how to separate Unit/Suite/APT/# from an address in Excel - excel

I have a data base 272,000 addresses But some addresses have unit/suite/STE/APT seeexample below
16 BRIARWOOD COURT UNIT B MONTVALE, NJ 07645
100 CROWN COURT #471 EDGEWATER, NJ 07020
23-05 HIGH ST APT A FAIR LAWN, NJ 07410
15-01 BROADWAY STE 6 FAIR LAWN, NJ 07410
80 BROADWAY, SUITE 1A CRESSKILL, N.J. 07626
300 GORGE ROAD APT 11 CLIFFSIDE PARK, N.J. 07010
I would like to split the text to the next column when it comes across unit/suite/STE/APT
I want to separate these so I can use Advance filter with unique records and create a master find and replace to clean the list....
Any formulas I can use for this would be helpful....


You can batch geocode your file on geocoder.ca
This is the result I got:
rawlocation Latitude Longitude Score StandardCivicNumber StandardAddress StandardCity StandardStateorProvinceAbbrv PostalZip Confidence
16 BRIARWOOD COURT UNIT B MONTVALE NJ 07645 41.035587 -74.06744 1
16 Briarwood Crt Montvale NJ 7677 0.7
100 CROWN COURT #471 EDGEWATER NJ 07020 40.822893 -73.978375 1 100 Crown Crt Edgewater NJ 07020-1137 0.8
23-05 HIGH ST APT A FAIR LAWN NJ 07410 40.940276 -74.120329 1 23 High St Fair Lawn NJ 07410-3574 0.8
15-01 BROADWAY STE 6 FAIR LAWN NJ 07410 40.920501 -74.091153 1 1 S Broadway Fair Lawn NJ 07410-5529 0.8
80 BROADWAY - 0
300 GORGE ROAD APT 11 CLIFFSIDE PARK N.J. 07010 40.814151 -73.990015 1 300 Gorge Rd Cliffside Park NJ 07010-2759 0.8
From the cleaned up version you can then street compare to extract additional entities.


Since not all addresses have a secondary number (such as APT C, or STE 312), I would recommend separating every time you come across a ZIP (5 digits) or a ZIP+4 (like 07010-2759). This will help you break that string into discrete addresses.
If you then want to clean up the list by correcting small typos and standardizing abbreviations, etc, I recommend using an address validation and standardization service like Melissa Data, or SmartyStreets. SmartyStreets has tools for validating/cleansing large lists of addresses and even extracting addresses out of text. (Full disclosure) I'm a software developer for SmartyStreets.

