Overview

Dataset statistics

Number of variables16
Number of observations45372
Missing cells67136
Missing cells (%)9.2%
Duplicate rows11
Duplicate rows (%)< 0.1%
Total size in memory5.5 MiB
Average record size in memory128.0 B

Variable types

Categorical6
Numeric9
DateTime1

Alerts

Dataset has 11 (< 0.1%) duplicate rowsDuplicates
belongs_to_collection has a high cardinality: 1695 distinct valuesHigh cardinality
original_language has a high cardinality: 89 distinct valuesHigh cardinality
overview has a high cardinality: 44234 distinct valuesHigh cardinality
tagline has a high cardinality: 20270 distinct valuesHigh cardinality
title has a high cardinality: 42197 distinct valuesHigh cardinality
budget is highly overall correlated with revenue and 1 other fieldsHigh correlation
popularity is highly overall correlated with vote_countHigh correlation
revenue is highly overall correlated with budget and 2 other fieldsHigh correlation
vote_count is highly overall correlated with popularity and 1 other fieldsHigh correlation
return is highly overall correlated with budget and 1 other fieldsHigh correlation
original_language is highly imbalanced (67.4%)Imbalance
status is highly imbalanced (97.0%)Imbalance
belongs_to_collection has 40883 (90.1%) missing valuesMissing
overview has 941 (2.1%) missing valuesMissing
tagline has 24975 (55.0%) missing valuesMissing
popularity is highly skewed (γ1 = 29.21606707)Skewed
return is highly skewed (γ1 = 138.3234284)Skewed
overview is uniformly distributedUniform
tagline is uniformly distributedUniform
title is uniformly distributedUniform
budget has 36480 (80.4%) zerosZeros
revenue has 37964 (83.7%) zerosZeros
runtime has 1534 (3.4%) zerosZeros
vote_average has 2947 (6.5%) zerosZeros
vote_count has 2849 (6.3%) zerosZeros
return has 40049 (88.3%) zerosZeros

Reproduction

Analysis started2023-06-07 13:25:36.600554
Analysis finished2023-06-07 13:26:51.704764
Duration1 minute and 15.1 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

belongs_to_collection
Categorical

HIGH CARDINALITY  MISSING 

Distinct1695
Distinct (%)37.8%
Missing40883
Missing (%)90.1%
Memory size354.6 KiB
The Bowery Boys
 
29
Totò Collection
 
27
James Bond Collection
 
26
Zatôichi: The Blind Swordsman
 
26
The Carry On Collection
 
25
Other values (1690)
4356 

Length

Max length54
Median length43
Mean length23.856538
Min length3

Characters and Unicode

Total characters107092
Distinct characters166
Distinct categories12 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)8.7%

Sample

1st rowToy Story Collection
2nd rowGrumpy Old Men Collection
3rd rowFather of the Bride Collection
4th rowJames Bond Collection
5th rowBalto Collection

Common Values

ValueCountFrequency (%)
The Bowery Boys 29
 
0.1%
Totò Collection 27
 
0.1%
James Bond Collection 26
 
0.1%
Zatôichi: The Blind Swordsman 26
 
0.1%
The Carry On Collection 25
 
0.1%
Pokémon Collection 22
 
< 0.1%
Charlie Chan (Sidney Toler) Collection 21
 
< 0.1%
Godzilla (Showa) Collection 16
 
< 0.1%
Uuno Turhapuro 15
 
< 0.1%
Dragon Ball Z (Movie) Collection 15
 
< 0.1%
Other values (1685) 4267
 
9.4%
(Missing) 40883
90.1%

Length

2023-06-07T08:26:52.242500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
collection 3744
25.3%
the 1146
 
7.8%
of 230
 
1.6%
series 147
 
1.0%
139
 
0.9%
trilogy 87
 
0.6%
and 84
 
0.6%
a 62
 
0.4%
man 62
 
0.4%
in 56
 
0.4%
Other values (2407) 9030
61.1%

Most occurring characters

ValueCountFrequency (%)
o 11117
 
10.4%
e 10452
 
9.8%
10299
 
9.6%
l 10203
 
9.5%
i 7560
 
7.1%
n 7404
 
6.9%
t 6489
 
6.1%
c 4848
 
4.5%
C 4475
 
4.2%
a 4461
 
4.2%
Other values (156) 29784
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81125
75.8%
Uppercase Letter 13888
 
13.0%
Space Separator 10299
 
9.6%
Other Punctuation 576
 
0.5%
Close Punctuation 335
 
0.3%
Open Punctuation 335
 
0.3%
Decimal Number 321
 
0.3%
Dash Punctuation 162
 
0.2%
Other Letter 37
 
< 0.1%
Final Punctuation 9
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11117
13.7%
e 10452
12.9%
l 10203
12.6%
i 7560
9.3%
n 7404
9.1%
t 6489
8.0%
c 4848
 
6.0%
a 4461
 
5.5%
r 3872
 
4.8%
s 2588
 
3.2%
Other values (69) 12131
15.0%
Uppercase Letter
ValueCountFrequency (%)
C 4475
32.2%
T 1527
 
11.0%
S 1064
 
7.7%
B 682
 
4.9%
M 631
 
4.5%
A 509
 
3.7%
D 505
 
3.6%
H 462
 
3.3%
P 432
 
3.1%
G 417
 
3.0%
Other values (33) 3184
22.9%
Other Letter
ValueCountFrequency (%)
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
2
 
5.4%
Other values (4) 8
21.6%
Other Punctuation
ValueCountFrequency (%)
. 172
29.9%
' 107
18.6%
: 99
17.2%
, 79
13.7%
& 52
 
9.0%
! 35
 
6.1%
/ 21
 
3.6%
* 4
 
0.7%
? 4
 
0.7%
3
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 80
24.9%
9 64
19.9%
3 54
16.8%
0 51
15.9%
2 21
 
6.5%
8 13
 
4.0%
5 12
 
3.7%
7 11
 
3.4%
6 10
 
3.1%
4 5
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 330
98.5%
] 5
 
1.5%
Open Punctuation
ValueCountFrequency (%)
( 330
98.5%
[ 5
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 160
98.8%
2
 
1.2%
Space Separator
ValueCountFrequency (%)
10299
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94599
88.3%
Common 12042
 
11.2%
Cyrillic 414
 
0.4%
Hiragana 15
 
< 0.1%
Hangul 10
 
< 0.1%
Katakana 9
 
< 0.1%
Han 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 11117
11.8%
e 10452
11.0%
l 10203
10.8%
i 7560
 
8.0%
n 7404
 
7.8%
t 6489
 
6.9%
c 4848
 
5.1%
C 4475
 
4.7%
a 4461
 
4.7%
r 3872
 
4.1%
Other values (70) 23718
25.1%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
Common
ValueCountFrequency (%)
10299
85.5%
) 330
 
2.7%
( 330
 
2.7%
. 172
 
1.4%
- 160
 
1.3%
' 107
 
0.9%
: 99
 
0.8%
1 80
 
0.7%
, 79
 
0.7%
9 64
 
0.5%
Other values (20) 322
 
2.7%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
Katakana
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Han
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106378
99.3%
Cyrillic 414
 
