Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells246
Missing cells (%)18.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory110.3 B

Variable types

Categorical4
Text4
Numeric5

Alerts

area_lc is highly overall correlated with ctprvn_cd and 4 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 4 other fieldsHigh correlation
ctprvn_cd is highly overall correlated with signgu_cd and 4 other fieldsHigh correlation
signgu_cd is highly overall correlated with ctprvn_cd and 4 other fieldsHigh correlation
adstrd_cd is highly overall correlated with ctprvn_cd and 4 other fieldsHigh correlation
lo_val is highly overall correlated with la_val and 2 other fieldsHigh correlation
la_val is highly overall correlated with ctprvn_cd and 7 other fieldsHigh correlation
mnuri_card_mrhst_cd is highly overall correlated with lo_val and 2 other fieldsHigh correlation
cl is highly overall correlated with lo_val and 2 other fieldsHigh correlation
addr has 41 (41.0%) missing valuesMissing
ctprvn_cd has 41 (41.0%) missing valuesMissing
signgu_cd has 41 (41.0%) missing valuesMissing
signgu_nm has 41 (41.0%) missing valuesMissing
adstrd_cd has 41 (41.0%) missing valuesMissing
adstrd_nm has 41 (41.0%) missing valuesMissing
mrhst_nm has unique valuesUnique
lo_val has 43 (43.0%) zerosZeros
la_val has 43 (43.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:59:53.151299
Analysis finished2023-12-10 10:00:00.268826
Duration7.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

mnuri_card_mrhst_cd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
ON
52 
OFF
48 

Length

Max length3
Median length2
Mean length2.48
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ON 52
52.0%
OFF 48
48.0%

Length

2023-12-10T19:00:00.405192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:00:00.606802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
on 52
52.0%
off 48
48.0%

area_lc
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
47 
경기
13 
경남
7 
부산
6 
경북
 
4
Other values (11)
23 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row충남
2nd row서울
3rd row서울
4th row광주
5th row서울

Common Values

ValueCountFrequency (%)
서울 47
47.0%
경기 13
 
13.0%
경남 7
 
7.0%
부산 6
 
6.0%
경북 4
 
4.0%
강원 4
 
4.0%
충남 3
 
3.0%
광주 3
 
3.0%
대전 3
 
3.0%
전북 2
 
2.0%
Other values (6) 8
 
8.0%

Length

2023-12-10T19:00:00.795950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 47
47.0%
경기 13
 
13.0%
경남 7
 
7.0%
부산 6
 
6.0%
경북 4
 
4.0%
강원 4
 
4.0%
충남 3
 
3.0%
광주 3
 
3.0%
대전 3
 
3.0%
전북 2
 
2.0%
Other values (6) 8
 
8.0%

mrhst_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:00:01.317333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length17
Mean length5.96
Min length1

Characters and Unicode

Total characters596
Distinct characters226
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row홍주문화회관
2nd row(주)맥스무비
3rd rowCGV
4th row티켓마루
5th row교보문고
ValueCountFrequency (%)
인터파크 2
 
1.7%
정보화마을 2
 
1.7%
스포츠 2
 
1.7%
티켓링크 2
 
1.7%
명문사 1
 
0.9%
브룩스호텔 1
 
0.9%
호미화방 1
 
0.9%
주식회사 1
 
0.9%
아쿠아리움 1
 
0.9%
주)롯데월드 1
 
0.9%
Other values (101) 101
87.8%
2023-12-10T19:00:02.194297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 20
 
3.4%
( 19
 
3.2%
15
 
2.5%
역 13
 
2.2%
주 12
 
2.0%
스 11
 
1.8%
화 11
 
1.8%
티 11
 
1.8%
아 10
 
1.7%
켓 9
 
1.5%
Other values (216) 465
78.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 501
84.1%
Uppercase Letter 28
 
4.7%
Close Punctuation 20
 
3.4%
Open Punctuation 19
 
3.2%
Space Separator 15
 
2.5%
Other Symbol 5
 
0.8%
Lowercase Letter 5
 
0.8%
Decimal Number 2
 
0.3%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 13
 
2.6%
주 12
 
2.4%
스 11
 
2.2%
화 11
 
2.2%
티 11
 
2.2%
아 10
 
2.0%
켓 9
 
1.8%
문 9
 
1.8%
텔 9
 
1.8%
재 8
 
1.6%
Other values (187) 398
79.4%
Uppercase Letter
ValueCountFrequency (%)
W 3
10.7%
E 3
10.7%
N 3
10.7%
C 3
10.7%
A 2
 
