I've created a script in python to parse two fields from a webpage - total revenue and it's concerning date. The fields I'm after are javascript encrypted. They are available in page source within json array. The following script can parse those two fields accordingly.
However, the problem is the date visible in that page is different from the one available in page source.
Webpage link
The date in that webpage is like this
The date in page source is like this
There is clearly a variation of one day.
After visiting that webpage when you click on this tab Quarterly you can see the results there:
I've tried with:
import re
import json
import requests
url = 'https://finance.yahoo.com/quote/GTX/financials?p=GTX'
res = requests.get(url)
data = re.findall(r'root.App.main[^{]+(.*);',res.text)[0]
jsoncontent = json.loads(data)
container = jsoncontent['context']['dispatcher']['stores']['QuoteSummaryStore']['incomeStatementHistoryQuarterly']['incomeStatementHistory']
total_revenue = container[0]['totalRevenue']['raw']
concerning_date = container[0]['endDate']['fmt']
print(total_revenue,concerning_date)
Result I get (revenue in million):
802000000 2019-06-30
Result I wish to get:
802000000 2019-06-29
When I try with this ticker AAPL, I get the exact date, so subtracing or adding a day is not an option.
How can I get the exact date from that site?
Btw, I know how to get them using selenium, so I would only like to stick to requests.
As mentioned in the comments, you need to convert the date to the appropriate timezone (EST), which can be done with datetime and dateutil.
Here is a working example:
import re
import json
import requests
from datetime import datetime, timezone
from dateutil import tz
url = 'https://finance.yahoo.com/quote/GTX/financials?p=GTX'
res = requests.get(url)
data = re.findall(r'root.App.main[^{]+(.*);',res.text)[0]
jsoncontent = json.loads(data)
container = jsoncontent['context']['dispatcher']['stores']['QuoteSummaryStore']['incomeStatementHistoryQuarterly']['incomeStatementHistory']
total_revenue = container[0]['totalRevenue']['raw']
EST = tz.gettz('EST')
raw_date = datetime.fromtimestamp(container[0]['endDate']['raw'], tz=EST)
concerning_date = raw_date.date().strftime('%d-%m-%Y')
print(total_revenue, concerning_date)
The updated section of this answer outlines the root cause of the date differences.
ORIGINAL ANSWER
Some of the raw values in your JSON are UNIX timestamps.
Reference from your code with modifications:
concerning_date_fmt = container[0]['endDate']['fmt']
concerning_date_raw = container[0]['endDate']['raw']
print(f'{concerning_date} -- {concerning_date_raw}')
# output
2019-07-28 -- 1564272000
'endDate': {'fmt': '2019-07-28', 'raw': 1564272000}
1564272000 is the number of elapsed seconds since January 01 1970. This date was the start of the Unix Epoch and the time is in Coordinated Universal Time (UTC). 1564272000 is the equivalent to: 07/28/2019 12:00am (UTC).
You can covert these timestamps to a standard datetime format by using built-in Python functions
from datetime import datetime
unix_timestamp = int('1548547200')
converted_timestamp = datetime.utcfromtimestamp(unix_timestamp).strftime('%Y-%m-%dT%H:%M:%SZ')
print (converted_timestamp)
# output Coordinated Universal Time (or UTC)
2019-07-28T00:00:00Z
reformatted_timestamp = datetime.strptime(converted_timestamp, '%Y-%m-%dT%H:%M:%SZ').strftime('%d-%m-%Y')
print (reformatted_timestamp)
# output
28-07-2019
This still does not solve your original problem related to JSON dates and column dates being different at times. But here is my current hypothesis related to the date disparities that are occurring.
The json date (fmt and raw) that are being extracted from root.App.main are in Coordinated Universal Time (UTC). This is clear because of the UNIX timestamp in raw.
