Develop Reference

How do I get the sum of radio button values in Tkinter?

I have the following codes for my radio buttons and this is for my full menu programming project with tkinter:
from tkinter import *
from time import sleep
class SchoolCampMenuGUI:
def __init__(self,parent):
#------------------------------Layout Of Menu------------------------------------------#
Top=Frame(parent,bg="white")
Top.pack(side=TOP) #frame for title School Camp Menu
lblTitle=Label(Top,font=('New York Times',15),text="\t\tSchool Camp Menu\t\t\n(Please choose 1 breakfast,lunch and dinner and below 8700KJ) ")
lblTitle.pack() #setting fonts and size
f4=Label(borderwidth=3,relief=SUNKEN)
f4.pack(side=BOTTOM,fill=X)
f1=Label(borderwidth=3,relief=SUNKEN,bg="white")
f1.pack(side=LEFT) #first label for Breakfast
f2=Label(borderwidth=3,relief=SUNKEN,bg="white")
f2.pack(side=RIGHT) #second label for Lunch
f3=Label(borderwidth=3,relief=SUNKEN,bg="white")
f3.pack() #third label for dinner
def onclick1():
r.set(None)
q.set(None)
v.set(None)
def clear():
sleep(0.5)
f4.configure(text="")#define the definition of RESET button all value set to None to reselect choices and clears all calculations.
b1=Button(f4,text="RESET",width=8,bg="red",command=lambda:[onclick1(),clear()])#calling the combined function
b1.pack(side=RIGHT)
def total():
total=int(v.get())+int(r.get())+int(q.get())
f4.configure(text="Your Current Total Is: "+total+" KJs.")
r=StringVar()
v=StringVar()
q=StringVar()
r.set(0)
v.set(0)
q.set(0)
#--------------------------------------Lunch--------------------------------------#
lblMeal=Label(f3,text="Lunch",font=('arial',14,'bold'),bg="white")
lblMeal.pack()
rb1=Radiobutton(f3,text="Chicken Burgers",variable=r,font=('arial',12,'bold'),value=1180,bg="white",command=total)
rb1.pack(anchor=W)
rb2=Radiobutton(f3,text="Chicken Curry and Rice",variable=r,font=('arial',12,'bold'),value=1800,bg="white",command=total)
rb2.pack(anchor=W)
rb3=Radiobutton(f3,text="Teriyaki Chicken Sushi *Gluten Free",variable=r,font=('arial',12,'bold'),value=1730,fg="violet",command=total)
rb3.pack(anchor=W)
rb4=Radiobutton(f3,text="Caprese Panini *Gluten Free",variable=r,font=('arial',12,'bold'),value=2449,fg="violet",command=total)
rb4.pack(anchor=W)
rb5=Radiobutton(f3,text="Vegetable Risotto *Vegetarian",variable=r,font=('arial',12,'bold'),value=1432,fg="blue",command=total)
rb5.pack(anchor=W)
rb6=Radiobutton(f3,text="Gourmet Vegetable Pizza *Vegetarian",variable=r,font=('arial',12,'bold'),value=1463,fg="blue",command=total)
rb6.pack(anchor=W)
#----------------------------------Breakfast----------------------------------#
Meal=Label(f1,text="Breakfast",font=('arial',14,'bold'),bg="white")
Meal.pack()
rb7=Radiobutton(f1,text="Bacon and Egg Muffin",variable=v,font=('arial',12,'bold'),value=1240,bg="white",command=total)
rb7.pack(anchor=W)
rb8=Radiobutton(f1,text="Scrambled Eggs & Bake Beans",variable=v,font=('arial',12,'bold'),value=1533,bg="white",command=total)
rb8.pack(anchor=W)
rb9=Radiobutton(f1,text="2 Weet-Bix w/ milk",variable=v,font=('arial',12,'bold'),value=1110,bg="white",command=total)
rb9.pack(anchor=W)
rb10=Radiobutton(f1,text="Pancakes w/ syrup",variable=v,font=('arial',12,'bold'),value=2019,bg="white",command=total)
rb10.pack(anchor=W)
rb11=Radiobutton(f1,text="Bread with jam",variable=v,font=('arial',12,'bold'),value=491,bg="white",command=total)
rb11.pack(anchor=W)
rb12=Radiobutton(f1,text="Cinnamon Roll Doughnuts",variable=v,font=('arial',12,'bold'),value=1130,bg="white",command=total)
rb12.pack(anchor=W)
#----------------------------------dinner-----------------------------------#
Dinner=Label(f2,text="Dinner",font=('arial',14,'bold'),bg="white")
Dinner.pack()
rb13=Radiobutton(f2,text="Spaghetti Bolongnese",variable=q,font=('arial',12,'bold'),value=1523,bg="white",command=total)
rb13.pack(anchor=W)
rb14=Radiobutton(f2,text="Beef Burgers w/ Chips and Salad",variable=q,font=('arial',12,'bold'),value=3620,bg="white",command=total)
rb14.pack(anchor=W)
rb15=Radiobutton(f2,text="Meatball and Butter Bean Stew *Gluten Free",variable=q,font=('arial',12,'bold'),value=1820,fg="violet",command=total)
rb15.pack(anchor=W)
rb16=Radiobutton(f2,text="Roast Beef *Gluten Free",variable=q,font=('arial',12,'bold'),value=2280,fg="violet",command=total)
rb16.pack(anchor=W)
rb17=Radiobutton(f2,text="Creamy Broccoli Gnocchi *Vegetarian",variable=q,font=('arial',12,'bold'),value=2800,fg="blue",command=total)
rb17.pack(anchor=W)
rb18=Radiobutton(f2,text="Vegetable Wellington *Vegetarian",variable=q,font=('arial',12,'bold'),value=2270,fg="blue",command=total)
rb18.pack(anchor=W)
Is there a way to add all values together but not getting them? This is for my school menu project. Any help appreciated.
Note: The values are in KJs for food. So far I have all the values but they are just put there, e.g. 11801800, but not adding it up. I used r.get()+v.get() but they don't actually add the values up.