0.4%
None 246
 
0.2%
Hiragana 15
 
< 0.1%
Punctuation 14
 
< 0.1%
Katakana 12
 
< 0.1%
Hangul 10
 
< 0.1%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 11117
 
10.5%
e 10452
 
9.8%
10299
 
9.7%
l 10203
 
9.6%
i 7560
 
7.1%
n 7404
 
7.0%
t 6489
 
6.1%
c 4848
 
4.6%
C 4475
 
4.2%
a 4461
 
4.2%
Other values (67) 29070
27.3%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
None
ValueCountFrequency (%)
é 45
18.3%
ä 40
16.3%
ô 35
14.2%
ò 28
11.4%
ö 19
7.7%
ı 14
 
5.7%
ó 14
 
5.7%
í 9
 
3.7%
á 4
 
1.6%
İ 4
 
1.6%
Other values (19) 34
13.8%
Punctuation
ValueCountFrequency (%)
9
64.3%
3
 
21.4%
2
 
14.3%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
CJK
ValueCountFrequency (%)
3
100.0%
Katakana
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%

budget
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1223
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4234895.6
Minimum0
Maximum3.8 × 108
Zeros36480
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:52.409204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25000000
Maximum3.8 × 108
Range3.8 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17441397
Coefficient of variation (CV)4.1184951
Kurtosis66.612554
Mean4234895.6
Median Absolute Deviation (MAD)0
Skewness7.1168368
Sum1.9214568 × 1011
Variance3.0420232 × 1014
MonotonicityNot monotonic
2023-06-07T08:26:52.569194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36480
80.4%
5000000 286
 
0.6%
10000000 261
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 192
 
0.4%
Other values (1213) 6816
 
15.0%
ValueCountFrequency (%)
0 36480
80.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 9
 
< 0.1%
4 8
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
380000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 3
 
< 0.1%
258000000 1
 
< 0.1%
255000000 1
 
< 0.1%
250000000 10
< 0.1%
245000000 2
 
< 0.1%
237000000 1
 
< 0.1%

id
Real number (ℝ)

Distinct45348
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108022.68
Minimum2
Maximum469172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:52.737394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5334.55
Q126387.75
median59859.5
Q3156443.5
95-th percentile357141.25
Maximum469172
Range469170
Interquartile range (IQR)130055.75

Descriptive statistics

Standard deviation112161.62
Coefficient of variation (CV)1.0383154
Kurtosis0.55979937
Mean108022.68
Median Absolute Deviation (MAD)44418.5
Skewness1.2831425
Sum4.9012052 × 109
Variance1.2580228 × 1010
MonotonicityNot monotonic
2023-06-07T08:26:52.891273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4912 4
 
< 0.1%
69234 4
 
< 0.1%
132641 4
 
< 0.1%
110428 4
 
< 0.1%
109962 2
 
< 0.1%
77221 2
 
< 0.1%
15028 2
 
< 0.1%
99080 2
 
< 0.1%
10991 2
 
< 0.1%
12600 2
 
< 0.1%
Other values (45338) 45344
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
469172 1
< 0.1%
468707 1
< 0.1%
468343 1
< 0.1%
467731 1
< 0.1%
465044 1
< 0.1%
464819 1
< 0.1%
464207 1
< 0.1%
464111 1
< 0.1%
463906 1
< 0.1%
463800 1
< 0.1%

original_language
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct89
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size354.6 KiB
en
32200 
fr
 
2438
it
 
1528
ja
 
1352
de
 
1077
Other values (84)
6766 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters90722
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en 32200
71.0%
fr 2438
 
5.4%
it 1528
 
3.4%
ja 1352
 
3.0%
de 1077
 
2.4%
es 991
 
2.2%
ru 822
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (79) 3593
 
7.9%

Length

2023-06-07T08:26:53.033994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en 32200
71.0%
fr 2438
 
5.4%
it 1528
 
3.4%
ja 1352
 
3.0%
de 1077
 
2.4%
es 991
 
2.2%
ru 822
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (79) 3593
 
7.9%

Most occurring characters

ValueCountFrequency (%)
e 34523
38.1%
n 32908
36.3%
r 3631
 
4.0%
f 2834
 
3.1%
i 2386
 
2.6%
t 2250
 
2.5%
a 1841
 
2.0%
s 1650
 
1.8%
j 1353
 
1.5%
d 1321
 
1.5%
Other values (16) 6025
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90722
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34523
38.1%
n 32908
36.3%
r 3631
 
4.0%
f 2834
 
3.1%
i 2386
 
2.6%
t 2250
 
2.5%
a 1841
 
2.0%
s 1650
 
1.8%
j 1353
 
1.5%
d 1321
 
1.5%
Other values (16) 6025
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 90722
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34523
38.1%
n 32908
36.3%
r 3631
 
4.0%
f 2834
 
3.1%
i 2386
 
2.6%
t 2250
 
2.5%
a 1841
 
2.0%
s 1650
 
1.8%
j 1353
 
1.5%
d 1321
 
1.5%
Other values (16) 6025
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34523
38.1%
n 32908
36.3%
r 3631
 
4.0%
f 2834
 
3.1%
i 2386
 
2.6%
t 2250
 
2.5%
a 1841
 
2.0%
s 1650
 
1.8%
j 1353
 
1.5%
d 1321
 
1.5%
Other values (16) 6025
 
6.6%

overview
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct44234
Distinct (%)99.6%
Missing941
Missing (%)2.1%
Memory size354.6 KiB
No overview found.
 
133
No Overview
 
7
 
5
Ten years into a marriage, the wife is disappointed by the husband's lack of financial success, meaning she has to work and can't treat herself and the husband finds the wife slovenly and mean-spirited: she neither cooks not cleans particularly well and is generally disagreeable. In turn, he alternately ignores her and treats her as a servant. Neither is particularly happy, not helped by their unsatisfactory lodgers. The husband is easily seduced by an ex-colleague, a widow with a small child who needs some security, and considers leaving his wife.
 
4
Television made him famous, but his biggest hits happened off screen. Television producer by day, CIA assassin by night, Chuck Barris was recruited by the CIA at the height of his TV career and trained to become a covert operative. Or so Barris said.
 
4
Other values (44229)
44278 

Length

Max length1000
Median length786
Mean length323.30693
Min length1

Characters and Unicode

Total characters14364850
Distinct characters429
Distinct categories25 ?
Distinct scripts13 ?
Distinct blocks21 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44187 ?
Unique (%)99.5%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.

