The Perfect Man

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6%58%

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actress arizona bad date baker's shop bakery bakery worker blog brooklyn accent brooklyn new york city buzzer cake cake in the face chat up line chick flick comic book confession courtship coworker coworker relationship crossword puzzle dancing date date with coworker dating daughter embarrassed by mother deception e mail failed relationship female chef fire sprinkler flirting flower delivery friendship girl wears eyeglasses guilt hair wrapped in a towel high school hurt feelings instant messaging interrupted wedding letter read aloud listening in on telephone call looking for love love letter lying to one's parents male female relationship manhattan new york city manipulation marriage proposal matchmaking milf mother daughter relationship moving new school new york new york city online chat online dating orchid perfect man potted plant pretending to be someone else princess pta pta meeting reference to heathcliff from wuthering heights reference to julia child reference to patsy cline reference to romeo reference to styx reference to william shakespeare rejecting a marriage proposal restaurant rock concert scheme search for love secret admirer serenade shut up with kiss single mother single parent split screen suitor teenage girl three word title title spoken by character two suitors unlucky in love voice over e mail woman punches a man working mother yellow rose younger sister

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Stars:

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Race	White	Mixed / Other	Asian	Jewish	Latino	Total	Visuals	Info
Kairos	7		2			9		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.kairos.com/demos
Ethnic	4	2		1		7		This is the most reliable data source for accurate ethnic breakdowns of famous people. Often the information is crowdsourced by users and they cite all their sources (family trees, obituaries, census archives, etc.) on the bottom of the page. The only issue is converting their ambiguous and inconsistent verdicts into quantifiable variables. Source: https://ethnicelebs.com/
Crowd	1					1		This is the crowdsourced dataset created by users like you! We greatly appreciate your cooperation in making ZR the most accurate and massive database on the internet. Sometimes the verdicts are submitted by looks alone, sometimes users attach an image and a source link. Due to this inconsistency please take the results with a grain of salt and click each actor for more info.
Forebears					1	1
Total	12	2	2	1	1	18
Cast Percentages	66.67%	11.11%	11.11%	5.56%	5.56%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":66.67,"color":"#2b908f","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":11.11,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Asian","y":11.11,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Jewish","y":5.56,"color":"#ff0066","sliced":true,"selected":true},{"name":"Latino","y":5.56,"color":"#eeaaee","sliced":true,"selected":true}],"prefix":""}
U.S. Population (2000) Percentage	69.1%	3.96%	4.23%	1.85%	12.5%		{"name":"U.S. Population (2000)","series":[{"name":"White","y":69.1,"color":"#2b908f"},{"name":"Arab","y":0.42,"color":"#DF5353"},{"name":"Asian","y":4.23,"color":"#90ee7e"},{"name":"Black","y":12.3,"color":"#f45b5b"},{"name":"Indian","y":0.6,"color":"#7798BF"},{"name":"Indigenous","y":0.9,"color":"#aaeeee"},{"name":"Latino","y":12.5,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":3.96,"color":"#55BF3B"},{"name":"Jewish","y":1.85,"color":"#ff0066"}],"prefix":""}	Source: US Census (Note: Racial and ethnic categories overlap so the sum will not equal 100%.)
U.S. Population (2000) Representation	-2.43	+7.15	+6.88	+3.71	-6.94		{"name":"U.S. Population (2000)","series":[["White",69.1],["Mixed \/ Other",3.96],["Asian",4.23],["Jewish",1.85],["Latino",12.5]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Mixed \/ Other","y":11.11,"color":"#55BF3B"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Jewish","y":5.56,"color":"#ff0066"},{"name":"Latino","y":5.56,"color":"#eeaaee"}]}
U.S. Buying Power (2000) Percentage	81.25%	0.76%	3.5%		6.26%		{"name":"U.S. Buying Power (2000)","series":[{"name":"White","y":6413300000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":276400000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":609100000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":40300000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":494000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":60300000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2000) Representation	-14.58	+10.35	+7.61		-0.7		{"name":"U.S. Buying Power (2000)","series":[["White",81.25],["Mixed \/ Other",0.76],["Asian",3.5],["Jewish",0],["Latino",6.26]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Mixed \/ Other","y":11.11,"color":"#55BF3B"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Jewish","y":5.56,"color":"#ff0066"},{"name":"Latino","y":5.56,"color":"#eeaaee"}]}
U.S. Average Individual Income (2018) Percentage	75.87%	3.01%	5.15%		6.17%		{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":12718864679181,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":863662829574,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1524222662869,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":118862444126,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1034308096500,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":504305695765,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	-9.2	+8.1	+5.96		-0.61		{"name":"U.S. Average Individual Income (2018)","series":[["White",75.87],["Mixed \/ Other",3.01],["Asian",5.15],["Jewish",0],["Latino",6.17]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Mixed \/ Other","y":11.11,"color":"#55BF3B"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Jewish","y":5.56,"color":"#ff0066"},{"name":"Latino","y":5.56,"color":"#eeaaee"}]}