They do add up. Your problem is that r.get() returns a string, not an integer. First convert them, then sum up.
int(r.get()) + int(v.get())

Text to PDF Positioning Lines

I have a text file that i am reading and writing line by line into a PDF. The lines are out of position on the PDF because the FPDF library is left aligning all my lines. I am using the property set x so i can position each line to my liking. I am trying to reposition the headers until "RATE CODE CY" the would like all the data under the columns to come after. Then another header appears. I would like to align all the headers that come after the data. I know a for loop needs to be done to bring rest of the data...the issue is a header will come again and there is where i have to make the change with set_x property.
pdf = FPDF("L", "mm", "A4")
pdf.add_page()
pdf.set_font('arial', style='', size=10.0)
lines = file.readlines()
header8 = lines[7]
header8_1 = " ".join(lines[8].split()[:4])
header8_2 = " ".join(lines[8].split()[4:])
header9_1 = " ".join(lines[9].split()[:5])
header9_2 = " ".join(lines[9].split()[5:])
pdf.cell(ln=0, h=5.0, align='L', w=0, txt=header8_1, border=0)
pdf.set_x(125)
pdf.cell(ln=1, h=5.0, align='L', w=0, txt=header8_2, border=0)
pdf.cell(ln=0, h=5.0, align='L', w=0, txt=header9_1, border=0)
pdf.set_x(125)
pdfcell(ln=1, h=5.0, align='L', w=0, txt=header9_2, border=0)
Current PDF file:
READ SVC B MAXIMUM TOTAL DUE METER NO REMARKS
ACCOUNT # SERVICE ADDRESS CITY DATE DAY C KWH KWD AMOUNT
RATE CODE CY CUSTOMER NAME MAILING ADDRESS
----------------------------------------------------------------------------------------------------
11211-22222 12345 TEST HWY #86 TITUSVIL 10/12/19 29 C 1,444 189.01 ABC1234
GS-1 3 Home & ASSOC INC 1234 Miami HWY APT49
22222-33333 12345 TEST HWY #88 TITUSVIL 10/04/19 29 C 256 41.50 ABC1235
GS-1 3 DGN & ASSOC INC 1234 Miami HWY APT49
READ SVC B MAXIMUM TOTAL DUE METER NO REMARKS
ACCOUNT # SERVICE ADDRESS CITY DATE DAY C KWH KWD AMOUNT
RATE CODE CY CUSTOMER NAME MAILING ADDRESS
----------------------------------------------------------------------------------------------------
11211-22222 12345 TEST HWY #86 TITUSVIL 10/12/19 29 C 1,444 189.01 ABC1234
GS-1 3 Home & ASSOC INC 1234 Miami HWY APT49
22222-33333 12345 TEST HWY #88 TITUSVIL 10/04/19 29 C 256 41.50 ABC1235
GS-1 3 DGN & ASSOC INC 1234 Miami HWY APT49

Is there a way to properly convert data from lists to a CSV file using BeautifulSoup?

I am trying to create a webscraper for a website. The problem is that after the collected data is stored in a list, I'm not able to write this to a csv file properly. I have been stuck for ages with this problem and hopefully someone has an idea about how to fix this one!
The loop to get the data from the web pages:
import csv
from htmlrequest import simple_get
from htmlrequest import BeautifulSoup
# Define variables
listData = ['Companies', 'Locations', 'Descriptions']
plus = 15
max = 30
count = 0
# while loop to repeat process till max is reached
while (count <= max):
start = 'https://www.companiesintheuk.co.uk/find?q=Activities+of+sport+clubs&start=' + str(count) + '&s=h&t=SicCodeSearch&location=&sicCode=93120'
raw_html = simple_get(start)
soup = BeautifulSoup(raw_html, 'html.parser')
for i, div in enumerate(soup.find_all('div', class_="search_result_title")):
listData[0] = listData[0].strip() + div.text
for i, div2 in enumerate(soup.find_all('div', class_="searchAddress")):
listData[1] = listData[1].strip() + div2.text
# This is extra information
# for i, div3 in enumerate(soup.find_all('div', class_="searchSicCode")):
# listData[2] = listData[2].strip() + div3.text
count = count + plus
output example if printed:
Companies
(AMG) AGILITY MANAGEMENT GROUP LTD
(KLA) LIONS/LIONESS FOOTBALL TEAMS WORLD CUP LTD
(Dissolved)
1 SPORT ORGANISATION LIMITED
100UK LTD
1066 GYMNASTICS
1066 SPECIALS
10COACHING LIMITED
147 LOUNGE LTD
147 SNOOKER AND POOL CLUB (LEICESTER) LIMITED
Locations
ENGLAND, BH8 9PS
LONDON, EC2M 2PL
ENGLAND, LS7 3JB
ENGLAND, LE2 8FN
UNITED KINGDOM, N18 2QX
AVON, BS5 0JH
UNITED KINGDOM, WC2H 9JQ
UNITED KINGDOM, SE18 5SZ
UNITED KINGDOM, EC1V 2NX
I've tried to get it into a CSV file by using this code but I can't figure out how to properly format my output! Any suggestions are welcome.
# writing to csv
with open('test.csv', 'w') as csvfile:
write = csv.writer(csvfile, delimiter=',')
write.writerow(['Name','Location'])
write.writerow([listData[0],listData[1]])
print("Writing has been done!")
I want the code to be able to format it properly in the csv file to be able to import the two rows in a database.
This is the output when I write the data on 'test.csv'
which will result into this when opened up
The expected outcome would be something like this!