Common Values

ValueCountFrequency (%)
No overview found. 133
 
0.3%
No Overview 7
 
< 0.1%
5
 
< 0.1%
Ten years into a marriage, the wife is disappointed by the husband's lack of financial success, meaning she has to work and can't treat herself and the husband finds the wife slovenly and mean-spirited: she neither cooks not cleans particularly well and is generally disagreeable. In turn, he alternately ignores her and treats her as a servant. Neither is particularly happy, not helped by their unsatisfactory lodgers. The husband is easily seduced by an ex-colleague, a widow with a small child who needs some security, and considers leaving his wife. 4
 
< 0.1%
Television made him famous, but his biggest hits happened off screen. Television producer by day, CIA assassin by night, Chuck Barris was recruited by the CIA at the height of his TV career and trained to become a covert operative. Or so Barris said. 4
 
< 0.1%
Winter, 1915. Confined by her family to an asylum in the South of France - where she will never sculpt again - the chronicle of Camille Claudel's reclusive life, as she waits for a visit from her brother, Paul Claudel. 4
 
< 0.1%
Count de Chagnie has discovered Christine's singing talent on a market place and sent her to his friend Carriere, the director of the Parisian opera. However just when she arrives Carriere's dismissed. His arrogant successor refuses to let a woman of low birth sing in his opera, but graciously employs Christine as gadrobiere for his wife Charlotta, who's installed as first singer. He also fights the phantom, an unknown guy who lives since many years in the catacombs below the opera and was granted privileges by Carriere. However the phantom knows how to defend himself and at the same time helps Christine to her career. 4
 
< 0.1%
Adaptation of the Jane Austen novel. 3
 
< 0.1%
No movie overview available. 3
 
< 0.1%
A few funny little novels about different aspects of life. 3
 
< 0.1%
Other values (44224) 44261
97.6%
(Missing) 941
 
2.1%

Length

2023-06-07T08:26:53.186541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 138080
 
5.6%
a 98883
 
4.0%
and 75257
 
3.1%
to 73315
 
3.0%
of 69566
 
2.8%
in 48138
 
2.0%
is 36500
 
1.5%
his 36160
 
1.5%
with 23895
 
1.0%
her 21483
 
0.9%
Other values (97093) 1827299
74.6%

Most occurring characters

ValueCountFrequency (%)
2406222
16.8%
e 1363771
 
9.5%
a 940453
 
6.5%
t 934680
 
6.5%
i 851458
 
5.9%
o 829761
 
5.8%
n 822527
 
5.7%
s 767828
 
5.3%
r 744250
 
5.2%
h 600777
 
4.2%
Other values (419) 4103123
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11149373
77.6%
Space Separator 2406260
 
16.8%
Uppercase Letter 390961
 
2.7%
Other Punctuation 312803
 
2.2%
Decimal Number 42221
 
0.3%
Dash Punctuation 36764
 
0.3%
Close Punctuation 10097
 
0.1%
Open Punctuation 10074
 
0.1%
Final Punctuation 4553
 
< 0.1%
Initial Punctuation 881
 
< 0.1%
Other values (15) 863
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1363771
12.2%
a 940453
 
8.4%
t 934680
 
8.4%
i 851458
 
7.6%
o 829761
 
7.4%
n 822527
 
7.4%
s 767828
 
6.9%
r 744250
 
6.7%
h 600777
 
5.4%
l 478780
 
4.3%
Other values (142) 2815088
25.2%
Uppercase Letter
ValueCountFrequency (%)
A 42753
 
10.9%
T 35971
 
9.2%
S 31123
 
8.0%
M 23948
 
6.1%
B 23701
 
6.1%
C 22831
 
5.8%
H 19433
 
5.0%
W 18647
 
4.8%
I 16799
 
4.3%
D 16309
 
4.2%
Other values (77) 139446
35.7%
Other Letter
ValueCountFrequency (%)
6
 
4.8%
6
 
4.8%
5
 
4.0%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
2
 
1.6%
م 2
 
1.6%
Other values (76) 88
70.4%
Other Punctuation
ValueCountFrequency (%)
, 133420
42.7%
. 124792
39.9%
' 31126
 
10.0%
" 11661
 
3.7%
: 3298
 
1.1%
? 2759
 
0.9%
; 2493
 
0.8%
! 1543
 
0.5%
/ 765
 
0.2%
& 453
 
0.1%
Other values (12) 493
 
0.2%
Nonspacing Mark
ValueCountFrequency (%)
́ 4
12.1%
ి 4
12.1%
3
9.1%
3
9.1%
3
9.1%
̈ 3
9.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
Other values (4) 5
15.2%
Decimal Number
ValueCountFrequency (%)
1 9748
23.1%
0 8265
19.6%
9 6406
15.2%
2 4250
10.1%
5 2442
 
5.8%
8 2378
 
5.6%
3 2340
 
5.5%
4 2176
 
5.2%
7 2131
 
5.0%
6 2085
 
4.9%
Spacing Mark
ValueCountFrequency (%)
11
40.7%
4
 
14.8%
3
 
11.1%
3
 
11.1%
ि 2
 
7.4%
2
 
7.4%
1
 
3.7%
ி 1
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 35241
95.9%
881
 
2.4%
633
 
1.7%
5
 
< 0.1%
4
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 45
70.3%
14
 
21.9%
¦ 2
 
3.1%
° 2
 
3.1%
1
 
1.6%
Math Symbol
ValueCountFrequency (%)
~ 20
50.0%
+ 11
27.5%
= 6
 
15.0%
| 2
 
5.0%
1
 
2.5%
Open Punctuation
ValueCountFrequency (%)
( 10021
99.5%
[ 50
 
0.5%
{ 2
 
< 0.1%
1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 317
96.4%
£ 10
 
3.0%
1
 
0.3%
1
 
0.3%
Space Separator
ValueCountFrequency (%)
2406222
> 99.9%
  36
 
< 0.1%
  2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 10045
99.5%
] 50
 
0.5%
} 2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
3845
84.4%
689
 
15.1%
» 19
 
0.4%
Initial Punctuation
ValueCountFrequency (%)
671
76.2%
192
 
21.8%
« 18
 
2.0%
Control
ValueCountFrequency (%)
104
96.3%
’ 3
 
2.8%
 1
 
0.9%
Modifier Symbol
ValueCountFrequency (%)
´ 25
65.8%
` 12
31.6%
¯ 1
 
2.6%
Format
ValueCountFrequency (%)
31
60.8%
­ 20
39.2%
Other Number
ValueCountFrequency (%)
½ 8
50.0%
¹ 8
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%
Line Separator
ValueCountFrequency (%)
7
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Paragraph Separator
ValueCountFrequency (%)
2
100.0%
Modifier Letter
ValueCountFrequency (%)
ʼ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11535102
80.3%
Common 2824329
 
19.7%
Cyrillic 4587
 
< 0.1%
Greek 648
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Han 10
 
< 0.1%
Hangul 9
 
< 0.1%
Other values (3) 19
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1363771
11.8%
a 940453
 
8.2%
t 934680
 
8.1%
i 851458
 
7.4%
o 829761
 
7.2%
n 822527
 
7.1%
s 767828
 
6.7%
r 744250
 
6.5%
h 600777
 
5.2%
l 478780
 
4.2%
Other values (132) 3200817
27.7%
Common
ValueCountFrequency (%)
2406222
85.2%
, 133420
 
4.7%
. 124792
 
4.4%
- 35241
 
1.2%
' 31126
 
1.1%
" 11661
 
0.4%
) 10045
 
0.4%
( 10021
 
0.4%
1 9748
 
0.3%
0 8265
 
0.3%
Other values (71) 43788
 
1.6%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Greek
ValueCountFrequency (%)
α 60
 
9.3%
ο 55
 
8.5%
τ 43
 
6.6%
ι 36
 
5.6%
η 36
 
5.6%
ν 34
 
5.2%
ρ 31
 
4.8%
ε 31
 
4.8%
ς 30
 
4.6%
π 30
 
4.6%
Other values (33) 262
40.4%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Han
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
Inherited
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14346856
99.9%
Punctuation 7266
 