7.1%
M 2
 
7.1%
J 2
 
7.1%
Q 1
 
3.6%
V 1
 
3.6%
G 1
 
3.6%
Other values (7) 7
25.0%
Lowercase Letter
ValueCountFrequency (%)
t 1
20.0%
e 1
20.0%
k 1
20.0%
c 1
20.0%
i 1
20.0%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
2 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 506
84.9%
Common 57
 
9.6%
Latin 33
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 13
 
2.6%
주 12
 
2.4%
스 11
 
2.2%
화 11
 
2.2%
티 11
 
2.2%
아 10
 
2.0%
켓 9
 
1.8%
문 9
 
1.8%
텔 9
 
1.8%
재 8
 
1.6%
Other values (188) 403
79.6%
Latin
ValueCountFrequency (%)
W 3
 
9.1%
E 3
 
9.1%
N 3
 
9.1%
C 3
 
9.1%
A 2
 
6.1%
M 2
 
6.1%
J 2
 
6.1%
Q 1
 
3.0%
V 1
 
3.0%
G 1
 
3.0%
Other values (12) 12
36.4%
Common
ValueCountFrequency (%)
) 20
35.1%
( 19
33.3%
15
26.3%
, 1
 
1.8%
3 1
 
1.8%
2 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 501
84.1%
ASCII 90
 
15.1%
None 5
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 20
22.2%
( 19
21.1%
15
16.7%
W 3
 
3.3%
E 3
 
3.3%
N 3
 
3.3%
C 3
 
3.3%
A 2
 
2.2%
M 2
 
2.2%
J 2
 
2.2%
Other values (18) 18
20.0%
Hangul
ValueCountFrequency (%)
역 13
 
2.6%
주 12
 
2.4%
스 11
 
2.2%
화 11
 
2.2%
티 11
 
2.2%
아 10
 
2.0%
켓 9
 
1.8%
문 9
 
1.8%
텔 9
 
1.8%
재 8
 
1.6%
Other values (187) 398
79.4%
None
ValueCountFrequency (%)
㈜ 5
100.0%

addr
Text

MISSING 

Distinct59
Distinct (%)100.0%
Missing41
Missing (%)41.0%
Memory size932.0 B
2023-12-10T19:00:02.855654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length24
Mean length17.118644
Min length12

Characters and Unicode

Total characters1010
Distinct characters166
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)100.0%

Sample

1st row충남 홍성군 홍성읍 내포로 164
2nd row경남 하동군 북천면 직전리 583
3rd row서울 중랑구 상봉로26길 23
4th row충북 청주시 청원구 향군로 79
5th row서울 강북구 월계로3길 43
ValueCountFrequency (%)
서울 11
 
4.3%
경남 7
 
2.8%
경기 6
 
2.4%
서울특별시 5
 
2.0%
부산 4
 
1.6%
경북 4
 
1.6%
동구 3
 
1.2%
강원 3
 
1.2%
충남 3
 
1.2%
하동군 3
 
1.2%
Other values (186) 204
80.6%
2023-12-10T19:00:03.733779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
210
 
20.8%
동 35
 
3.5%
구 34
 
3.4%
2 32
 
3.2%
1 31
 
3.1%
시 31
 
3.1%
로 29
 
2.9%
서 24
 
2.4%
3 24
 
2.4%
경 19
 
1.9%
Other values (156) 541
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 588
58.2%
Space Separator 210
 
20.8%
Decimal Number 194
 
19.2%
Dash Punctuation 18
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 35
 
6.0%
구 34
 
5.8%
시 31
 
5.3%
로 29
 
4.9%
서 24
 
4.1%
경 19
 
3.2%
남 18
 
3.1%
울 17
 
2.9%
북 15
 
2.6%
산 12
 
2.0%
Other values (144) 354
60.2%
Decimal Number
ValueCountFrequency (%)
2 32
16.5%
1 31
16.0%
3 24
12.4%
4 19
9.8%
7 17
8.8%
5 17
8.8%
6 16
8.2%
8 14
7.2%
0 14
7.2%
9 10
 
