Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells81
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory149.3 B

Variable types

Categorical4
Numeric9
Text4

Alerts

stats_year has constant value ""Constant
sn is highly overall correlated with fclty_la and 4 other fieldsHigh correlation
fclty_la is highly overall correlated with sn and 4 other fieldsHigh correlation
hldy_viewng_end_time is highly overall correlated with sn and 5 other fieldsHigh correlation
adult_viewng_ct is highly overall correlated with sn and 4 other fieldsHigh correlation
yngbgs_viewng_ct is highly overall correlated with sn and 4 other fieldsHigh correlation
child_viewng_ct is highly overall correlated with sn and 5 other fieldsHigh correlation
wkday_viewng_begin_time is highly overall correlated with hldy_viewng_begin_timeHigh correlation
hldy_viewng_begin_time is highly overall correlated with hldy_viewng_end_time and 2 other fieldsHigh correlation
viewng_ct_relate_cn has 81 (81.0%) missing valuesMissing
sn has unique valuesUnique
fclty_nm has unique valuesUnique
fclty_rn_addr has unique valuesUnique
oper_instt_tel_no has 2 (2.0%) zerosZeros
hldy_viewng_end_time has 67 (67.0%) zerosZeros
adult_viewng_ct has 66 (66.0%) zerosZeros
yngbgs_viewng_ct has 65 (65.0%) zerosZeros
child_viewng_ct has 65 (65.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:06:56.618505
Analysis finished2023-12-10 10:07:12.757628
Duration16.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

stats_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 100
100.0%

Length

2023-12-10T19:07:12.896489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:13.092976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 100
100.0%

sn
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean295.97
Minimum1
Maximum1877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:13.325360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.9
Q148.75
median183
Q3400
95-th percentile804.3
Maximum1877
Range1876
Interquartile range (IQR)351.25

Descriptive statistics

Standard deviation369.87755
Coefficient of variation (CV)1.249713
Kurtosis5.1795361
Mean295.97
Median Absolute Deviation (MAD)142.5
Skewness2.1396413
Sum29597
Variance136809.4
MonotonicityNot monotonic
2023-12-10T19:07:13.583020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
271 1
 
1.0%
53 1
 
1.0%
59 1
 
1.0%
58 1
 
1.0%
57 1
 
1.0%
1619 1
 
1.0%
56 1
 
1.0%
563 1
 
1.0%
562 1
 
1.0%
561 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
8 1
1.0%
16 1
1.0%
17 1
1.0%
19 1
1.0%
20 1
1.0%
21 1
1.0%
22 1
1.0%
23 1
1.0%
ValueCountFrequency (%)
1877 1
1.0%
1619 1
1.0%
1504 1
1.0%
1444 1
1.0%
1190 1
1.0%
784 1
1.0%
779 1
1.0%
758 1
1.0%
728 1
1.0%
727 1
1.0%

fclty_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:13.976071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length15
Mean length9.28
Min length5

Characters and Unicode

Total characters928
Distinct characters237
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row동산박물관
2nd row공주기독교박물관
3rd row월산미술관
4th row순창공립미술관
5th row원광대학교 박물관
ValueCountFrequency (%)
박물관 7
 
5.1%
미술관 2
 
1.5%
art 2
 
1.5%
동산박물관 1
 
0.7%
파파월드(휴관 1
 
0.7%
보석앤화석박물관(휴관 1
 
0.7%
창조자연사박물관 1
 
0.7%
세종대학교박물관 1
 
0.7%
조병화문학관 1
 
0.7%
조명박물관 1
 
0.7%
Other values (119) 119
86.9%
2023-12-10T19:07:14.712682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 108
 
11.6%
물 70
 
7.5%
박 69
 
7.4%
38
 
4.1%
교 25
 
2.7%
학 20
 
2.2%
대 19
 
2.0%
술 18
 
1.9%
) 16
 
1.7%
( 16
 
1.7%
Other values (227) 529
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 817
88.0%
Space Separator 38
 
4.1%
Uppercase Letter 21
 
2.3%
Close Punctuation 16
 
1.7%
Open Punctuation 16
 
1.7%
Lowercase Letter 15
 
1.6%
Decimal Number 3
 
0.3%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 108
 
13.2%
물 70
 
8.6%
박 69
 
8.4%
교 25
 
3.1%
학 20
 
2.4%
대 19
 
2.3%
술 18
 
2.2%
휴 14
 
1.7%
한 14
 
1.7%
미 14
 
1.7%
Other values (204) 446
54.6%
Uppercase Letter
ValueCountFrequency (%)
A 5
23.8%
M 5
23.8%
C 3
14.3%
O 2
 
9.5%
K 2
 
9.5%
J 1
 
4.8%
I 1
 
4.8%
D 1
 
4.8%
U 1
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
u 4
26.7%
m 2
13.3%
r 2
13.3%
t 2
13.3%
e 2
13.3%
s 2
13.3%
f 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
6 1
33.3%
5 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 817
88.0%
Common 75
 