0.1%
None 5930
 
< 0.1%
Cyrillic 4587
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Letterlike Symbols 14
 
< 0.1%
CJK 10
 
< 0.1%
Other values (11) 41
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2406222
16.8%
e 1363771
 
9.5%
a 940453
 
6.6%
t 934680
 
6.5%
i 851458
 
5.9%
o 829761
 
5.8%
n 822527
 
5.7%
s 767828
 
5.4%
r 744250
 
5.2%
h 600777
 
4.2%
Other values (82) 4085129
28.5%
Punctuation
ValueCountFrequency (%)
3845
52.9%
881
 
12.1%
689
 
9.5%
671
 
9.2%
633
 
8.7%
303
 
4.2%
192
 
2.6%
31
 
0.4%
7
 
0.1%
5
 
0.1%
Other values (4) 9
 
0.1%
None
ValueCountFrequency (%)
é 1550
26.1%
ä 294
 
5.0%
á 293
 
4.9%
ö 250
 
4.2%
í 243
 
4.1%
è 209
 
3.5%
ü 178
 
3.0%
ı 165
 
2.8%
ó 164
 
2.8%
ç 158
 
2.7%
Other values (141) 2426
40.9%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Letterlike Symbols
ValueCountFrequency (%)
14
100.0%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Alphabetic PF
ValueCountFrequency (%)
4
100.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Diacriticals
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Number Forms
ValueCountFrequency (%)
2
100.0%
Modifier Letters
ValueCountFrequency (%)
ʼ 2
100.0%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
CJK
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Katakana
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Specials
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct43733
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9262654
Minimum0
Maximum547.4883
Zeros40
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:53.474948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.020835
Q10.3888395
median1.130301
Q33.6903083
95-th percentile11.063679
Maximum547.4883
Range547.4883
Interquartile range (IQR)3.3014688

Descriptive statistics

Standard deviation6.0097826
Coefficient of variation (CV)2.053738
Kurtosis1923.7198
Mean2.9262654
Median Absolute Deviation (MAD)0.9675455
Skewness29.216067
Sum132770.51
Variance36.117486
MonotonicityNot monotonic
2023-06-07T08:26:53.660332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 × 10-656
 
0.1%
0.000308 42
 
0.1%
0 40
 
0.1%
0.00022 39
 
0.1%
0.001177 38
 
0.1%
0.000844 38
 
0.1%
0.000578 38
 
0.1%
0.002001 27
 
0.1%
0.003013 21
 
< 0.1%
0.00353 19
 
< 0.1%
Other values (43723) 45014
99.2%
ValueCountFrequency (%)
0 40
0.1%
1 × 10-656
0.1%
2 × 10-66
 
< 0.1%
3 × 10-66
 
< 0.1%
4 × 10-65
 
< 0.1%
5 × 10-61
 
< 0.1%
6 × 10-62
 
< 0.1%
7 × 10-61
 
< 0.1%
8 × 10-66
 
< 0.1%
9 × 10-62
 
< 0.1%
ValueCountFrequency (%)
547.488298 1
< 0.1%
294.337037 1
< 0.1%
287.253654 1
< 0.1%
228.032744 1
< 0.1%
213.849907 1
< 0.1%
187.860492 1
< 0.1%
185.330992 1
< 0.1%
185.070892 1
< 0.1%
183.870374 1
< 0.1%
154.801009 1
< 0.1%
Distinct17334
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
Minimum1874-12-09 00:00:00
Maximum2020-12-16 00:00:00
2023-06-07T08:26:53.821814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:53.982071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6863
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11232509
Minimum0
Maximum2.7879651 × 109
Zeros37964
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:54.142930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile48022158
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64392834
Coefficient of variation (CV)5.7327205
Kurtosis237.05384
Mean11232509
Median Absolute Deviation (MAD)0
Skewness12.25405
Sum5.0964139 × 1011
Variance4.1464371 × 1015
MonotonicityNot monotonic
2023-06-07T08:26:54.280342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37964
83.7%
12000000 20
 
< 0.1%
10000000 19
 
< 0.1%
11000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7263
 
16.0%
ValueCountFrequency (%)
0 37964
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

Distinct353
Distinct (%)0.8%
Missing246
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean94.18435
Minimum0
Maximum1256
Zeros1534
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:54.962467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.342049
Coefficient of variation (CV)0.40709575
Kurtosis93.922609
Mean94.18435
Median Absolute Deviation (MAD)11
Skewness4.491173
Sum4250163
Variance1470.1127
MonotonicityNot monotonic
2023-06-07T08:26:55.146738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2548
 
5.6%
0 1534
 
3.4%
100 1470
 
3.2%
95 1412
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1078
 
2.4%
94 1062
 
2.3%
91 1055
 
2.3%
88 1030
 
2.3%
Other values (343) 31619
69.7%
ValueCountFrequency (%)
0 1534
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 50
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%

status
Categorical

Distinct6
Distinct (%)< 0.1%
Missing80
Missing (%)0.2%
Memory size354.6 KiB
Released
44933 
Rumored
 
229
Post Production
 
97
In Production
 
19
Planned
 
13

Length

Max length15
Median length8
Mean length8.011746
Min length7

Characters and Unicode

Total characters362868
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 44933
99.0%
Rumored 229
 
0.5%
Post Production 97
 
0.2%
In Production 19
 
< 0.1%
Planned 13
 
< 0.1%
Canceled 1
 
< 0.1%
(Missing) 80
 
0.2%

Length

2023-06-07T08:26:55.300004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T08:26:55.453493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
released 44933
99.0%
rumored 229
 
0.5%
production 116
 
0.3%
post 97
 
0.2%
in 19
 
< 0.1%
planned 13
 
< 0.1%
canceled 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135043
37.2%
d 45292
 
12.5%
R 45162
 
12.4%
s 45030
 
12.4%
l 44947
 
12.4%
a 44947
 
12.4%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
Other values (8) 970
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 317344
87.5%
Uppercase Letter 45408
 
12.5%
Space Separator 116
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 135043
42.6%
d 45292
 
14.3%
s 45030
 
14.2%
l 44947
 
14.2%
a 44947
 
14.2%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
t 213
 
0.1%
Other values (3) 395
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
R 45162
99.5%
P 226
 
0.5%
I 19
 
< 0.1%
C 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 362752
> 99.9%
Common 116
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 135043
37.2%
d 45292
 
12.5%
R 45162
 
12.4%
s 45030
 
12.4%
l 44947
 
12.4%
a 44947
 
12.4%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
Other values (7) 854
 
0.2%
Common
ValueCountFrequency (%)
116
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 135043
37.2%
d 45292
 
12.5%
R 45162
 
12.4%
s 45030
 
12.4%
l 44947
 
12.4%
a 44947
 
12.4%
o 558
 
0.2%
r 345
 
0.1%
u 345
 
0.1%
m 229
 
0.1%
Other values (8) 970
 
0.3%

tagline
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct20270
Distinct (%)99.4%
Missing24975
Missing (%)55.0%
Memory size354.6 KiB
Based on a true story.
 
