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Is there a way where I can replace the first '.' with '-' in my code for a domain generator

last time I've gotten some help on making a website name generator. I feel bad but i'm stuck at the moment and I need some help again to improve it. in my code there's a .txt file called combined which included these lines.
After that i created a variable to add to the domain
web = 'web'
suffix = 'co.id'
And then i write it out so that the it would print the line output to the Combined.txt
output_count = 50
subdomain_count = 2
for i in range(output_count):
out = []
for j in range(subdomain_count):
out.append(random.choice(Test))
out.append(web)
out.append(suffix)
Example.write('.'.join(out)+"\n")
with open("dictionaries/examples.txt") as f:
websamples = [line.rstrip() for line in f]
I want the output where instead of just login.download.web.co.id there would be more variety like login-download.web.co.id or login.download-web.co.id In the code i used Example.write('.'.join(out)+"\n") so that the. would be a separator for each characters. I was thinking of adding more, by making a similar code line and save it to a different .txt files but I feel like it would be too long. Is there a way where I can variate each character separation with this symbol - or _ instead of just a . in the output?
Thanks!

Sure just iterate through a list of delimiters to add each of them to the output.
web = 'web'
suffix = 'co.id'
output_count = 50
subdomain_count = 2
delimeters = [ '-', '.']
for i in range(output_count):
out = []
for j in range(subdomain_count):
out.append(random.choice(Test))
for delimeter in delimeters:
addr = delimeter.join(out)
addrs = '.'.join([addr, web, suffix])
print(addrs)
Example.write(addrs + '\n')
output
my_pay.web.co.id
my-pay.web.co.id
my.pay.web.co.id
pay_download.web.co.id
pay-download.web.co.id
pay.download.web.co.id
group_login.web.co.id
group-login.web.co.id
group.login.web.co.id
install_group.web.co.id
install-group.web.co.id
install.group.web.co.id
...
...
update
import itertools
Test = ['download', 'login', 'my', 'ip', 'site', 'ssl', 'pay', 'install']
delimeters = [ '-', '.']
web = 'web'
suffix = 'co.id'
output_count = 50
subdomain_count = 2
for combo in itertools.combinations(Test, 2):
out = ''
for i, d in enumerate(delimeters):
out = d.join(combo)
out = delimeters[i-1].join([out, web])
addr = '.'.join([out, suffix])
print(addr)
# Example.write(addr+'\n')
output
download-login.web.co.id
download.login-web.co.id
download-my.web.co.id
download.my-web.co.id
download-ip.web.co.id
download.ip-web.co.id
download-site.web.co.id
download.site-web.co.id
download-ssl.web.co.id
download.ssl-web.co.id
download-pay.web.co.id
download.pay-web.co.id
download-install.web.co.id
download.install-web.co.id
login-my.web.co.id
login.my-web.co.id
login-ip.web.co.id
login.ip-web.co.id
login-site.web.co.id
login.site-web.co.id
login-ssl.web.co.id
login.ssl-web.co.id
login-pay.web.co.id
login.pay-web.co.id
login-install.web.co.id
login.install-web.co.id
my-ip.web.co.id
my.ip-web.co.id
my-site.web.co.id
my.site-web.co.id
my-ssl.web.co.id
my.ssl-web.co.id
my-pay.web.co.id
my.pay-web.co.id
my-install.web.co.id
my.install-web.co.id
ip-site.web.co.id
ip.site-web.co.id
ip-ssl.web.co.id
ip.ssl-web.co.id
ip-pay.web.co.id
ip.pay-web.co.id
ip-install.web.co.id
ip.install-web.co.id
site-ssl.web.co.id
site.ssl-web.co.id
site-pay.web.co.id
site.pay-web.co.id
site-install.web.co.id
site.install-web.co.id
ssl-pay.web.co.id
ssl.pay-web.co.id
ssl-install.web.co.id
ssl.install-web.co.id
pay-install.web.co.id
pay.install-web.co.id

