Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations13130
Missing cells6479
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1000.8 KiB
Average record size in memory78.1 B

Variable types

Numeric7
Categorical2
Text3
DateTime2

Alerts

AnimeID is highly overall correlated with SeasonYearHigh correlation
Favourites is highly overall correlated with MeanScore and 1 other fieldsHigh correlation
MeanScore is highly overall correlated with Favourites and 1 other fieldsHigh correlation
Popularity is highly overall correlated with Favourites and 1 other fieldsHigh correlation
SeasonYear is highly overall correlated with AnimeIDHigh correlation
EnglishTitle has 5923 (45.1%) missing values Missing
EndDate has 206 (1.6%) missing values Missing
Episodes is highly skewed (γ1 = 23.10140966) Skewed
AnimeID has unique values Unique
Favourites has 1194 (9.1%) zeros Zeros

Reproduction

Analysis started2025-01-09 22:04:23.283537
Analysis finished2025-01-09 22:04:30.360942
Duration7.08 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

AnimeID
Real number (ℝ)

High correlation  Unique 

Distinct13130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49953.676
Minimum1
Maximum185793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2025-01-10T06:04:30.441942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile714.45
Q13956.5
median15572
Q3105079.25
95-th percentile163293.1
Maximum185793
Range185792
Interquartile range (IQR)101122.75

Descriptive statistics

Standard deviation59236.339
Coefficient of variation (CV)1.1858254
Kurtosis-0.93265485
Mean49953.676
Median Absolute Deviation (MAD)13296.5
Skewness0.84570811
Sum6.5589176 × 108
Variance3.5089438 × 109
MonotonicityNot monotonic
2025-01-10T06:04:30.758687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185793 1
 
< 0.1%
9884 1
 
< 0.1%
19989 1
 
< 0.1%
9438 1
 
< 0.1%
17667 1
 
< 0.1%
19505 1
 
< 0.1%
168578 1
 
< 0.1%
6872 1
 
< 0.1%
12767 1
 
< 0.1%
20381 1
 
< 0.1%
Other values (13120) 13120
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
ValueCountFrequency (%)
185793 1
< 0.1%
185777 1
< 0.1%
185670 1
< 0.1%
185669 1
< 0.1%
185663 1
< 0.1%
185644 1
< 0.1%
185642 1
< 0.1%
185613 1
< 0.1%
185541 1
< 0.1%
185539 1
< 0.1%

Season
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
FALL
3620 
SPRING
3581 
SUMMER
3131 
WINTER
2798 

Length

Max length6
Median length6
Mean length5.448591
Min length4

Characters and Unicode

Total characters71540
Distinct characters14
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowWINTER
2nd rowWINTER
3rd rowSPRING
4th rowSPRING
5th rowSPRING

Common Values

ValueCountFrequency (%)
FALL 3620
27.6%
SPRING 3581
27.3%
SUMMER 3131
23.8%
WINTER 2798
21.3%

Length

2025-01-10T06:04:30.923239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-10T06:04:31.047240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
fall 3620
27.6%
spring 3581
27.3%
summer 3131
23.8%
winter 2798
21.3%

Most occurring characters

ValueCountFrequency (%)
R 9510
13.3%
L 7240
10.1%
S 6712
9.4%
I 6379
8.9%
N 6379
8.9%
M 6262
8.8%
E 5929
8.3%
F 3620
 
5.1%
A 3620
 
5.1%
P 3581
 
5.0%
Other values (4) 12308
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 9510
13.3%
L 7240
10.1%
S 6712
9.4%
I 6379
8.9%
N 6379
8.9%
M 6262
8.8%
E 5929
8.3%
F 3620
 
5.1%
A 3620
 
5.1%
P 3581
 
5.0%
Other values (4) 12308
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 9510
13.3%
L 7240
10.1%
S 6712
9.4%
I 6379
8.9%
N 6379
8.9%
M 6262
8.8%
E 5929
8.3%
F 3620
 