Related

Creating multiple named dataframes by a for loop

I have a database that contains 60,000+ rows of college football recruit data. From there, I want to create seperate dataframes where each one contains just one value. This is what a sample of the dataframe looks like:
,Primary Rank,Other Rank,Name,Link,Highschool,Position,Height,weight,Rating,National Rank,Position Rank,State Rank,Team,Class
0,1,,D.J. Williams,https://247sports.com/Player/DJ-Williams-49931,"De La Salle (Concord, CA)",ILB,6-2,235,0.9998,1,1,1,Miami,2000
1,2,,Brock Berlin,https://247sports.com/Player/Brock-Berlin-49926,"Evangel Christian Academy (Shreveport, LA)",PRO,6-2,190,0.9998,2,1,1,Florida,2000
2,3,,Charles Rogers,https://247sports.com/Player/Charles-Rogers-49984,"Saginaw (Saginaw, MI)",WR,6-4,195,0.9988,3,1,1,Michigan State,2000
3,4,,Travis Johnson,https://247sports.com/Player/Travis-Johnson-50043,"Notre Dame (Sherman Oaks, CA)",SDE,6-4,265,0.9982,4,1,2,Florida State,2000
4,5,,Marcus Houston,https://247sports.com/Player/Marcus-Houston-50139,"Thomas Jefferson (Denver, CO)",RB,6-0,208,0.9980,5,1,1,Colorado,2000
5,6,,Kwame Harris,https://247sports.com/Player/Kwame-Harris-49999,"Newark (Newark, DE)",OT,6-7,320,0.9978,6,1,1,Stanford,2000
6,7,,B.J. Johnson,https://247sports.com/Player/BJ-Johnson-50154,"South Grand Prairie (Grand Prairie, TX)",WR,6-1,190,0.9976,7,2,1,Texas,2000
7,8,,Bryant McFadden,https://247sports.com/Player/Bryant-McFadden-50094,"McArthur (Hollywood, FL)",CB,6-1,182,0.9968,8,1,1,Florida State,2000
8,9,,Sam Maldonado,https://247sports.com/Player/Sam-Maldonado-50071,"Harrison (Harrison, NY)",RB,6-2,215,0.9964,9,2,1,Ohio State,2000
9,10,,Mike Munoz,https://247sports.com/Player/Mike-Munoz-50150,"Archbishop Moeller (Cincinnati, OH)",OT,6-7,290,0.9960,10,2,1,Tennessee,2000
10,11,,Willis McGahee,https://247sports.com/Player/Willis-McGahee-50179,"Miami Central (Miami, FL)",RB,6-1,215,0.9948,11,3,2,Miami,2000
11,12,,Antonio Hall,https://247sports.com/Player/Antonio-Hall-50175,"McKinley (Canton, OH)",OT,6-5,295,0.9946,12,3,2,Kentucky,2000
12,13,,Darrell Lee,https://247sports.com/Player/Darrell-Lee-50580,"Kirkwood (Saint Louis, MO)",WDE,6-5,230,0.9940,13,1,1,Florida,2000
13,14,,O.J. Owens,https://247sports.com/Player/OJ-Owens-50176,"North Stanly (New London, NC)",S,6-1,195,0.9932,14,1,1,Tennessee,2000
14,15,,Jeff Smoker,https://247sports.com/Player/Jeff-Smoker-50582,"Manheim Central (Manheim, PA)",PRO,6-3,190,0.9922,15,2,1,Michigan State,2000
15,16,,Marco Cooper,https://247sports.com/Player/Marco-Cooper-50171,"Cass Technical (Detroit, MI)",OLB,6-2,235,0.9918,16,1,2,Ohio State,2000
16,17,,Chance Mock,https://247sports.com/Player/Chance-Mock-50163,"The Woodlands (The Woodlands, TX)",PRO,6-2,190,0.9918,17,3,2,Texas,2000
17,18,,Roy Williams,https://247sports.com/Player/Roy-Williams-55566,"Permian (Odessa, TX)",WR,6-4,202,0.9916,18,3,3,Texas,2000
18,19,,Matt Grootegoed,https://247sports.com/Player/Matt-Grootegoed-50591,"Mater Dei (Santa Ana, CA)",OLB,5-11,205,0.9914,19,2,3,USC,2000
19,20,,Yohance Buchanan,https://247sports.com/Player/Yohance-Buchanan-50182,"Douglass (Atlanta, GA)",S,6-1,210,0.9912,20,2,1,Florida State,2000
20,21,,Mac Tyler,https://247sports.com/Player/Mac-Tyler-50572,"Jess Lanier (Hueytown, AL)",DT,6-6,320,0.9912,21,1,1,Alabama,2000
21,22,,Jason Respert,https://247sports.com/Player/Jason-Respert-55623,"Northside (Warner Robins, GA)",OC,6-3,300,0.9902,22,1,2,Tennessee,2000
22,23,,Casey Clausen,https://247sports.com/Player/Casey-Clausen-50183,"Bishop Alemany (Mission Hills, CA)",PRO,6-4,215,0.9896,23,4,4,Tennessee,2000
23,24,,Albert Means,https://247sports.com/Player/Albert-Means-55968,"Trezevant (Memphis, TN)",SDE,6-6,310,0.9890,24,2,1,Alabama,2000
24,25,,Albert Hollis,https://247sports.com/Player/Albert-Hollis-55958,"Christian Brothers (Sacramento, CA)",RB,6-0,190,0.9890,25,4,5,Georgia,2000