As an alternative of replacing the final output, you could make the seperator random:
import random
seperators = ['-', '_', '.']
Example.write(random.choice(seperators).join(out)+"\n")

In order to ensure compliance with RFC 1035 I would suggest:
from random import choices as CHOICES, choice as CHOICE
output_count = 50
subdomain_count = 2
web = 'web'
suffix = 'co.id'
dotdash = '.-'
filename = 'output.txt'
Test = [
'auth',
'access',
'account',
'admin'
# etc
]
with open(filename, 'w') as output:
for _ in range(output_count):
sd = CHOICE(dotdash).join(CHOICES(Test, k=subdomain_count))
print('.'.join((sd, web, suffix)), file=output)

XML Parsing Python ElementTree - Nested for loops

I'm using Jupyter Notebook and ElementTree (Python 3) to create a dataframe and save as csv from an XML file. Here is the XML format (in Estonian):
<asutused hetk="2020-04-14T03:53:33" ver="2">
<asutus>
<registrikood>10000515</registrikood>
<nimi>Osaühing B.Braun Medical</nimi>
<aadress />
<tegevusload>
<tegevusluba>
<tegevusloa_number>L04647</tegevusloa_number>
<alates>2019-12-10</alates>
<kuni />
<loaliik_kood>1</loaliik_kood>
<loaliik_nimi>Eriarstiabi</loaliik_nimi>
<haiglaliik_kood />
<haiglaliik_nimi />
<tegevuskohad>
<tegevuskoht>
<aadress>Harju maakond, Tallinn, Mustamäe linnaosa, J. Sütiste tee 17/1</aadress>
<teenused>
<teenus>
<kood>T0038</kood>
<nimi>ambulatoorsed üldkirurgiateenused</nimi>
</teenus>
<teenus>
<kood>T0236</kood>
<nimi>õe vastuvõtuteenus</nimi>
</teenus>
</teenused>
</tegevuskoht>
<tegevuskoht>
<aadress>Harju maakond, Tallinn, Mustamäe linnaosa, J. Sütiste tee 17/1</aadress>
<teenused>
<teenus>
<kood>T0038</kood>
<nimi>ambulatoorsed üldkirurgiateenused</nimi>
</teenus>
<teenus>
<kood>T0236</kood>
<nimi>õe vastuvõtuteenus</nimi>
</teenus>
</teenused>
</tegevuskoht>
</tegevuskohad>
</tegevusluba>
<tegevusluba>
<tegevusloa_number>L04651</tegevusloa_number>
<alates>2019-12-11</alates>
<kuni />
<loaliik_kood>2</loaliik_kood>
<loaliik_nimi>Õendusabi</loaliik_nimi>
<haiglaliik_kood />
<haiglaliik_nimi />
<tegevuskohad>
<tegevuskoht>
<aadress>Harju maakond, Tallinn, Mustamäe linnaosa, J. Sütiste tee 17/1</aadress>
<teenused>
<teenus>
<kood>T0038</kood>
<nimi>ambulatoorsed üldkirurgiateenused</nimi>
</teenus>
<teenus>
<kood>T0236</kood>
<nimi>õe vastuvõtuteenus</nimi>
</teenus>
</teenused>
</tegevuskoht>
<tegevuskoht>
<aadress>Harju maakond, Tallinn, Mustamäe linnaosa, J. Sütiste tee 17/1</aadress>
<teenused>
<teenus>
<kood>T0038</kood>
<nimi>ambulatoorsed üldkirurgiateenused</nimi>
</teenus>
<teenus>
<kood>T0236</kood>
<nimi>õe vastuvõtuteenus</nimi>
</teenus>
</teenused>
</tegevuskoht>
</tegevuskohad>
</tegevusluba>
</tegevusload>
<tootajad>
<tootaja>
<kood>D03091</kood>
<eesnimi>Evo</eesnimi>
<perenimi>Kaha</perenimi>
<kutse_kood>11</kutse_kood>
<kutse_nimi>Arst</kutse_nimi>
<erialad>
<eriala>
<kood>E420</kood>
