Overview

Dataset statistics

Number of variables20
Number of observations356
Missing cells174
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.5 KiB
Average record size in memory168.4 B

Variable types

Categorical10
Numeric6
Text4

Dataset

Description농촌체험휴양마을 및 농촌민박 사업자를 대상으로 경관 및 서비스, 체험, 숙박, 음식 4개 분야에 대한 심사를 통해 결정된등급 정보(민박은 경관 및 서비스, 숙박 2개 분야)
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220217000000002072

Alerts

STAYNG_LMPPOINT_RT has a high cardinality: 51 distinct valuesHigh cardinality
BSNS_SE is highly overall correlated with ORDR and 4 other fieldsHigh correlation
SIGNGU_CD is highly overall correlated with ORDR and 14 other fieldsHigh correlation
STAYNG_LMPPOINT_RT is highly overall correlated with STAYNG_SCORE and 2 other fieldsHigh correlation
STAYNG_GRAD is highly overall correlated with STAYNG_SCORE and 2 other fieldsHigh correlation
EXPRN_GRAD is highly overall correlated with EXPRN_SCORE and 1 other fieldsHigh correlation
FD_GRAD is highly overall correlated with FD_SCORE and 3 other fieldsHigh correlation
FD_GTSR_RT is highly overall correlated with FD_SCORE and 4 other fieldsHigh correlation
SCENE_GRAD is highly overall correlated with SCENE_SCORE and 1 other fieldsHigh correlation
CTPRVN is highly overall correlated with ORDR and 2 other fieldsHigh correlation
YEAR is highly overall correlated with ORDR and 4 other fieldsHigh correlation
ORDR is highly overall correlated with CTPRVN_CD and 4 other fieldsHigh correlation
SCENE_SCORE is highly overall correlated with EXPRN_SCORE and 3 other fieldsHigh correlation
EXPRN_SCORE is highly overall correlated with SCENE_SCORE and 5 other fieldsHigh correlation
STAYNG_SCORE is highly overall correlated with EXPRN_SCORE and 4 other fieldsHigh correlation
FD_SCORE is highly overall correlated with SCENE_SCORE and 7 other fieldsHigh correlation
CTPRVN_CD is highly overall correlated with ORDR and 2 other fieldsHigh correlation
BSNS_SE is highly imbalanced (74.8%)Imbalance
SIGNGU_CD is highly imbalanced (96.5%)Imbalance
ORDR has 56 (15.7%) missing valuesMissing
EXPRN_SCORE has 17 (4.8%) missing valuesMissing
EXPRN_GTSR_RT has 17 (4.8%) missing valuesMissing
STAYNG_SCORE has 27 (7.6%) missing valuesMissing
FD_SCORE has 55 (15.4%) missing valuesMissing
VILAGENM has unique valuesUnique

Reproduction

Analysis started2023-12-11 03:27:20.670228
Analysis finished2023-12-11 03:27:27.471174
Duration6.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YEAR
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2014
300 
2015
56 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014 300
84.3%
2015 56
 
15.7%

Length

2023-12-11T12:27:27.560080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:27.730815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 300
84.3%
2015 56
 
15.7%

BSNS_SE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
체험휴양마을
341 
농어촌민박
 
15

Length

Max length6
Median length6
Mean length5.9578652
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
체험휴양마을 341
95.8%
농어촌민박 15
 
4.2%

Length

2023-12-11T12:27:27.888780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:28.036144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체험휴양마을 341
95.8%
농어촌민박 15
 
4.2%

ORDR
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct286
Distinct (%)95.3%
Missing56
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean137.15333
Minimum1
Maximum286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-11T12:27:28.194163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q161.75
median136.5
Q3211.25
95-th percentile271.05
Maximum286
Range285
Interquartile range (IQR)149.5

Descriptive statistics

Standard deviation85.710389
Coefficient of variation (CV)0.62492385
Kurtosis-1.2366519
Mean137.15333
Median Absolute Deviation (MAD)75
Skewness0.03523602
Sum41146
Variance7346.2707
MonotonicityNot monotonic
2023-12-11T12:27:28.387753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 2
 
0.6%
5 2
 
0.6%
3 2
 
0.6%
2 2
 
0.6%
1 2
 
0.6%
14 2
 
0.6%
13 2
 
0.6%
4 2
 
0.6%
11 2
 
0.6%
7 2
 
0.6%
Other values (276) 280
78.7%
(Missing) 56
 
15.7%
ValueCountFrequency (%)
1 2
0.6%
2 2
0.6%
3 2
0.6%
4 2
0.6%
5 2
0.6%
6 2
0.6%
7 2
0.6%
8 2
0.6%
9 2
0.6%
10 2
0.6%
ValueCountFrequency (%)
286 1
0.3%
285 1
0.3%
284 1
0.3%
283 1
0.3%
282 1
0.3%
281 1
0.3%
280 1
0.3%
279 1
0.3%
278 1
0.3%
277 1
0.3%