5.1%
A 3620
 
5.1%
P 3581
 
5.0%
Other values (4) 12308
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 9510
13.3%
L 7240
10.1%
S 6712
9.4%
I 6379
8.9%
N 6379
8.9%
M 6262
8.8%
E 5929
8.3%
F 3620
 
5.1%
A 3620
 
5.1%
P 3581
 
5.0%
Other values (4) 12308
17.2%

SeasonYear
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.7064
Minimum1940
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2025-01-10T06:04:31.186239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1940
5-th percentile1983
Q12001
median2011
Q32017
95-th percentile2023
Maximum2024
Range84
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.833594
Coefficient of variation (CV)0.0063921667
Kurtosis1.0055369
Mean2007.7064
Median Absolute Deviation (MAD)8
Skewness-1.0765501
Sum26361185
Variance164.70113
MonotonicityNot monotonic
2025-01-10T06:04:31.337239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2016 571
 
4.3%
2014 551
 
4.2%
2017 546
 
4.2%
2018 513
 
3.9%
2021 499
 
3.8%
2023 485
 
3.7%
2012 482
 
3.7%
2013 476
 
3.6%
2011 458
 
3.5%
2022 456
 
3.5%
Other values (71) 8093
61.6%
ValueCountFrequency (%)
1940 1
< 0.1%
1941 1
< 0.1%
1943 2
< 0.1%
1945 1
< 0.1%
1947 1
< 0.1%
1948 1
< 0.1%
1949 1
< 0.1%
1951 1
< 0.1%
1952 1
< 0.1%
1953 1
< 0.1%
ValueCountFrequency (%)
2024 442
3.4%
2023 485
3.7%
2022 456
3.5%
2021 499
3.8%
2020 427
3.3%
2019 456
3.5%
2018 513
3.9%
2017 546
4.2%
2016 571
4.3%
2015 354
2.7%

EnglishTitle
Text

Missing 

Distinct7180
Distinct (%)99.6%
Missing5923
Missing (%)45.1%
Memory size102.7 KiB
2025-01-10T06:04:31.598339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length133
Median length90
Mean length25.96656
Min length1

Characters and Unicode

Total characters187141
Distinct characters134
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

Unique7156 ?
Unique (%)99.3%

Sample

1st rowCyborg 009
2nd rowPictures at an Exhibition
3rd rowAdventures of the Monkey King
4th rowManual of Ninja Martial Arts
5th rowBaby Kangaroo's Birthday Surprise
ValueCountFrequency (%)
the 2211
 
7.1%
of 1082
 
3.5%
397
 
1.3%
2 365
 
1.2%
a 360
 
1.2%
and 284
 
0.9%
in 277
 
0.9%
season 257
 
0.8%
movie 222
 
0.7%
to 207
 
0.7%
Other values (7486) 25557
81.9%
2025-01-10T06:04:32.068385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24030
 
12.8%
e 15768
 
8.4%
a 12233
 
6.5%
o 11106
 
5.9%
i 10522
 
5.6%
n 9315
 
5.0%
r 9285
 
5.0%
t 8046
 
4.3%
s 6826
 
3.6%
l 5550
 
3.0%
Other values (124) 74460
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 187141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24030
 
12.8%
e 15768
 
8.4%
a 12233
 
6.5%
o 11106
 
5.9%
i 10522
 
5.6%
n 9315
 
5.0%
r 9285
 
5.0%
t 8046
 
4.3%
s 6826
 
3.6%
l 5550
 
3.0%
Other values (124) 74460
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 187141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24030
 
12.8%
e 15768
 
8.4%
a 12233
 
6.5%
o 11106
 
5.9%
i 10522
 
5.6%
n 9315
 
5.0%
r 9285
 
5.0%
t 8046
 
4.3%
s 6826
 
3.6%
l 5550
 
3.0%
Other values (124) 74460
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 187141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24030
 