7
Some things are better left top secret.
 
4
Trust no one.
 
4
-
 
4
Be careful what you wish for.
 
4
Other values (20265)
20374 

Length

Max length297
Median length204
Mean length46.997058
Min length1

Characters and Unicode

Total characters958599
Distinct characters170
Distinct categories17 ?
Distinct scripts6 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20167 ?
Unique (%)98.9%

Sample

1st rowRoll the dice and unleash the excitement!
2nd rowStill Yelling. Still Fighting. Still Ready for Love.
3rd rowFriends are the people who let you be yourself... and never let you forget it.
4th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!
5th rowA Los Angeles Crime Saga

Common Values

ValueCountFrequency (%)
Based on a true story. 7
 
< 0.1%
Some things are better left top secret. 4
 
< 0.1%
Trust no one. 4
 
< 0.1%
- 4
 
< 0.1%
Be careful what you wish for. 4
 
< 0.1%
Classic Albums 3
 
< 0.1%
There is no turning back 3
 
< 0.1%
How far would you go? 3
 
< 0.1%
Some doors should never be opened. 3
 
< 0.1%
Know Your Enemy 3
 
< 0.1%
Other values (20260) 20359
44.9%
(Missing) 24975
55.0%

Length

2023-06-07T08:26:56.014834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 10993
 
6.3%
a 6812
 
3.9%
of 4404
 
2.5%
to 3582
 
2.1%
is 2793
 
1.6%
in 2693
 
1.5%
and 2682
 
1.5%
you 2389
 
1.4%
1580
 
0.9%
for 1523
 
0.9%
Other values (15100) 134463
77.3%

Most occurring characters

ValueCountFrequency (%)
153665
16.0%
e 94407
 
9.8%
t 57265
 
6.0%
o 56558
 
5.9%
a 51468
 
5.4%
n 47494
 
5.0%
i 46030
 
4.8%
r 44980
 
4.7%
s 42360
 
4.4%
h 37161
 
3.9%
Other values (160) 327211
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 680411
71.0%
Space Separator 153665
 
16.0%
Uppercase Letter 74992
 
7.8%
Other Punctuation 44582
 
4.7%
Decimal Number 2687
 
0.3%
Dash Punctuation 1942
 
0.2%
Final Punctuation 98
 
< 0.1%
Open Punctuation 56
 
< 0.1%
Close Punctuation 55
 
< 0.1%
Currency Symbol 37
 
< 0.1%
Other values (7) 74
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 94407
13.9%
t 57265
 
8.4%
o 56558
 
8.3%
a 51468
 
7.6%
n 47494
 
7.0%
i 46030
 
6.8%
r 44980
 
6.6%
s 42360
 
6.2%
h 37161
 
5.5%
l 30172
 
4.4%
Other values (43) 172516
25.4%
Other Letter
ValueCountFrequency (%)
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (24) 24
70.6%
Uppercase Letter
ValueCountFrequency (%)
T 10008
 
13.3%
A 6873
 
9.2%
S 5653
 
7.5%
H 4402
 
5.9%
I 4387
 
5.8%
E 4306
 
5.7%
W 3679
 
4.9%
O 3478
 
4.6%
N 3195
 
4.3%
L 3194
 
4.3%
Other values (20) 25817
34.4%
Other Punctuation
ValueCountFrequency (%)
. 26648
59.8%
! 5784
 
13.0%
' 5674
 
12.7%
, 4224
 
9.5%
? 1159
 
2.6%
" 582
 
1.3%
148
 
0.3%
: 138
 
0.3%
& 83
 
0.2%
* 42
 
0.1%
Other values (7) 100
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 802
29.8%
1 516
19.2%
2 299
 
11.1%
3 208
 
7.7%
9 208
 
7.7%
5 168
 
6.3%
4 140
 
5.2%
6 121
 
4.5%
7 121
 
4.5%
8 104
 
3.9%
Math Symbol
ValueCountFrequency (%)
+ 5
35.7%
= 5
35.7%
| 2
 
14.3%
~ 1
 
7.1%
1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 1925
99.1%
9
 
0.5%
8
 
0.4%
Final Punctuation
ValueCountFrequency (%)
82
83.7%
15
 
15.3%
» 1
 
1.0%
Initial Punctuation
ValueCountFrequency (%)
14
73.7%
4
 
21.1%
« 1
 
5.3%
Open Punctuation
ValueCountFrequency (%)
( 49
87.5%
[ 7
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 48
87.3%
] 7
 
12.7%
Other Number
ValueCountFrequency (%)
½ 2
66.7%
² 1
33.3%
Modifier Letter
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Space Separator
ValueCountFrequency (%)
153665
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 37
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 755403
78.8%
Common 203161
 
21.2%
Han 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 94407
 
12.5%
t 57265
 
7.6%
o 56558
 
7.5%
a 51468
 
6.8%
n 47494
 
6.3%
i 46030
 
6.1%
r 44980
 
6.0%
s 42360
 
5.6%
h 37161
 
4.9%
l 30172
 
4.0%
Other values (73) 247508
32.8%
Common
ValueCountFrequency (%)
153665
75.6%
. 26648
 
13.1%
! 5784
 
2.8%
' 5674
 
2.8%
, 4224
 
2.1%
- 1925
 
0.9%
? 1159
 
0.6%
0 802
 
0.4%
" 582
 
0.3%
1 516
 
0.3%
Other values (42) 2182
 
1.1%
Han
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 958169
> 99.9%
Punctuation 280
 
< 0.1%
None 110
 
< 0.1%
CJK 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%
IPA Ext 2
 
< 0.1%
Modifier Letters 2
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153665
16.0%
e 94407
 
9.9%
t 57265
 
6.0%
o 56558
 
5.9%
a 51468
 
5.4%
n 47494
 
5.0%
i 46030
 
4.8%
r 44980
 
4.7%
s 42360
 
4.4%
h 37161
 
3.9%
Other values (78) 326781
34.1%
Punctuation
ValueCountFrequency (%)
148
52.9%
82
29.3%
15
 
5.4%
14
 
5.0%
9
 
3.2%
8
 
2.9%
4
 
1.4%
None
ValueCountFrequency (%)
é 18
16.4%
ä 16
14.5%
ö 8
 
7.3%
á 6
 
5.5%
ó 6
 
5.5%
ü 5
 
4.5%
í 5
 
4.5%
ı 5
 
4.5%
· 4
 
3.6%
ć 3
 
2.7%
Other values (26) 34
30.9%
IPA Ext
ValueCountFrequency (%)
ə 2
100.0%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
CJK
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Modifier Letters
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct42197
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
Cinderella
 
11
Hamlet
 
9
Alice in Wonderland
 
9
Beauty and the Beast
 
8
Les Misérables
 
8
Other values (42192)
45327 

Length

Max length105
Median length79
Mean length16.704002
Min length1

Characters and Unicode

Total characters757894
Distinct characters287
Distinct categories17 ?
Distinct scripts7 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39877 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II

Common Values

ValueCountFrequency (%)
Cinderella 11
 
< 0.1%
Hamlet 9
 
< 0.1%
Alice in Wonderland 9
 
< 0.1%
Beauty and the Beast 8
 
< 0.1%
Les Misérables 8
 
< 0.1%
Treasure Island 7
 
< 0.1%
A Christmas Carol 7
 
< 0.1%
The Three Musketeers 7
 
< 0.1%
Wuthering Heights 6
 
< 0.1%
First Love 6
 
< 0.1%
Other values (42187) 45294
99.8%

Length

2023-06-07T08:26:56.209577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 14555
 