I'm not sure how it is improperly formatted, but maybe you just need to replace with open('test.csv', 'w') with with open('test.csv', 'w+', newline='')
I've combined your code (taking out htmlrequests for requests and bs4 modules and also not using listData, but instead creating my own lists. I've left your lists but they do nothing):
import csv
import bs4
import requests
# Define variables
listData = ['Companies', 'Locations', 'Descriptions']
company_list = []
locations_list = []
plus = 15
max = 30
count = 0
# while loop to repeat process till max is reached
while count <= max:
start = 'https://www.companiesintheuk.co.uk/find?q=Activities+of+sport+clubs&start={}&s=h&t=SicCodeSearch&location=&sicCode=93120'.format(count)
res = requests.get(start)
soup = bs4.BeautifulSoup(res.text, 'html.parser')
for i, div in enumerate(soup.find_all('div', class_="search_result_title")):
listData[0] = listData[0].strip() + div.text
company_list.append(div.text.strip())
for i, div2 in enumerate(soup.find_all('div', class_="searchAddress")):
listData[1] = listData[1].strip() + div2.text
locations_list.append(div2.text.strip())
# This is extra information
# for i, div3 in enumerate(soup.find_all('div', class_="searchSicCode")):
# listData[2] = listData[2].strip() + div3.text
count = count + plus
if len(company_list) == len(locations_list):
with open('test.csv', 'w+', newline='') as csvfile:
writer = csv.writer(csvfile, delimiter=',')
writer.writerow(['Name', 'Location'])
for i in range(len(company_list)):
writer.writerow([company_list[i], locations_list[i]])
Which generates a csv file like:
Name,Location
(AMG) AGILITY MANAGEMENT GROUP LTD,"UNITED KINGDOM, M6 6DE"
"(KLA) LIONS/LIONESS FOOTBALL TEAMS WORLD CUP LTD
(Dissolved)","ENGLAND, BD1 2PX"
0161 STUDIOS LTD,"UNITED KINGDOM, HD6 3AX"
1 CLICK SPORTS MANAGEMENT LIMITED,"ENGLAND, E10 5PW"
1 SPORT ORGANISATION LIMITED,"UNITED KINGDOM, CR2 6NF"
100UK LTD,"UNITED KINGDOM, BN14 9EJ"
1066 GYMNASTICS,"EAST SUSSEX, BN21 4PT"
1066 SPECIALS,"EAST SUSSEX, TN40 1HE"
10COACHING LIMITED,"UNITED KINGDOM, SW6 6LR"
10IS ACADEMY LIMITED,"ENGLAND, PE15 9PS"
"10TH MAN LIMITED
(Dissolved)","GLASGOW, G3 6AN"
12 GAUGE EAST MANCHESTER COMMUNITY MMA LTD,"ENGLAND, OL9 8DQ"
121 MAKING WAVES LIMITED,"TYNE AND WEAR, NE30 1AR"
121 WAVES LTD,"TYNE AND WEAR, NE30 1AR"
1-2-KICK LTD,"ENGLAND, BH8 9PS"
"147 HAVANA LIMITED
(Liquidation)","LONDON, EC2M 2PL"
147 LOUNGE LTD,"ENGLAND, LS7 3JB"
147 SNOOKER AND POOL CLUB (LEICESTER) LIMITED,"ENGLAND, LE2 8FN"
1ACTIVE LTD,"UNITED KINGDOM, N18 2QX"
1ON1 KING LTD,"AVON, BS5 0JH"
1PUTT LTD,"UNITED KINGDOM, WC2H 9JQ"
1ST SPORTS LTD,"UNITED KINGDOM, SE18 5SZ"
2 BRO PRO EVENTS LTD,"UNITED KINGDOM, EC1V 2NX"
2 SPLASH SWIM SCHOOL LTD,"ENGLAND, B36 0EY"
2 STEPPERS C.I.C.,"SURREY, CR0 6BX"
2017 MOTO LIMITED,"UNITED KINGDOM, ME2 4NW"
2020 ARCHERY LTD,"LONDON, SE16 6SS"
21 LEISURE LIMITED,"LONDON, EC4M 7WS"
261 FEARLESS CLUB UNITED KINGDOM CIC,"LANCASHIRE, LA2 8RF"
2AIM4 LIMITED,"HERTFORDSHIRE, SG2 0JD"
2POINT4 FM LTD,"LONDON, NW10 8LW"
3 LIONS SCHOOL OF SPORT LTD,"BRISTOL, BS20 8BU"
3 PT LTD,"ANTRIM, BT40 2FB"
3 PUTT LIFE LTD,"UNITED KINGDOM, LU3 2DP"
3 THIRTY SEVEN LTD,"KENT, DA9 9RS"
3:30 SOCCER SCHOOL LTD,"UNITED KINGDOM, EH6 7JB"
30 MINUTE WORKOUT (LLANISHEN) LTD,"PONTYCLUN, CF72 9UA"
321 RELAX LTD,"MID GLAMORGAN, CF83 3HL"
360 MOTOR RACING CLUB LTD,"HALSTEAD, CO9 2ET"
3LIONSATHLETICS LIMITED,"ENGLAND, S3 8DB"
3S SWIM ROMFORD LTD,"UNITED KINGDOM, DA9 9DR"
3XL EVENT MANAGEMENT LIMITED,"KENT, BR3 4NW"
3XL MOTORSPORT MANAGEMENT LIMITED,"KENT, BR3 4NW"
4 CORNER FOOTBALL LTD,"BROMLEY, BR1 5DD"
4 PRO LTD,"UNITED KINGDOM, FY5 5HT"
Which seems fine to me, but your post was very unclear about how you expected it to be formatted so I really have no idea

Cannot find containers with BeautifulSoup

I'm trying to make a very simple script which will scrape the top 50 sounds on SoundCloud, add them to a dictionary, then save them to a file. When I try to find all the items I get none back (as seen by a debug message I put in). I was wondering what I did wrong and if anyone could help me figure it out, thanks!
from bs4 import BeautifulSoup as Bs
import requests
website = "https://soundcloud.com/charts/top?genre=rock&country=all-countries"
session = requests.session()
def get_songs():
songs = {}
response = session.get(website)
soup = Bs(response.text, "html.parser")
print(soup.title.text)
containers = soup.find_all("li", {"class": "chartTracks__item"})
if len(containers) == 0:
print("Could not find any containers")
for element in containers:
chart_track_div = element.div("chartTrack")
details_div = chart_track_div.div("chartTrack__details")
artist = details_div.div("chartTrack__username").text
song_name = details_div.div("chartTrack__title").text
songs[song_name] = artist
return songs
def create_file(songs_dictionary):
# Just printing out key&value for now
for key, value in songs_dictionary:
print("Song: " + key)
print("Artist: " + value)
toSave = get_songs()
create_file(toSave)
This is what I get after I run it: http://prntscr.com/m78dfr

A few things that I needed to change.
First, it is a dynamic page, so if you want to grab that info into containers using the soup.find_all("li", {"class": "chartTracks__item"}), you'd have to render the page first, either with Selenium or requests-html, then do the .find_all
However, the data you are pulling is found in the html source, but under different tags, so I just went ahead and grabbed the info you were grabbing.
Second, I didn't know if this was exactly your intention, but you were saving artist as the user name, and song as the title. Unfortunately, each of those songs have slightly different formats listed by soundcloud. If you were to truly grab strictly the artist - title, it'll require some filtering and re-working with the strings. But I kept it as you had it, and you can choose what to do from there.