8.1%
Latin 36
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 108
 
13.2%
물 70
 
8.6%
박 69
 
8.4%
교 25
 
3.1%
학 20
 
2.4%
대 19
 
2.3%
술 18
 
2.2%
휴 14
 
1.7%
한 14
 
1.7%
미 14
 
1.7%
Other values (204) 446
54.6%
Latin
ValueCountFrequency (%)
A 5
13.9%
M 5
13.9%
u 4
11.1%
C 3
8.3%
O 2
 
5.6%
K 2
 
5.6%
m 2
 
5.6%
r 2
 
5.6%
t 2
 
5.6%
e 2
 
5.6%
Other values (6) 7
19.4%
Common
ValueCountFrequency (%)
38
50.7%
) 16
21.3%
( 16
21.3%
. 2
 
2.7%
6 1
 
1.3%
5 1
 
1.3%
1 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 817
88.0%
ASCII 111
 
12.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 108
 
13.2%
물 70
 
8.6%
박 69
 
8.4%
교 25
 
3.1%
학 20
 
2.4%
대 19
 
2.3%
술 18
 
2.2%
휴 14
 
1.7%
한 14
 
1.7%
미 14
 
1.7%
Other values (204) 446
54.6%
ASCII
ValueCountFrequency (%)
38
34.2%
) 16
14.4%
( 16
14.4%
A 5
 
4.5%
M 5
 
4.5%
u 4
 
3.6%
C 3
 
2.7%
O 2
 
1.8%
. 2
 
1.8%
K 2
 
1.8%
Other values (13) 18
16.2%

fclty_se_nm
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
사립
66 
대학
19 
공립
15 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사립
2nd row사립
3rd row사립
4th row공립
5th row대학

Common Values

ValueCountFrequency (%)
사립 66
66.0%
대학 19
 
19.0%
공립 15
 
15.0%

Length

2023-12-10T19:07:14.949397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:15.180627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립 66
66.0%
대학 19
 
19.0%
공립 15
 
15.0%

fclty_rn_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:15.660124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length21.2
Min length13

Characters and Unicode

Total characters2120
Distinct characters209
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row충청남도 공주시 반포면 정광터1길 108-8
2nd row충청남도 공주시 제민1길 18(봉항동10)
3rd row강원도 동해시 삼화로 520
4th row전라북도 순창군 순창읍 남계로 81
5th row전라북도 익산시 익산대로 460
ValueCountFrequency (%)
경기도 48
 
9.9%
강원도 9
 
1.9%
충청북도 9
 
1.9%
충청남도 8
 
1.6%
파주시 8
 
1.6%
탄현면 6
 
1.2%
용인시 6
 
1.2%
전라북도 6
 
1.2%
제주특별자치시 5
 
1.0%
남양주시 5
 
1.0%
Other values (324) 375
77.3%
2023-12-10T19:07:16.613209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
385
 
18.2%
시 89
 
4.2%
도 87
 
4.1%
1 69
 
3.3%
로 58
 
2.7%
경 53
 
2.5%
기 51
 
2.4%
길 45
 
2.1%
2 44
 
2.1%
주 42
 
2.0%
Other values (199) 1197
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1341
63.3%
Space Separator 385
 
18.2%
Decimal Number 342
 
16.1%
Dash Punctuation 34
 
1.6%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Math Symbol 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 89
 
6.6%
도 87
 
6.5%
로 58
 
4.3%
경 53
 
4.0%
기 51
 
3.8%
길 45
 
3.4%
주 42
 
3.1%
면 40
 
3.0%
산 26
 
1.9%
남 26
 
1.9%
Other values (182) 824
61.4%
Decimal Number
ValueCountFrequency (%)
1 69
20.2%
2 44
12.9%
3 35
10.2%
6 35
10.2%
4 31
9.1%
7 29
8.5%
5 28
8.2%
0 26
 
7.6%
8 24
 
7.0%
9 21
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
385
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1341
63.3%
Common 777
36.7%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 89
 
6.6%
도 87
 
6.5%
로 58
 
4.3%
경 53
 
4.0%
기 51
 
3.8%
길 45
 
3.4%
주 42
 
3.1%
면 40
 
3.0%
산 26
 
1.9%
남 26
 
1.9%
Other values (182) 824
61.4%
Common
ValueCountFrequency (%)
385
49.5%
1 69
 
8.9%
2 44
 
5.7%
3 35
 
4.5%
6 35
 
4.5%
- 34
 
4.4%
4 31
 
4.0%
7 29
 
3.7%
5 28
 
3.6%
0 26
 
3.3%
Other values (5) 61
 
7.9%
Latin
ValueCountFrequency (%)
B 1
50.0%
K 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1341
63.3%
ASCII 779
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
385
49.4%
1 69
 
8.9%
2 44
 
5.6%
3 35
 
4.5%
6 35
 
4.5%
- 34
 
4.4%
4 31
 
4.0%
7 29
 
3.7%
5 28
 
3.6%
0 26
 
3.3%
Other values (7) 63
 
8.1%
Hangul
ValueCountFrequency (%)
시 89
 
6.6%
도 87
 
6.5%
로 58
 
4.3%
경 53
 
4.0%
기 51
 
3.8%
길 45
 
3.4%
주 42
 
3.1%
면 40
 
3.0%
산 26
 
1.9%
남 26
 
1.9%
Other values (182) 824
61.4%

fclty_la
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.866005
Minimum33.31968
Maximum38.471884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:16.864358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.31968
5-th percentile34.906144
Q136.408722
median37.258621
Q337.571413
95-th percentile37.873353
Maximum38.471884
Range5.1522043
Interquartile range (IQR)1.1626909