10.7%
of 4932
 
3.6%
a 2243
 
1.6%
in 1693
 
1.2%
and 1631
 
1.2%
to 1054
 
0.8%
757
 
0.6%
man 666
 
0.5%
love 664
 
0.5%
for 601
 
0.4%
Other values (24354) 107389
78.9%

Most occurring characters

ValueCountFrequency (%)
90835
 
12.0%
e 76246
 
10.1%
a 48944
 
6.5%
o 45669
 
6.0%
n 40825
 
5.4%
r 40011
 
5.3%
i 39772
 
5.2%
t 36722
 
4.8%
s 29519
 
3.9%
h 28520
 
3.8%
Other values (277) 280831
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 534155
70.5%
Uppercase Letter 117263
 
15.5%
Space Separator 90835
 
12.0%
Other Punctuation 10488
 
1.4%
Decimal Number 3858
 
0.5%
Dash Punctuation 981
 
0.1%
Close Punctuation 87
 
< 0.1%
Open Punctuation 85
 
< 0.1%
Final Punctuation 38
 
< 0.1%
Other Letter 25
 
< 0.1%
Other values (7) 79
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 76246
14.3%
a 48944
9.2%
o 45669
 
8.5%
n 40825
 
7.6%
r 40011
 
7.5%
i 39772
 
7.4%
t 36722
 
6.9%
s 29519
 
5.5%
h 28520
 
5.3%
l 25929
 
4.9%
Other values (121) 121998
22.8%
Uppercase Letter
ValueCountFrequency (%)
T 16017
13.7%
S 10335
 
8.8%
M 8035
 
6.9%
B 7655
 
6.5%
C 7170
 
6.1%
A 6786
 
5.8%
D 6334
 
5.4%
L 5869
 
5.0%
H 5170
 
4.4%
W 5167
 
4.4%
Other values (65) 38725
33.0%
Other Letter
ValueCountFrequency (%)
چ 2
 
8.0%
ه 2
 
8.0%
ی 2
 
8.0%
ک 2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
ª 1
 
4.0%
Other values (11) 11
44.0%
Other Punctuation
ValueCountFrequency (%)
: 3718
35.5%
' 2504
23.9%
. 1603
15.3%
, 1133
 
10.8%
! 647
 
6.2%
& 458
 
4.4%
? 269
 
2.6%
/ 79
 
0.8%
* 19
 
0.2%
# 13
 
0.1%
Other values (8) 45
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 861
22.3%
1 701
18.2%
0 616
16.0%
3 482
12.5%
9 232
 
6.0%
4 229
 
5.9%
5 227
 
5.9%
7 193
 
5.0%
8 161
 
4.2%
6 156
 
4.0%
Math Symbol
ValueCountFrequency (%)
+ 17
70.8%
× 3
 
12.5%
1
 
4.2%
= 1
 
4.2%
1
 
4.2%
1
 
4.2%
Other Number
ValueCountFrequency (%)
½ 12
63.2%
² 3
 
15.8%
³ 2
 
10.5%
1
 
5.3%
1
 
5.3%
Other Symbol
ValueCountFrequency (%)
° 3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
Currency Symbol
ValueCountFrequency (%)
$ 18
85.7%
¢ 2
 
9.5%
£ 1
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 966
98.5%
15
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 82
94.3%
] 5
 
5.7%
Open Punctuation
ValueCountFrequency (%)
( 80
94.1%
[ 5
 
5.9%
Final Punctuation
ValueCountFrequency (%)
37
97.4%
1
 
2.6%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
90835
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Format
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 650903
85.9%
Common 106451
 
14.0%
Cyrillic 346
 
< 0.1%
Greek 170
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
Han 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 76246
 
11.7%
a 48944
 
7.5%
o 45669
 
7.0%
n 40825
 
6.3%
r 40011
 
6.1%
i 39772
 
6.1%
t 36722
 
5.6%
s 29519
 
4.5%
h 28520
 
4.4%
l 25929
 
4.0%
Other values (107) 238746
36.7%
Common
ValueCountFrequency (%)
90835
85.3%
: 3718
 
3.5%
' 2504
 
2.4%
. 1603
 
1.5%
, 1133
 
1.1%
- 966
 
0.9%
2 861
 
0.8%
1 701
 
0.7%
! 647
 
0.6%
0 616
 
0.6%
Other values (50) 2867
 
2.7%
Cyrillic
ValueCountFrequency (%)
е 32
 
9.2%
о 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
в 14
 
4.0%
л 14
 
4.0%
Other values (38) 124
35.8%
Greek
ValueCountFrequency (%)
α 20
 
11.8%
ι 14
 
8.2%
ο 14
 
8.2%
τ 9
 
5.3%
λ 8
 
4.7%
ρ 8
 
4.7%
ά 8
 
4.7%
ν 7
 
4.1%
η 6
 
3.5%
ς 6
 
3.5%
Other values (32) 70
41.2%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
چ 2
18.2%
ه 2
18.2%
ی 2
18.2%
ک 2
18.2%
س 1
9.1%
ا 1
9.1%
ج 1
9.1%
Han
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756330
99.8%
None 1123
 
0.1%
Cyrillic 346
 
< 0.1%
Punctuation 62
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
CJK 5
 
< 0.1%
Misc Symbols 3
 
< 0.1%
Letterlike Symbols 2
 
< 0.1%
Math Operators 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90835
 
12.0%
e 76246
 
10.1%
a 48944
 
6.5%
o 45669
 
6.0%
n 40825
 
5.4%
r 40011
 
5.3%
i 39772
 
5.3%
t 36722
 
4.9%
s 29519
 
3.9%
h 28520
 
3.8%
Other values (76) 279267
36.9%
None
ValueCountFrequency (%)
é 218
19.4%
ä 127
 
11.3%
ö 55
 
4.9%
è 53
 
4.7%
ô 44
 
3.9%
ü 39
 
3.5%
ó 37
 
3.3%
á 35
 
3.1%
ı 35
 
3.1%
í 33
 
2.9%
Other values (108) 447
39.8%
Punctuation
ValueCountFrequency (%)
37
59.7%
15
24.2%
5
 
8.1%
2
 
3.2%
1
 
1.6%
1
 
1.6%
1
 
1.6%
Cyrillic
ValueCountFrequency (%)
е 32
 
9.2%
о 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
в 14
 
4.0%
л 14
 
4.0%
Other values (38) 124
35.8%
Arabic
ValueCountFrequency (%)
چ 2
18.2%
ه 2
18.2%
ی 2
18.2%
ک 2
18.2%
س 1
9.1%
ا 1
9.1%
ج 1
9.1%
Misc Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
CJK
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arrows
ValueCountFrequency (%)
1
100.0%

vote_average
Real number (ℝ)

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6239729
Minimum0
Maximum10
Zeros2947
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:56.385857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.9154534
Coefficient of variation (CV)0.34058723
Kurtosis2.5417895
Mean5.6239729
Median Absolute Deviation (MAD)0.9
Skewness-1.5244593
Sum255170.9
Variance3.6689616
MonotonicityNot monotonic
2023-06-07T08:26:56.537757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2947
 