5.2%
Space Separator
ValueCountFrequency (%)
210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 588
58.2%
Common 422
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 35
 
6.0%
구 34
 
5.8%
시 31
 
5.3%
로 29
 
4.9%
서 24
 
4.1%
경 19
 
3.2%
남 18
 
3.1%
울 17
 
2.9%
북 15
 
2.6%
산 12
 
2.0%
Other values (144) 354
60.2%
Common
ValueCountFrequency (%)
210
49.8%
2 32
 
7.6%
1 31
 
7.3%
3 24
 
5.7%
4 19
 
4.5%
- 18
 
4.3%
7 17
 
4.0%
5 17
 
4.0%
6 16
 
3.8%
8 14
 
3.3%
Other values (2) 24
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 588
58.2%
ASCII 422
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210
49.8%
2 32
 
7.6%
1 31
 
7.3%
3 24
 
5.7%
4 19
 
4.5%
- 18
 
4.3%
7 17
 
4.0%
5 17
 
4.0%
6 16
 
3.8%
8 14
 
3.3%
Other values (2) 24
 
5.7%
Hangul
ValueCountFrequency (%)
동 35
 
6.0%
구 34
 
5.8%
시 31
 
5.3%
로 29
 
4.9%
서 24
 
4.1%
경 19
 
3.2%
남 18
 
3.1%
울 17
 
2.9%
북 15
 
2.6%
산 12
 
2.0%
Other values (144) 354
60.2%

cl
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
숙박
21 
공연
19 
도서
15 
교통수단
13 
여행사
8 
Other values (6)
24 

Length

Max length5
Median length2
Mean length2.54
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공연
2nd row영상
3rd row영상
4th row공연
5th row도서

Common Values

ValueCountFrequency (%)
숙박 21
21.0%
공연 19
19.0%
도서 15
15.0%
교통수단 13
13.0%
여행사 8
 
8.0%
미술 6
 
6.0%
영상 5
 
5.0%
스포츠관람 4
 
4.0%
관광지 4
 
4.0%
음악 3
 
3.0%

Length

2023-12-10T19:00:03.997947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박 21
21.0%
공연 19
19.0%
도서 15
15.0%
교통수단 13
13.0%
여행사 8
 
8.0%
미술 6
 
6.0%
영상 5
 
5.0%
스포츠관람 4
 
4.0%
관광지 4
 
4.0%
음악 3
 
3.0%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)27.1%
Missing41
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean25.508475
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:04.228630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median26
Q334.5
95-th percentile38
Maximum39
Range28
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation10.341368
Coefficient of variation (CV)0.40540911
Kurtosis-1.4173112
Mean25.508475
Median Absolute Deviation (MAD)9
Skewness-0.32485748
Sum1505
Variance106.94389
MonotonicityNot monotonic
2023-12-10T19:00:04.454143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11 16
 
16.0%
38 7
 
7.0%
31 7
 
7.0%
21 5
 
5.0%
37 4
 
4.0%
25 3
 
3.0%
32 3
 
3.0%
34 3
 
3.0%
35 2
 
2.0%
23 2
 
2.0%
Other values (6) 7
 
7.0%
(Missing) 41
41.0%
ValueCountFrequency (%)
11 16
16.0%
21 5
 
5.0%
22 1
 
1.0%
23 2
 
2.0%
24 2
 
2.0%
25 3
 
3.0%
26 1
 
1.0%
31 7
7.0%
32 3
 
3.0%
33 1
 
1.0%
ValueCountFrequency (%)
39 1
 
1.0%
38 7
7.0%
37 4
4.0%
36 1
 
1.0%
35 2
 
2.0%
34 3
3.0%
33 1
 
1.0%
32 3
3.0%
31 7
7.0%
26 1
 
1.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
41 
서울특별시
16 
경상남도
7 
경기도
7 
부산광역시
5 
Other values (12)
24 

Length

Max length7
Median length4
Mean length4.23
Min length3

Unique

Unique5 ?
Unique (%)5.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 41
41.0%
서울특별시 16
 