25,26,,Eric Moore,https://247sports.com/Player/Eric-Moore-55973,"Pahokee (Pahokee, FL)",OLB,6-4,226,0.9884,26,3,3,Florida State,2000
26,27,,Willie Dixon,https://247sports.com/Player/Willie-Dixon-55626,"Stockton Christian School (Stockton, CA)",WR,5-11,182,0.9884,27,4,6,Miami,2000
27,28,,Cory Bailey,https://247sports.com/Player/Cory-Bailey-50586,"American (Hialeah, FL)",S,5-10,175,0.9880,28,3,4,Florida,2000
28,29,,Sean Young,https://247sports.com/Player/Sean-Young-55972,"Northwest Whitfield County (Tunnel Hill, GA)",OG,6-6,293,0.9878,29,1,3,Tennessee,2000
29,30,,Johnnie Morant,https://247sports.com/Player/Johnnie-Morant-60412,"Parsippany Hills (Morris Plains, NJ)",WR,6-5,225,0.9871,30,5,1,Syracuse,2000
30,31,,Wes Sims,https://247sports.com/Player/Wes-Sims-60243,"Weatherford (Weatherford, OK)",OG,6-5,310,0.9869,31,2,1,Oklahoma,2000
31,33,,Jason Campbell,https://247sports.com/Player/Jason-Campbell-55976,"Taylorsville (Taylorsville, MS)",PRO,6-5,190,0.9853,33,5,1,Auburn,2000
32,34,,Antwan Odom,https://247sports.com/Player/Antwan-Odom-50168,"Alma Bryant (Irvington, AL)",SDE,6-7,260,0.9851,34,3,2,Alabama,2000
33,35,,Sloan Thomas,https://247sports.com/Player/Sloan-Thomas-55630,"Klein (Spring, TX)",WR,6-2,188,0.9847,35,6,5,Texas,2000
34,36,,Raymond Mann,https://247sports.com/Player/Raymond-Mann-60804,"Hampton (Hampton, VA)",ILB,6-1,233,0.9847,36,2,1,Virginia,2000
35,37,,Alphonso Townsend,https://247sports.com/Player/Alphonso-Townsend-55975,"Lima Central Catholic (Lima, OH)",DT,6-6,280,0.9847,37,2,3,Ohio State,2000
36,38,,Greg Jones,https://247sports.com/Player/Greg-Jones-50158,"Battery Creek (Beaufort, SC)",RB,6-2,245,0.9837,38,6,1,Florida State,2000
37,39,,Paul Mociler,https://247sports.com/Player/Paul-Mociler-60319,"St. John Bosco (Bellflower, CA)",OG,6-5,300,0.9833,39,3,7,UCLA,2000
38,40,,Chris Septak,https://247sports.com/Player/Chris-Septak-57555,"Millard West (Omaha, NE)",TE,6-3,245,0.9833,40,1,1,Nebraska,2000
39,41,,Eric Knott,https://247sports.com/Player/Eric-Knott-60823,"Henry Ford II (Sterling Heights, MI)",TE,6-4,235,0.9831,41,2,3,Michigan State,2000
40,42,,Harold James,https://247sports.com/Player/Harold-James-57524,"Osceola (Osceola, AR)",S,6-1,220,0.9827,42,4,1,Alabama,2000
For example, if I don't use a for loop, this line of code is what I use if I just want to create one dataframe:
recruits2022 = recruits_final[recruits_final['Class'] == 2022]
However, I want to have a named dataframe for each recruiting class.
In other words, recruits2000 would be a dataframe for all rows that have a class value equal to 2000, recruits2001 would be a dataframe for all rows that have a class value to 2001, and so forth.
This is what I tried recently, but have no luck saving the dataframe outside of the for loop.
databases = ['recruits2000', 'recruits2001', 'recruits2002', 'recruits2003', 'recruits2004',
'recruits2005', 'recruits2006', 'recruits2007', 'recruits2008', 'recruits2009',
'recruits2010', 'recruits2011', 'recruits2012', 'recruits2013', 'recruits2014',
'recruits2015', 'recruits2016', 'recruits2017', 'recruits2018', 'recruits2019',
'recruits2020', 'recruits2021', 'recruits2022', 'recruits2023']
for i in range(len(databases)):
year = pd.to_numeric(databases[i][-4:], errors = 'coerce')
db = recruits_final[recruits_final['Class'] == year]
db.name = databases[i]
print(db)
print(db.name)
print(year)
recruits2023
I would get this error instead of what I wanted
NameError Traceback (most recent call last)
<ipython-input-49-7cb5d12ab92f> in <module>()
29
30 # print(db.name)
---> 31 recruits2023
32
33
NameError: name 'recruits2023' is not defined
Is there something that I am missing to get this for loop to work? Any assistance is truly appreciated. Thanks in advance.