World population (2005) Percentage	15.78%	1.86%	33.46%	0.34%	5.73%		{"name":"World population (2005) ","series":[{"name":"Mixed \/ Other","y":12059233362,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Latino","y":37152006149,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Indigenous","y":8921391088,"color":"#aaeeee","sliced":true,"selected":true},{"name":"White","y":102383610376,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":78238938078,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Asian","y":217077547173,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Arab","y":51316658810,"color":"#DF5353","sliced":true,"selected":true},{"name":"Indian","y":139376519734,"color":"#7798BF","sliced":true,"selected":true},{"name":"Jewish","y":2191204385,"color":"#ff0066","sliced":true,"selected":true}],"prefix":""}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World population (2005) Representation	+50.89	+9.25	-22.35	+5.22	-0.17		{"name":"World population (2005) ","series":[["White",15.78],["Mixed \/ Other",1.86],["Asian",33.46],["Jewish",0.34],["Latino",5.73]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Mixed \/ Other","y":11.11,"color":"#55BF3B"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Jewish","y":5.56,"color":"#ff0066"},{"name":"Latino","y":5.56,"color":"#eeaaee"}]}
World Buying Power Percentage	36.48%	1.81%	29.46%	1.11%	9.35%		{"name":"World Buying Power","series":[{"name":"White","y":3010602822170000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Latino","y":771525888400000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Jewish","y":91700962050000,"color":"#ff0066","sliced":true,"selected":true},{"name":"Black","y":526264226360000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indigenous","y":174285727610000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":148984454560000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Arab","y":526400066100000,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":2431392083750000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Indian","y":572687873900000,"color":"#7798BF","sliced":true,"selected":true}],"prefix":"$"}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World Buying Power Representation	+30.19	+9.3	-18.35	+4.45	-3.79		{"name":"World Buying Power","series":[["White",36.48],["Mixed \/ Other",1.81],["Asian",29.46],["Jewish",1.11],["Latino",9.35]],"cast":[{"name":"White","y":66.67,"color":"#2b908f"},{"name":"Mixed \/ Other","y":11.11,"color":"#55BF3B"},{"name":"Asian","y":11.11,"color":"#90ee7e"},{"name":"Jewish","y":5.56,"color":"#ff0066"},{"name":"Latino","y":5.56,"color":"#eeaaee"}]}
Notes		The "Mixed / Other" categories overlap, and the 2 or more category was not an option until the 2000 Census. So for the combined column, all data will be taken from the "Other" column except for 2000, 2010, & 2020. For those, the average between the "2 or More Races" and "Some Other Race" will be used.	The US Census considers Pakistani, Sri Lankan, Indian, and Bangladeshi as Asians. At ZR we group Pakistanis with the Arab world. And we group Sri Lankan and Bangladeshi with India because 1.) they are all genetically similar as "South Asians and 2.) there are only a handful of actors from those regions in our database. However, you are viewing the "Asian Alone" category from the US Census when comparing it with Domestic representation. Also note that the 2000 and 2010 estimates include MultiRacial Asians not just "Asian Alone" estimates, and in some categories they include Native Hawaiians as "Asian." So there is a margin of error less than 1% and some inconsistencies with the grouping.	If there were any inconsistencies with percentages calculated using our Census data, the Jewish Data Bank percentages were the ones used. Also please review the Jewish Data Bank pdf to understand the difference between the "Core" Jewish population and the "Extended" / "Law of Return" Jewish populations. We are displaying the "Core" Jewish populations since that is what the Jewish Data Bank and Wikipedia articles chose. Also, the 1942 population estimates were used for the 1940 row. For the 2000 estimates, the "Core" definition was not used, so we used the 5.2 mil broader estimate instead of the 4.3 mil estimate since the 5.2 broader estimate seemed too drastic of a drop and the definitions weren't congruent with Core and Law of Return.	Many Latinos check "White" or "Other" on the Census. So this overlaps with "Mixed" race and "White." Also, when there was a discrepancy with the percentage listed on the Wikipedia table and our calculated numbers, the Wiki table's numbers were used. Likewise, when the population total was missing from the Census pdf, the estimate was calculated using the percentage from the Wiki table. NOTE: It turns out that the reason America doesn't have a separate "Latino" race is because the League of Latin American Citizens protested against discrimination towards Mexicans and got themselves to legally be considered "White." Check the research links in our Google Doc for more info.