16.0%
경상남도 7
 
7.0%
경기도 7
 
7.0%
부산광역시 5
 
5.0%
경상북도 4
 
4.0%
강원도 3
 
3.0%
대전광역시 3
 
3.0%
충청남도 3
 
3.0%
전라북도 2
 
2.0%
Other values (7) 9
 
9.0%

Length

2023-12-10T19:00:05.098038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 41
41.0%
서울특별시 16
 
16.0%
경상남도 7
 
7.0%
경기도 7
 
7.0%
부산광역시 5
 
5.0%
경상북도 4
 
4.0%
대전광역시 3
 
3.0%
충청남도 3
 
3.0%
강원도 3
 
3.0%
전라북도 2
 
2.0%
Other values (7) 9
 
9.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)84.7%
Missing41
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean25606.678
Minimum11010
Maximum39010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:05.359236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11029
Q111215
median26030
Q334695
95-th percentile38360
Maximum39010
Range28000
Interquartile range (IQR)23480

Descriptive statistics

Standard deviation10359.672
Coefficient of variation (CV)0.40456914
Kurtosis-1.4199333
Mean25606.678
Median Absolute Deviation (MAD)9020
Skewness-0.31913864
Sum1510794
Variance1.073228 × 108
MonotonicityNot monotonic
2023-12-10T19:00:05.676343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38360 3
 
3.0%
11130 2
 
2.0%
11030 2
 
2.0%
37070 2
 
2.0%
11010 2
 
2.0%
21080 2
 
2.0%
25040 2
 
2.0%
11070 2
 
2.0%
25010 1
 
1.0%
11210 1
 
1.0%
Other values (40) 40
40.0%
(Missing) 41
41.0%
ValueCountFrequency (%)
11010 2
2.0%
11020 1
1.0%
11030 2
2.0%
11070 2
2.0%
11080 1
1.0%
11090 1
1.0%
11120 1
1.0%
11130 2
2.0%
11140 1
1.0%
11160 1
1.0%
ValueCountFrequency (%)
39010 1
 
1.0%
38360 3
3.0%
38320 1
 
1.0%
38080 1
 
1.0%
38050 1
 
1.0%
38030 1
 
1.0%
37070 2
2.0%
37060 1
 
1.0%
37040 1
 
1.0%
36030 1
 
1.0%

signgu_nm
Text

MISSING 

Distinct45
Distinct (%)76.3%
Missing41
Missing (%)41.0%
Memory size932.0 B
2023-12-10T19:00:06.104286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9491525
Min length2

Characters and Unicode

Total characters174
Distinct characters58
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)57.6%

Sample

1st row홍성군
2nd row하동군
3rd row중랑구
4th row청주시 청원구
5th row강북구
ValueCountFrequency (%)
하동군 3
 
5.0%
동구 3
 
5.0%
중구 3
 
5.0%
영천시 2
 
3.3%
북구 2
 
3.3%
용산구 2
 
3.3%
서구 2
 
3.3%
종로구 2
 
3.3%
중랑구 2
 
3.3%
서대문구 2
 
3.3%
Other values (36) 37
61.7%
2023-12-10T19:00:06.785910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 31
17.8%
시 24
 
13.8%
동 8
 
4.6%
서 7
 
4.0%
주 6
 
3.4%
군 5
 
2.9%
중 5
 
2.9%
성 5
 
2.9%
산 5
 
2.9%
북 4
 
2.3%
Other values (48) 74
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 173
99.4%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 31
17.9%
시 24
 
13.9%
동 8
 
4.6%
서 7
 
4.0%
주 6
 
3.5%
군 5
 
2.9%
중 5
 
2.9%
성 5
 
2.9%
산 5
 
2.9%
북 4
 
2.3%
Other values (47) 73
42.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 173
99.4%
Common 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 31
17.9%
시 24
 
13.9%
동 8
 
4.6%
서 7
 
4.0%
주 6
 
3.5%
군 5
 
2.9%
중 5
 
2.9%
성 5
 
2.9%
산 5
 
2.9%
북 4
 
2.3%
Other values (47) 73
42.2%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 173
99.4%
ASCII 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 31
17.9%
시 24
 