<nimi>üldkirurgia</nimi>
</eriala>
</erialad>
</tootaja>
<tootaja>
<kood>N01146</kood>
<eesnimi>Karmen</eesnimi>
<perenimi>Mežulis</perenimi>
<kutse_kood>15</kutse_kood>
<kutse_nimi>Õde</kutse_nimi>
</tootaja>
<tootaja>
<kood>N01153</kood>
<eesnimi>Nele</eesnimi>
<perenimi>Terras</perenimi>
<kutse_kood>15</kutse_kood>
<kutse_nimi>Õde</kutse_nimi>
</tootaja>
<tootaja>
<kood>N02767</kood>
<eesnimi>Helena</eesnimi>
<perenimi>Tern</perenimi>
<kutse_kood>15</kutse_kood>
<kutse_nimi>Õde</kutse_nimi>
</tootaja>
<tootaja>
<kood>N12882</kood>
<eesnimi>Hanna</eesnimi>
<perenimi>Leemet</perenimi>
<kutse_kood>15</kutse_kood>
<kutse_nimi>Õde</kutse_nimi>
</tootaja>
</tootajad>
</asutus>
</asutused>
Each "asutus" is a hospital and I need some of the information inside. Here is my code:
tree = ET.parse("od_asutused.xml")
root = tree.getroot()
# open a file for writing
data = open('EE.csv', 'w')
# create the csv writer object
csvwriter = csv.writer(data, delimiter=';')
head = []
count = 0
for member in root.findall('asutus'):
hospital = []
if count == 0:
ident = member.find('registrikood').tag
head.append(id)
name = member.find('nimi').tag
head.append(name)
address = member.find('aadress').tag
head.append(address)
facility_type = member.find('./tegevusload/tegevusluba/haiglaliik_nimi').tag
head.append(facility_type)
site_address = member.find('./tegevusload/tegevusluba/tegevuskohad/tegevuskoht/aadress').tag
head.append(site_address)
for elem in member.findall('tegevusload'):
list_specs = elem.find('./tegevusluba/tegevuskohad/tegevuskoht/teenused/teenus/nimi').tag
head.append(list_specs)
csvwriter.writerow(head)
count = count + 1
ident = member.find('registrikood').text
hospital.append(ident)
name = member.find('nimi').text
hospital.append(name)
address = member.find('aadress').text
hospital.append(address)
facility_type = member.find('./tegevusload/tegevusluba/haiglaliik_nimi').text
hospital.append(facility_type)
site_address = member.find('./tegevusload/tegevusluba/tegevuskohad/tegevuskoht/aadress').text
hospital.append(site_address)
for spec in elem.findall('tegevusload'):
list_specs = spec.find('./tegevusluba/tegevuskohad/tegevuskoht/teenused/teenus/nimi').text
hospital.append(list_specs)
csvwriter.writerow(hospital)
data.close()
#Upload csv for geocoding
df = pd.read_csv(r'EE.csv', na_filter= False, delimiter=';')
#Rename columns
df.rename(columns = {'<built-in function id>':'id',
'nimi':'name',
'aadress':'address',
'haiglaliik_nimi':'facility_type',
'haiglaliik_kood':'facility_type_c',
'aadress.1':'site_address',
'nimi.1':'list_specs'},
inplace = True)
#Add columns
df['country'] = 'Estonia'
df['cc'] = 'EE'
df.head(10)
And the result of the df.head(10):
Result of dataframe
The "list_specs" is blank no matter what I do. How can I populate this field with a list of each 'nimi' for each site address? Thank you.