List use a dictionary of dataframes using groupby:
dict_dfs = dict(tuple(df.groupby('Class')))
Access you individual dataframes using
dict_dfs[2022]

You override variable db at each iteration and recruits2023 is not a variable so you can't use it like that:
You can use a dict to store your data:
recruits = {}
for year in recruits_final['Class'].unique():
recruits[year] = recruits_final[recruits_final['Class'] == year]
>>> recruits[2000]
Primary Rank Other Rank Name Link ... Position Rank State Rank Team Class
0 1 NaN D.J. Williams https://247sports.com/Player/DJ-Williams-49931 ... 1 1 Miami 2000
1 2 NaN Brock Berlin https://247sports.com/Player/Brock-Berlin-49926 ... 1 1 Florida 2000
2 3 NaN Charles Rogers https://247sports.com/Player/Charles-Rogers-49984 ... 1 1 Michigan State 2000
3 4 NaN Travis Johnson https://247sports.com/Player/Travis-Johnson-50043 ... 1 2 Florida State 2000
...
38 40 NaN Chris Septak https://247sports.com/Player/Chris-Septak-57555 ... 1 1 Nebraska 2000
39 41 NaN Eric Knott https://247sports.com/Player/Eric-Knott-60823 ... 2 3 Michigan State 2000
40 42 NaN Harold James https://247sports.com/Player/Harold-James-57524 ... 4 1 Alabama 2000
>>> recruits.keys()
dict_keys([2000])

Split one column into multiple columns by multiple delimiters in Pandas

Given a dataframe as follows:
player score
0 Sergio Agüero Forward — Manchester City 209.98
1 Eden Hazard Midfield — Chelsea 274.04
2 Alexis Sánchez Forward — Arsenal 223.86
3 Yaya Touré Midfield — Manchester City 197.91
4 Angel María Midfield — Manchester United 132.23
How could split player into three new columns name, position and team?
player score name position team
0 Sergio Agüero Forward — Manchester City 209.98 Sergio Forward Manchester City
1 Eden Hazard Midfield — Chelsea 274.04 Eden Midfield Chelsea
2 Alexis Sánchez Forward — Arsenal 223.86 Alexis Forward Arsenal
3 Yaya Touré Midfield — Manchester City 197.91 Yaya Midfield Manchester City
4 Angel María Midfield — Manchester United 132.23 Angel Midfield Manchester United
I have considered split it two columns with df[['name_position', 'team']] = df['player'].str.split(pat= ' — ', expand=True), then split name_position to name and position. But is there any better solutions?
Many thanks.

You can use str.extract as well if you want to do it in one go:
print(df["player"].str.extract(r"(?P<name>.*?)\s.*?\s(?P<position>[A-Za-z]+)\s—\s(?P<team>.*)"))
name position team
0 Sergio Forward Manchester City
1 Eden Midfield Chelsea
2 Alexis Forward Arsenal
3 Yaya Midfield Manchester City
4 Angel Midfield Manchester United

You can split a python string by space with string.split(). This will break up your text into 'words', then you can simply access the one you like, like this:
string = "Sergio Agüero Forward — Manchester City"
name = string.split()[0]
position = string.split()[2]
team = string.split()[4] + (string.split().has_key(5) ? string.split()[5] : '')
For more complex patterns, you can use regex, which is a powerful string pattern finding tool.
Hope this helped :)

How to identify where each person have lived in different cities in each time?

Here is a small set of the dataset that I am currently working on.
FirstName LastName cities occupation time
---------------------------------------------------------------
---------------------------------------------------------------
Alice Oumi Queens software engineer 1/1/2019
Alice Oumi New York software engineer 12/3/2018
Sam Charles Santa Clara Engineer 2/5/2017
Sam Charles Santa Monica Engineer 8/9/2018
Sam Charles Santa Clara Engineer 12/12/2019
Alice Oumi New York software engineer 1/2/2017
As you see above, the same person could be living in a same place but for a different duration of a time. I want to make clean this dataset that should what places did Alice and Sam live. For example, instead of having 2 rows of Alice living in New York, I only need to have one. Something similar to the following table
FirstName LastName cities FirstTime SecondTime
---------------------------------------------------------------
---------------------------------------------------------------
Alice Oumi Queens 1/1/2019 NA
Alice Oumi New York 1/2/2017 12/3/2018
Sam Charles Santa Clara 2/5/2017 12/12/2019
Sam Charles Santa Monica 8/9/2018 NA
I am kinda new to python and trying to learn. but i have tried to use for loops using iterrows() but didn't work.
What can use to achieve this table?
Thank you so much in advance

You can do that as follows:
# number the times a person lived in the same city (with the same occupation)
df['sequence']= df.groupby(['FirstName', 'LastName', 'cities', 'occupation']).cumcount()+1
# now create the "pivot" table
result= df.set_index(['FirstName', 'LastName', 'cities', 'occupation', 'sequence']).unstack()
# rename the columns
result.columns= ['FirstTime', 'SecondTime']
# reset the index (it was just needed for "pivoting"
result.reset_index(inplace=True)
The result looks like:
Out[483]:
FirstName LastName cities occupation FirstTime SecondTime
0 Alice Oumi New York software engineer 12/3/2018 1/2/2017
1 Alice Oumi Queens software engineer 1/1/2019 NaN
2 Sam Charles Santa Clara Engineer 2/5/2017 12/12/2019
3 Sam Charles Santa Monica Engineer 8/9/2018 None NaN

How to clean up troublesome characters in clipboard data so I can paste into a python script in IDLE?