13.9%
동 8
 
4.6%
서 7
 
4.0%
주 6
 
3.5%
군 5
 
2.9%
중 5
 
2.9%
성 5
 
2.9%
산 5
 
2.9%
북 4
 
2.3%
Other values (47) 73
42.2%
ASCII
ValueCountFrequency (%)
1
100.0%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)98.3%
Missing41
Missing (%)41.0%
Infinite0
Infinite (%)0.0%
Mean2560725.5
Minimum1101061
Maximum3901063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:07.185700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101061
5-th percentile1102970.2
Q11121557
median2603053
Q33469538
95-th percentile3836013.3
Maximum3901063
Range2800002
Interquartile range (IQR)2347981

Descriptive statistics

Standard deviation1035958.6
Coefficient of variation (CV)0.40455667
Kurtosis-1.4199336
Mean2560725.5
Median Absolute Deviation (MAD)901999
Skewness-0.31914047
Sum1.510828 × 108
Variance1.0732101 × 1012
MonotonicityNot monotonic
2023-12-10T19:00:07.478160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1107073 2
 
2.0%
1108084 1
 
1.0%
3901063 1
 
1.0%
3125051 1
 
1.0%
2603053 1
 
1.0%
3205053 1
 
1.0%
2102064 1
 
1.0%
3404051 1
 
1.0%
3207051 1
 
1.0%
2309075 1
 
1.0%
Other values (48) 48
48.0%
(Missing) 41
41.0%
ValueCountFrequency (%)
1101061 1
1.0%
1101067 1
1.0%
1102054 1
1.0%
1103072 1
1.0%
1103073 1
1.0%
1107073 2
2.0%
1108084 1
1.0%
1109069 1
1.0%
1112074 1
1.0%
1113073 1
1.0%
ValueCountFrequency (%)
3901063 1
1.0%
3836040 1
1.0%
3836034 1
1.0%
3836011 1
1.0%
3832032 1
1.0%
3808055 1
1.0%
3805063 1
1.0%
3803067 1
1.0%
3707054 1
1.0%
3707032 1
1.0%

adstrd_nm
Text

MISSING 

Distinct56
Distinct (%)94.9%
Missing41
Missing (%)41.0%
Memory size932.0 B
2023-12-10T19:00:07.892379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.4576271
Min length2

Characters and Unicode

Total characters204
Distinct characters79
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)91.5%

Sample

1st row홍성읍
2nd row북천면
3rd row망우본동
4th row우암동
5th row삼양동
ValueCountFrequency (%)
중앙동 3
 
5.1%
망우본동 2
 
3.4%
충장동 1
 
1.7%
구포1동 1
 
1.7%
충현동 1
 
1.7%
아라동 1
 
1.7%
경안동 1
 
1.7%
화정동 1
 
1.7%
삼수동 1
 
1.7%
충무동 1
 
1.7%
Other values (46) 46
78.0%
2023-12-10T19:00:08.540532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 54
26.5%
1 14
 
6.9%
중 5
 
2.5%
면 5
 
2.5%
우 4
 
2.0%
· 4
 
2.0%
현 4
 
2.0%
천 4
 
2.0%
신 4
 
2.0%
안 3
 
1.5%
Other values (69) 103
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 179
87.7%
Decimal Number 21
 
10.3%
Other Punctuation 4
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 54
30.2%
중 5
 
2.8%
면 5
 
2.8%
우 4
 
2.2%
현 4
 
2.2%
천 4
 
2.2%
신 4
 
2.2%
안 3
 
1.7%
충 3
 
1.7%
앙 3
 
1.7%
Other values (64) 90
50.3%
Decimal Number
ValueCountFrequency (%)
1 14
66.7%
3 3
 
14.3%
2 3
 
14.3%
4 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
· 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 179
87.7%
Common 25
 