12.8%
e 15768
 
8.4%
a 12233
 
6.5%
o 11106
 
5.9%
i 10522
 
5.6%
n 9315
 
5.0%
r 9285
 
5.0%
t 8046
 
4.3%
s 6826
 
3.6%
l 5550
 
3.0%
Other values (124) 74460
39.8%
Distinct12719
Distinct (%)97.6%
Missing97
Missing (%)0.7%
Memory size102.7 KiB
2025-01-10T06:04:32.280615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length85
Median length60
Mean length14.328858
Min length1

Characters and Unicode

Total characters186748
Distinct characters2504
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

Unique12428 ?
Unique (%)95.4%

Sample

1st rowおそ松くん(1966年版)
2nd row戦え! オスパー
3rd rowレインボー戦隊ロビン
4th rowハリスの旋風
5th row海賊王子
ValueCountFrequency (%)
the 568
 
2.4%
animation 295
 
1.3%
劇場版 151
 
0.6%
2 141
 
0.6%
ova 139
 
0.6%
of 122
 
0.5%
93
 
0.4%
movie 68
 
0.3%
season 66
 
0.3%
one 48
 
0.2%
Other values (14957) 21707
92.8%
2025-01-10T06:04:32.691860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10038
 
5.4%
5455
 
2.9%
4486
 
2.4%
4242
 
2.3%
2398
 
1.3%
2371
 
1.3%
2278
 
1.2%
A 2024
 
1.1%
1912
 
1.0%
E 1878
 
1.0%
Other values (2494) 149666
80.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 186748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10038
 
5.4%
5455
 
2.9%
4486
 
2.4%
4242
 
2.3%
2398
 
1.3%
2371
 
1.3%
2278
 
1.2%
A 2024
 
1.1%
1912
 
1.0%
E 1878
 
1.0%
Other values (2494) 149666
80.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 186748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10038
 
5.4%
5455
 
2.9%
4486
 
2.4%
4242
 
2.3%
2398
 
1.3%
2371
 
1.3%
2278
 
1.2%
A 2024
 
1.1%
1912
 
1.0%
E 1878
 
1.0%
Other values (2494) 149666
80.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 186748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10038
 
5.4%
5455
 
2.9%
4486
 
2.4%
4242
 
2.3%
2398
 
1.3%
2371
 
1.3%
2278
 
1.2%
A 2024
 
1.1%
1912
 
1.0%
E 1878
 
1.0%
Other values (2494) 149666
80.1%
Distinct13109
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size102.7 KiB
2025-01-10T06:04:32.945447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length156
Median length109
Mean length27.092612
Min length1

Characters and Unicode

Total characters355726
Distinct characters163
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

Unique13088 ?
Unique (%)99.7%

Sample

1st rowOsomatsu-kun
2nd rowTatakae! Osper
3rd rowRainbow Sentai Robin
4th rowHarisu no Kaze
5th rowKaizoku Ouji
ValueCountFrequency (%)
no 3922
 
7.0%
the 772
 
1.4%
to 717
 
1.3%
607
 
1.1%
ni 502
 
0.9%
wa 468
 
0.8%
2 421
 
0.7%
animation 361
 
0.6%
wo 313
 
0.6%
ga 305
 
0.5%
Other values (13864) 47768
85.1%
2025-01-10T06:04:33.407539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43040
 
12.1%
a 29967
 
8.4%
o 25740
 
7.2%
i 25449
 
7.2%
n 21874
 
6.1%
e 20618
 
5.8%
u 19477
 
5.5%
r 12092
 
3.4%
s 11826
 
3.3%
t 11174
 
3.1%
Other values (153) 134469
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 355726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
43040
 
12.1%
a 29967
 
8.4%
o 25740
 
7.2%
i 25449
 
7.2%
n 21874
 
6.1%
e 20618
 
5.8%
u 19477
 
5.5%
r 12092
 
3.4%
s 11826
 
3.3%
t 11174
 
3.1%
Other values (153) 134469
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 355726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
43040
 
12.1%
a 29967
 
8.4%
o 25740
 
7.2%
i 25449
 
7.2%
n 21874
 
6.1%
e 20618
 
5.8%
u 19477
 
5.5%
r 12092
 
3.4%
s 11826
 
3.3%
t 11174
 
3.1%
Other values (153) 134469
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 355726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
43040
 