CTPRVN
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
강원
86 
전남
48 
충남
40 
경기
39 
전북
36 
Other values (8)
107 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
강원 86
24.2%
전남 48
13.5%
충남 40
11.2%
경기 39
11.0%
전북 36
10.1%
경남 35
9.8%
경북 34
 
9.6%
충북 23
 
6.5%
제주 7
 
2.0%
인천 4
 
1.1%
Other values (3) 4
 
1.1%

Length

2023-12-11T12:27:28.615527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원 86
24.2%
전남 48
13.5%
충남 40
11.2%
경기 39
11.0%
전북 36
10.1%
경남 35
9.8%
경북 34
 
9.6%
충북 23
 
6.5%
제주 7
 
2.0%
인천 4
 
1.1%
Other values (3) 4
 
1.1%

SIGNGU
Text

Distinct117
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-11T12:27:28.948412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.011236
Min length2

Characters and Unicode

Total characters716
Distinct characters98
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

Unique34 ?
Unique (%)9.6%

Sample

1st row인제
2nd row인제
3rd row인제
4th row인제
5th row인제
ValueCountFrequency (%)
인제 22
 
6.2%
횡성 9
 
2.5%
서천 8
 
2.2%
평창 8
 
2.2%
청양 7
 
2.0%
남원 7
 
2.0%
산청 7
 
2.0%
양평 7
 
2.0%
남해 6
 
1.7%
금산 6
 
1.7%
Other values (107) 269
75.6%
2023-12-11T12:27:29.429902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 54
 
7.5%
주 46
 
6.4%
양 44
 
6.1%
성 30
 
4.2%
산 28
 
3.9%
제 27
 
3.8%
인 24
 
3.4%
안 23
 
3.2%
평 21
 
2.9%
원 20
 
2.8%
Other values (88) 399
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 716
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 54
 
7.5%
주 46
 
6.4%
양 44
 
6.1%
성 30
 
4.2%
산 28
 
3.9%
제 27
 
3.8%
인 24
 
3.4%
안 23
 
3.2%
평 21
 
2.9%
원 20
 
2.8%
Other values (88) 399
55.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 716
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 54
 
7.5%
주 46
 
6.4%
양 44
 
6.1%
성 30
 
4.2%
산 28
 
3.9%
제 27
 
3.8%
인 24
 
3.4%
안 23
 
3.2%
평 21
 
2.9%
원 20
 
2.8%
Other values (88) 399
55.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 716
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 54
 
7.5%
주 46
 
6.4%
양 44
 
6.1%
성 30
 
4.2%
산 28
 
3.9%
제 27
 
3.8%
인 24
 
3.4%
안 23
 
3.2%
평 21
 
2.9%
원 20
 
2.8%
Other values (88) 399
55.7%

VILAGENM
Text

UNIQUE 

Distinct356
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-11T12:27:29.721244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14.5
Mean length6.5646067
Min length2

Characters and Unicode

Total characters2337
Distinct characters359
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique356 ?
Unique (%)100.0%

Sample

1st row아다펜
2nd row솔마루민박
3rd row페이지펜션
4th row쌀라네집
5th row강뜨락펜션
ValueCountFrequency (%)
아다펜 1
 
0.3%
신덕마을 1
 
0.3%
승곡마을 1
 
0.3%
녹색농심인삼마을 1
 
0.3%
한밤실마을(대율마을 1
 
0.3%
달사과마을 1
 
0.3%
천지갑산마을 1
 
0.3%
가송마을 1
 
0.3%
암산마을 1
 
0.3%
옛날솜씨마을 1
 
0.3%
Other values (347) 347
97.2%
2023-12-11T12:27:30.227654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 396
 
16.9%
을 391
 
16.7%
( 70
 
3.0%
) 70
 
3.0%
리 39
 
1.7%
산 36
 
1.5%
지 26
 
1.1%
동 25
 
1.1%
천 21
 
0.9%
촌 20
 
0.9%
Other values (349) 1243
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2187
93.6%
Open Punctuation 70
 
3.0%
Close Punctuation 70
 
3.0%
Decimal Number 8
 
0.3%
Other Punctuation 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 396
 
18.1%
을 391
 
17.9%
리 39
 
1.8%
산 36
 
1.6%
지 26
 
1.2%
동 25
 
1.1%
천 21
 
1.0%
촌 20
 
0.9%
대 19
 
0.9%
수 19
 
0.9%
Other values (340) 1195
54.6%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
2 2
25.0%
3 1
 