Third, you didn't pass any parameters into your first function:
def get_songs():
songs = {}
response = session.get(website)
So it's not going to do anything, since it's referring to website, but it's never passed in. So I changed that to:
def get_songs(website):
songs = {}
response = session.get(website)
Fourth, you can't iterate through the dictionary with for key, value in songs_dictionary:. It's asking for 2 values, but can only unpack 1. To do what you are trying, you have 2 options:
for key, value in songs_dictionary.items():
print("Song: " + key)
print("Artist: " + value)
or
for key in songs_dictionary:
print("Song: " + key)
print("Artist: " + songs_dictionary[key])
I think that's all that I found, but full code here:
from bs4 import BeautifulSoup as Bs
import requests
website = "https://soundcloud.com/charts/top?genre=rock&country=all-countries"
session = requests.session()
def get_songs(website):
songs = {}
response = session.get(website)
soup = Bs(response.text, "html.parser")
print(soup.title.text)
containers = soup.find_all("section", {"class": "sounds"})
songs_ranks = containers[0].find_all('li')
if len(songs_ranks) == 0:
print("Could not find any containers")
for element in songs_ranks:
artist = element.find_all('a')[1].text
song_name = element.find('a', {'itemprop':'url'}).text
songs[song_name] = artist
return songs
def create_file(songs_dictionary):
# Just printing out key&value for now
for key, value in songs_dictionary.items():
print("Song: " + key)
print("Artist: " + value)
toSave = get_songs(website)
create_file(toSave)
output:
Song: KING
Artist: XXXTENTACION
Song: Queen - Bohemian Rhapsody
Artist: rizky.rilos
Song: áá
©á·áá
¡á¯ (Brit Rock Remix For áá
¡áá
­áá
¢áá
®á¨áá
¦) - BTS
Artist: BTS
Song: XXXTENTACION - NUMB
Artist: conrad foxx
Song: In The End
Artist: LINKIN_PARK
Song: I Write Sins Not Tragedies
Artist: Panic! At The Disco
Song: Man Upon The Hill
Artist: Stars and Rabbit
Song: Nirvana - Smells like teen spirit
Artist: Rocio Araujo
Song: Nickelback - Rockstar
Artist: Roadrunner USA
Song: xxxtentacion - valentine
Artist: ó
Song: Zombie
Artist: Bad Wolves
Song: Marília Mendonça â Amante Não Tem Lar
Artist: Sertanejo Repost
Song: sleep thru ur alarms
Artist: Lontalius
Song: Angel With A Shotgun
Artist: NightCore
Song: Nightcore - My Demons
Artist: NightCore
Song: Armada - Harusnya Aku
Artist: DJCantik.com
Song: Dont Stop Me Now - Queen
Artist: Zinay Hernandez
Song: Sing To Me feat. Karen O
Artist: waltermartinmusic
Song: Everytime
Artist: boy pablo
Song: Tongue Tied - Grouplove
Artist: Atlantic Records
Song: For Beginners
Artist: M. Ward
Song: This Is Gospel
Artist: Panic! At The Disco
Song: Skillet - Hero
Artist: Warner Music Nashville
Song: Wonderwall - Oasis
Artist: Florian.N.
Song: High Hopes - Panic! At the disco
Artist: IrisDH
Song: Another One Bites The Dust (Remastered 2011)
Artist: Queen
Song: Panic! At The Disco - Bohemian Rhapsody (from Suicide Squad: The Album) (Audio)
Artist: Panic! At The Disco
Song: Killer Queen (Remastered 2011)
Artist: Queen
Song: Blue Bird-Naruto Shippuden 3rd Opening Theme
Artist: flaviogomes23
Song: Virzha-tentang rindu mp3
Artist: Arjuna Bilal
Song: Tipe-X - Mawar Hitam
Artist: Tora Loaadiing
Song: Lolot - Galungan Lan Kuningan
Artist: I Made Suwita
Song: Red Hot Chili peppers - Californication
Artist: arthyum
Song: Nickelback - How You Remind Me
Artist: Roadrunner USA
Song: 2004 Green Day "Boulevard of broken dreams" Vinyl rip
Artist: Collin Codeïne
Song: Zé Neto E Cristiano - Seu Polícia (DVD Zé Neto E Cristiano Ao Vivo Em São José Do Rio Preto)
Artist: Sertanejo universitario (2018)
Song: Pink Floyd - Wish You Were Here
Artist: Ulviyya Ali
Song: Apocalypse
Artist: Cigarettes After Sex
Song: Linkin Park - In The End
Artist: ALLMusic
Song: Come As You Are
Artist: Nirvana
Song: Avenged Sevenfold - Dear God
Artist: Malik Hamza Sajjad
Song: Kaleo - Way Down We Go
Artist: AminAshkan
Song: Ya Qurban, Khumariyaan, Coke Studio Season 11, Episode 7
Artist: CokeStudio
Song: IDOL (Korean classical music ver.)_2018MMA VER.
Artist: Atm Soo
Song: Gym Best Music For Workout vol 2
Artist: Gym Best MusicFor Workout
Song: Do I Wanna Know? - Arctic Monkeys
Artist: Teenage Kicks.
Song: Um44k - Nossa Música âªâ«
Artist: Portal do Rap
Song: Nanatsu No Taizai (The Seven Deadly Sins) Anime OST - Perfect Time (POWER SONG)
Artist: cobritsa
Song: Tipe X - Genit
Artist: Hilmie CintaSederhana
Song: Kodaline - All I want - Acoustic Performance
Artist: Andy Wells 1

BeautifulSoup, extract a table (from poorly designed site) and turn it into a CSV

I'm trying to extract this table in whole - any tips? I've tried the following code 8 different ways, with no avail. Thank you!