12.1%
a 29967
 
8.4%
o 25740
 
7.2%
i 25449
 
7.2%
n 21874
 
6.1%
e 20618
 
5.8%
u 19477
 
5.5%
r 12092
 
3.4%
s 11826
 
3.3%
t 11174
 
3.1%
Other values (153) 134469
37.8%

Format
Categorical

Distinct7
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size13.3 KiB
TV
4125 
OVA
3452 
MOVIE
2027 
SPECIAL
1366 
ONA
1191 
Other values (2)
968 

Length

Max length8
Median length7
Mean length3.7766776
Min length2

Characters and Unicode

Total characters49584
Distinct characters16
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
TV 4125
31.4%
OVA 3452
26.3%
MOVIE 2027
15.4%
SPECIAL 1366
 
10.4%
ONA 1191
 
9.1%
TV_SHORT 956
 
7.3%
MUSIC 12
 
0.1%
(Missing) 1
 
< 0.1%

Length

2025-01-10T06:04:33.544538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-10T06:04:33.675538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
tv 4125
31.4%
ova 3452
26.3%
movie 2027
15.4%
special 1366
 
10.4%
ona 1191
 
9.1%
tv_short 956
 
7.3%
music 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
V 10560
21.3%
O 7626
15.4%
T 6037
12.2%
A 6009
12.1%
I 3405
 
6.9%
E 3393
 
6.8%
S 2334
 
4.7%
M 2039
 
4.1%
C 1378
 
2.8%
P 1366
 
2.8%
Other values (6) 5437
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 10560
21.3%
O 7626
15.4%
T 6037
12.2%
A 6009
12.1%
I 3405
 
6.9%
E 3393
 
6.8%
S 2334
 
4.7%
M 2039
 
4.1%
C 1378
 
2.8%
P 1366
 
2.8%
Other values (6) 5437
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 10560
21.3%
O 7626
15.4%
T 6037
12.2%
A 6009
12.1%
I 3405
 
6.9%
E 3393
 
6.8%
S 2334
 
4.7%
M 2039
 
4.1%
C 1378
 
2.8%
P 1366
 
2.8%
Other values (6) 5437
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 10560
21.3%
O 7626
15.4%
T 6037
12.2%
A 6009
12.1%
I 3405
 
6.9%
E 3393
 
6.8%
S 2334
 
4.7%
M 2039
 
4.1%
C 1378
 
2.8%
P 1366
 
2.8%
Other values (6) 5437
11.0%

MeanScore
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.233435
Minimum8
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2025-01-10T06:04:33.830634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile40
Q154
median63
Q369
95-th percentile78
Maximum100
Range92
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.680925
Coefficient of variation (CV)0.19076058
Kurtosis0.63676489
Mean61.233435
Median Absolute Deviation (MAD)7
Skewness-0.60098058
Sum803995
Variance136.44402
MonotonicityNot monotonic
2025-01-10T06:04:33.978633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 526
 
4.0%
68 514
 
3.9%
67 506
 
3.9%
66 492
 
3.7%
65 481
 
3.7%
62 481
 
3.7%
70 463
 
3.5%
64 456
 
3.5%
69 451
 
3.4%
61 451
 
3.4%
Other values (71) 8309
63.3%
ValueCountFrequency (%)
8 1
 
< 0.1%
10 23
0.2%
13 1
 
< 0.1%
15 4
 
< 0.1%
16 2
 
< 0.1%
17 1
 
< 0.1%
19 2
 
< 0.1%
20 7
 
0.1%
21 2
 
< 0.1%
22 3
 
< 0.1%
ValueCountFrequency (%)
100 2
 
< 0.1%
97 1
 
< 0.1%
91 3
 
< 0.1%
90 3
 
< 0.1%
89 9
 
0.1%
88 12
 
0.1%
87 19
0.1%
86 21
0.2%
85 38
0.3%
84 43
0.3%

Popularity
Real number (ℝ)