12.5%
6 1
 
12.5%
5 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2186
93.5%
Common 150
 
6.4%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 396
 
18.1%
을 391
 
17.9%
리 39
 
1.8%
산 36
 
1.6%
지 26
 
1.2%
동 25
 
1.1%
천 21
 
1.0%
촌 20
 
0.9%
대 19
 
0.9%
수 19
 
0.9%
Other values (339) 1194
54.6%
Common
ValueCountFrequency (%)
( 70
46.7%
) 70
46.7%
1 3
 
2.0%
2 2
 
1.3%
, 1
 
0.7%
1
 
0.7%
3 1
 
0.7%
6 1
 
0.7%
5 1
 
0.7%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2186
93.5%
ASCII 150
 
6.4%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 396
 
18.1%
을 391
 
17.9%
리 39
 
1.8%
산 36
 
1.6%
지 26
 
1.2%
동 25
 
1.1%
천 21
 
1.0%
촌 20
 
0.9%
대 19
 
0.9%
수 19
 
0.9%
Other values (339) 1194
54.6%
ASCII
ValueCountFrequency (%)
( 70
46.7%
) 70
46.7%
1 3
 
2.0%
2 2
 
1.3%
, 1
 
0.7%
1
 
0.7%
3 1
 
0.7%
6 1
 
0.7%
5 1
 
0.7%
CJK
ValueCountFrequency (%)
休 1
100.0%

SCENE_SCORE
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)14.1%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean65.788732
Minimum20
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-11T12:27:30.388499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile47
Q160
median68
Q374
95-th percentile79
Maximum84
Range64
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.69098
Coefficient of variation (CV)0.16250472
Kurtosis1.9345452
Mean65.788732
Median Absolute Deviation (MAD)6
Skewness-1.1797235
Sum23355
Variance114.29705
MonotonicityNot monotonic
2023-12-11T12:27:30.566302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 24
 
6.7%
76 21
 
5.9%
72 18
 
5.1%
74 18
 
5.1%
68 17
 
4.8%
62 14
 
3.9%
70 14
 
3.9%
77 14
 
3.9%
69 13
 
3.7%
66 13
 
3.7%
Other values (40) 189
53.1%
ValueCountFrequency (%)
20 1
 
0.3%
23 1
 
0.3%
24 1
 
0.3%
25 1
 
0.3%
36 1
 
0.3%
37 1
 
0.3%
39 1
 
0.3%
40 2
0.6%
41 3
0.8%
43 1
 
0.3%
ValueCountFrequency (%)
84 1
 
0.3%
83 1
 
0.3%
82 4
 
1.1%
81 3
 
0.8%
80 4
 
1.1%
79 6
 
1.7%
78 6
 
1.7%
77 14
3.9%
76 21
5.9%
75 12
3.4%

SCENE_GTSR_RT
Text

Distinct51
Distinct (%)14.4%
Missing1
Missing (%)0.3%
Memory size2.9 KiB
2023-12-11T12:27:30.818875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0197183
Min length3

Characters and Unicode

Total characters1072
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)3.7%

Sample

1st row81%
2nd row70%
3rd row70%
4th row100%
5th row87%
ValueCountFrequency (%)
92 27
 
7.6%
91 20
 
5.6%
87 17
 
4.8%
96 17
 
4.8%
86 16
 
4.5%
80 15
 
4.2%
85 15
 
4.2%
95 14
 
3.9%
88 14
 
3.9%
77 14
 
3.9%
Other values (41) 186
52.4%
2023-12-11T12:27:31.238637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 355
33.1%
8 153
14.3%
9 121
 
11.3%
7 107
 
10.0%
6 87
 
8.1%
2 57
 
5.3%
1 50
 
4.7%
5 49
 
4.6%
0 48
 
4.5%
3 33
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 717
66.9%
Other Punctuation 355
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 153
21.3%
9 121
16.9%
7 107
14.9%
6 87
12.1%
2 57
 
7.9%
1 50
 
7.0%
5 49
 
6.8%
0 48
 
6.7%
3 33
 
4.6%
4 12
 
1.7%
Other Punctuation
ValueCountFrequency (%)
% 355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1072
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 355
33.1%
8 153
14.3%
9 121
 
11.3%
7 107
 
10.0%
6 87
 
8.1%
2 57
 
5.3%
1 50
 
4.7%
5 49
 
4.6%
0 48
 
4.5%
3 33
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 355
33.1%
8 153
14.3%
9 121
 
11.3%
7 107
 
10.0%
6 87
 
8.1%
2 57
 
5.3%
1 50
 
4.7%
5 49
 
4.6%
0 48
 
4.5%
3 33
 
3.1%

SCENE_GRAD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
1등급
126 
2등급
116 
3등급
67 
등외
46 
<NA>
 