6.5%
6 2463
 
5.4%
5 1998
 
4.4%
7 1886
 
4.2%
6.5 1722
 
3.8%
6.3 1602
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1349
 
3.0%
6.7 1340
 
3.0%
Other values (82) 27315
60.2%
ValueCountFrequency (%)
0 2947
6.5%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 103
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 185
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 2
 
< 0.1%
9 158
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1820
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.10517
Minimum0
Maximum14075
Zeros2849
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:56.699119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.76439
Coefficient of variation (CV)4.466315
Kurtosis150.91449
Mean110.10517
Median Absolute Deviation (MAD)8
Skewness10.440282
Sum4995692
Variance241832.22
MonotonicityNot monotonic
2023-06-07T08:26:56.855356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3242
 
7.1%
2 3127
 
6.9%
0 2849
 
6.3%
3 2783
 
6.1%
4 2477
 
5.5%
5 2096
 
4.6%
6 1747
 
3.9%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22928
50.5%
ValueCountFrequency (%)
0 2849
6.3%
1 3242
7.1%
2 3127
6.9%
3 2783
6.1%
4 2477
5.5%
5 2096
4.6%
6 1747
3.9%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

release_year
Real number (ℝ)

Distinct135
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.8813
Minimum1874
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:57.028818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1874
5-th percentile1941
Q11978
median2001
Q32010
95-th percentile2015
Maximum2020
Range146
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.054491
Coefficient of variation (CV)0.012076267
Kurtosis0.83959553
Mean1991.8813
Median Absolute Deviation (MAD)12
Skewness-1.2247249
Sum90375638
Variance578.61851
MonotonicityNot monotonic
2023-06-07T08:26:57.174444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 1974
 
4.4%
2015 1905
 
4.2%
2013 1890
 
4.2%
2012 1723
 
3.8%
2011 1667
 
3.7%
2016 1604
 
3.5%
2009 1585
 
3.5%
2010 1501
 
3.3%
2008 1470
 
3.2%
2007 1319
 
2.9%
Other values (125) 28734
63.3%
ValueCountFrequency (%)
1874 1
 
< 0.1%
1878 1
 
< 0.1%
1883 1
 
< 0.1%
1887 1
 
< 0.1%
1888 2
 
< 0.1%
1890 5
 
< 0.1%
1891 6
< 0.1%
1892 3
 
< 0.1%
1893 1
 
< 0.1%
1894 13
< 0.1%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2018 5
 
< 0.1%
2017 531
 
1.2%
2016 1604
3.5%
2015 1905
4.2%
2014 1974
4.4%
2013 1890
4.2%
2012 1723
3.8%
2011 1667
3.7%
2010 1501
3.3%

return
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1256
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean660.10103
Minimum0
Maximum12396383
Zeros40049
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2023-06-07T08:26:57.344254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.54
Maximum12396383
Range12396383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74696.586
Coefficient of variation (CV)113.15932
Kurtosis20671.134
Mean660.10103
Median Absolute Deviation (MAD)0
Skewness138.32343
Sum29950104
Variance5.57958 × 109
MonotonicityNot monotonic
2023-06-07T08:26:57.512543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40049
88.3%
0.01 64
 
0.1%
0.02 38
 
0.1%
1 34
 
0.1%
0.08 29
 
0.1%
0.06 27
 
0.1%
0.03 27
 
0.1%
1.1 26
 
0.1%
0.62 25
 
0.1%
1.2 23
 
0.1%
Other values (1246) 5030
 
11.1%
ValueCountFrequency (%)
0 40049
88.3%
0.01 64
 
0.1%
0.02 38
 
0.1%
0.03 27
 
0.1%
0.04 19
 
< 0.1%
0.05 22
 
< 0.1%
0.06 27
 
0.1%
0.07 18
 
< 0.1%
0.08 29
 
0.1%
0.09 16
 
< 0.1%
ValueCountFrequency (%)
12396383 1
< 0.1%
8500000 1
< 0.1%
4197476.62 1
< 0.1%
2755584 1
< 0.1%
1018619.28 1
< 0.1%
1000000 1
< 0.1%
26881.72 1
< 0.1%
12890.39 1
< 0.1%
5330.34 1
< 0.1%
4133.33 1
< 0.1%

Interactions

2023-06-07T08:26:47.666067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:34.368597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.003764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:37.242990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:38.498155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:39.777298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:42.811257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:44.272991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:46.071374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:47.817966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:34.713632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.147693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:37.394888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:38.650051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:39.945188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:42.956777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:44.432872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:46.271248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:47.953876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:34.883552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.275582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:37.522801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:38.769973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:40.097087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:43.084691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:44.658198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:46.487796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:48.097784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:35.011466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.403501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:37.650731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:38.905879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:40.249000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:43.221511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:44.810853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:46.751893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:48.241684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:35.147376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.539458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:37.778632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:39.041787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:40.408911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:43.373780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:45.019961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:46.896307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:48.377610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:35.291281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.683400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:37.914539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:39.177697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:40.560814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:43.614270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:45.188540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:47.048207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:48.513501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:35.564059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.811279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:38.042454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:39.329598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:40.736662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:43.798563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:45.389118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:47.193053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:48.657422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:35.715957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:36.955183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:38.186363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:39.481498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:40.987833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:43.966452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:45.637530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:47.337061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:48.801309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:35.859863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:37.099085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:38.346251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:39.633633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:42.652216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:44.126816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:45.871116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-06-07T08:26:47.504955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-06-07T08:26:58.204563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
budgetidpopularityrevenueruntimevote_averagevote_countrelease_yearreturnoriginal_languagestatus
budget1.000-0.2550.4630.6450.2270.0720.4840.1410.7710.0000.000
id-0.2551.000-0.410-0.278-0.206-0.149-0.4330.392-0.2630.0710.056
popularity0.463-0.4101.0000.4910.3070.2410.8930.1860.4460.0000.000
revenue0.645-0.2780.4911.0000.2540.1270.5130.1040.8490.0000.000
runtime0.227-0.2060.3070.2541.0000.1930.2900.0340.2340.1110.000
vote_average0.072-0.1490.2410.1270.1931.0000.318-0.0080.1210.0700.019
vote_count0.484-0.4330.8930.5130.2900.3181.0000.1970.4730.0000.000
release_year0.1410.3920.1860.1040.034-0.0080.1971.0000.0850.1440.028
return0.771-0.2630.4460.8490.2340.1210.4730.0851.0000.0000.000
original_language0.0000.0710.0000.0000.1110.0700.0000.1440.0001.0000.000
status0.0000.0560.0000.0000.0000.0190.0000.0280.0000.0001.000