I found in your code the following points to change:
At least on my computer, calling csv.writer causes that newline chars
are doubled. The remedy I found is to open the output file with
additional parameters:
data = open('EE.csv', 'w', newline='\n', encoding='utf-8')
There is no sense to write head with Estonian column names and then
rename the columns. Note also that in head.append(id) you use an undeclared
variable (id).
But this is not so important, as I changed this whole section with writing
target column names (see below).
As you write the CSV file to be read by read_csv, it should contain a
fixed number of columns. So it is a bad practice to use a loop to write
one element.
Your instruction list_specs = elem.findall(...) was wrong, because
elem is not set in the current loop. Instead you should use member (but
I solved this detail other way).
There is no sense to create a variable only in order to use it once.
More concise and readable code is e.g. hospital.append(member.findtext('nimi')).
To avoid long XPath expressions, with repeated initial part, I decided
to set a temporary variable "in the middle" of this path, e.g.
tgvLb = member.find('tegevusload/tegevusluba') and then use a relative
XPath starting from this node.
Your rename instruction contains one not needed column, namely facility_type_c. You read only 6 columns, not 7.
So change the middle part of your code to:
data = open('EE.csv', 'w', newline='\n', encoding='utf-8')
csvwriter = csv.writer(data, delimiter=';')
head = ['id', 'name', 'address', 'facility_type', 'site_address', 'list_specs']
csvwriter.writerow(head)
for member in root.findall('asutus'):
hospital = []
hospital.append(member.findtext('registrikood'))
hospital.append(member.findtext('nimi'))
hospital.append(member.findtext('aadress'))
tgvLb = member.find('tegevusload/tegevusluba')
hospital.append(tgvLb.findtext('haiglaliik_nimi'))
tgvKoht = tgvLb.find('tegevuskohad/tegevuskoht')
hospital.append(tgvKoht.findtext('aadress'))
hospital.append(tgvKoht.findtext('teenused/teenus/nimi'))
csvwriter.writerow(hospital)
data.close()
df = pd.read_csv(r'EE.csv', na_filter= False, delimiter=';')
and drop df.rename from your code.