The dates being displayed in the table columns seem to be in the Eastern Standard Time (EST) timezone. EST is currently UTC-4. Which means that 2019-07-28 22:00 (10pm) EST would be 2019-07-29 02:00 (2am) UTC. The server hosting finance.yahoo.com looks to be in the United States, based on the traceroute
results. These values are also in the json file:
'exchangeTimezoneName': 'America/New_York'
'exchangeTimezoneShortName': 'EDT'
There is also the possibility that some of the date differences are linked to the underlying React code, which the site uses. This issue is harder to diagnose, because the code isn't visible.
At this time I believe that the best solution would be to use the UNIX timestamp as your ground truth time reference. This reference could be used to replace the table column's date.
There is definitely some type of conversion happening between the JSON file and the columns.
NVIDIA JSON FILE: 'endDate': {'raw': 1561766400, 'fmt': '2019-06-29'}
NVIDIA Associated Total Revenue column: 6/30/2019
BUT the Total Revenue column date should be 6/28/2019 (EDT), because the UNIX time stamp for 1561766400 is 06/29/2019 12:00am (UTC).
The disparity with DELL is greater than a basic UNIX timestamp and a EDT timestamp conversion.
DELL JSON FILE:{"raw":1564704000,"fmt":"2019-08-02"}
DELL Associated Total Revenue column: 7/31/2019
If we convert the UNIX timestamp to an EDT timestamp, the result would be 8/1/2019, but that is not the case in the DELL example, which is 7/31/2019. Something within the Yahoo code base has to be causing this difference.
I'm starting to believe that React might be the culprit with these date differences, but I cannot be sure without doing more research.
If React is the root cause then the best option would be to use the date elements from the JSON data.
UPDATED ANSWER 10-17-2019
This problem is very interesting, because it seems that these column dates are linked to a company's official end of fiscal quarter and not a date conversation issue.
Here are several examples for
Apple Inc. (AAPL)
Atlassian Corporation Plc (TEAM)
Arrowhead Pharmaceuticals, Inc. (ARWR):
Their column dates are:
6/30/2019
3/31/2019
12/31/2018
9/30/2018
These dates match to these fiscal quarters.
Quarter 1 (Q1): January 1 - March 31.
Quarter 2 (Q2): April 1 - June 30.
Quarter 3 (Q3): July 1 - September 30.
Quarter 4 (Q4): October 1 - December 31
These fiscal quarter end dates can vary greatly as this DELL example shows.
DELL (posted in NASDAQ)
End of fiscal quarter: July 2019
Yahoo Finance
Column date: 7/31/2019
JSON date: 2019-08-02
From the company's website:
When does Dell Technologies’ fiscal year end?
Our fiscal year is the 52- or 53-week period ending on the Friday nearest January 31. Our 2020 fiscal year will end on January 31, 2020. For prior fiscal years, see list below: Our 2019 fiscal year ended on February 1, 2019 Our 2018 fiscal year ended on February 2, 2018 Our 2017 fiscal year ended on February 3, 2017 Our 2016 fiscal year ended on January 29, 2016 Our 2015 fiscal year ended on January 30, 2015 Our 2014 fiscal year ended on January 31, 2014 Our 2013 fiscal year ended on February 1, 2013
NOTE: The 05-03-19 and 08-02-19 dates.
These are from the JSON quarter data for DELL:
{'raw': 1564704000, 'fmt': '2019-08-02'}
{'raw': 1556841600, 'fmt': '2019-05-03'}
It seems that these column dates are linked to a company's fiscal quarter end dates. So I would recommend that you either use the JSON date as you primary reference element or the corresponding column date.
P.S. There is some type of date voodoo occurring at Yahoo, because they seem to move these column quarter dates based on holidays, weekends and end of month.
Instead of getting the fmt of the concerning_date, It's better to get the timestamp.
concerning_date = container[0]['endDate']['raw']
In the example above you will get the result 1561852800 which you can transfer into a date with a certain timezone. (Hint: use datetime and pytz). This timestamp will yield the following results based on timezone:
Date in Los Angeles*: 29/06/2019, 17:00:00
Date in Berlin* :30/06/2019, 02:00:00
Date in Beijing*: 30/06/2019, 07:00:00
Date in New York* :29/06/2019, 19:00:00
Related
I am working with REIGN data that documents elections and leaders in countries around the world (https://www.oneearthfuture.org/datasets/reign)
In the dataset there is an boolean election anticipation variable that turns from 0 to 1 to denote that an election is anticipated in at least the next 6 months, possibly sooner.