data = []
table = soup.find_all("tbody")
rows = table.find_all("tr")
for row in rows:
cols = row.find_all("td")
cols = [ele.text.strip() for ele in cols]
data.append([ele for ele in cols if ele])

Code:
import requests
from bs4 import BeautifulSoup
html = requests.get('http://www.boxofficemojo.com/alltime/adjusted.htm').text
soup = BeautifulSoup(html, 'html.parser')
table = soup.find('table', cellspacing='1')
f = open('data.csv','w')
for row in table.find_all('tr'):
print(''.join(row.findAll(text=True)).replace('\n', '|'))
f.write(''.join(row.findAll(text=True)).replace('\n', '|') + '\n')
f.close()
Output:
1|Gone with the Wind|MGM|$1,854,769,700|$198,676,459|1939^|
2|Star Wars|Fox|$1,635,137,900|$460,998,007|1977^|
3|The Sound of Music|Fox|$1,307,373,200|$158,671,368|1965|
4|E.T.: The Extra-Terrestrial|Uni.|$1,302,222,800|$435,110,554|1982^|
5|Titanic|Par.|$1,244,347,300|$659,363,944|1997^|
6|The Ten Commandments|Par.|$1,202,580,000|$65,500,000|1956|
7|Jaws|Uni.|$1,175,763,500|$260,000,000|1975|
8|Doctor Zhivago|MGM|$1,139,563,500|$111,721,910|1965|
9|The Exorcist|WB|$1,015,300,400|$232,906,145|1973^|
10|Snow White and the Seven Dwarfs|Dis.|$1,000,620,000|$184,925,486|1937^|
11|Star Wars: The Force Awakens|BV|$992,496,600|$936,662,225|2015|
12|101 Dalmatians|Dis.|$917,240,400|$144,880,014|1961^|
13|The Empire Strikes Back|Fox|$901,298,200|$290,475,067|1980^|
14|Ben-Hur|MGM|$899,640,000|$74,000,000|1959|
15|Avatar|Fox|$893,301,900|$760,507,625|2009^|
16|Return of the Jedi|Fox|$863,465,400|$309,306,177|1983^|
17|Jurassic Park|Uni.|$843,843,500|$402,453,882|1993^|
18|Star Wars: Episode I - The Phantom Menace|Fox|$829,064,800|$474,544,677|1999^|
19|The Lion King|BV|$818,364,200|$422,783,777|1994^|
20|The Sting|Uni.|$818,331,400|$156,000,000|1973|
21|Raiders of the Lost Ark|Par.|$812,675,900|$248,159,971|1981^|
22|The Graduate|AVCO|$785,595,300|$104,945,305|1967^|
23|Fantasia|Dis.|$762,339,100|$76,408,097|1941^|
24|Jurassic World|Uni.|$725,671,700|$652,270,625|2015|
25|The Godfather|Par.|$724,509,200|$134,966,411|1972^|
26|Forrest Gump|Par.|$721,682,300|$330,252,182|1994^|
27|Mary Poppins|Dis.|$717,709,100|$102,272,727|1964^|
28|Grease|Par.|$706,577,200|$188,755,690|1978^|
29|Marvel's The Avengers|BV|$705,769,500|$623,357,910|2012|
30|Thunderball|UA|$686,664,000|$63,595,658|1965|
31|The Dark Knight|WB|$683,575,000|$534,858,444|2008^|
32|The Jungle Book|Dis.|$676,381,600|$141,843,612|1967^|
33|Sleeping Beauty|Dis.|$667,166,200|$51,600,000|1959^|
34|Ghostbusters|Col.|$653,374,800|$242,212,467|1984^|
35|Shrek 2|DW|$652,247,500|$441,226,247|2004|
36|Butch Cassidy and the Sundance Kid|Fox|$647,721,100|$102,308,889|1969|
37|Love Story|Par.|$642,583,000|$106,397,186|1970|
38|Spider-Man|Sony|$637,870,000|$403,706,375|2002|
39|Independence Day|Fox|$635,888,300|$306,169,268|1996^|
40|Home Alone|Fox|$621,799,900|$285,761,243|1990|
41|Pinocchio|Dis.|$618,762,600|$84,254,167|1940^|
42|Cleopatra (1963)|Fox|$616,744,200|$57,777,778|1963|
43|Beverly Hills Cop|Par.|$616,437,200|$234,760,478|1984|
44|Star Wars: The Last Jedi|BV|$615,738,300|$615,738,279|2017|
45|Goldfinger|UA|$608,634,000|$51,081,062|1964|
46|Airport|Uni.|$606,901,600|$100,489,151|1970|
47|American Graffiti|Uni.|$603,257,100|$115,000,000|1973|
48|The Robe|Fox|$600,872,700|$36,000,000|1953|
49|Pirates of the Caribbean: Dead Man's Chest|BV|$593,288,400|$423,315,812|2006|
50|Around the World in 80 Days|UA|$593,169,200|$42,000,000|1956|
51|Bambi|RKO|$584,880,300|$102,247,150|1942^|
52|Blazing Saddles|WB|$580,539,700|$119,601,481|1974^|
53|Batman|WB|$577,923,400|$251,188,924|1989|
54|The Bells of St. Mary's|RKO|$576,000,000|$21,333,333|1945|
55|The Lord of the Rings: The Return of the King|NL|$565,852,400|$377,845,905|2003^|
56|Finding Nemo|BV|$565,364,200|$380,843,261|2003^|
57|The Towering Inferno|Fox|$563,428,600|$116,000,000|1974|
58|Rogue One: A Star Wars Story|BV|$554,854,100|$532,177,324|2016|
59|Cinderella (1950)|Dis.|$553,567,100|$93,141,149|1950^|
60|Spider-Man 2|Sony|$552,257,300|$373,585,825|2004|
61|My Fair Lady|WB|$550,800,000|$72,000,000|1964|
62|The Greatest Show on Earth|Par.|$550,800,000|$36,000,000|1952|
63|National Lampoon's Animal House|Uni.|$549,792,700|$141,600,000|1978^|
64|The Passion of the Christ|NM|$548,090,400|$370,782,930|2004^|
65|Star Wars: Episode III - Revenge of the Sith|Fox|$544,599,700|$380,270,577|2005^|
66|Back to the Future|Uni.|$542,085,000|$210,609,762|1985|
67|The Lord of the Rings: The Two Towers|NL|$529,918,100|$342,551,365|2002^|
68|The Dark Knight Rises|WB|$528,601,000|$448,139,099|2012|
