2011, 10, 10)
and then comparing these two is trivial:
x > timelimit
Related
I have a model named 'DemoModel' it has a field called demo_date.
I want to fetch the last 5 months i.e;(from current month records to past 5 months records) records by querying on the demo_date field.
My models look like
class DemoModel(models.Model):
demo_date = models.DateTimeField()
from datetime import datetime, timedelta
today = datetime.today()
long_ago = today + timedelta(days=-150)
retrieved_data = DemoModel.objects.filter(demo_date__gte=long_ago)
Use
dateutil.relativedelta import relativedelta
to calculate the five_months_ago parameter accurately.
And then get the objects like this:
target_set = DemoModel.objects.filter(demo_date__gte=five_months_ago)
This function give subscription or add months
def monthdelta(date, delta):
m, y = (date.month+delta) % 12, date.year + ((date.month)+delta-1) // 12
if not m: m = 12
d = min(date.day, [31,
29 if y%4==0 and not y%400==0 else 28,31,30,31,30,31,31,30,31,30,31][m-1])
return date.replace(day=d,month=m, year=y)
query goes here
from datetime import datetime
query= DemoModel.objects.filter(demo_date__gte=monthdelta(datetime.now(), -5)
)
I am getting an error and I'm not sure how to fix it.
Here is my code:
from matplotlib.finance import quotes_historical_yahoo_ochl
from datetime import date
from datetime import datetime
import pandas as pd
today = date.today()
start = (today.year-1, today.month, today.day)
quotes = quotes_historical_yahoo_ochl('AXP', start, today)
fields = ['date', 'open', 'close', 'high', 'low', 'volume']
list1 = []
for i in range(len(quotes)):
x = date.fromordinal(int(quotes[i][0]))
y = datetime.strftime(x, '%Y-%m-%d')
list1.append(y)
quotesdf = pd.DataFrame(quotes, index = list1, columns = fields)
quotesdf = quotesdf.drop(['date'], axis = 1)
print quotesdf
How can I change my code to achieve my goal, change the dateform and delete the original one?
In principle your code should work, you just need to indent it correctly, that is, you need to append the value of y to list1 inside the for loop.
for i in range(len(quotes)):
x = date.fromordinal(int(quotes[i][0]))
y = datetime.strftime(x, '%Y-%m-%d')
list1.append(y)
Thereby list1 will have as many entries as quotes instead of only one (the last one). And the final dataframe will not complain about misshaped data.
I would like to convert a Python date object to a Unix timestamp.
I already added a time object, so it would be that specific date at midnight, but I don't know how to continue from there.
d = date(2014, 10, 27)
t = time(0, 0, 0)
dt = datetime.combine(d, t)
#How to convert this to Unix timestamp?
I am using Python 2.7
You can get the unix time like this:
import time
from datetime import date
d = date(2014, 10, 27)
unixtime = time.mktime(d.timetuple())
Unix time can be derived from a datetime object like this:
d = date(2014, 10, 27)
t = time(0, 0, 0)
dt = datetime.combine(d, t)
unix = dt.strftime('%s')
# returns 1414364400, which is 2014-10-27 00:00:00
You can use easy_date to make it easy:
import date_converter
timestamp = date_converter.date_to_timestamp(d)
>>> import datetime
>>> d = datetime.date(2014, 10, 27)
>>> int("{:%s}".format(d))
1414364400
>>> datetime.datetime.fromtimestamp(1414364400)
datetime.datetime(2014, 10, 27, 0, 0)
Please note that %s formatting of times is not supported on Windows.
I'm relatively new to Python, and I am having trouble separating out unique rows from a data set that I had recently converted into lists. I broke separated out the data's unixtime recordings and converted them into datetime. Then when I recombined the data into a list I tried to separate out the unique rows of data. But instead I get the error.
[[[datetime.datetime(2014, 6, 20, 0, 0) -16.0]
[datetime.datetime(2014, 6, 20, 0, 0) -16.0]........
Traceback (most recent call last):
File "C:\Users\lenovo\Favorites\Microsoft 网站\Downloads\OTdataparser.py", line 33, in <module>
indicies = np.unique(okdat, return_index = True) #<-- NOT WORKING
File "C:\Python27\lib\site-packages\numpy\lib\arraysetops.py", line 180, in unique
perm = ar.argsort(kind='mergesort')
TypeError: can't compare datetime.datetime to float
My script is below.