Python - How to count specific section in a list

I'm brand new to python and I'm struggling how to add certain sections of a cvs file in python. I'm not allowed to use "import cvs"
I'm importing the TipJoke CVS file from https://vincentarelbundock.github.io/Rdatasets/datasets.html
This is the only code I have so far that worked and I'm at a total loss on where to go from here.
if __name__ == '__main__':
from pprint import pprint
from string import punctuation
f = open("TipJoke.csv", "r")
tipList = []
for line in f:
#deletes the quotes
line = line.replace('"', '')
tipList.append(line)
pprint(tipList[])
Output:
[',Card,Tip,Ad,Joke,None\n',
'1,None,1,0,0,1\n',
'2,Joke,1,0,1,0\n',
'3,Ad,0,1,0,0\n',
'4,None,0,0,0,1\n',
'5,None,1,0,0,1\n',
'6,None,0,0,0,1\n',
'7,Ad,0,1,0,0\n',
'8,Ad,0,1,0,0\n',
'9,None,0,0,0,1\n',
'10,None,0,0,0,1\n',
'11,None,1,0,0,1\n',
'12,Ad,0,1,0,0\n',
'13,None,0,0,0,1\n',
'14,Ad,1,1,0,0\n',
'15,Joke,1,0,1,0\n',
'16,Joke,0,0,1,0\n',
'17,Joke,1,0,1,0\n',
'18,None,0,0,0,1\n',
'19,Joke,0,0,1,0\n',
'20,None,0,0,0,1\n',
'21,Ad,1,1,0,0\n',
'22,Ad,1,1,0,0\n',
'23,Ad,0,1,0,0\n',
'24,Joke,0,0,1,0\n',
'25,Joke,1,0,1,0\n',
'26,Joke,0,0,1,0\n',
'27,None,1,0,0,1\n',
'28,Joke,1,0,1,0\n',
'29,Joke,1,0,1,0\n',
'30,None,1,0,0,1\n',
'31,Joke,0,0,1,0\n',
'32,None,1,0,0,1\n',
'33,Joke,1,0,1,0\n',
'34,Ad,0,1,0,0\n',
'35,Joke,0,0,1,0\n',
'36,Ad,1,1,0,0\n',
'37,Joke,0,0,1,0\n',
'38,Ad,0,1,0,0\n',
'39,Joke,0,0,1,0\n',
'40,Joke,0,0,1,0\n',
'41,Joke,1,0,1,0\n',
'42,None,0,0,0,1\n',
'43,None,0,0,0,1\n',
'44,Ad,0,1,0,0\n',
'45,None,0,0,0,1\n',
'46,None,0,0,0,1\n',
'47,Ad,0,1,0,0\n',
'48,Joke,0,0,1,0\n',
'49,Joke,1,0,1,0\n',
'50,None,1,0,0,1\n',
'51,None,0,0,0,1\n',
'52,Joke,1,0,1,0\n',
'53,Joke,1,0,1,0\n',
'54,Joke,0,0,1,0\n',
'55,None,1,0,0,1\n',
'56,Ad,0,1,0,0\n',
'57,Joke,0,0,1,0\n',
'58,None,0,0,0,1\n',
'59,Ad,0,1,0,0\n',
'60,Joke,1,0,1,0\n',
'61,Ad,0,1,0,0\n',
'62,None,1,0,0,1\n',
'63,Joke,0,0,1,0\n',
'64,Ad,0,1,0,0\n',
'65,Joke,0,0,1,0\n',
'66,Ad,0,1,0,0\n',
'67,Ad,0,1,0,0\n',
'68,Ad,0,1,0,0\n',
'69,None,0,0,0,1\n',
'70,Joke,1,0,1,0\n',
'71,None,1,0,0,1\n',
'72,None,0,0,0,1\n',
'73,None,0,0,0,1\n',
'74,Joke,0,0,1,0\n',
'75,Ad,1,1,0,0\n',
'76,Ad,0,1,0,0\n',
'77,Ad,1,1,0,0\n',
'78,Joke,0,0,1,0\n',
'79,Joke,0,0,1,0\n',
'80,Ad,1,1,0,0\n',
'81,Ad,0,1,0,0\n',
'82,None,0,0,0,1\n',
'83,Ad,0,1,0,0\n',
'84,Joke,0,0,1,0\n',
'85,Joke,0,0,1,0\n',
'86,Ad,1,1,0,0\n',
'87,None,1,0,0,1\n',
'88,Joke,1,0,1,0\n',
'89,Ad,0,1,0,0\n',
'90,None,0,0,0,1\n',
'91,None,0,0,0,1\n',
'92,Joke,0,0,1,0\n',
'93,Joke,0,0,1,0\n',
'94,Ad,0,1,0,0\n',
'95,Ad,0,1,0,0\n',
'96,Ad,0,1,0,0\n',
'97,Joke,1,0,1,0\n',
'98,None,0,0,0,1\n',
'99,None,0,0,0,1\n',