Descriptive statistics

Standard deviation1.0746586
Coefficient of variation (CV)0.029150394
Kurtosis2.7134821
Mean36.866005
Median Absolute Deviation (MAD)0.4517845
Skewness-1.6283328
Sum3686.6005
Variance1.1548911
MonotonicityNot monotonic
2023-12-10T19:07:17.113431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.483831 2
 
2.0%
36.4025739 1
 
1.0%
37.4333408 1
 
1.0%
37.8360961 1
 
1.0%
37.1120797 1
 
1.0%
37.552054 1
 
1.0%
37.4337151 1
 
1.0%
33.344244 1
 
1.0%
33.412436 1
 
1.0%
33.324067 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
33.31968 1
1.0%
33.324067 1
1.0%
33.344244 1
1.0%
33.412436 1
1.0%
34.8855358 1
1.0%
34.907229 1
1.0%
34.9965623 1
1.0%
35.1692383 1
1.0%
35.231982 1
1.0%
35.2524129 1
1.0%
ValueCountFrequency (%)
38.4718843 1
1.0%
38.1454768 1
1.0%
38.0683553 1
1.0%
38.0115903 1
1.0%
37.9226458 1
1.0%
37.8707582 1
1.0%
37.8360961 1
1.0%
37.7908045 1
1.0%
37.78926 1
1.0%
37.7876293 1
1.0%

fclty_lo
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.30861
Minimum126.25856
Maximum129.07206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:17.370435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.25856
5-th percentile126.68658
Q1126.96571
median127.17567
Q3127.53497
95-th percentile128.56448
Maximum129.07206
Range2.8134973
Interquartile range (IQR)0.5692675

Descriptive statistics

Standard deviation0.57451231
Coefficient of variation (CV)0.0045127529
Kurtosis1.7139421
Mean127.30861
Median Absolute Deviation (MAD)0.2918397
Skewness1.2221823
Sum12730.861
Variance0.33006439
MonotonicityNot monotonic
2023-12-10T19:07:17.974398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.48339 2
 
2.0%
127.23221 1
 
1.0%
126.8783874 1
 
1.0%
126.9675329 1
 
1.0%
127.2216269 1
 
1.0%
127.073644 1
 
1.0%
126.786472 1
 
1.0%
126.84355 1
 
1.0%
126.39346 1
 
1.0%
126.25856 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.25856 1
1.0%
126.38617 1
1.0%
126.39346 1
1.0%
126.53974 1
1.0%
126.6803558 1
1.0%
126.6869104 1
1.0%
126.6875615 1
1.0%
126.6973166 1
1.0%
126.6978711 1
1.0%
126.697936 1
1.0%
ValueCountFrequency (%)
129.0720573 1
1.0%
129.0542004 1
1.0%
129.0213133 1
1.0%
128.8789901 1
1.0%
128.6856444 1
1.0%
128.5581006 1
1.0%
128.4221775 1
1.0%
128.2692246 1
1.0%
128.2534197 1
1.0%
128.0105868 1
1.0%

oper_instt_tel_no
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9703631 × 108
Minimum0
Maximum7.0778089 × 109
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:18.223955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1114989 × 108
Q13.1540973 × 108
median3.1976002 × 108
Q34.3740832 × 108
95-th percentile6.4794772 × 108
Maximum7.0778089 × 109
Range7.0778089 × 109
Interquartile range (IQR)1.2199859 × 108

Descriptive statistics

Standard deviation7.8627449 × 108
Coefficient of variation (CV)1.5819257
Kurtosis52.489535
Mean4.9703631 × 108
Median Absolute Deviation (MAD)17794222
Skewness6.8427226
Sum4.9703631 × 1010
Variance6.1822757 × 1017
MonotonicityNot monotonic
2023-12-10T19:07:18.456321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.0%
418585009 1
 
1.0%
319495675 1
 
1.0%
317718577 1
 
1.0%
7077808911 1
 
1.0%
316740307 1
 
1.0%
234083277 1
 
1.0%
314351009 1
 
1.0%
647877831 1
 
1.0%
647997272 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0 2
2.0%
23201323 1
1.0%
25024123 1
1.0%
28980505 1
1.0%
220737755 1
1.0%
221380952 1
1.0%
221837373 1
1.0%
231443700 1
1.0%
234083277 1
1.0%
312012014 1
1.0%
ValueCountFrequency (%)
7077808911 1
1.0%
3180203001 1
1.0%
3180052389 1
1.0%
656343464 1
1.0%
647997272 1
1.0%
647945115 1
1.0%
647877831 1
1.0%
647393906 1
1.0%
638505483 1
1.0%
638417277 1
1.0%

wkday_viewng_begin_time
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1000
48 
900
44 
1100
 