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
import math
ds5 = np.genfromtxt("gpsdata.dat.140620", delimiter = '',
usecols = (2,4,5), dtype = object)
print ds5
ds = np.array([x for x in ds5 if x[0] == "06/20/2014"])
dot = ds[:,2].astype(float)
print ds
rndsht = np.genfromtxt(ds[:,1], delimiter = ".", dtype = float) #Rm decimal
print rndsht
dutc = np.array([datetime.utcfromtimestamp(x) for x in rndsht[:,0]])
print dutc
#dutc = np.array([datetime.utcfromtimestamp(x) for x in ds[:,1].astype(float)])
okdat = np.dstack((dutc,dot))
#okdat.astype(object)
print okdat
#indicies = np.unique(dutc, return_index=True) #<-- WORKS! BUT okdat??
#print indicies
indicies = np.unique(okdat, return_index = True) #<-- NOT WORKING
print indicies
#Can't figure out how to use indicies to limit dot
You could write your own unique function.
Here is quick example (you can probably do better). Note that is doesn't preserve order, but you could use insert and do that.
def
def unique(data):
x = 0
while x < len(data):
i = data[x]
c = 0
while (i in data):
c += 1
data.remove(i)
data.append(i)
if (c <= 1):
x += 1
return data
I've got a model similar to this:
class Person(models.Model):
name = models.CharField(max_length=40)
birthday = DateTimeField() # their next birthday
I would like to get a list of the total birthdays for each day for the next 30 days. So for example, the list would look like this:
[[9, 0], [10, 3], [11, 1], [12, 1], [13, 5], ... #30 entries in list
Each list entry in the list is a date number followed by the number of birthdays on that day. So for example on the 9th of May there are 0 birthdays.
UPDATES
My db is sqlite3 - will be moving to postgres in the future.
from django.db.models import Count
import datetime
today = datetime.date.today()
thirty_days = today + datetime.timedelta(days=30)
birthdays = dict(Person.objects.filter(
birthday__range=[today, thirty_days]
).values_list('birthday').annotate(Count('birthday')))
for day in range(30):
date = today + datetime.timedelta(day)
print "[%s, %s]" % (date, birthdays.get(date, 0))
I would get the list of days and birthday count this way:
from datetime import date, timedelta
today = date.today()
thirty_days = today + timedelta(days=30)
# get everyone with a birthday
people = Person.objects.filter(birthday__range=[today, thirty_days])
birthday_counts = []
for date in [today + timedelta(x) for x in range(30)]:
# use filter to get only birthdays on given date's day, use len to get total
birthdays = [date.day, len(filter(lambda x: x.birthday.day == date.day, people))]
birthday_counts.append(birthdays)
Something like this --
from datetime import date, timedelta
class Person(models.Model):
name = models.CharField(max_length=40)
birthday = models.DateField()
#staticmethod
def upcoming_birthdays(days=30):
today = date.today()
where = 'DATE_ADD(birthday, INTERVAL (YEAR(NOW()) - YEAR(birthday)) YEAR) BETWEEN DATE(NOW()) AND DATE_ADD(NOW(), INTERVAL %S DAY)'
birthdays = Person.objects.extra(where=where, params=[days]).values_list('birthday', flat=True)
data = []
for offset in range(0, days):
i = 0
d = today + timedelta(days=offset)
for b in birthdays:
if b.day == d.day and b.month == d.month:
i += 1
data.append((d.day, i))
return data
print Person.upcoming_birthdays()
(Queryset of people with a birthday in the next X days)
Found cool solution for this!
For me it works!
from datetime import datetime, timedelta
import operator
from django.db.models import Q
def birthdays_within(days):
now = datetime.now()
then = now + timedelta(days)
# Build the list of month/day tuples.
monthdays = [(now.month, now.day)]
while now <= then:
monthdays.append((now.month, now.day))
now += timedelta(days=1)
# Tranform each into queryset keyword args.
monthdays = (dict(zip(("birthday__month", "birthday__day"), t))
for t in monthdays)
# Compose the djano.db.models.Q objects together for a single query.
query = reduce(operator.or_, (Q(**d) for d in monthdays))
# Run the query.
return Person.objects.filter(query)
But it get a list of persons that have a birthday in date range. You should change a bit.