'100,None,1,0,0,1\n',
'101,Joke,0,0,1,0\n',
'102,Joke,0,0,1,0\n',
'103,Ad,1,1,0,0\n',
'104,Ad,0,1,0,0\n',
'105,Ad,0,1,0,0\n',
'106,Ad,1,1,0,0\n',
'107,Ad,0,1,0,0\n',
'108,None,0,0,0,1\n',
'109,Ad,0,1,0,0\n',
'110,Joke,1,0,1,0\n',
'111,None,0,0,0,1\n',
'112,Ad,0,1,0,0\n',
'113,Ad,0,1,0,0\n',
'114,None,0,0,0,1\n',
'115,Ad,0,1,0,0\n',
'116,None,0,0,0,1\n',
'117,None,0,0,0,1\n',
'118,Ad,0,1,0,0\n',
'119,None,1,0,0,1\n',
'120,Ad,1,1,0,0\n',
'121,Ad,0,1,0,0\n',
'122,Ad,1,1,0,0\n',
'123,None,0,0,0,1\n',
'124,None,0,0,0,1\n',
'125,Joke,1,0,1,0\n',
'126,Joke,1,0,1,0\n',
'127,Ad,0,1,0,0\n',
'128,Joke,0,0,1,0\n',
'129,Joke,0,0,1,0\n',
'130,Ad,0,1,0,0\n',
'131,None,0,0,0,1\n',
'132,None,0,0,0,1\n',
'133,None,0,0,0,1\n',
'134,Joke,1,0,1,0\n',
'135,Ad,0,1,0,0\n',
'136,None,0,0,0,1\n',
'137,Joke,0,0,1,0\n',
'138,Ad,0,1,0,0\n',
'139,Ad,0,1,0,0\n',
'140,None,0,0,0,1\n',
'141,Joke,0,0,1,0\n',
'142,None,0,0,0,1\n',
'143,Ad,0,1,0,0\n',
'144,None,1,0,0,1\n',
'145,Joke,0,0,1,0\n',
'146,Ad,0,1,0,0\n',
'147,Ad,0,1,0,0\n',
'148,Ad,0,1,0,0\n',
'149,Joke,1,0,1,0\n',
'150,Ad,1,1,0,0\n',
'151,Joke,1,0,1,0\n',
'152,None,0,0,0,1\n',
'153,Ad,0,1,0,0\n',
'154,None,0,0,0,1\n',
'155,None,0,0,0,1\n',
'156,Ad,0,1,0,0\n',
'157,Ad,0,1,0,0\n',
'158,Joke,0,0,1,0\n',
'159,None,0,0,0,1\n',
'160,Joke,1,0,1,0\n',
'161,None,1,0,0,1\n',
'162,Ad,1,1,0,0\n',
'163,Joke,0,0,1,0\n',
'164,Joke,0,0,1,0\n',
'165,Ad,0,1,0,0\n',
'166,Joke,1,0,1,0\n',
'167,Joke,1,0,1,0\n',
'168,Ad,0,1,0,0\n',
'169,Joke,1,0,1,0\n',
'170,Joke,0,0,1,0\n',
'171,Ad,0,1,0,0\n',
'172,Joke,0,0,1,0\n',
'173,Joke,0,0,1,0\n',
'174,Ad,0,1,0,0\n',
'175,None,0,0,0,1\n',
'176,Joke,1,0,1,0\n',
'177,Ad,0,1,0,0\n',
'178,Joke,0,0,1,0\n',
'179,Joke,0,0,1,0\n',
'180,None,0,0,0,1\n',
'181,None,0,0,0,1\n',
'182,Ad,0,1,0,0\n',
'183,None,0,0,0,1\n',
'184,None,0,0,0,1\n',
'185,None,0,0,0,1\n',
'186,None,0,0,0,1\n',
'187,Ad,0,1,0,0\n',
'188,None,1,0,0,1\n',
'189,Ad,0,1,0,0\n',
'190,Ad,0,1,0,0\n',
'191,Ad,0,1,0,0\n',
'192,Joke,1,0,1,0\n',
'193,Joke,0,0,1,0\n',
'194,Ad,0,1,0,0\n',
'195,None,0,0,0,1\n',
'196,Joke,1,0,1,0\n',
'197,Joke,0,0,1,0\n',
'198,Joke,1,0,1,0\n',
'199,Ad,0,1,0,0\n',
'200,None,0,0,0,1\n',
'201,Joke,1,0,1,0\n',
'202,Joke,0,0,1,0\n',
'203,Joke,0,0,1,0\n',
'204,Ad,0,1,0,0\n',
'205,None,0,0,0,1\n',
'206,Ad,0,1,0,0\n',
'207,Ad,0,1,0,0\n',
'208,Joke,0,0,1,0\n',
'209,Ad,0,1,0,0\n',
'210,Joke,0,0,1,0\n',
'211,None,0,0,0,1\n']
I'm currently trying to find the Total number of entries of the specified card type and the Percentage of tips given for the specified card type with two decimal places of precision. The tip column is the 0 or 1 right after the card type (None, Ad, Joke).