I want to copy tables of data displayed in websites and paste as text directly into scripts as string variables using IDLE. This sometimes doesn't work because of something in the copied material that IDLE won't accept as savable. The resulting behavior is not an error message, but IDLE simply ignoring the save request. It just sits there until I close without saving.
That behavior is fine with me at the moment - I'd of course not want to save a python script that contains troublesome characters.
Is there some way I can get those pesky characters out of what's in my computer's clip board so I can get on with my script?
If I just needed to do this once, I could go in and look at the html of the site and possibly extract it, or in the case of the table of satellites on this page maybe I can go into the google app and get it.
But for the purposes of this question, I'd like a way to "fix" the data in my clip board to I can paste as a string into a script using IDLE and run it.
I've tried "Paste and Match Style" in a .txt file first to clean it up, no luck. I have Sublime Text 2 but not very familliar with it, if there is a relatively easy to use function in there, that would be OK.
Trying to paste inside triple quotes thing = """ """ at the prompt gives the following error message: Unsupported characters in input:
note: using Python and IDLE versions '2.7.11', Tk version '8.5.9' (I know, these are a year old) in OSX.
EDIT: Here is a chunk of data from my clip board, as suggested in the comments. Copying from here (as shown) results in unsuccessful save attempts in IDLE, so at least some of the pesky symbols are in here. I'm pasting between a pair of triple quotes, e.g. thing = """ """
1 2/6/2000 PICOSAT 1&2 (TETHERED) Aerospace Corporation mil Opal Opal T 5 N Minotaur-1
2 2/10/2000 PICOSAT 3 (JAK) Santa Clara University uni Opal Opal E 2 N Minotaur-1
3 2/10/2000 PICOSAT 6 (StenSat) Stensat Group. LLC civ Opal Opal C 2 N Minotaur-1
4 2/12/2000 PICOSAT 4 (Thelma) Santa Clara University uni Opal Opal S 2 N Minotaur-1
5 2/12/2000 PICOSAT 5 (Louise) Santa Clara University uni Opal Opal S 2 N Minotaur-1
6 9/6/2001 PICOSAT 7&8 (TETHERED) Aerospace Corporation mil Opal Opal T 2 D Minotaur-1
7 12/2/2002 MEPSI Aerospace Corporation mil 2U SSPL T 2 D Shuttle
8 6/30/2003 DTUSAT 1 Technical University of Denmark uni 1U PPOD E 2 N Rokot-KM
9 6/30/2003 CUTE-1 (CO-55) Tokyo Institute of Technology uni 1U PPOD E 3 N Rokot-KM
10 6/30/2003 QUAKESAT 1 Stanford University uni 3U PPOD S 5 N Rokot-KM
11 6/30/2003 AAU CUBESAT 1 Aalborg University uni 1U PPOD E 2 N Rokot-KM
12 6/30/2003 CANX-1 UTIAS (University of Toronto) uni 1U PPOD E 2 N Rokot-KM
13 6/30/2003 CUBESAT XI-IV (CO-57) University of Tokyo uni 1U PPOD E 4 S Rokot-KM
14 10/27/2005 UWE-1 University of Würzburg uni 1U TPOD E 3 N Kosmos-3M
15 10/27/2005 CUBESAT XI-V (CO-58) University of Tokyo uni 1U TPOD E 5 N Kosmos-3M
16 10/27/2005 Ncube 2 Norweigan Universities uni 1U TPOD E 2 N Kosmos-3M
17 2/21/2006 CUTE 1.7 Tokyo Institute of Technology uni 2U JPOD C 2 D M-5 (2)
18 7/26/2006 AeroCube 1 Aerospace Corporation mil 1U PPOD T 1 D Dnepr-1
19 7/26/2006 SEEDS Nihon University uni 1U PPOD E 1 D Dnepr-1
20 7/26/2006 SACRED University of Arizona uni 1U PPOD E 1 D Dnepr-1

I'd try to scan the string and find the characters outside the normal printable range. Maybe the strange character will be easier to identify.
text = """ <here comes your pasted text> """
def normal(c):
return (32 <= ord(c) <= 127) or (c in '\n\r\t')
strange = set(ord(c) for c in text if not normal(c))
print strange
I wonder what character codes may end up in strange.