69|The Sixth Sense|BV|$528,576,400|$293,506,292|1999|
70|Superman|WB|$526,547,600|$134,218,018|1978|
71|Tootsie|Col.|$522,378,200|$177,200,000|1982|
72|Smokey and the Bandit|Uni.|$521,726,300|$126,737,428|1977|
73|Beauty and the Beast (2017)|BV|$521,407,600|$504,014,165|2017|
74|Finding Dory|BV|$515,531,300|$486,295,561|2016|
75|West Side Story|MGM|$513,807,200|$43,656,822|1961|
76|Close Encounters of the Third Kind|Col.|$513,370,800|$135,189,114|1977^|
77|Harry Potter and the Sorcerer's Stone|WB|$513,281,200|$317,575,550|2001|
78|Lady and the Tramp|Dis.|$511,646,200|$93,602,326|1955^|
79|Lawrence of Arabia|Col.|$508,421,000|$44,824,144|1962^|
80|The Rocky Horror Picture Show|Fox|$505,537,300|$112,892,319|1975|
81|Rocky|UA|$505,267,000|$117,235,147|1976|
82|The Best Years of Our Lives|RKO|$504,900,000|$23,650,000|1946|
83|The Poseidon Adventure|Fox|$504,000,000|$84,563,118|1972|
84|The Lord of the Rings: The Fellowship of the Ring|NL|$503,057,400|$315,544,750|2001^|
85|Twister|WB|$502,037,000|$241,721,524|1996|
86|Men in Black|Sony|$501,381,100|$250,690,539|1997|
87|The Bridge on the River Kwai|Col.|$499,392,000|$27,200,000|1957|
88|Transformers: Revenge of the Fallen|P/DW|$494,810,500|$402,111,870|2009|
89|It's a Mad, Mad, Mad, Mad World|MGM|$494,576,300|$46,332,858|1963|
90|Swiss Family Robinson|Dis.|$493,957,400|$40,356,000|1960|
91|One Flew Over the Cuckoo's Nest|UA|$492,831,600|$108,981,275|1975|
92|M.A.S.H.|Fox|$492,821,000|$81,600,000|1970|
93|Indiana Jones and the Temple of Doom|Par.|$491,431,300|$179,870,271|1984|
94|Avengers: Age of Ultron|BV|$491,377,100|$459,005,868|2015|
95|Star Wars: Episode II - Attack of the Clones|Fox|$490,840,600|$310,676,740|2002^|
96|Toy Story 3|BV|$489,656,000|$415,004,880|2010|
97|Mrs. Doubtfire|Fox|$483,642,600|$219,195,243|1993|
98|Aladdin|BV|$481,420,700|$217,350,219|1992|
99|Ghost|Par.|$472,450,700|$217,631,306|1990|
100|The Hunger Games: Catching Fire|LGF|$469,232,400|$424,668,047|2013|
101|Duel in the Sun|Selz.|$468,367,300|$20,408,163|1946|
102|The Hunger Games|LGF|$466,924,700|$408,010,692|2012|
103|Pirates of the Caribbean: The Curse of the Black Pearl|BV|$464,956,900|$305,413,918|2003|
104|House of Wax|WB|$463,883,000|$23,750,000|1953|
105|Rear Window|Par.|$462,256,500|$36,764,313|1954^|
106|The Lost World: Jurassic Park|Uni.|$458,173,400|$229,086,679|1997|
107|Indiana Jones and the Last Crusade|Par.|$453,643,400|$197,171,806|1989|
108|Monsters, Inc.|BV|$453,061,600|$289,916,256|2001^|
109|Frozen|BV|$450,196,500|$400,738,009|2013|
110|Spider-Man 3|Sony|$449,033,200|$336,530,303|2007|
111|Iron Man 3|BV|$448,060,700|$409,013,994|2013|
112|Terminator 2: Judgment Day|TriS|$447,732,400|$205,881,154|1991^|
113|Sergeant York|WB|$441,770,900|$16,361,885|1941|
114|How the Grinch Stole Christmas|Uni.|$441,620,600|$260,044,825|2000|
115|Top Gun|Par.|$440,917,900|$179,800,601|1986^|
116|Harry Potter and the Deathly Hallows Part 2|WB|$440,547,300|$381,011,219|2011|
117|Toy Story 2|BV|$439,139,300|$245,852,179|1999^|
118|Shrek|DW|$434,128,000|$267,665,011|2001|
119|Shrek the Third|P/DW|$430,606,000|$322,719,944|2007|
120|Despicable Me 2|Uni.|$430,487,800|$368,061,265|2013|
121|Captain America: Civil War|BV|$429,213,000|$408,084,349|2016|
122|The Matrix Reloaded|WB|$428,668,600|$281,576,461|2003|
123|Transformers|P/DW|$425,970,900|$319,246,193|2007|
124|Crocodile Dundee|Par.|$424,138,600|$174,803,506|1986|
125|Wonder Woman|WB|$423,340,500|$412,563,408|2017|
126|The Four Horsemen of the Apocalypse|MPC|$421,530,600|$9,183,673|1921|
127|Saving Private Ryan|DW|$419,958,100|$216,540,909|1998|
128|Young Frankenstein|Fox|$419,041,900|$86,273,333|1974|
129|Peter Pan|Dis.|$418,824,000|$87,404,651|1953^|
130|Gremlins|WB|$417,526,300|$153,083,102|1984^|
131|Beauty and the Beast|BV|$416,438,900|$218,967,620|1991^|
132|The Chronicles of Narnia: The Lion, the Witch and the Wardrobe|BV|$414,717,600|$291,710,957|2005|
133|Harry Potter and the Goblet of Fire|WB|$414,709,000|$290,013,036|2005|
134|Pirates of the Caribbean: At World's End|BV|$412,860,400|$309,420,425|2007|
135|Harry Potter and the Chamber of Secrets|WB|$412,327,800|$261,988,482|2002|
136|The Fugitive|WB|$407,567,300|$183,875,760|1993|
137|The Caine Mutiny|Col.|$407,479,600|$21,750,000|1954|
138|Iron Man|Par.|$407,095,000|$318,412,101|2008|
139|Transformers: Dark of the Moon|P/DW|$406,315,000|$352,390,543|2011|
140|Meet the Fockers|Uni.|$405,508,300|$279,261,160|2004|
141|Indiana Jones and the Kingdom of the Crystal Skull|Par.|$405,430,100|$317,101,119|2008|
142|Toy Story|BV|$402,711,200|$191,796,233|1995^|
143|Dances with Wolves|Orion|$401,159,500|$184,208,848|1990|