4
1030
 
3
1300
 
1

Length

Max length4
Median length4
Mean length3.56
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1000
2nd row1000
3rd row900
4th row900
5th row1000

Common Values

ValueCountFrequency (%)
1000 48
48.0%
900 44
44.0%
1100 4
 
4.0%
1030 3
 
3.0%
1300 1
 
1.0%

Length

2023-12-10T19:07:18.687035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:18.890533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1000 48
48.0%
900 44
44.0%
1100 4
 
4.0%
1030 3
 
3.0%
1300 1
 
1.0%

wkday_viewng_end_time
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1771.6
Minimum1500
Maximum2200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:19.020582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile1600
Q11700
median1800
Q31800
95-th percentile1800
Maximum2200
Range700
Interquartile range (IQR)100

Descriptive statistics

Standard deviation77.273226
Coefficient of variation (CV)0.043617762
Kurtosis10.256689
Mean1771.6
Median Absolute Deviation (MAD)0
Skewness0.66367536
Sum177160
Variance5971.1515
MonotonicityNot monotonic
2023-12-10T19:07:19.181045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1800 69
69.0%
1700 21
 
21.0%
1600 5
 
5.0%
1830 2
 
2.0%
1900 1
 
1.0%
1500 1
 
1.0%
2200 1
 
1.0%
ValueCountFrequency (%)
1500 1
 
1.0%
1600 5
 
5.0%
1700 21
 
21.0%
1800 69
69.0%
1830 2
 
2.0%
1900 1
 
1.0%
2200 1
 
1.0%
ValueCountFrequency (%)
2200 1
 
1.0%
1900 1
 
1.0%
1830 2
 
2.0%
1800 69
69.0%
1700 21
 
21.0%
1600 5
 
5.0%
1500 1
 
1.0%

hldy_viewng_begin_time
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
67 
1000
27 
1100
 
4
1030
 
2

Length

Max length4
Median length1
Mean length1.99
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67
67.0%
1000 27
27.0%
1100 4
 
4.0%
1030 2
 
2.0%

Length

2023-12-10T19:07:19.377029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:19.522549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 67
67.0%
1000 27
27.0%
1100 4
 
4.0%
1030 2
 
2.0%

hldy_viewng_end_time
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.3
Minimum0
Maximum2200
Zeros67
Zeros (%)67.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:19.635128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31725
95-th percentile1800
Maximum2200
Range2200
Interquartile range (IQR)1725

Descriptive statistics

Standard deviation849.87801
Coefficient of variation (CV)1.4348776
Kurtosis-1.4464518
Mean592.3
Median Absolute Deviation (MAD)0
Skewness0.74793734
Sum59230
Variance722292.64
MonotonicityNot monotonic
2023-12-10T19:07:19.806891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
67.0%
1800 22
 
22.0%
1700 8
 
8.0%
2000 1
 
1.0%
1830 1
 
1.0%
2200 1
 
1.0%
ValueCountFrequency (%)
0 67
67.0%
1700 8
 
8.0%
1800 22
 
22.0%
1830 1
 
1.0%
2000 1
 
1.0%
2200 1
 
1.0%
ValueCountFrequency (%)
2200 1
 
1.0%
2000 1
 
1.0%
1830 1
 
1.0%
1800 22
 
22.0%
1700 8
 
8.0%
0 67
67.0%

close_relate_cn
Text

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:20.132344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length31
Mean length13.05
Min length3

Characters and Unicode

Total characters1305
Distinct characters103
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)46.0%

Sample

1st row공휴일
2nd row매주 월요일/ 일요일
3rd row관람시각 정보 없음
4th row매주 월요일 /관람시각 정보 없음
5th row공휴일/학교기념일/원불교기념일
ValueCountFrequency (%)
관람시각 44
13.1%
정보 43
12.8%
없음 43
12.8%
매주 28
 
8.4%
공휴일 18
 
5.4%
월요일 17
 
5.1%
추석 11
 
3.3%
설날 9
 
2.7%
월 8
 
2.4%
및 7
 
2.1%
Other values (70) 107
31.9%
2023-12-10T19:07:20.846182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
243
18.6%
일 94
 
7.2%
/ 83
 
6.4%
관 55
 
4.2%
정 55
 
4.2%
월 50
 
3.8%
음 45
 
3.4%
시 44
 
3.4%
각 44
 
3.4%
람 44
 
3.4%
Other values (93) 548
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 921
70.6%
Space Separator 243
 
18.6%
Other Punctuation 94
 
7.2%
Decimal Number 33
 
2.5%
Open Punctuation 6
 
0.5%
Close Punctuation 6
 
0.5%
Math Symbol 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 94
 
10.2%
관 55
 
6.0%
정 55
 
6.0%
월 50
 
5.4%
음 45
 
4.9%
시 44
 
4.8%
각 44
 
4.8%
람 44
 
4.8%
보 43
 
4.7%
없 43
 
4.7%
Other values (77) 404
43.9%
Decimal Number
ValueCountFrequency (%)
1 15
45.5%
0 7
21.2%
2 6
 
18.2%
5 3
 
9.1%
7 1
 
3.0%
6 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/ 83
88.3%
. 5
 
5.3%
, 4
 
4.3%
: 1
 
1.1%
? 1
 
1.1%
Space Separator
ValueCountFrequency (%)
243
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 921
70.6%
Common 384
29.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 94
 