12.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 54
30.2%
중 5
 
2.8%
면 5
 
2.8%
우 4
 
2.2%
현 4
 
2.2%
천 4
 
2.2%
신 4
 
2.2%
안 3
 
1.7%
충 3
 
1.7%
앙 3
 
1.7%
Other values (64) 90
50.3%
Common
ValueCountFrequency (%)
1 14
56.0%
· 4
 
16.0%
3 3
 
12.0%
2 3
 
12.0%
4 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 179
87.7%
ASCII 21
 
10.3%
None 4
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 54
30.2%
중 5
 
2.8%
면 5
 
2.8%
우 4
 
2.2%
현 4
 
2.2%
천 4
 
2.2%
신 4
 
2.2%
안 3
 
1.7%
충 3
 
1.7%
앙 3
 
1.7%
Other values (64) 90
50.3%
ASCII
ValueCountFrequency (%)
1 14
66.7%
3 3
 
14.3%
2 3
 
14.3%
4 1
 
4.8%
None
ValueCountFrequency (%)
· 4
100.0%

lo_val
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.745807
Minimum0
Maximum129.42688
Zeros43
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:08.841372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median126.80005
Q3127.35778
95-th percentile129.02404
Maximum129.42688
Range129.42688
Interquartile range (IQR)127.35778

Descriptive statistics

Standard deviation63.505532
Coefficient of variation (CV)0.8729786
Kurtosis-1.9568391
Mean72.745807
Median Absolute Deviation (MAD)2.2027646
Skewness-0.28676314
Sum7274.5807
Variance4032.9526
MonotonicityNot monotonic
2023-12-10T19:00:09.288379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 43
43.0%
128.997992 1
 
1.0%
129.0351404 1
 
1.0%
126.5705897 1
 
1.0%
127.256581 1
 
1.0%
129.426877 1
 
1.0%
128.958891 1
 
1.0%
129.023459 1
 
1.0%
126.997485 1
 
1.0%
129.166239 1
 
1.0%
Other values (48) 48
48.0%
ValueCountFrequency (%)
0.0 43
43.0%
126.4589 1
 
1.0%
126.5705897 1
 
1.0%
126.646279 1
 
1.0%
126.672768 1
 
1.0%
126.688259 1
 
1.0%
126.720152 1
 
1.0%
126.7552888 1
 
1.0%
126.844819 1
 
1.0%
126.874498 1
 
1.0%
ValueCountFrequency (%)
129.426877 1
1.0%
129.166239 1
1.0%
129.122119 1
1.0%
129.104095 1
1.0%
129.0351404 1
1.0%
129.023459 1
1.0%
129.007645 1
1.0%
128.997992 1
1.0%
128.958891 1
1.0%
128.940847 1
1.0%

la_val
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.804131
Minimum0
Maximum37.777057
Zeros43
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:09.618703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median35.106054
Q337.111241
95-th percentile37.596158
Maximum37.777057
Range37.777057
Interquartile range (IQR)37.111241

Descriptive statistics

Standard deviation18.179679
Coefficient of variation (CV)0.87384949
Kurtosis-1.9521378
Mean20.804131
Median Absolute Deviation (MAD)2.4773692
Skewness-0.28071349
Sum2080.4131
Variance330.50074
MonotonicityNot monotonic
2023-12-10T19:00:09.900389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 43
43.0%
35.2062018 1
 
1.0%
35.1031867 1
 
1.0%
33.4506415 1
 
1.0%
37.4099471 1
 
1.0%
35.494666 1
 
1.0%
37.26897 1
 
1.0%
35.0959261 1
 
1.0%
36.7882332 1
 
1.0%
37.4429404 1
 
1.0%
Other values (48) 48
48.0%
ValueCountFrequency (%)
0.0 43
43.0%
33.4506415 1
 
1.0%
34.8319879 1
 
1.0%
34.954999 1
 
1.0%
35.070212 1
 
1.0%
35.0959261 1
 
1.0%
35.1031867 1
 
1.0%
35.1034182 1
 
1.0%
35.1086894 1
 
1.0%
35.1124645 1
 
1.0%
ValueCountFrequency (%)
37.777057 1
1.0%
37.6189266 1
1.0%
37.6116932 1
1.0%
37.610589 1
1.0%
37.6051005 1
1.0%
37.5956869 1
1.0%
37.5947098 1
1.0%
37.5721361 1
1.0%
37.571855 1
1.0%
37.5623646 1
1.0%

