I'm analysing a e-mail network. I loaded the following information in a directed igraph on R:
Vertex types: person, e-mail
V(g)[ type == "person" ]
V(g)[ type == "email" ]
Edge types: sends, receives
E(g)[ type == "send" ]
E(g)[ type == "receive" ]
So for example:
John --send--> email1 --receive--> Mary
John --send--> email2 --receive--> Mary
Mary --send--> email3 --receive--> John
I would like to generate a summary of the e-mail activity, with edges with an attribute representing the number of emails:
John --2--> Mary
Mary --1--> John
How would I go about to do that?
Thanks,
Mulone
Related
How do I extract a specific word after keyword in R.
I have the following input text which contains details about policy. I need to extract specific words value like FirstName , SurName , FatherName and dob.
input.txt
In Case of unit linked plan, Investment risk in Investment Portfolio is borne by the policyholder.
ly
c I ROPOSAL FORM z
Insurance
Proposal Form Number: 342525 PF 42242
Advisor Coe aranch Code 2
Ff roanumber =F SSOS™S™~™S~S rancid ate = |
IBR. Code S535353424
re GFN ——
INSTRUCTION FOR FILLING THES APPLICATION FORM ; 1. Compiets the proocsal form in CAPITAL LETTERS using = Black Ball Point P]n. 2. Sless= mark your selection by marking “X" insides the
Boe. 3. Slnsse bases 2 Blank soece after eect word, letter or initial 4. Slssse write "MA" for questions whic are not apolicatie. 5.00 NOT USE the Sor") to identify your initial or seperate the sddressiiine.
6. Sulmissson of age proof ie mandatory along wall Ge propel fonm.
IMPORTANT INSTRUCTIONS WITH REGARD TO DISCLOSURE OF INFORMATION: Inturance it a contract of UTMOST GOOD FAITH and itis required by disclose all material and nelevant
fach: complebehy, DO) NOT suppress any fac: in response by the questions in the priposal form. FAILURE TO PROVIDE COMPLETE AND ACCURATE INFORMATION OR
MISREPRESENTATION OF THE FACTS COULD DECLARE THES POLICY CONTRACT NULL AND VOID AFTER PAYMENT OF SURRENDER VALUE, IF ANY, SUBJECT TO SECTION 45 OF
INSURANCE ACT, 1998 As AMENDED FROM TIME TO TIME,
Section I - Details of the Life to be Assured
1. Tite E-] Mr. LJ Mrs. LJ Miss [J Or. LJ Others (Specify)
2. FirstName PETER PAUL
3. Surname T
44. Father's Name
46, Mother's Name ERIKA RESWE D
5. Date of Birth 13/02/1990 6, Gender E] Male ] Female
7. Age Proof L] School Certificate [] Driving License [] Passport {Birth Certificate E"] PAN Card
3, Marital Status D) Single EF] Married 0 Widower) 0 Civorcee
9, Spouse Name ERISEWQ FR
10. Maiden Name
iL. Nationality -] Resident Indian National [J Non Resident Indian (MRI) L] Others (Specify)
12, Education J Postgraduate / Doctorate Ee) Graduate [] 12thstd. Pass [J 10thstd. Pass [J Below 10th std.
OO Dliterate / Uneducated CJ Others (Specify)
13. Address For No 7¥%a vaigai street Flower
Communication Nagar selaiyur
Landmark
City Salem
Pin Code BO00 73: State TAMIL NADU
Address proof [] Passport ([] Driving License [] Voter ID [] Bank Statement [] Utility Bill G4 Others (Specify) Aadhaar Card
14, Permanent No 7¥a vaigai street Flower
Address :
Nagar selaiyur
Landmark
City Salem
Pin Code 5353535 state (TAMIL NADU
Address proof CJ] Passport [9 DrivingLicense [J Voter ID [ Bank Statement [ Utility Bill B] Others (Specify) Aadhaar Card
15. Contact Details Mobile 424242424 Phone (Home)
Office / Business
E-mail fdgrgtr13#yahoo.com
Preferred mode: ((] Letter EF) E-Mail
Preferred Language for Letter {other than English): [] Hindi [] Kannada [-] Tamil J Telugu C] Malayalam C) Gujarati
Bengali GOriya =D] Marathi
16. Occupation CL] Salaried-Govt /PSU ( Salaried-other [9 Self Employed Professional [J Aagriculturist {Farmer [Part Time Business
LJ Retired ] Landlord J Student (current Std) -] Others (Specify) Salaried - MNC
17. Full Name of the Capio software
Employers Businnes/
School/College
18, Designation & Exact nature of Work / Business Manager
19. AnnualIncomein 1,200,000.00 20. Annual Income of Husband / Father = 1,500,000.00
Figures (%) (for female and minor lives)
21. Exact nature of work / business of Husband / Father for female and minor lives Government Employee
Page 10fé
The below code works for me but the problem is if line order changes everything get changed. Is there a way to extract keyword value irrespective of line order. ?