144|An Officer and a Gentleman|Par.|$400,769,900|$129,795,554|1982|
145|Guardians of the Galaxy Vol. 2|BV|$399,848,900|$389,813,101|2017|
146|2001: A Space Odyssey|MGM|$397,829,200|$56,954,992|1968^|
147|Rain Man|MGM|$397,417,800|$172,825,435|1988|
148|The Secret Life of Pets|Uni.|$397,253,600|$368,384,330|2016|
149|Guess Who's Coming to Dinner|Col.|$397,099,200|$56,666,667|1967|
150|Inside Out|BV|$396,452,900|$356,461,711|2015|
151|American Sniper|WB|$395,474,400|$350,126,372|2014|
152|Kramer Vs. Kramer|Col.|$394,925,800|$106,260,000|1979|
153|Armageddon|BV|$394,560,300|$201,578,182|1998|
154|Psycho|Uni.|$391,680,100|$32,000,000|1960|
155|Rocky III|UA|$390,271,700|$125,049,125|1982^|
156|Harry Potter and the Order of the Phoenix|WB|$389,622,600|$292,004,738|2007|
157|Rambo: First Blood Part II|TriS|$388,961,600|$150,415,432|1985|
158|Batman Forever|WB|$388,369,100|$184,031,112|1995|
159|Deadpool|Fox|$388,249,600|$363,070,709|2016|
160|Pretty Woman|BV|$387,179,600|$178,406,268|1990|
161|Earthquake|Uni.|$386,952,300|$79,666,653|1974|
162|Alice in Wonderland (2010)|BV|$385,896,200|$334,191,110|2010|
163|The Incredibles|BV|$385,835,000|$261,441,092|2004|
164|Cast Away|Fox|$384,588,700|$233,632,142|2000|
165|Home Alone 2: Lost in New York|Fox|$384,179,200|$173,585,516|1992|
166|The Jungle Book (2016)|BV|$382,904,500|$364,001,123|2016|
167|Three Men and a Baby|BV|$382,840,700|$167,780,960|1987|
168|My Big Fat Greek Wedding|IFC|$380,230,800|$241,438,208|2002|
169|Guardians of the Galaxy|BV|$378,010,100|$333,176,600|2014|
170|Furious 7|Uni.|$376,598,400|$353,007,020|2015|
171|Mission: Impossible|Par.|$375,885,400|$180,981,856|1996|
172|The Hunger Games: Mockingjay - Part 1|LGF|$373,872,900|$337,135,885|2014|
173|Minions|Uni.|$373,756,800|$336,045,770|2015|
174|Saturday Night Fever|Par.|$372,751,500|$94,213,184|1977|
175|On Golden Pond|Uni.|$372,564,100|$119,285,432|1981|
176|Austin Powers: The Spy Who Shagged Me|NL|$372,332,300|$206,040,086|1999|
177|Harry Potter and the Half-Blood Prince|WB|$371,524,900|$301,959,197|2009|
178|Bruce Almighty|Uni.|$369,680,400|$242,829,261|2003|
179|Harry Potter and the Prisoner of Azkaban|WB|$368,886,800|$249,541,069|2004|
180|Funny Girl|Col.|$367,562,200|$52,223,306|1968^|
181|Mission: Impossible II|Par.|$366,876,200|$215,409,889|2000|
182|Rush Hour 2|NL|$366,817,700|$226,164,286|2001|
183|Apollo 13|Uni.|$365,894,000|$173,837,933|1995^|
184|Patton|Fox|$365,718,000|$61,749,765|1970|
185|Fatal Attraction|Par.|$364,269,300|$156,645,693|1987|
186|Zootopia|BV|$363,584,000|$341,268,248|2016|
187|Liar Liar|Uni.|$362,821,200|$181,410,615|1997|
188|Robin Hood: Prince of Thieves|WB|$360,863,200|$165,493,908|1991|
189|Beverly Hills Cop II|Par.|$360,778,800|$153,665,036|1987|
190|Iron Man 2|Par.|$360,772,100|$312,433,331|2010|
191|Up|BV|$360,533,300|$293,004,164|2009|
192|Batman Returns|WB|$360,191,600|$162,831,698|1992|
193|Signs|BV|$360,164,800|$227,966,634|2002|
194|Jumanji: Welcome to the Jungle|Sony|$358,036,900|$358,036,871|2017|
195|The Twilight Saga: Eclipse|Sum.|$357,823,200|$300,531,751|2010|
196|Superman II|WB|$357,246,300|$108,185,706|1981|
197|The Twilight Saga: New Moon|Sum.|$357,194,500|$296,623,634|2009|
198|What's Up, Doc?|WB|$356,400,000|$66,000,000|1972|
199|9 to 5|Fox|$352,493,200|$103,290,500|1980|
200|Batman v Superman: Dawn of Justice|WB|$351,232,600|$330,360,194|2016|
201|The Firm|Par.|$351,120,300|$158,348,367|1993|
202|Suicide Squad|WB|$350,483,800|$325,100,054|2016|
203|Who Framed Roger Rabbit|BV|$349,448,400|$156,452,370|1988|
204|Inception|WB|$348,133,400|$292,576,195|2010|
205|Skyfall|Sony|$347,389,600|$304,360,277|2012|
206|The Hobbit: An Unexpected Journey|WB (NL)|$347,313,400|$303,003,568|2012|
207|Porky's|Fox|$346,289,600|$111,289,673|1982^|
208|Air Force One|Sony|$345,835,200|$172,956,409|1997|
209|Stir Crazy|Col.|$345,700,400|$101,300,000|1980|
210|A Star Is Born (1976)|WB|$344,788,700|$80,000,000|1976|
211|There's Something About Mary|Fox|$344,053,800|$176,484,651|1998|
212|Spider-Man: Homecoming|Sony|$343,499,000|$334,201,140|2017|
213|Cars|BV|$342,088,800|$244,082,982|2006|
214|The Hangover|WB|$341,182,900|$277,322,503|2009|
215|Lethal Weapon 2|WB|$340,501,700|$147,253,986|1989|
216|Night at the Museum|Fox|$340,041,900|$250,863,268|2006|
217|Harry Potter and the Deathly Hallows Part 1|WB|$339,560,700|$295,983,305|2010|
218|I Am Legend|WB|$337,126,200|$256,393,010|2007|
219|Austin Powers in Goldmember|NL|$337,033,800|$213,307,889|2002|
220|War of the Worlds|Par.|$335,521,600|$234,280,354|2005|
221|It|WB (NL)|$335,148,900|$327,481,748|2017|