High correlation 

Distinct7565
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16981.422
Minimum9
Maximum828708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2025-01-10T06:04:34.127634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile130
Q1601
median2033.5
Q310335.5
95-th percentile82588.05
Maximum828708
Range828699
Interquartile range (IQR)9734.5

Descriptive statistics

Standard deviation49020.563
Coefficient of variation (CV)2.8867173
Kurtosis60.156937
Mean16981.422
Median Absolute Deviation (MAD)1787.5
Skewness6.6102982
Sum2.2296607 × 108
Variance2.4030156 × 109
MonotonicityNot monotonic
2025-01-10T06:04:34.278635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187 16
 
0.1%
142 14
 
0.1%
249 13
 
0.1%
230 13
 
0.1%
105 13
 
0.1%
159 13
 
0.1%
91 12
 
0.1%
217 12
 
0.1%
183 12
 
0.1%
163 12
 
0.1%
Other values (7555) 13000
99.0%
ValueCountFrequency (%)
9 1
 
< 0.1%
11 5
< 0.1%
13 7
0.1%
14 3
< 0.1%
15 7
0.1%
16 6
< 0.1%
17 5
< 0.1%
18 4
< 0.1%
19 3
< 0.1%
20 5
< 0.1%
ValueCountFrequency (%)
828708 1
< 0.1%
786001 1
< 0.1%
754353 1
< 0.1%
734243 1
< 0.1%
710530 1
< 0.1%
671375 1
< 0.1%
596090 1
< 0.1%
588689 1
< 0.1%
582602 1
< 0.1%
577592 1
< 0.1%

Episodes
Real number (ℝ)

Skewed 

Distinct191
Distinct (%)1.5%
Missing127
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean13.41152
Minimum1
Maximum1818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.7 KiB
2025-01-10T06:04:34.424637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q313
95-th percentile50.9
Maximum1818
Range1817
Interquartile range (IQR)12

Descriptive statistics

Standard deviation40.040023
Coefficient of variation (CV)2.9854947
Kurtosis831.64114
Mean13.41152
Median Absolute Deviation (MAD)3
Skewness23.10141
Sum174390
Variance1603.2034
MonotonicityNot monotonic
2025-01-10T06:04:34.606633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4609
35.1%
12 1820
 
13.9%
2 1198
 
9.1%
13 828
 
6.3%
3 533
 
4.1%
26 501
 
3.8%
4 408
 
3.1%
6 333
 
2.5%
24 285
 
2.2%
25 206
 
1.6%
Other values (181) 2282
17.4%
ValueCountFrequency (%)
1 4609
35.1%
2 1198
 
9.1%
3 533
 
4.1%
4 408
 
3.1%
5 147
 
1.1%
6 333
 
2.5%
7 78
 
0.6%
8 116
 
0.9%
9 55
 
0.4%
10 180
 
1.4%
ValueCountFrequency (%)
1818 1
< 0.1%
1787 1
< 0.1%
1428 1
< 0.1%
1006 1
< 0.1%
936 1
< 0.1%
773 1
< 0.1%
744 1
< 0.1%
726 1
< 0.1%
694 1
< 0.1%
526 1
< 0.1%

Favourites
Real number (ℝ)

High correlation  Zeros 

Distinct1675
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.69581
Minimum0
Maximum82676
Zeros1194
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2025-01-10T06:04:34.764633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median19
Q3107
95-th percentile1682.55
Maximum82676
Range82676
Interquartile range (IQR)103

Descriptive statistics

Standard deviation2232.1378
Coefficient of variation (CV)5.1946929
Kurtosis331.03071
Mean429.69581
Median Absolute Deviation (MAD)18
Skewness14.670886
Sum5641906
Variance4982439
MonotonicityNot monotonic
2025-01-10T06:04:34.915644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1194
 