10.2%
관 55
 
6.0%
정 55
 
6.0%
월 50
 
5.4%
음 45
 
4.9%
시 44
 
4.8%
각 44
 
4.8%
람 44
 
4.8%
보 43
 
4.7%
없 43
 
4.7%
Other values (77) 404
43.9%
Common
ValueCountFrequency (%)
243
63.3%
/ 83
 
21.6%
1 15
 
3.9%
0 7
 
1.8%
( 6
 
1.6%
2 6
 
1.6%
) 6
 
1.6%
. 5
 
1.3%
, 4
 
1.0%
5 3
 
0.8%
Other values (6) 6
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 921
70.6%
ASCII 384
29.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
243
63.3%
/ 83
 
21.6%
1 15
 
3.9%
0 7
 
1.8%
( 6
 
1.6%
2 6
 
1.6%
) 6
 
1.6%
. 5
 
1.3%
, 4
 
1.0%
5 3
 
0.8%
Other values (6) 6
 
1.6%
Hangul
ValueCountFrequency (%)
일 94
 
10.2%
관 55
 
6.0%
정 55
 
6.0%
월 50
 
5.4%
음 45
 
4.9%
시 44
 
4.8%
각 44
 
4.8%
람 44
 
4.8%
보 43
 
4.7%
없 43
 
4.7%
Other values (77) 404
43.9%

adult_viewng_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1575
Minimum0
Maximum9000
Zeros66
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:21.171533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33000
95-th percentile7000
Maximum9000
Range9000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation2541.9461
Coefficient of variation (CV)1.613934
Kurtosis0.69446399
Mean1575
Median Absolute Deviation (MAD)0
Skewness1.3971516
Sum157500
Variance6461489.9
MonotonicityNot monotonic
2023-12-10T19:07:21.578255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 66
66.0%
5000 9
 
9.0%
2000 4
 
4.0%
6000 4
 
4.0%
3000 4
 
4.0%
9000 2
 
2.0%
7000 2
 
2.0%
1000 2
 
2.0%
4000 2
 
2.0%
8000 2
 
2.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
0 66
66.0%
1000 2
 
2.0%
1500 1
 
1.0%
2000 4
 
4.0%
3000 4
 
4.0%
3500 1
 
1.0%
4000 2
 
2.0%
5000 9
 
9.0%
5500 1
 
1.0%
6000 4
 
4.0%
ValueCountFrequency (%)
9000 2
 
2.0%
8000 2
 
2.0%
7000 2
 
2.0%
6000 4
4.0%
5500 1
 
1.0%
5000 9
9.0%
4000 2
 
2.0%
3500 1
 
1.0%
3000 4
4.0%
2000 4
4.0%

yngbgs_viewng_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1633
Minimum0
Maximum40000
Zeros65
Zeros (%)65.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:21.812670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32125
95-th percentile5525
Maximum40000
Range40000
Interquartile range (IQR)2125

Descriptive statistics

Standard deviation4379.3375
Coefficient of variation (CV)2.6817743
Kurtosis60.289924
Mean1633
Median Absolute Deviation (MAD)0
Skewness7.0254689
Sum163300
Variance19178597
MonotonicityNot monotonic
2023-12-10T19:07:22.047408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 65
65.0%
5000 7
 
7.0%
3000 5
 
5.0%
4000 5
 
5.0%
2000 4
 
4.0%
1500 3
 
3.0%
2500 2
 
2.0%
7000 2
 
2.0%
1000 1
 
1.0%
5500 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0 65
65.0%
600 1
 
1.0%
700 1
 
1.0%
1000 1
 
1.0%
1500 3
 
3.0%
2000 4
 
4.0%
2500 2
 
2.0%
3000 5
 
5.0%
4000 5
 
5.0%
5000 7
 
7.0%
ValueCountFrequency (%)
40000 1
 
1.0%
8000 1
 
1.0%
7000 2
 
2.0%
6000 1
 
1.0%
5500 1
 
1.0%
5000 7
7.0%
4000 5
5.0%
3000 5
5.0%
2500 2
 
2.0%
2000 4
4.0%

child_viewng_ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1156
Minimum0
Maximum8000
Zeros65
Zeros (%)65.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:22.257753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32000
95-th percentile5000
Maximum8000
Range8000
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation1937.838
Coefficient of variation (CV)1.6763305
Kurtosis2.0186989
Mean1156
Median Absolute Deviation (MAD)0
Skewness1.6781037
Sum115600
Variance3755216.2
MonotonicityNot monotonic
2023-12-10T19:07:22.471637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 65
65.0%
3000 9
 