1

Length

Max length4
Median length3
Mean length2.8735955
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row2등급
2nd row3등급
3rd row3등급
4th row1등급
5th row2등급

Common Values

ValueCountFrequency (%)
1등급 126
35.4%
2등급 116
32.6%
3등급 67
18.8%
등외 46
 
12.9%
<NA> 1
 
0.3%

Length

2023-12-11T12:27:31.422861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:31.570092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1등급 126
35.4%
2등급 116
32.6%
3등급 67
18.8%
등외 46
 
12.9%
na 1
 
0.3%

EXPRN_SCORE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)17.4%
Missing17
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean61.60767
Minimum13
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-11T12:27:31.717690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile36
Q154
median64
Q372
95-th percentile79
Maximum86
Range73
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.593292
Coefficient of variation (CV)0.22064285
Kurtosis0.37986329
Mean61.60767
Median Absolute Deviation (MAD)8
Skewness-0.80519388
Sum20885
Variance184.77757
MonotonicityNot monotonic
2023-12-11T12:27:31.922944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
5.3%
62 14
 
3.9%
69 13
 
3.7%
61 11
 
3.1%
58 11
 
3.1%
64 11
 
3.1%
77 11
 
3.1%
71 11
 
3.1%
65 11
 
3.1%
72 11
 
3.1%
Other values (49) 216
60.7%
(Missing) 17
 
4.8%
ValueCountFrequency (%)
13 1
 
0.3%
19 2
0.6%
23 1
 
0.3%
27 3
0.8%
30 2
0.6%
31 1
 
0.3%
32 2
0.6%
34 1
 
0.3%
35 3
0.8%
36 3
0.8%
ValueCountFrequency (%)
86 1
 
0.3%
85 2
 
0.6%
84 3
 
0.8%
83 2
 
0.6%
82 2
 
0.6%
81 3
 
0.8%
80 3
 
0.8%
79 2
 
0.6%
78 9
2.5%
77 11
3.1%

EXPRN_GTSR_RT
Text

MISSING 

Distinct57
Distinct (%)16.8%
Missing17
Missing (%)4.8%
Memory size2.9 KiB
2023-12-11T12:27:32.172855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0176991
Min length3

Characters and Unicode

Total characters1023
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)2.1%

Sample

1st row45%
2nd row82%
3rd row70%
4th row93%
5th row92%
ValueCountFrequency (%)
85 20
 
5.9%
81 16
 
4.7%
92 14
 
4.1%
77 14
 
4.1%
91 14
 
4.1%
80 13
 
3.8%
88 11
 
3.2%
86 11
 
3.2%
72 11
 
3.2%
76 11
 
3.2%
Other values (47) 204
60.2%
2023-12-11T12:27:32.506074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 339
33.1%
8 143
14.0%
7 111
 
10.9%
9 73
 
7.1%
6 73
 
7.1%
5 70
 
6.8%
1 54
 
5.3%
0 50
 
4.9%
3 45
 
4.4%
2 43
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 684
66.9%
Other Punctuation 339
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 143
20.9%
7 111
16.2%
9 73
10.7%
6 73
10.7%
5 70
10.2%
1 54
 
7.9%
0 50
 
7.3%
3 45
 
6.6%
2 43
 
6.3%
4 22
 
3.2%
Other Punctuation
ValueCountFrequency (%)
% 339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1023
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 339
33.1%
8 143
14.0%
7 111
 
10.9%
9 73
 
7.1%
6 73
 
7.1%
5 70
 
6.8%
1 54
 
5.3%
0 50
 
4.9%
3 45
 
4.4%
2 43
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 339
33.1%
8 143
14.0%
7 111
 
10.9%
9 73
 
7.1%
6 73
 
7.1%
5 70
 
6.8%
1 54
 
5.3%
0 50
 
4.9%
3 45
 
4.4%
2 43
 
4.2%

EXPRN_GRAD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2등급
103 
등외
92 
3등급
73 
1등급
71 
<NA>
17 

Length

Max length4
Median length3
Mean length2.7893258
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
2등급 103
28.9%
등외 92
25.8%
3등급 73
20.5%
1등급 71
19.9%
<NA> 17
 
4.8%

Length

2023-12-11T12:27:32.637524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:32.742858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2등급 103
28.9%
등외 92
25.8%
3등급 73
20.5%
1등급 71
19.9%
na 17
 