9.1%
1 770
 
5.9%
2 551
 
4.2%
3 503
 
3.8%
4 487
 
3.7%
5 405
 
3.1%
6 341
 
2.6%
7 303
 
2.3%
8 269
 
2.0%
10 225
 
1.7%
Other values (1665) 8082
61.6%
ValueCountFrequency (%)
0 1194
9.1%
1 770
5.9%
2 551
4.2%
3 503
3.8%
4 487
3.7%
5 405
 
3.1%
6 341
 
2.6%
7 303
 
2.3%
8 269
 
2.0%
9 224
 
1.7%
ValueCountFrequency (%)
82676 1
< 0.1%
66905 1
< 0.1%
51619 1
< 0.1%
50172 1
< 0.1%
48965 1
< 0.1%
41663 1
< 0.1%
41458 1
< 0.1%
41379 1
< 0.1%
41247 1
< 0.1%
32640 1
< 0.1%

Duration
Real number (ℝ)

Distinct153
Distinct (%)1.2%
Missing94
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean29.641378
Minimum1
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.7 KiB
2025-01-10T06:04:35.229866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q117
median24
Q330
95-th percentile93
Maximum168
Range167
Interquartile range (IQR)13

Descriptive statistics

Standard deviation25.776836
Coefficient of variation (CV)0.86962343
Kurtosis3.4909632
Mean29.641378
Median Absolute Deviation (MAD)6
Skewness1.8755554
Sum386405
Variance664.4453
MonotonicityNot monotonic
2025-01-10T06:04:35.389863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 2732
20.8%
25 1105
 
8.4%
30 1053
 
8.0%
23 843
 
6.4%
5 429
 
3.3%
3 421
 
3.2%
2 383
 
2.9%
20 376
 
2.9%
1 345
 
2.6%
4 321
 
2.4%
Other values (143) 5028
38.3%
ValueCountFrequency (%)
1 345
2.6%
2 383
2.9%
3 421
3.2%
4 321
2.4%
5 429
3.3%
6 155
 
1.2%
7 109
 
0.8%
8 124
 
0.9%
9 89
 
0.7%
10 174
1.3%
ValueCountFrequency (%)
168 1
 
< 0.1%
163 1
 
< 0.1%
162 1
 
< 0.1%
161 1
 
< 0.1%
160 1
 
< 0.1%
155 1
 
< 0.1%
153 1
 
< 0.1%
152 1
 
< 0.1%
150 4
< 0.1%
147 1
 
< 0.1%
Distinct6355
Distinct (%)48.5%
Missing31
Missing (%)0.2%
Memory size102.7 KiB
Minimum1940-09-19 00:00:00
Maximum2024-12-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-10T06:04:35.548863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:35.714865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EndDate
Date

Missing 

Distinct6331
Distinct (%)49.0%
Missing206
Missing (%)1.6%
Memory size102.7 KiB
Minimum1940-09-19 00:00:00
Maximum2025-03-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-10T06:04:35.870873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:36.033873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-01-10T06:04:28.943770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:24.321512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.145756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.972073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.686075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.402116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.182116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:29.066770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:24.482734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.253752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.082074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.796077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.519116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.293116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:29.174770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:24.590755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.349755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.181290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.894077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.624116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.420614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:29.290771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:24.702757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.446075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.280518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.991075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.728116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.518731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:29.414770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:24.810754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.544074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.375080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.086116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.834116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.618731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:29.528772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:24.926753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.652073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.485076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.195116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.946117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.738729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:29.633770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.029755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:25.748074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:26.578075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:27.292116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.064117image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-10T06:04:28.833770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-10T06:04:36.142873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AnimeIDDurationEpisodesFavouritesFormatMeanScorePopularitySeasonSeasonYear
AnimeID1.000-0.285-0.0460.0500.150-0.031-0.0070.0540.785
Duration-0.2851.000-0.3800.1230.4430.1830.0820.054-0.311
Episodes-0.046-0.3801.0000.2310.0510.1650.1810.0210.041
Favourites0.0500.1230.2311.0000.0470.7360.9410.0000.351
Format0.1500.4430.0510.0471.0000.1770.1060.0880.183
MeanScore-0.0310.1830.1650.7360.1771.0000.6890.0250.178
Popularity-0.0070.0820.1810.9410.1060.6891.0000.0000.339
Season0.0540.0540.0210.0000.0880.0250.0001.0000.055
SeasonYear0.785-0.3110.0410.3510.1830.1780.3390.0551.000