Excel sheet of data in question
I want to create a new column that returns the earliest date of when anticipation (column N) turns 1 (i.e. when was the election first anticipated).
So for example, with Afghanistan in column we have an election in 2014 and in 2017.
In column N we see it turn from 0 to 1 on Oct, 2014 (election anticipated) and then we see it go back to 0 on July, 2014 (election concluded) until it goes back to 1 on Jan, 2019 (election anticipated) and then turns back to 0 on Oct, 2019.
So if successful, I would capture Oct, 2014 (election anticipated) and Jan, 2019 (election anticipated) as election announcement dates in a new column along with any other dates an election was anticipated.
Currently I have the following:
#bringing in Reign CSV
regin = pd.read_csv('REIGN_2021_7(1).csv')
#shows us the first 5 rows to make sure they look good
print(regin.head())
#show us the rows and columns in the file
regin.shape
#Getting our index
print(regin.columns)
#adding in a date column that concatenates year and month
regin['date'] = pd.to_datetime(regin[['year', 'month']].assign(DAY=1))
regin.head
#def conditions(s):
if (s['anticipation'] == 1):
return (s['date'])
else:
return 0
regin['announced_date'] = regin.apply(conditions, axis=1)
print(regin.head)
Biggest issue for me is that while this returns the date of when a 1 appears, it does not display the earliest date. How I can loop through the anticipation column and return the minimum date, but do so multiple times as a country will have many elections over the years and there are therefore multiple instances in column N for one country of the anticipation turning on(1) and off(0).
Thanks in advance for any assistance! Let me know if anything is unclear.
If you can loop over your dates, you will probably want to use the datetime module (assuming all dates have the same format):
from datetime import datetime
[...]
earliest_date = datetime.today()
[... loop over data, by country ...]
date1 = datetime.strptime(input_date_string1, date_string_format)
if date1 < earliest_date:
earliest_date = date1
[...]
This module supports (among other things):
parsing date objects from a string (.strptime(in_str, format))
comparison of date objects (date1 > date2)
datetime object from current date + time (.today())
datetime object from arbitrary date (.date(year, month, day))
docs: https://docs.python.org/3/library/datetime.html
I would like to extract the first date of the week from the year and the calender week (in Europe where the first calender week is the week that includes the 4th of January)
My code works correctly for
import datetime
year=20
week=53
print(datetime.datetime.strptime(str(year) + "-"+ str(week-1) +'-1-CET', "%y-%U-%w-%Z"))
the output is 2020-12-28 00:00:00 which is correct
for
import datetime
year=21
week=1
print(datetime.datetime.strptime(str(year) + "-"+ str(week-1) +'-1-CET', "%y-%U-%w-%Z"))
I get also 2020-12-28 00:00:00.The correct output would be 2021-01-04.
Could you please tell me where my mistake is?
Thanks
Using %V instead of %U should make things easier.
Any idea how to localize a date, which should only displays day and month respectively month and day?
I know how to format the whole date:
formats.date_format(datetime.now(), format="DATE_FORMAT", use_l10n=True)
Which returns the date as: Feb. 6, 2020 or 6 Feb. 2020 according to the locale setting.
I need the same Output, but without the year.
You can use MONTH_DAY_FORMAT.
formats.date_format(datetime.now(), format="MONTH_DAY_FORMAT", use_l10n=True)
I have a long series of stock daily prices and I am trying to get week prices to do some calculations. I have been reading the documentation and I see you can set offsets get a specific date of the week which is what I want. This is the code assume stock is part of a loop I am runing.