4.8%

STAYNG_SCORE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)13.4%
Missing27
Missing (%)7.6%
Infinite0
Infinite (%)0.0%
Mean64.702128
Minimum13
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-11T12:27:32.852613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile46.4
Q160
median67
Q372
95-th percentile76
Maximum78
Range65
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.5691974
Coefficient of variation (CV)0.14789618
Kurtosis3.4278059
Mean64.702128
Median Absolute Deviation (MAD)5
Skewness-1.493009
Sum21287
Variance91.569538
MonotonicityNot monotonic
2023-12-11T12:27:32.998695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
72 21
 
5.9%
73 20
 
5.6%
66 19
 
5.3%
70 19
 
5.3%
69 17
 
4.8%
68 17
 
4.8%
67 17
 
4.8%
71 16
 
4.5%
74 15
 
4.2%
65 14
 
3.9%
Other values (34) 154
43.3%
(Missing) 27
 
7.6%
ValueCountFrequency (%)
13 1
0.3%
25 1
0.3%
33 1
0.3%
35 2
0.6%
36 1
0.3%
39 1
0.3%
40 1
0.3%
41 1
0.3%
43 2
0.6%
44 1
0.3%
ValueCountFrequency (%)
78 1
 
0.3%
77 11
3.1%
76 8
 
2.2%
75 10
2.8%
74 15
4.2%
73 20
5.6%
72 21
5.9%
71 16
4.5%
70 19
5.3%
69 17
4.8%

STAYNG_LMPPOINT_RT
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
27 
92%
 
21
88%
 
20
97%
 
20
96%
 
19
Other values (46)
249 

Length

Max length4
Median length3
Mean length3.1376404
Min length3

Unique

Unique12 ?
Unique (%)3.4%

Sample

1st row89%
2nd row80%
3rd row96%
4th row98%
5th row91%

Common Values

ValueCountFrequency (%)
<NA> 27
 
7.6%
92% 21
 
5.9%
88% 20
 
5.6%
97% 20
 
5.6%
96% 19
 
5.3%
89% 17
 
4.8%
94% 15
 
4.2%
93% 14
 
3.9%
86% 13
 
3.7%
85% 13
 
3.7%
Other values (41) 177
49.7%

Length

2023-12-11T12:27:33.141633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 27
 
7.6%
92 21
 
5.9%
88 20
 
5.6%
97 20
 
5.6%
96 19
 
5.3%
89 17
 
4.8%
94 15
 
4.2%
93 14
 
3.9%
86 13
 
3.7%
85 13
 
3.7%
Other values (41) 177
49.7%

STAYNG_GRAD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
1등급
140 
2등급
97 
3등급
53 
등외
39 
<NA>
27 

Length

Max length4
Median length3
Mean length2.9662921
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2등급
2nd row2등급
3rd row1등급
4th row1등급
5th row1등급

Common Values

ValueCountFrequency (%)
1등급 140
39.3%
2등급 97
27.2%
3등급 53
 
14.9%
등외 39
 
11.0%
<NA> 27
 
7.6%

Length

2023-12-11T12:27:33.253230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:33.368991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1등급 140
39.3%
2등급 97
27.2%
3등급 53
 
14.9%
등외 39
 
11.0%
na 27
 
7.6%

FD_SCORE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)19.3%
Missing55
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean50.139535
Minimum11
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-11T12:27:33.501006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile28
Q142
median50
Q357
95-th percentile74
Maximum80
Range69
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.026145
Coefficient of variation (CV)0.25979789
Kurtosis-0.050658073
Mean50.139535
Median Absolute Deviation (MAD)8
Skewness0.018449769
Sum15092
Variance169.68047
MonotonicityNot monotonic
2023-12-11T12:27:33.646917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 16
 
4.5%
50 14
 
3.9%
55 12
 
3.4%
46 12
 
3.4%
47 12
 
3.4%
57 11
 
3.1%
42 10
 
2.8%
54 10
 
2.8%
41 10
 
2.8%
48 9
 
2.5%
Other values (48) 185
52.0%
(Missing) 55
 
15.4%
ValueCountFrequency (%)
11 1
 
0.3%
14 1
 
0.3%
20 1
 
0.3%
21 1
 
0.3%
22 1
 
0.3%
25 2
0.6%
26 2
0.6%
27 4
1.1%
28 4
1.1%
29 3
0.8%
ValueCountFrequency (%)
80 2
 
0.6%
78 2
 
0.6%
77 3
0.8%
76 1
 
0.3%
75 2
 
0.6%
74 6
1.7%
73 4
1.1%
72 6
1.7%
71 1
 
0.3%
70 1
 
0.3%

FD_GTSR_RT
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
55 
85%
 
22
80%
 
18
91%
 
15
93%
 
14
Other values (44)
232 

Length

Max length4
Median length3
Mean length3.1601124
Min length3

Unique

Unique7 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 55
 
15.4%
85% 22
 
6.2%
80% 18
 
5.1%
91% 15
 
4.2%
93% 14
 
3.9%
90% 14
 
3.9%
88% 13
 
3.7%
75% 12
 
3.4%
74% 12
 
3.4%
96% 12
 
3.4%
Other values (39) 169
47.5%

Length

2023-12-11T12:27:33.787873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 55
 