9.0%
5000 5
 
5.0%
2000 5
 
5.0%
1000 4
 
4.0%
4000 3
 
3.0%
7000 2
 
2.0%
4500 1
 
1.0%
700 1
 
1.0%
400 1
 
1.0%
Other values (4) 4
 
4.0%
ValueCountFrequency (%)
0 65
65.0%
400 1
 
1.0%
700 1
 
1.0%
1000 4
 
4.0%
1500 1
 
1.0%
2000 5
 
5.0%
2500 1
 
1.0%
3000 9
 
9.0%
4000 3
 
3.0%
4500 1
 
1.0%
ValueCountFrequency (%)
8000 1
 
1.0%
7000 2
 
2.0%
6000 1
 
1.0%
5000 5
5.0%
4500 1
 
1.0%
4000 3
 
3.0%
3000 9
9.0%
2500 1
 
1.0%
2000 5
5.0%
1500 1
 
1.0%

viewng_ct_relate_cn
Text

MISSING 

Distinct15
Distinct (%)78.9%
Missing81
Missing (%)81.0%
Memory size932.0 B
2023-12-10T19:07:22.872034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length115
Median length100
Mean length88.578947
Min length1

Characters and Unicode

Total characters1683
Distinct characters38
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)57.9%

Sample

1st row특별전시입장료-일반(5000)/특별전시입장료-미취학(3000)/특별전시입장료-유치원/초등학생(3000)/ 특별전시입장료-청소년 (3000)/ 특별전시입장료-19~25세 (5000)
2nd row특별전시입장료-일반(3000)/특별전시입장료-미취학(2000)/특별전시입장료-유치원/초등학생(2000)/ 특별전시입장료-청소년 (2500)/ 특별전시입장료-19~25세 (3000)
3rd row특별전시입장료-일반(7000)/특별전시입장료-미취학(7000)/특별전시입장료-유치원/초등학생(7000)/ 특별전시입장료-청소년 (7000)/ 특별전시입장료-19~25세 (7000)
4th row특별전시입장료-일반(5000)/특별전시입장료-유치원/초등학생(3000)/ 특별전시입장료-청소년 (5000)/ 특별전시입장료-19~25세 (5000)
5th row특별전시입장료-일반(6000)/특별전시입장료-미취학(5000)/특별전시입장료-유치원/초등학생(5000)/ 특별전시입장료-청소년 (5000)/ 특별전시입장료-19~25세 (6000)
ValueCountFrequency (%)
특별전시입장료-청소년 17
19.5%
특별전시입장료-19~25세 17
19.5%
3000 7
8.0%
5000 7
8.0%
6000 6
 
6.9%
4000 5
 
5.7%
특별전시입장료-일반(5000)/특별전시입장료-미취학(3000)/특별전시입장료-유치원/초등학생(3000 4
 
4.6%
8000 3
 
3.4%
5500 2
 
2.3%
7000 2
 
2.3%
Other values (14) 17
19.5%
2023-12-10T19:07:23.611177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 254
 
15.1%
- 83
 
4.9%
별 83
 
4.9%
/ 83
 
4.9%
) 83
 
4.9%
( 83
 
4.9%
특 83
 
4.9%
료 83
 
4.9%
장 83
 
4.9%
입 83
 
4.9%
Other values (28) 682
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 847
50.3%
Decimal Number 416
24.7%
Dash Punctuation 83
 
4.9%
Other Punctuation 83
 
4.9%
Close Punctuation 83
 
4.9%
Open Punctuation 83
 
4.9%
Space Separator 68
 
4.0%
Math Symbol 20
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
별 83
9.8%
특 83
9.8%
료 83
9.8%
장 83
9.8%
입 83
9.8%
시 83
9.8%
전 83
9.8%
학 32
 
3.8%
세 17
 
2.0%
년 17
 
2.0%
Other values (12) 200
23.6%
Decimal Number
ValueCountFrequency (%)
0 254
61.1%
5 40
 
9.6%
2 28
 
6.7%
3 19
 
4.6%
1 19
 
4.6%
9 17
 
4.1%
6 15
 
3.6%
4 12
 
2.9%
8 7
 
1.7%
7 5
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Space Separator
ValueCountFrequency (%)
68
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 847
50.3%
Common 836
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
별 83
9.8%
특 83
9.8%
료 83
9.8%
장 83
9.8%
입 83
9.8%
시 83
9.8%
전 83
9.8%
학 32
 
3.8%
세 17
 
2.0%
년 17
 
2.0%
Other values (12) 200
23.6%
Common
ValueCountFrequency (%)
0 254
30.4%
- 83
 
9.9%
/ 83
 
9.9%
) 83
 
9.9%
( 83
 
9.9%
68
 
8.1%
5 40
 
4.8%
2 28
 
3.3%
~ 20
 
2.4%
3 19
 
2.3%
Other values (6) 75
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 847
50.3%
ASCII 836
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 254
30.4%
- 83
 
9.9%
/ 83
 
9.9%
) 83
 
9.9%
( 83
 
9.9%
68
 
8.1%
5 40
 
4.8%
2 28
 
3.3%
~ 20
 
2.4%
3 19
 
2.3%
Other values (6) 75
 
9.0%
Hangul
ValueCountFrequency (%)
별 83
9.8%
특 83
9.8%
료 83
9.8%
장 83
9.8%
입 83
9.8%
시 83
9.8%
전 83
9.8%
학 32
 