Missing values

2025-01-10T06:04:29.804770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-10T06:04:30.066942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-10T06:04:30.251943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AnimeIDSeasonSeasonYearEnglishTitleNativeTitleRomajiTitleFormatMeanScorePopularityEpisodesFavouritesDurationStartDateEndDate
09884WINTER1966Noneおそ松くん(1966年版)Osomatsu-kunTV63112756.0825.01966-02-051967-03-25
119989WINTER1966None戦え! オスパーTatakae! OsperTV6819252.0224.01965-12-141967-10-11
29438SPRING1966Noneレインボー戦隊ロビンRainbow Sentai RobinTV4636648.0226.01966-04-231967-03-24
317667SPRING1966Noneハリスの旋風Harisu no KazeTV7430870.0725.01966-05-051967-08-31
419505SPRING1966None海賊王子Kaizoku OujiTV4529631.0225.01966-05-021966-11-28
5168578SPRING1966None孫悟空が始まるよー 黄風大王の巻Son Gokuu ga Hajimaru yoo: Koufuu Daiou no MakiMOVIE35651.0023.01966-06-121966-06-12
66872SUMMER1966Cyborg 009サイボーグ009Cyborg 009 (Movie)MOVIE5511481.0565.01966-07-211966-07-21
712767SUMMER1966None遊星仮面Yuusei KamenTV4125539.0225.01966-06-031967-02-21
820381SUMMER1966Noneジャングル大帝 劇場版Jungle Taitei MovieMOVIE645641.0275.01966-07-311966-07-31
92565FALL1966Noneジャングル大帝・進めレオJungle Taitei: Susume Leo!TV6370626.0620.01966-10-051967-03-29
AnimeIDSeasonSeasonYearEnglishTitleNativeTitleRomajiTitleFormatMeanScorePopularityEpisodesFavouritesDurationStartDateEndDate
13120184099FALL2024NoneAgent BlueAgent BlueONA441981.011.02024-11-102024-11-10
13121184288FALL2024Noneカズヒホとマリーのようこそキッチン。Kazuhiho to Marie no Youkoso Kitchen.ONA60876.033.02024-11-142024-12-19
13122184694FALL2024Solo Leveling -ReAwakening-俺だけレベルアップな件 -ReAwakening-Ore dake Level Up na Ken: ReAwakeningMOVIE8279271.0348114.02024-11-292024-11-29
13123184998FALL2024Noneまぁるい彼女と残念な彼氏 第2期Maarui Kanojo to Zannen na Kareshi 2nd SeasonTV_SHORT60516.0011.02024-11-162024-12-21
13124185281FALL2024Noneゴムをつけてといいましたよね...Gomu wo Tsukete to Iimashita yo ne...ONA703232.02216.02024-12-132024-12-27
13125185538FALL2024NoneどこでもマキバオーDokodemo MakibaoONA4030NaN05.02024-12-16NaT
13126185539FALL2024Noneアルプスの老人ハイジのおじいさんAlps no Roujin Heidi no Ojii-sanONA2559NaN01.02024-12-18NaT
13127185541FALL2024Future Folktales Season 2アサティール2 未来の昔ばなしAsatir 2: Mirai no MukashibanashiONA5777NaN325.02024-11-03NaT
13128185613FALL2024The Lord of the Rings: The War of the Rohirrimロード・オブ・ザ・リング/ローハンの戦いThe Lord of the Rings: Rohan no TatakaiMOVIE6815121.037134.02024-12-132024-12-13
13129185793FALL2024Noneユーチューニャークリスマス特別編YouTuNya: Christmas Tokubetsu-henSPECIAL68471.003.02024-12-242024-12-24