15.4%
85 22
 
6.2%
80 18
 
5.1%
91 15
 
4.2%
93 14
 
3.9%
90 14
 
3.9%
88 13
 
3.7%
75 12
 
3.4%
74 12
 
3.4%
96 12
 
3.4%
Other values (39) 169
47.5%

FD_GRAD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2등급
91 
등외
87 
1등급
68 
<NA>
55 
3등급
55 

Length

Max length4
Median length3
Mean length2.9101124
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
2등급 91
25.6%
등외 87
24.4%
1등급 68
19.1%
<NA> 55
15.4%
3등급 55
15.4%

Length

2023-12-11T12:27:33.913571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:34.045333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2등급 91
25.6%
등외 87
24.4%
1등급 68
19.1%
na 55
15.4%
3등급 55
15.4%

CTPRVN_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.974719
Minimum28
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-11T12:27:34.152487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile41
Q142
median44
Q346
95-th percentile48
Maximum50
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2339626
Coefficient of variation (CV)0.073541404
Kurtosis7.3373145
Mean43.974719
Median Absolute Deviation (MAD)2
Skewness-1.7566385
Sum15655
Variance10.458514
MonotonicityNot monotonic
2023-12-11T12:27:34.244261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
42 86
24.2%
46 48
13.5%
44 40
11.2%
41 39
11.0%
45 36
10.1%
48 35
9.8%
47 34
 
9.6%
43 23
 
6.5%
50 7
 
2.0%
28 4
 
1.1%
Other values (3) 4
 
1.1%
ValueCountFrequency (%)
28 4
 
1.1%
30 2
 
0.6%
31 1
 
0.3%
36 1
 
0.3%
41 39
11.0%
42 86
24.2%
43 23
 
6.5%
44 40
11.2%
45 36
10.1%
46 48
13.5%
ValueCountFrequency (%)
50 7
 
2.0%
48 35
9.8%
47 34
 
9.6%
46 48
13.5%
45 36
10.1%
44 40
11.2%
43 23
 
6.5%
42 86
24.2%
41 39
11.0%
36 1
 
0.3%

SIGNGU_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
354 
30110
 
1
30140
 
1

Length

Max length5
Median length4
Mean length4.005618
Min length4

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 354
99.4%
30110 1
 
0.3%
30140 1
 
0.3%

Length

2023-12-11T12:27:34.381124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:34.510948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 354
99.4%
30110 1
 