3.8%
세 17
 
2.0%
년 17
 
2.0%
Other values (12) 200
23.6%

Interactions

2023-12-10T19:07:10.441530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:58.139180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.426166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.542082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.621403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.127945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.885635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:07.101511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:08.661361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:10.624253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:58.310348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.589839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.675540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.748511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.385886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.095327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:07.242923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:08.893067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:10.833187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:58.457473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.731766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.786272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.873976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.596771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.258389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:07.422970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:09.071280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:10.990465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:58.605434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.847168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.876112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.984246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.786929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.508867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:07.619462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:09.300358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:11.162691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:58.758026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.996533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.986826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:02.190155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:03.997706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.711291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:07.800551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:09.477908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:11.341468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:58.892631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.145324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.094615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:02.367747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.173375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:05.879672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:07.964313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:09.652495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:11.502748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.025232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.239398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.185340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:02.526152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.296419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:06.014317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:08.092045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:09.905355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:11.653881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.132041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.328594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.281986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:02.709354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.440294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:06.146886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:08.227829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:10.069533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:11.848687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:06:59.276915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:00.426314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:01.477683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:02.910414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:04.641054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:06.842455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:08.437768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:10.236725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:07:23.815279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
snfclty_nmfclty_se_nmfclty_rn_addrfclty_lafclty_looper_instt_tel_nowkday_viewng_begin_timewkday_viewng_end_timehldy_viewng_begin_timehldy_viewng_end_timeclose_relate_cnadult_viewng_ctyngbgs_viewng_ctchild_viewng_ctviewng_ct_relate_cn
sn1.0001.0000.3441.0000.6790.4550.0000.6970.0000.4170.4170.8760.0000.0000.0000.000
fclty_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
fclty_se_nm0.3441.0001.0001.0000.3390.3100.4300.1870.3480.2150.2250.8340.1100.2240.1560.000
fclty_rn_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
fclty_la0.6791.0000.3391.0001.0000.6450.0000.1090.0380.4030.4730.0000.0000.0000.0000.000
fclty_lo0.4551.0000.3101.0000.6451.0000.0000.0000.0000.0000.1860.2600.4510.0000.0000.000
oper_instt_tel_no0.0001.0000.4301.0000.0000.0001.0000.0000.4890.0000.1941.0000.0000.9400.4381.000
wkday_viewng_begin_time0.6971.0000.1871.0000.1090.0000.0001.0000.7200.8330.4340.9420.4380.0000.6050.000
wkday_viewng_end_time0.0001.0000.3481.0000.0380.0000.4890.7201.0000.3110.7250.8890.5470.2110.7861.000
hldy_viewng_begin_time0.4171.0000.2151.0000.4030.0000.0000.8330.3111.0000.9070.7110.6780.3270.7860.765
hldy_viewng_end_time0.4171.0000.2251.0000.4730.1860.1940.4340.7250.9071.0000.8880.6850.4110.7670.963
close_relate_cn0.8761.0000.8341.0000.0000.2601.0000.9420.8890.7110.8881.0000.6070.9200.8270.757
adult_viewng_ct0.0001.0000.1101.0000.0000.4510.0000.4380.5470.6780.6850.6071.0000.6700.9481.000
yngbgs_viewng_ct0.0001.0000.2241.0000.0000.0000.9400.0000.2110.3270.4110.9200.6701.0000.8321.000
child_viewng_ct0.0001.0000.1561.0000.0000.0000.4380.6050.7860.7860.7670.8270.9480.8321.0001.000
viewng_ct_relate_cn0.0001.0000.0001.0000.0000.0001.0000.0001.0000.7650.9630.7571.0001.0001.0001.000
2023-12-10T19:07:24.126382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fclty_se_nmwkday_viewng_begin_timehldy_viewng_begin_time
fclty_se_nm1.0000.1400.202
wkday_viewng_begin_time0.1401.0000.801
hldy_viewng_begin_time0.2020.8011.000
2023-12-10T19:07:24.337929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
snfclty_lafclty_looper_instt_tel_nowkday_viewng_end_timehldy_viewng_end_timeadult_viewng_ctyngbgs_viewng_ctchild_viewng_ctfclty_se_nmwkday_viewng_begin_timehldy_viewng_begin_time
sn1.000-0.5530.2570.4020.006-0.740-0.551-0.590-0.6010.1540.4860.270
fclty_la-0.5531.000-0.151-0.4750.0580.5530.5090.5350.5310.2210.0600.183
fclty_lo0.257-0.1511.0000.285-0.011-0.309-0.209-0.255-0.2540.1860.0000.000
oper_instt_tel_no0.402-0.4750.2851.000-0.099-0.397-0.333-0.321-0.3360.1620.0000.000
wkday_viewng_end_time0.0060.058-0.011-0.0991.0000.2250.1620.1620.1720.2910.3340.239
hldy_viewng_end_time-0.7400.553-0.309-0.3970.2251.0000.6800.7160.7260.2130.3650.599
adult_viewng_ct-0.5510.509-0.209-0.3330.1620.6801.0000.9670.9730.0550.1890.463
yngbgs_viewng_ct-0.5900.535-0.255-0.3210.1620.7160.9671.0000.9960.0700.0000.313
child_viewng_ct-0.6010.531-0.254-0.3360.1720.7260.9730.9961.0000.0850.2850.585
fclty_se_nm0.1540.2210.1860.1620.2910.2130.0550.0700.0851.0000.1400.202
wkday_viewng_begin_time0.4860.0600.0000.0000.3340.3650.1890.0000.2850.1401.0000.801
hldy_viewng_begin_time0.2700.1830.0000.0000.2390.5990.4630.3130.5850.2020.8011.000