Missing values

2023-06-07T08:26:49.249013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-07T08:26:49.648747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-07T08:26:51.383946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

belongs_to_collectionbudgetidoriginal_languageoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevote_averagevote_countrelease_yearreturn
0Toy Story Collection30000000862enLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.9469431995-10-3037355403381.0ReleasedNaNToy Story7.75415199512.45
1NaN650000008844enWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.0155391995-12-15262797249104.0ReleasedRoll the dice and unleash the excitement!Jumanji6.9241319954.04
2Grumpy Old Men Collection015602enA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.7129001995-12-220101.0ReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old Men6.59219950.00
3NaN1600000031357enCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.8594951995-12-2281452156127.0ReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to Exhale6.13419955.09
4Father of the Bride Collection011862enJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.3875191995-02-1076578911106.0ReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part II5.717319950.00
5NaN60000000949enObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.9249271995-12-15187436818170.0ReleasedA Los Angeles Crime SagaHeat7.7188619953.12
6NaN5800000011860enAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.6772771995-12-150127.0ReleasedYou are cordially invited to the most surprising merger of the year.Sabrina6.214119950.00
7NaN045325enA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.5611611995-12-22097.0ReleasedThe Original Bad Boys.Tom and Huck5.44519950.00
8NaN350000009091enInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.2315801995-12-2264350171106.0ReleasedTerror goes into overtime.Sudden Death5.517419951.84
9James Bond Collection58000000710enJames Bond must unmask the mysterious head of the Janus Syndicate and prevent the leader from utilizing the GoldenEye weapons system to inflict devastating revenge on Britain.14.6860361995-11-16352194034130.0ReleasedNo limits. No fears. No substitutes.GoldenEye6.6119419956.07
belongs_to_collectionbudgetidoriginal_languageoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevote_averagevote_countrelease_yearreturn
45362NaN067179itSentenced to life imprisonment for illegal activities, Italian International member Giulio Manieri holds on to his political ideals while struggling against madness in the loneliness of his prison cell.0.2250511972-01-01090.0ReleasedNaNSt. Michael Had a Rooster6.0319720.0
45363NaN084419enAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.2228141946-03-29065.0ReleasedMeet...The CREEPER!House of Horrors6.3819460.0
45364NaN0390959enIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.0760612000-10-22045.0ReleasedNaNShadow of the Blair Witch7.0220000.0
45365NaN0289923enA film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.3864502000-10-03030.0ReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 77.0120000.0
45366NaN0222848enIt's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.6615581995-01-01085.0ReleasedNaNCaged Heat 30003.5119950.0
45367NaN030840enYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.6837531991-05-130104.0ReleasedNaNRobin Hood5.72619910.0
45368NaN0111109tlAn artist struggles to finish his work while a storyline about a cult plays in his head.0.1782412011-11-170360.0ReleasedNaNCentury of Birthing9.0320110.0
45369NaN067758enWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.9030072003-08-01090.0ReleasedA deadly game of wits.Betrayal3.8620030.0
45370NaN0227506enIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.0035031917-10-21087.0ReleasedNaNSatan Triumphant0.0019170.0
45371NaN0461257en50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.1630152017-06-09075.0ReleasedNaNQueerama0.0020170.0

Duplicate rows

Most frequently occurring

belongs_to_collectionbudgetidoriginal_languageoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevote_averagevote_countrelease_yearreturn# duplicates
0Why We Fight0159849enThe third film of Frank Capra's 'Why We Fight" propaganda film series, dealing with the Nazi conquest of Western Europe in 1940.0.4733221943-01-01057.0ReleasedNaNWhy We Fight: Divide and Conquer5.0119430.002
1NaN099080enOriginally called White Thunder, American producer Varick Frissell's 1931 film was inspired by his love for the Canadian Arctic Circle. Set in a beautifully black-and-white filmed Newfoundland, it is the story of a rivalry between two seal hunters that plays out on the ice floes during a hunt. Unsatisfied with the first cut, Frissell arranged for the crew to accompany an actual Newfoundland seal hunt on The SS Viking, on which an explosion of dynamite (carried regularly at the time on Arctic ships to combat ice jams) killed many members of the crew, including Frissell. The film was renamed in honor of the dead.0.0023621931-06-21070.0ReleasedActually produced during the Great Newfoundland Seal Hunt and You see the REAL thingThe Viking0.0019310.002
2NaN0132641jaTen years into a marriage, the wife is disappointed by the husband's lack of financial success, meaning she has to work and can't treat herself and the husband finds the wife slovenly and mean-spirited: she neither cooks not cleans particularly well and is generally disagreeable. In turn, he alternately ignores her and treats her as a servant. Neither is particularly happy, not helped by their unsatisfactory lodgers. The husband is easily seduced by an ex-colleague, a widow with a small child who needs some security, and considers leaving his wife.0.0960791953-04-29089.0ReleasedNaNWife0.0019530.002
3NaN0132641jaTen years into a marriage, the wife is disappointed by the husband's lack of financial success, meaning she has to work and can't treat herself and the husband finds the wife slovenly and mean-spirited: she neither cooks not cleans particularly well and is generally disagreeable. In turn, he alternately ignores her and treats her as a servant. Neither is particularly happy, not helped by their unsatisfactory lodgers. The husband is easily seduced by an ex-colleague, a widow with a small child who needs some security, and considers leaving his wife.0.6193881953-04-29089.0ReleasedNaNWife0.0019530.002
4NaN980000298721thIn a hospital, ten soldiers are being treated for a mysterious sleeping sickness. In a story in which dreams can be experienced by others, and in which goddesses can sit casually with mortals, a nurse learns the reason why the patients will never be cured, and forms a telepathic bond with one of them.2.5354192015-09-020122.0ReleasedNaNCemetery of Splendour4.45020150.002
5NaN3512454110428frWinter, 1915. Confined by her family to an asylum in the South of France - where she will never sculpt again - the chronicle of Camille Claudel's reclusive life, as she waits for a visit from her brother, Paul Claudel.0.1100652013-03-1311586095.0ReleasedNaNCamille Claudel 19157.02020130.032
6NaN3512454110428frWinter, 1915. Confined by her family to an asylum in the South of France - where she will never sculpt again - the chronicle of Camille Claudel's reclusive life, as she waits for a visit from her brother, Paul Claudel.0.1340142013-03-1311586095.0ReleasedNaNCamille Claudel 19157.02020130.032
7NaN1000000069234enCount de Chagnie has discovered Christine's singing talent on a market place and sent her to his friend Carriere, the director of the Parisian opera. However just when she arrives Carriere's dismissed. His arrogant successor refuses to let a woman of low birth sing in his opera, but graciously employs Christine as gadrobiere for his wife Charlotta, who's installed as first singer. He also fights the phantom, an unknown guy who lives since many years in the catacombs below the opera and was granted privileges by Carriere. However the phantom knows how to defend himself and at the same time helps Christine to her career.0.4384901990-03-180168.0ReleasedNaNThe Phantom of the Opera5.0319900.002
8NaN1000000069234enCount de Chagnie has discovered Christine's singing talent on a market place and sent her to his friend Carriere, the director of the Parisian opera. However just when she arrives Carriere's dismissed. His arrogant successor refuses to let a woman of low birth sing in his opera, but graciously employs Christine as gadrobiere for his wife Charlotta, who's installed as first singer. He also fights the phantom, an unknown guy who lives since many years in the catacombs below the opera and was granted privileges by Carriere. However the phantom knows how to defend himself and at the same time helps Christine to her career.0.4418721990-03-180168.0ReleasedNaNThe Phantom of the Opera5.0319900.002
9NaN300000004912enTelevision made him famous, but his biggest hits happened off screen. Television producer by day, CIA assassin by night, Chuck Barris was recruited by the CIA at the height of his TV career and trained to become a covert operative. Or so Barris said.7.6458272002-12-3033013805113.0ReleasedSome things are better left top secret.Confessions of a Dangerous Mind6.628120021.102