0.3%
30140 1
 
0.3%

Interactions

2023-12-11T12:27:25.496610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:22.488986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.050694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.610004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.138943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.804464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.609786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:22.599503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.140602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.710422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.225368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.902586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.721446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:22.683185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.228221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.803649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.329093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.002764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.829449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:22.770849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.315963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.897749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.458162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.123082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.953658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:22.886932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.413099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.980216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.567410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.257442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:26.050441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:22.969833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:23.529064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.060263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:24.682687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:25.369452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:27:34.598192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
YEARBSNS_SEORDRCTPRVNSCENE_SCORESCENE_GTSR_RTSCENE_GRADEXPRN_SCOREEXPRN_GTSR_RTEXPRN_GRADSTAYNG_SCORESTAYNG_LMPPOINT_RTSTAYNG_GRADFD_SCOREFD_GTSR_RTFD_GRADCTPRVN_CDSIGNGU_CD
YEAR1.0000.000NaN0.1770.3550.4710.4290.7250.6780.6230.2140.4880.4010.9950.7070.6150.218NaN
BSNS_SE0.0001.0000.6690.2730.4210.3760.0000.0000.0000.0000.0000.5740.052NaNNaNNaN0.291NaN
ORDRNaN0.6691.0000.9000.0990.2610.1200.1800.0500.1650.0000.4590.1320.1530.0000.1270.854NaN
CTPRVN0.1770.2730.9001.0000.0000.2140.1920.0000.0000.1920.4200.5610.3660.4240.6360.1671.000NaN
SCENE_SCORE0.3550.4210.0990.0001.0000.9880.8930.6390.7970.5540.4850.7180.3960.5670.8080.4330.064NaN
SCENE_GTSR_RT0.4710.3760.2610.2140.9881.0001.0000.8410.8860.6580.7220.8500.6170.7300.7870.5910.3060.000
SCENE_GRAD0.4290.0000.1200.1920.8931.0001.0000.5650.6700.7270.3970.5830.5920.4470.5450.6270.272NaN
EXPRN_SCORE0.7250.0000.1800.0000.6390.8410.5651.0000.9970.9520.5000.7300.5210.8150.7450.5800.000NaN
EXPRN_GTSR_RT0.6780.0000.0500.0000.7970.8860.6700.9971.0001.0000.7530.8230.5690.7820.8180.6570.0000.000
EXPRN_GRAD0.6230.0000.1650.1920.5540.6580.7270.9521.0001.0000.4850.5680.6860.6150.6770.7520.000NaN
STAYNG_SCORE0.2140.0000.0000.4200.4850.7220.3970.5000.7530.4851.0001.0000.8850.5220.8240.6570.3550.000
STAYNG_LMPPOINT_RT0.4880.5740.4590.5610.7180.8500.5830.7300.8230.5681.0001.0001.0000.8060.8770.5800.6180.000
STAYNG_GRAD0.4010.0520.1320.3660.3960.6170.5920.5210.5690.6860.8851.0001.0000.5620.6510.6940.2520.000
FD_SCORE0.995NaN0.1530.4240.5670.7300.4470.8150.7820.6150.5220.8060.5621.0000.9870.8430.3960.000
FD_GTSR_RT0.707NaN0.0000.6360.8080.7870.5450.7450.8180.6770.8240.8770.6510.9871.0001.0000.6960.000
FD_GRAD0.615NaN0.1270.1670.4330.5910.6270.5800.6570.7520.6570.5800.6940.8431.0001.0000.000NaN
CTPRVN_CD0.2180.2910.8541.0000.0640.3060.2720.0000.0000.0000.3550.6180.2520.3960.6960.0001.000NaN
SIGNGU_CDNaNNaNNaNNaNNaN0.000NaNNaN0.000NaN0.0000.0000.0000.0000.000NaNNaN1.000