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Drugs3/5

Dove

Sex1/5
Language1/5
Violence1/5
Drugs1/5
Nudity1/5
Other2/5

This film gets a 3.00/5 family friendly score

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RottenTomatoes:

51%57%

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A Christmas Prince (2017)

Summary: When a reporter goes undercover as a tutor to get the inside scoop on a playboy prince, she gets tangled in some royal intrigue and ends up finding love - but will she be able to keep up her lie?

Release Date: November 17, 2017

Genres: Comedy, Family, Romance

Director: Alex Zamm

Writers: Karen Schaler

, Nate Atkins

Runtime: 1 h 32 min

MPAA: TV-PG

Production: Motion Picture Corporation of America (MPCA)

Distributor: Netflix

Country: United States

Language: English

The average Metacritic audience score is 67%.

Metacritic User:

67/100

39%13%

39% of the Stars & Main Cast are Female.

13% of the Stars & Main Cast are "diverse."

White87.5%Asian6.25%Black6.25%

N/A

BechdeltestPassed!No MPAA or IMDb parental guidance data has been imported yet.
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Total found: 29

Other sources

Parental Guide: +add

This film gets a 2.75/5 family friendly score

MPAA Certification	No MPAA rating found yet. Add Family Friendly Rating?
IMDb Rating https://www.imdb.com/title/tt0380623/parentalguide last updated: 2024-10-26Update data
Commonsensemedia Rating https://www.commonsensemedia.org/movie-reviews/the-perfect-man last updated: 2024-10-26Update data
Message 0/5	Daughter lies to her mother throughout, learns a lesson.
Sex 3/5	Male bartender flirts with men. Jokes about breasts and 5th graders losing their virginity.
Consumerism 3/5	Focus on restaurant business; Mac computers figure prominently.
Dove Rating https://dove.org/review/4934-the-perfect-man/
Faith 0/5	None
Sex 2/5	Gay man acts stereotypical, portrayed in humorous manner; man tells woman about cheating on her with two women. Girl called a "skin virgin" because she has no piercings or tattoos.
Language 1/5	OMG-1; woman refers to a girl's breasts as "speed bumps."
Violence 0/5	None
Drugs 1/5	Couple instances of alcohol consumption.
Nudity 0/5	None
Other 0/5	None