if you are allowed with pandas library then
import pandas as pd
df = pd.read_csv("TipJoke.csv")
df is a pandas dataframe object in which you can perform multiple filtering task according to your need.
for example if you want to get data for Joke you can filter like this:
print(df[df["Card"] == "Joke"])
Though, i'm just providing you the direction , not whole logic for your question.

This works
from pprint import pprint
from string import punctuation
counts = {"Joke": 0, "Ad": 0, "None": 0}
with open("TipJoke.csv", "r") as f:
for line in f:
line_clean = line.replace('"', "").replace("\n", "").split(",")
try:
counts[line_clean[1]] += int(line_clean[2])
except:
pass
print(counts)

how to add hyphen in between string using python

import csv
x=[]
y=[]
with open ('x_wind.txt','r') as csvfile:
plots= csv.reader(csvfile, delimiter=',')
for row in plots:
x.append(int(row[0]))
y.append(float(row[1]))
I have wrote above code to extract data from a file
now , i want my data should print as 2018-06-14:1
sample data is
2018061402,6.8750
2018061403,8.0000
2018061404,7.7500
2018061405,7.3750
2018061406,6.7500
2018061407,6.1250
2018061408,5.7500
2018061409,5.6250
2018061410,5.5000
2018061411,5.5000
2018061412,5.3750
2018061413,5.1250
2018061414,4.6250
2018061415,3.8750
2018061416,3.5000
2018061417,3.1250
2018061418,3.6250
2018061419,4.2500
2018061420,4.7500
2018061421,5.8750
2018061422,6.2500
2018061423,6.6250
2018061500,6.7500
2018061501,6.7500
2018061502,7.3750
2018061503,7.1250
2018061504,6.1250
2018061505,5.2500
2018061506,4.7500
2018061507,4.1250
2018061508,4.0000
2018061509,3.8750
2018061510,3.8750
2018061511,4.1250
2018061512,4.5000
2018061513,4.3750
2018061514,3.5000
2018061515,3.1250
2018061516,3.1250
2018061517,3.0000
2018061518,3.0000
2018061519,3.5000
2018061520,3.8750
2018061521,4.1250
2018061522,4.3750
2018061523,4.6250
2018061600,5.1250
2018061601,4.8750
2018061602,6.0000
2018061603,5.5000
2018061604,4.7500
2018061605,3.8750
2018061606,3.3750
2018061607,2.7500
2018061608,2.3750
2018061609,2.5000
2018061610,2.7500
2018061611,3.1250
2018061612,3.3750
2018061613,3.6250
2018061614,3.2500
2018061615,2.7500
2018061616,3.1250
2018061617,2.8750
2018061618,1.5000
2018061619,1.5000
2018061620,1.6250
2018061621,1.8750
2018061622,2.6250
2018061623,3.3750
2018061700,4.1250
2018061701,4.7500
2018061702,6.1250
2018061703,6.1250
2018061704,5.5000
2018061705,5.0000
2018061706,4.2500
2018061707,4.0000
2018061708,3.8750
2018061709,4.0000
2018061710,4.3750
2018061711,4.5000
2018061712,4.5000
2018061713,4.0000
2018061714,3.5000
2018061715,3.0000
2018061716,2.7500
2018061717,2.2500
2018061718,0.3750
2018061719,1.5000
2018061720,2.1250
2018061721,2.1250
2018061722,2.2500
2018061723,3.1250
2018061800,4.2500
2018061801,5.5000
2018061802,7.1250
2018061803,7.1250
2018061804,6.3750
2018061805,5.7500
2018061806,5.3750
2018061807,5.0000
2018061808,5.1250
2018061809,5.0000
2018061810,5.0000
2018061811,4.7500
2018061812,4.6250
2018061813,4.5000
2018061814,4.2500
2018061815,3.7500
2018061816,3.3750
2018061817,3.2500
2018061818,3.1250
2018061819,3.0000
2018061820,3.1250
2018061821,3.2500
2018061822,3.3750
2018061823,3.3750
2018061900,3.5000
2018061901,3.3750
2018061902,4.3750
2018061903,5.8750
2018061904,5.8750
2018061905,5.3750
2018061906,4.7500
2018061907,3.6250
2018061908,3.5000
2018061909,2.6250
2018061910,3.0000
2018061911,2.5000
2018061912,2.0000
2018061913,1.3750
2018061914,0.5000
2018061915,-0.2500
2018061916,-0.5000
2018061917,0.6250
2018061918,2.2500
2018061919,2.0000
2018061920,2.1250
2018061921,2.1250
2018061922,2.7500
2018061923,3.1250
2018062000,3.1250
2018062001,3.0000
2018062002,3.5000
2018062003,5.0000
2018062004,5.1250
2018062005,4.5000
2018062006,3.7500
2018062007,3.2500
2018062008,3.3750
2018062009,2.8750
2018062010,2.7500
2018062011,2.5000
2018062012,1.8750
2018062013,1.2500
2018062014,0.3750
2018062015,0.1250
2018062016,0.5000
2018062017,1.8750