df_clean_BW[WEEKLY_PricesFriday'] = stock.resample('W-FRI').last()
But for US stock market there are many days where it is a holiday on Friday so then I saw you can adjust this for USCalendar Holidays. This is the code I was using
from pandas.tseries.offsets import CustomBusinessDay
from pandas.tseries.holiday import USFederalHolidayCalendar
bday_us = CustomBusinessDay(calendar=USFederalHolidayCalendar())
But I dont know how to combine the two so that if there is a holiday on Friday to take the day prior (the Thursday instead). So something like this but this throws an error
df_clean_BW[WEEKLY_PricesFriday'] = stock.resample('W-FRI' & bday_us).last()
I have a long list of dates so I don't want to create a list of exception days because that would be too long. Here is an example of the output I would want. In this case Jan 1, 2016 was a Friday so I just want to take December 31, 2015 instead. This must be a common request for anyone who looks at stock data but I cant figure out a way to do it.
Date Price Week Price
12/30/2015 103.3227
12/31/2015 101.3394
1/4/2016 101.426 101.3394 << Take 12/31 as 1.1 is holiday
1/5/2016 98.8844
1/6/2016 96.9492
1/7/2016 92.8575
1/8/2016 93.3485 93.3485
First generate your array of Fridays including holidays. Then use np.busday_offset() to offset them like this:
np.busday_offset(fridays, 0, roll='backward', busdaycal=bday_us.calendar)
I have to write a program where I take stocks from yahoo finance and print out certain information for the site. One of the pieces of data is the date. I need to take a date such as 3/21/2012 and converter to the following format: Mar 21, 2012.
Here is my code for the entire project.
def getStockData(company="GOOG"):
baseurl ="http://quote.yahoo.com/d/quotes.csv?s={0}&f=sl1d1t1c1ohgvj1pp2owern&e=.csv"
url = baseurl.format(company)
conn = u.urlopen(url)
content = conn.readlines()
data = content[0].decode("utf-8")
data = data.split(",")
date = data[2][1:-1]
date_new = datetime.strptime(date, "%m/%d/%Y").strftime("%B[0:3] %d, %Y")
print("The last trade for",company, "was", data[1],"and the change was", data[4],"on", date_new)
company = input("What company would you like to look up?")
getStockData(company)
co = ["VOD.L", "AAPL", "YHOO", "S", "T"]
for company in co:
getStockData(company)
You should really specify what about your code is not working (i.e., what output are you getting that you don't expect? What error message are you getting, if any?). However, I suspect your problem is with this part:
strftime('%B[0:3] %d, %Y')
Since Python won't do what you think with that attempt to slice '%B'. You should instead use '%b', which as noted in the documentation for strftime(), corresponds to the locale-abbreviated month name.
EDIT
Here is a fully functional script based on what you posted above with my suggested modifications:
import urllib2 as u
from datetime import datetime
def getStockData(company="GOOG"):
baseurl ="http://quote.yahoo.com/d/quotes.csv?s={0}&f=sl1d1t1c1ohgvj1pp2owern&e=.csv"
url = baseurl.format(company)
conn = u.urlopen(url)
content = conn.readlines()
data = content[0].decode("utf-8")
data = data.split(",")
date = data[2][1:-1]
date_new = datetime.strptime(date, "%m/%d/%Y").strftime("%b %d, %Y")
print("The last trade for",company, "was", data[1],"and the change was", data[4],"on", date_new)
for company in ["VOD.L", "AAPL", "YHOO", "S", "T"]:
getStockData(company)
The output of this script is:
The last trade for VOD.L was 170.00 and the change was -1.05 on Mar 06, 2012
The last trade for AAPL was 530.26 and the change was -2.90 on Mar 06, 2012
The last trade for YHOO was 14.415 and the change was -0.205 on Mar 06, 2012
The last trade for S was 2.39 and the change was -0.04 on Mar 06, 2012
The last trade for T was 30.725 and the change was -0.265 on Mar 06, 2012
For what it's worth, I'm running this on Python 2.7.1. I also had the line from __future__ import print_function to make this compatible with the Python3 print function you appear to be using.
Check out Dateutil. You can use it to parse a string into python datetime object and then print that object using strftime.
I've since come to a conclusion that auto detection of datetime value is not always a good idea. It's much better to use strptime and specify what format you want.