Multi Criterion Max If Statement

My dataset looks like this...
State Close Date Probability Highest Prob/State
WA 12/31/2016 50% FALSE
WA 12/19/2016 80% FALSE
WA 10/15/2016 80% TRUE
My objective is to build a formula to populate the right-most column. The formula should assess Close Dates and Probabilities within each state. First, it should select the highest probability, then it should select the nearest close date if there is a tie on probability (as in the example). For that record, it should read "TRUE".
I assume this would include a MAX IF statement but haven't been able to get it to work.
Here is a more robust set of data I'm working with. It may actually be easier to first find the highest probability within each Region then select the minimum (oldest) date if there is a tie on probability. This too will serve my purposes.
Region Forecast Close Date Probability (%)
Okeechobee FL 6/27/2016 90
Okeechobee West FL 7/1/2016 40
Albany GA 3/11/2016 100
Emerald Coast FL 6/30/2016 60
Emerald Coast FL 10/1/2016 40
Cullman_Hartselle TN 4/30/2016 10
North MS 10/1/2016 25
Roanoke VA 8/31/2016 25
Roanoke VA 8/1/2016 40
Gardena CA 6/1/2016 80
Gardena CA 6/1/2016 80
Lomita-Harbor City 6/30/2016 60
Lomita-Harbor City 6/30/2016 0
Lomita-Harbor City 6/30/2016 40
Eastern NC 6/30/2016 60
Northwest NC 9/16/2016 10
Fort Collins_Greeley CO 3/1/2016 100
Northwest OK 6/30/2016 100
Southwest MO 7/29/2016 90
Northern NH-VT 3/1/2016 20
South DE 12/1/2016 0
South DE 12/1/2016 20
Kingston NY 12/30/2016 5
Longview WA 11/30/2016 5
North DE 12/1/2016 20
North DE 12/1/2016 0
Salt Lake City UT 8/31/2016 20
Idaho Panhandle 8/26/2016 0
Bridgeton_Salem NJ 7/1/2016 25
Bridgeton_Salem NJ 7/1/2016 65
Layton_Ogden UT 3/25/2016 5
Central OR 6/30/2016 10

The following Array formula should work:
=(ABS(B2-$F$2)=MIN(IF(($A$2:$A$33=A2)*(C2=MAX(IF($A$2:$A$33=A2,$C$2:$C$33))),ABS($B$2:$B$33-$F$2))))*(C2=MAX(IF($A$2:$A$33=A2,$C$2:$C$33)))>0
Being an array formula use Ctrl-Shift-Enter when exiting Edit mode. If done properly Excel will put {} around the formula.
Edit
Added #tigeravatar suggestion to avoid volatile functions.

I think this is OK now but needs to be checked against the more complete set of data provided by OP.
It counts:-
(1) Any rows with same state but higher probability
(2) Any rows with same state and probability, in the future (or present) and nearer to today's date
(3) Any rows with same state and probability, in the past and nearer to today's date.
If all these are zero, you should have the right one.
=COUNTIFS($A$2:$A$100,$A2,$C$2:$C$100,">"&$C2)
+COUNTIFS($A$2:$A$100,$A2,$C$2:$C$100,$C2,$B$2:$B$100,"<"&$G$2+IF ($B2>=$G$2,DATEDIF($G$2,$B2,"d"),DATEDIF($B2,$G$2,"d")),$B$2:$B$100,">="&$G$2)
+COUNTIFS($A$2:$A$100,$A2,$C$2:$C$100,$C2,$B$2:$B$100,">"&$G$2-IF($B2>=$G$2,DATEDIF($G$2,$B2,"d"),DATEDIF($B2,$G$2,"d")),$B$2:$B$100,"<"&$G$2)
=0
If the dates are all in the future, it can be simplified a lot:-
=COUNTIFS($A$2:$A$100,$A2,$C$2:$C$100,">"&$C2)
+COUNTIFS($A$2:$A$100,$A2,$C$2:$C$100,$C2,$B$2:$B$100,"<"&$G$2+DATEDIF($G$2,$B2,"d"))
=0

Resources