Current Code
path <- getwd()
my_txt <- readLines(paste(path, "/input.txt", sep = ""))
fName <- sub('.*FirstName', '', my_txt[7])
SName <- sub('.*Surname', '', my_txt[8])
FatherNm <- sub(".*Father's Name", '', my_txt[9])
dob <- sub("6, Gender.*", '',sub(".*Date of Birth", '', my_txt[11]))
You can combine the text together as one string and extract the values based on pattern in the data. This approach will work irrespective of the line number in the data provided the pattern in the data is always valid for all the files.
my_txt <- readLines(paste(path, "/input.txt", sep = ""))
#Collapse data in one string
text <- paste0(my_txt, collapse = '\n')
#Extract text after FirstName till '\n'
fName <- sub('.*FirstName (.*?)\n.*', '\\1', text)
fName
#[1] "John Woo"
#Extract text after Surname till '\n'
SName <- sub('.*Surname (.*?)\n.*', '\\1', text)
SName
#[1] "T"
#Extract text after Father's Name till '\n'
FatherNm <- sub(".*Father's Name (.*?)\n.*", '\\1', text)
FatherNm
#[1] "Bill Woo"
#Extract numbers which come after Date of Birth.
dob <- sub(".*Date of Birth (\\d+/\\d+/\\d+).*", '\\1', text)
dob
#[1] "13/07/1970"
I have the following vertices -
Person1 -> Device1 <- Person2
^
| |
v
Email1 <- Person3
Now I want to write a gremlin query (janusgraph) which will give me all persons connected to the device(only) with which person1 is connected.
So according to the above graph, our output should be - [Person2].
Person3 is not in output because Person3 is also connected with "Email1" of "Person1".
g.addV('person').property('name', 'Person1').as('p1').
addV('person').property('name', 'Person2').as('p2').
addV('person').property('name', 'Person3').as('p3').
addV('device').as('d1').
addV('email').as('e1').
addE('HAS_DEVICE').from('p1').to('d1').
addE('HAS_EMAIL').from('p1').to('e1').
addE('HAS_DEVICE').from('p2').to('d1').
addE('HAS_DEVICE').from('p3').to('d1').
addE('HAS_EMAIL').from('p3').to('e1')
The following traversal will give you the person vertices that are connected to "Person1" via one or more "device" vertices and not connected via any other type of vertices.enter code here
g.V().has('person', 'name', 'Person1').as('p1').
out().as('connector').
in().where(neq('p1')).
group().
by().
by(select('connector').label().fold()).
unfold().
where(
select(values).
unfold().dedup().fold(). // just in case the persons are connected by multiple devices
is(eq(['device']))
).
select(keys)
I have a list in Excel. One code in Column A and another in Column B.
There is a website in which I need to input both the details in two different boxes and it takes to another page.
That page contains certain details which I need to scrape in Excel.
Any help in this?
Ok. Give this a shot:
import pandas as pd
import requests
df = pd.read_excel('C:/test/data.xlsx')
url = 'http://rla.dgft.gov.in:8100/dgft/IecPrint'
results = pd.DataFrame()
for row in df.itertuples():
payload = {
'iec': '%010d' %row[1],
'name':row[2]}
response = requests.post(url, params=payload)
print ('IEC: %010d\tName: %s' %(row[1],row[2]))
try:
dfs = pd.read_html(response.text)
except:
print ('The name Given By you does not match with the data OR you have entered less than three letters')
temp_df = pd.DataFrame([['%010d' %row[1],row[2], 'ERROR']],
columns = ['IEC','Party Name and Address','ERROR'])
results = results.append(temp_df, sort=False).reset_index(drop=True)
continue
generalData = dfs[0]
generalData = generalData.iloc[:,[0,-1]].set_index(generalData.columns[0]).T.reset_index(drop=True)
directorData = dfs[1]
directorData = directorData.iloc[:,[-1]].T.reset_index(drop=True)
directorData.columns = [ 'director_%02d' %(each+1) for each in directorData.columns ]
try:
branchData = dfs[2]
branchData = branchData.iloc[:,[-1]].T.reset_index(drop=True)
branchData.columns = [ 'branch_%02d' %(each+1) for each in branchData.columns ]
except:
branchData = pd.DataFrame()
print ('No Branch Data.')