Looks like you have a list of datetime object.
You can use the datetime module to convert it to your required format.
Ex:
import datetime
data = "2018061402"
print( datetime.datetime.strptime(data[:-2], "%Y%m%d").strftime("%Y-%m-%d") )
Output:
2018-06-14

insert a list 2xn obtained from a json in python

Hi I'm trying to access a json to save it in a list to perform a sort of append and create a pdf in ReportLab, I have the following code but I have several problems the first is that I would like to have a list of 2xn to always it has columns and rows be dynamic according to the json.
If anyone can help me be grateful much
import json
json_data = []
attributesName = []
testTable = { "attributes":[] }
attributesValue = []
path="prueba2.pdf"
doc = SimpleDocTemplate(path, pagesize=letter)
styleSheet = getSampleStyleSheet()
text = []
with open("prueba.json") as json_file:
document = json.load(json_file)
for item in document:
for data_item in item['data']:
attributesName.append([str(data_item['name'])
attributesValue.append([data_item['value']])
testTable[attributesName].extend({data_item['name'], data_item['value']})
print attributesName[0]
print testTable[0]
parts = []
p = Paragraph('''<para align=left fontsize=9>{0}</para>'''.format(text), styleSheet["BodyText"])
parts.append(p)
doc.build(parts)
I implemented the following,but it prints the list
[[['RFC', 'NOMBRE', 'APELLIDO PATERNO', 'APELLIDO MATERNO', 'FECHA NACIMIENTO', 'CALLE', 'No. EXTERI
OR', 'No. INTERIOR', 'C.P.', 'ENTIDAD', 'MUNICIPIO', 'COLONIA', 'DOCUMENTO']], [['MORR910304FL2', 'R
JOSE', 'MONTIEL', 'ROBLES', '1992-02-04', 'AMOR', '4', '2', '55064', 'EDO DE MEX', 'ECATEPEC', 'INDUSTRIAL', 'Documento']]]
I want some like this
[['RFC'], ['22232446']]
[['NOMBRE'], ['22239952']]
[['APELLIDO'], ['22245430']]

if you change your code with the next code
with open("prueba.json") as json_file:
document = json.load(json_file)
for item in document:
for data_item in item['data']:
attributesName.append(str(data_item["name"]))
attributesValue.append(str(data_item["value"]))
tabla.append([[attributesName],[attributesValue]])
print attributesName
print attributesValue
for Y in tabla:
print(Y)