2023-12-11T12:27:34.781118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BSNS_SESIGNGU_CDSTAYNG_LMPPOINT_RTSTAYNG_GRADEXPRN_GRADFD_GRADFD_GTSR_RTSCENE_GRADCTPRVNYEAR
BSNS_SE1.0001.0000.4260.0340.0001.0001.0000.0000.2500.000
SIGNGU_CD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
STAYNG_LMPPOINT_RT0.4261.0001.0000.9270.2960.3040.3090.3060.1950.360
STAYNG_GRAD0.0341.0000.9271.0000.3320.3380.3360.2660.1740.268
EXPRN_GRAD0.0001.0000.2960.3321.0000.3890.3580.3660.1110.431
FD_GRAD1.0001.0000.3040.3380.3891.0000.9230.2880.0960.424
FD_GTSR_RT1.0001.0000.3090.3360.3580.9231.0000.2650.2290.530
SCENE_GRAD0.0001.0000.3060.2660.3660.2880.2651.0000.1110.288
CTPRVN0.2501.0000.1950.1740.1110.0960.2290.1111.0000.162
YEAR0.0001.0000.3600.2680.4310.4240.5300.2880.1621.000
2023-12-11T12:27:35.252570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ORDRSCENE_SCOREEXPRN_SCORESTAYNG_SCOREFD_SCORECTPRVN_CDYEARBSNS_SECTPRVNSCENE_GRADEXPRN_GRADSTAYNG_LMPPOINT_RTSTAYNG_GRADFD_GTSR_RTFD_GRADSIGNGU_CD
ORDR1.0000.1100.0380.0210.1290.9641.0000.5130.6720.0660.0850.1640.0890.0000.0951.000
SCENE_SCORE0.1101.0000.6660.3930.5440.0620.3510.4170.0000.8050.3860.3270.2620.4140.2881.000
EXPRN_SCORE0.0380.6661.0000.5800.7470.0230.5610.0000.0000.3710.8720.3220.3350.3370.3831.000
STAYNG_SCORE0.0210.3930.5801.0000.5860.0350.2110.0000.1920.2620.3290.9300.7900.4230.3401.000
FD_SCORE0.1290.5440.7470.5861.0000.0710.9271.0000.1890.2790.4140.4010.3680.8280.6791.000
CTPRVN_CD0.9640.0620.0230.0350.0711.0000.1620.2160.9930.1230.0000.2780.1740.3020.0001.000
YEAR1.0000.3510.5610.2110.9270.1621.0000.0000.1620.2880.4310.3600.2680.5300.4241.000
BSNS_SE0.5130.4170.0000.0001.0000.2160.0001.0000.2500.0000.0000.4260.0341.0001.0001.000
CTPRVN0.6720.0000.0000.1920.1890.9930.1620.2501.0000.1110.1110.1950.1740.2290.0961.000
SCENE_GRAD0.0660.8050.3710.2620.2790.1230.2880.0000.1111.0000.3660.3060.2660.2650.2881.000
EXPRN_GRAD0.0850.3860.8720.3290.4140.0000.4310.0000.1110.3661.0000.2960.3320.3580.3891.000
STAYNG_LMPPOINT_RT0.1640.3270.3220.9300.4010.2780.3600.4260.1950.3060.2961.0000.9270.3090.3041.000
STAYNG_GRAD0.0890.2620.3350.7900.3680.1740.2680.0340.1740.2660.3320.9271.0000.3360.3381.000
FD_GTSR_RT0.0000.4140.3370.4230.8280.3020.5301.0000.2290.2650.3580.3090.3361.0000.9231.000
FD_GRAD0.0950.2880.3830.3400.6790.0000.4241.0000.0960.2880.3890.3040.3380.9231.0001.000
SIGNGU_CD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T12:27:26.206830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:27:26.612367image/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.
2023-12-11T12:27:26.939332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YEARBSNS_SEORDRCTPRVNSIGNGUVILAGENMSCENE_SCORESCENE_GTSR_RTSCENE_GRADEXPRN_SCOREEXPRN_GTSR_RTEXPRN_GRADSTAYNG_SCORESTAYNG_LMPPOINT_RTSTAYNG_GRADFD_SCOREFD_GTSR_RTFD_GRADCTPRVN_CDSIGNGU_CD
02014농어촌민박4강원인제아다펜4781%2등급<NA><NA><NA>7089%2등급<NA><NA><NA>42<NA>
12014농어촌민박5강원인제솔마루민박4170%3등급<NA><NA><NA>6380%2등급<NA><NA><NA>42<NA>
22014농어촌민박6강원인제페이지펜션4170%3등급<NA><NA><NA>7596%1등급<NA><NA><NA>42<NA>
32014농어촌민박7강원인제쌀라네집58100%1등급<NA><NA><NA>7798%1등급<NA><NA><NA>42<NA>
42014농어촌민박8강원인제강뜨락펜션5187%2등급<NA><NA><NA>7191%1등급<NA><NA><NA>42<NA>
52014농어촌민박9강원인제구름타고온선녀5594%1등급<NA><NA><NA>7191%1등급<NA><NA><NA>42<NA>
62014농어촌민박10강원인제백담별채4679%3등급<NA><NA><NA>6279%3등급<NA><NA><NA>42<NA>
72014농어촌민박11강원인제백담계곡5187%2등급<NA><NA><NA>6887%2등급<NA><NA><NA>42<NA>
82014농어촌민박12강원인제열둘선녀계곡4984%2등급<NA><NA><NA>6583%2등급<NA><NA><NA>42<NA>
92014농어촌민박13강원인제물가에5493%1등급<NA><NA><NA>7292%1등급<NA><NA><NA>42<NA>
YEARBSNS_SEORDRCTPRVNSIGNGUVILAGENMSCENE_SCORESCENE_GTSR_RTSCENE_GRADEXPRN_SCOREEXPRN_GTSR_RTEXPRN_GRADSTAYNG_SCORESTAYNG_LMPPOINT_RTSTAYNG_GRADFD_SCOREFD_GTSR_RTFD_GRADCTPRVN_CDSIGNGU_CD
3462015체험휴양마을<NA>전남장성대곡마을7692%1등급<NA><NA><NA>7195%1등급<NA><NA><NA>46<NA>
3472015체험휴양마을<NA>전남진도의신사천마을7084%2등급7184%2등급6992%1등급6885%2등급46<NA>
3482015체험휴양마을<NA>경북영천정각마을(별빛마을)6680%2등급7386%2등급7093%1등급7290%1등급47<NA>
3492015체험휴양마을<NA>경북청도성곡권역농촌체험휴양마을8096%1등급8499%1등급7397%1등급6885%2등급47<NA>
3502015체험휴양마을<NA>경북칠곡가산산성마을7084%2등급7892%1등급77103%1등급7796%1등급47<NA>
3512015체험휴양마을<NA>경남사천초량농어촌체험휴양마을7692%1등급7791%1등급<NA><NA><NA>7796%1등급48<NA>
3522015체험휴양마을<NA>경남거창개금약초마을(개금)7692%1등급7082%2등급6181%2등급7290%1등급48<NA>
3532015체험휴양마을<NA>제주제주유수암마을7287%2등급6981%2등급7195%1등급<NA><NA><NA>50<NA>
3542015체험휴양마을<NA>제주서귀포가시마을7287%2등급7689%2등급7296%1등급6885%2등급50<NA>
3552015농어촌민박<NA>전남담양매화나무집3688%2등급7388%2등급<NA><NA><NA><NA><NA><NA>46<NA>