222|Every Which Way But Loose|WB|$334,232,400|$85,196,485|1978|
223|The Twilight Saga: Breaking Dawn Part 2|LG/S|$333,495,700|$292,324,737|2012|
224|The Love Bug|Dis.|$331,410,900|$51,264,000|1969|
225|The Twilight Saga: Breaking Dawn Part 1|Sum.|$329,680,800|$281,287,133|2011|
226|You Only Live Twice|UA|$329,598,600|$43,084,787|1967|
227|X-Men: The Last Stand|Fox|$328,465,300|$234,362,462|2006|
228|The Mummy Returns|Uni.|$327,657,500|$202,019,785|2001|
229|X2: X-Men United|Fox|$327,236,800|$214,949,694|2003|
230|Platoon|Orion|$325,302,500|$138,530,565|1986|
231|Rocky IV|UA|$324,855,400|$127,873,716|1985|
232|Pearl Harbor|BV|$322,017,800|$198,542,554|2001|
233|True Lies|Fox|$321,261,400|$146,282,411|1994|
234|Heaven Can Wait (1978)|Par.|$320,281,100|$81,640,278|1978|
235|Lethal Weapon 3|WB|$320,153,100|$144,731,527|1992|
236|Look Who's Talking|TriS|$319,854,500|$140,088,813|1989|
237|Gladiator|DW|$319,592,900|$187,705,427|2000|
238|Man of Steel|WB|$318,830,300|$291,045,518|2013|
239|Jaws 2|Uni.|$318,717,900|$81,766,007|1978^|
240|Star Trek|Par.|$317,150,800|$257,730,019|2009|
241|The Santa Clause|BV|$316,776,400|$144,833,357|1994|
242|The Amityville Horror|AIP|$316,113,900|$86,432,000|1979|
243|Thor: Ragnarok|BV|$314,143,200|$314,143,225|2017|
244|The Waterboy|BV|$314,053,600|$161,491,646|1998|
245|A Bug's Life|BV|$313,363,900|$162,798,565|1998|
246|A Few Good Men|Col.|$313,069,200|$141,340,178|1992|
247|The Odd Couple|Par.|$312,030,500|$44,527,234|1968|
248|Rocky II|UA|$311,542,700|$85,182,160|1979|
249|Jerry Maguire|Sony|$311,468,800|$153,952,592|1996|
250|The Perfect Storm|WB|$311,027,300|$182,618,434|2000|
251|King Kong|Uni.|$310,014,100|$218,080,025|2005|
252|The Matrix|WB|$309,879,100|$171,479,930|1999|
253|The Amazing Spider-Man|Sony|$309,163,500|$262,030,663|2012|
254|Tarzan|BV|$309,122,000|$171,091,819|1999|
255|Sister Act|BV|$308,813,300|$139,605,150|1992|
256|Hooper|WB|$306,000,000|$78,000,000|1978|
257|The Blind Side|WB|$305,701,600|$255,959,475|2009|
258|The Da Vinci Code|Sony|$304,882,700|$217,536,138|2006|
259|Monsters University|BV|$304,779,900|$268,492,764|2013|
260|All the President's Men|WB|$304,276,100|$70,600,000|1976|
261|What Women Want|Par.|$303,763,400|$182,811,707|2000|
262|The Bourne Ultimatum|Uni.|$303,515,200|$227,471,070|2007|
263|Gravity|WB|$302,369,300|$274,092,705|2013|
264|Honey, I Shrunk the Kids|BV|$302,279,100|$130,724,172|1989|
265|Terms of Endearment|Par.|$301,824,600|$108,423,489|1983|
266|Men in Black II|Sony|$300,868,300|$190,418,803|2002|
267|Star Trek: The Motion Picture|Par.|$300,849,700|$82,258,456|1979|
268|Wedding Crashers|NL|$299,683,200|$209,255,921|2005|
269|Despicable Me|Uni.|$299,217,100|$251,513,985|2010|
270|Pocahontas|BV|$298,782,100|$141,579,773|1995|
271|Arthur|WB|$298,725,900|$95,461,682|1981|
272|The Hunger Games: Mockingjay - Part 2|LGF|$297,446,700|$281,723,902|2015|
273|The LEGO Movie|WB|$296,654,200|$257,760,692|2014|
274|Batman Begins|WB|$295,860,600|$206,852,432|2005^|
275|Apocalypse Now|MGM|$295,789,400|$83,471,511|1979^|
276|Charlie and the Chocolate Factory|WB|$295,677,800|$206,459,076|2005|
277|Big Daddy|Sony|$295,422,100|$163,479,795|1999|
278|Ocean's Eleven|WB|$294,446,200|$183,417,150|2001|
279|Jurassic Park III|Uni.|$293,844,100|$181,171,875|2001|
280|Teenage Mutant Ninja Turtles|NL|$293,555,800|$135,265,915|1990|
281|Planet of the Apes (2001)|Fox|$291,948,200|$180,011,740|2001|
282|Alien|Fox|$291,755,600|$80,931,801|1979^|
283|Hancock|Sony|$291,441,100|$227,946,274|2008|
284|As Good as It Gets|Sony|$290,776,100|$148,478,011|1997|
285|The Hangover Part II|WB|$289,972,400|$254,464,305|2011|
286|Midnight Cowboy|UA|$289,525,900|$44,785,053|1969|
287|The Hobbit: The Desolation of Smaug|WB (NL)|$289,308,500|$258,366,855|2013|
288|The French Connection|Fox|$287,640,000|$51,700,000|1971|
289|The Flintstones|Uni.|$286,669,000|$130,531,208|1994|
290|Captain America: The Winter Soldier|BV|$286,373,800|$259,766,572|2014|
291|Coming to America|Par.|$286,238,000|$128,152,301|1988|
292|National Treasure: Book of Secrets|BV|$286,164,000|$219,964,115|2007|
293|WALL-E|BV|$286,150,300|$223,808,164|2008|
294|The Hobbit: The Battle of the Five Armies|WB (NL)|$285,304,300|$255,119,788|2014|
295|The Silence of the Lambs|Orion|$285,087,900|$130,742,922|1991|
296|The Karate Kid Part II|Col.|$284,812,500|$115,103,979|1986|
297|Airplane!|Par.|$284,796,800|$83,453,539|1980|
298|Alvin and the Chipmunks|Fox|$284,128,700|$217,326,974|2007|
299|Meet the Parents|Uni.|$282,676,300|$166,244,045|2000|
300|Ransom|BV|$282,366,800|$136,492,681|1996|