temp_df = pd.concat([generalData, directorData, branchData], axis=1)
results = results.append(temp_df, sort=False).reset_index(drop=True)
results.to_excel('path.new_file.xlsx', index=False)
Output:
print (results.to_string())
IEC IEC Allotment Date File Number File Date Party Name and Address Phone No e_mail Exporter Type IEC Status Date of Establishment BIN (PAN+Extension) PAN ISSUE DATE PAN ISSUED BY Nature Of Concern Banker Detail director_01 director_02 director_03 branch_01 branch_02 branch_03 branch_04 branch_05 branch_06 branch_07 branch_08 branch_09
0 0305008111 03.05.2005 04/04/131/51473/AM20/ 20.08.2019 NISSAN MOTOR INDIA PVT. LTD. PLOT-1A,SIPCOT IN... 918939917907 shailesh.kumar#rnaipl.com 5 Merchant/Manufacturer Valid IEC 2005-02-07 AACCN0695D FT001 NaN NaN 3 Private Limited STANDARD CHARTERED BANK A/C Type:1 CA A/C No :... HARDEEP SINGH BRAR GURMEL SINGH BRAR HOUSE NO ... JEROME YVES MARIE SAIGOT THIERRY SAIGOT A9/2, ... KOJI KAWAKITA KIHACHI KAWAKITA 3-21-3, NAGATAK... Branch Code:165TH FLOOR ORCHID BUSINESS PARK,S... Branch Code:14NRPDC , WAREHOUSE NO.B -2A,PATAU... Branch Code:12EQUINOX BUSINESS PARK TOWER 3 4T... Branch Code:8GRAND PALLADIUM,5TH FLR.,B WING,,... Branch Code:6TVS LOGISTICS SERVICES LTD.SING,C... Branch Code:2PLOT 1A SIPCOT INDUL PARK,ORAGADA... Branch Code:5BLDG.NO.3 PART,124A,VALLAM A,SRIP... Branch Code:15SURVEY NO. 678 679 680 681 682 6... Branch Code:10INDOSPACE SKCL INDL.PARK,BULD.NO...
I am making a graph database using Neo4j and I'm wondering what's the best way to model this case:
Person1 > told > quote > to > Person2 > who told it to -> Person3 -> who told it to -> Person4 > Who told it to -> Person1
I've thought about quote being an attribute of link. But then maybe quote needs also to be a node. In this case the edges would be "told" and "was_told". Like:
Person1 -> created > quote
Quote attributes: id, text
Person attributes: id, name
Person2 > told: {to: Person 3} > quote
Person3 > was_told: {by: Person2} > quote
or:
Person3 > told:quote > Person1
What's the best approach to use to model this database?
I think you need the following model:
A fragment (talk) of a conversation (including time)
Who was the speaker of this fragment
Who was an audience of this fragment
Content (quote) of this fragment
For example, here's the code for creating the first fragment:
MERGE (P1:Person {name:'Person1'})
MERGE (P2:Person {name:'Person2'})
MERGE (Q:Quote {name:'Quote1', text:'Quote1 text'})
MERGE (P1)<-[:has_speaker]-(T1:Talk {name:'Talk1', time: 1})-[:has_audience]->(P2)
MERGE (T1)-[:talk_about]->(Q)
Visualization:
The query for the entire life cycle of a quote:
MATCH (Q:Quote {name:'Quote1', text:'Quote1 text'})<-[:talk_about]-(T:Talk)
WITH Q, T
MATCH (P1:Person)<-[:has_speaker]-(T)-[:has_audience]->(P2)
WITH Q, T, P1 as speaker, collect(P2.name) as audience ORDER BY T.time ASC
RETURN Q as quote,
collect( {time: T.time,
speaker: speaker.name,
audience: audience}
) as quoteTimeline
I have a string
string <- "You know that song Mary had a little lamb? Mary is my friend."
I'd like to split it such that
> string[1]
[1] "You know that song "
> string[2]
[1] " had a little lamb? Mary is my friend."
I want to split it on the first instance of "Mary".
Closer to my actual problem, suppose I had the following string:
string <- "Name: Mary
Some stuff about Mary goes here, for a page
Name: Mary
There's more stuff about her.
Name: Sue
Now the name is different. I want to split on Sue here.
Name: Sue
Sue appears again, but because the name is Sue again I don't want to splt.
Name: Beth
The name changed again, so I want to split on Beth above (following Name: ).
Name: Amy
The name changed again and now I want to split on the 'Amy' immediately following Name: ."
Essentially, I want to split this document so that each element corresponds to information about one person so that:
> string
[1] "Name: Mary\n Some stuff about Mary goes here, for a page\n Name: Mary\n There's more stuff about her.\n Name: "
[2] "Sue\n Now the name is different. I want to split on Sue here.\n Name: Sue\n Sue appears again, but because the name is Sue again I don't want to splt.\n Name: "
[3] "Beth\n The name changed again, so I want to split on Beth above (following Name: ).\n Name: "
[4] "Amy\n The name changed again and now I want to split on the 'Amy' immediately following Name: ."
May be this helps
strsplit(string, '(\\b\\S+\\b)(?=.*\\b\\1\\b.*)', perl=TRUE)[[1]]
##[1] "You know that song "
#[2] " had a little lamb? Mary is my friend."
Another case
string1 <- "You know that song Mary had a little lamb? Mary is my friend and she is also a friend of another friend"
strsplit(string1, '(\\b\\S+\\b)(?=.*\\b\\1\\b.*)', perl=TRUE)[[1]]
#[1] "You know that song " " had " " little lamb? Mary "
#[4] " my " " and she is also a " " of another friend"
NOTE: I am not sure whether this is the way the OP wants to split for the second example.
Try this one:
regmatches(string, regexpr("Mary", string), invert = TRUE)