Missing values

2023-12-10T19:07:12.172135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:07:12.580896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stats_yearsnfclty_nmfclty_se_nmfclty_rn_addrfclty_lafclty_looper_instt_tel_nowkday_viewng_begin_timewkday_viewng_end_timehldy_viewng_begin_timehldy_viewng_end_timeclose_relate_cnadult_viewng_ctyngbgs_viewng_ctchild_viewng_ctviewng_ct_relate_cn
02020271동산박물관사립충청남도 공주시 반포면 정광터1길 108-836.402574127.232214185850091000160000공휴일000<NA>
12020275공주기독교박물관사립충청남도 공주시 제민1길 18(봉항동10)36.450971127.1225024185370071000170000매주 월요일/ 일요일200020001000<NA>
22020758월산미술관사립강원도 동해시 삼화로 52037.463933129.021313335348855900180000관람시각 정보 없음900010001000<NA>
32020779순창공립미술관공립전라북도 순창군 순창읍 남계로 8135.374084127.144398636501638900180000매주 월요일 /관람시각 정보 없음000<NA>
42020300원광대학교 박물관대학전라북도 익산시 익산대로 46035.971714126.9602246385054831000160000공휴일/학교기념일/원불교기념일000<NA>
52020784선화기독교미술관사립충청남도 논산시 벌곡면 황룡재로 32436.207708127.238287425253141900180000관람시각 정보 없음000<NA>
62020328충간공보물제651호박물관사립전라북도 익산시 삼기면 미륵산1길 836.026413127.0163456384172771000180000매주일월/ 공휴일000<NA>
720201화성시향토박물관공립경기도 화성시 향남읍 행정동로 9637.132615126.928013136937971000180010001800매주 월요일(단/ 월요일이 공휴일인 경우 당일은 개관하고 그 다음 첫번째 평일에 휴관)000<NA>
820202한국만화박물관공립경기도 부천시 길주로 137.508569126.7437143231030901000180010001800매주월요일/1월1일/ 설/추석 당일 및 그 전날500050005000<NA>
9202019예당국악박물관(휴관)사립경기도 남양주시 수석동 강변북로661번길 2137.59018127.16833315906981900180000예당국악박물관-2(2017.12.05.) /관람시각 정보 없음500030003000특별전시입장료-일반(5000)/특별전시입장료-미취학(3000)/특별전시입장료-유치원/초등학생(3000)/ 특별전시입장료-청소년 (3000)/ 특별전시입장료-19~25세 (5000)
stats_yearsnfclty_nmfclty_se_nmfclty_rn_addrfclty_lafclty_looper_instt_tel_nowkday_viewng_begin_timewkday_viewng_end_timehldy_viewng_begin_timehldy_viewng_end_timeclose_relate_cnadult_viewng_ctyngbgs_viewng_ctchild_viewng_ctviewng_ct_relate_cn
902020712오랜미래신화미술관사립강원도 원주시 문막읍 취병로 71137.350084127.769747337465256900180000관람시각 정보 없음300050003000<NA>
912020722내설악예술인촌 공공미술관공립강원도 인제군 북면 예술인촌길66-1238.145477128.25342334634081900180000월요일 명절당일 1월1일000<NA>
922020218동곡박물관사립충청북도 영동군 영동읍 대학로 1036.178176127.7795674374236521000180000주말/ 공휴일000<NA>
932020219농민문학기념관사립충청북도 영동군 매곡면 노천2길 5-136.188867127.9299944374351861000180000매주 월요일000<NA>
942020256선문대학교박물관대학충청남도 아산시 선문로 221번길 7036.798431127.073144153027821000170000토요일/ 국정 공휴일000<NA>
952020727KUMA 계원예술대학교 미술관대학경기도 의왕시 계원대학로 66 계원예술대학교37.378918126.98067314201735900180000관람시각 정보 없음000<NA>
962020258공주교육대학교 박물관대학충청남도 공주시 웅진로 2736.445203127.1196554185013511000170000주말 및 공휴일000<NA>
9720201877한국차박물관대학부산광역시 부산진구 진남로506 (양정동)35.169238129.0720575185031121000170000공휴일 관람시각 정보 없음000<NA>
98202082화폐박물관사립경기도 파주시 탄현면 헤이리마을길 93-8937.790805126.6973173194965921000180010001800매주 월/ 설날/ 추석300030002000특별전시입장료-일반(3000)/특별전시입장료-미취학(2000)/특별전시입장료-유치원/초등학생(2000)/ 특별전시입장료-청소년 (3000)/ 특별전시입장료-19~25세 (3000)
992020728DIMA M.O.A(Museum Of Art)대학경기도 안성시 삼죽면 동아예대길 4737.06236127.353294316706938900180000매주 일요일/ 공휴일 휴관000<NA>