Interactions

2023-12-10T18:59:58.502909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:54.917102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:55.836319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:56.705355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:57.597863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:58.693349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:55.080892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:56.005951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:56.881792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:57.804718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:58.863219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:55.259166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:56.179599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:57.040814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:57.965692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:59.026010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:55.458160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:56.361075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:57.214466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:58.154036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:59.193771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:55.618137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:56.525000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:57.404579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:58.324580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:00:10.123594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
mnuri_card_mrhst_cdarea_lcmrhst_nmaddrclctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmlo_valla_val
mnuri_card_mrhst_cd1.0000.6101.0001.0000.7560.0000.2440.0000.0000.0000.0000.9570.579
area_lc0.6101.0001.0001.0000.5031.0001.0000.9870.9401.0000.9760.5520.886
mrhst_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
cl0.7560.5031.0001.0001.0000.3620.5080.3340.9050.3560.9520.7020.742
ctprvn_cd0.0001.0001.0001.0000.3621.0001.0000.9990.9411.0000.7670.0000.000
ctprvn_nm0.2441.0001.0001.0000.5081.0001.0000.9870.9401.0000.9760.0000.821
signgu_cd0.0000.9871.0001.0000.3340.9990.9871.0000.9360.9990.8380.0000.000
signgu_nm0.0000.9401.0001.0000.9050.9410.9400.9361.0000.9340.9951.0001.000
adstrd_cd0.0001.0001.0001.0000.3561.0001.0000.9990.9341.0000.7640.0000.000
adstrd_nm0.0000.9761.0001.0000.9520.7670.9760.8380.9950.7641.0001.0001.000
lo_val0.9570.5521.0001.0000.7020.0000.0000.0001.0000.0001.0001.0001.000
la_val0.5790.8861.0001.0000.7420.0000.8210.0001.0000.0001.0001.0001.000
2023-12-10T19:00:10.441438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
clarea_lcmnuri_card_mrhst_cdctprvn_nm
cl1.0000.2060.7080.208
area_lc0.2061.0000.4471.000
mnuri_card_mrhst_cd0.7080.4471.0000.153
ctprvn_nm0.2081.0000.1531.000
2023-12-10T19:00:10.756836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_cdsigngu_cdadstrd_cdlo_valla_valmnuri_card_mrhst_cdarea_lcclctprvn_nm
ctprvn_cd1.0000.9880.9880.292-0.5540.0000.9090.0410.909
signgu_cd0.9881.0001.0000.279-0.5690.0000.8720.1560.872
adstrd_cd0.9881.0001.0000.279-0.5690.0000.9090.1890.909
lo_val0.2920.2790.2791.0000.7020.8120.4030.6520.000
la_val-0.554-0.569-0.5690.7021.0000.8440.7160.5700.581
mnuri_card_mrhst_cd0.0000.0000.0000.8120.8441.0000.4470.7080.153
area_lc0.9090.8720.9090.4030.7160.4471.0000.2061.000
cl0.0410.1560.1890.6520.5700.7080.2061.0000.208
ctprvn_nm0.9090.8720.9090.0000.5810.1531.0000.2081.000

Missing values

2023-12-10T18:59:59.435573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:59:59.765951image/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.
2023-12-10T19:00:00.102410image/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

mnuri_card_mrhst_cdarea_lcmrhst_nmaddrclctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmlo_valla_val
0ON충남홍주문화회관충남 홍성군 홍성읍 내포로 164공연34충청남도34360홍성군3436011홍성읍0.00.0
1ON서울(주)맥스무비<NA>영상<NA><NA><NA><NA><NA><NA>0.00.0
2ON서울CGV<NA>영상<NA><NA><NA><NA><NA><NA>0.00.0
3ON광주티켓마루<NA>공연<NA><NA><NA><NA><NA><NA>0.00.0
4ON서울교보문고<NA>도서<NA><NA><NA><NA><NA><NA>0.00.0
5OFF경남북천역경남 하동군 북천면 직전리 583교통수단38경상남도38360하동군3836040북천면127.89445935.112465
6ON서울강동아트센터<NA>공연<NA><NA><NA><NA><NA><NA>0.00.0
7OFF서울칼튼서울 중랑구 상봉로26길 23숙박11서울특별시11070중랑구1107073망우본동127.09432837.595687
8ON서울펜션라이프<NA>숙박<NA><NA><NA><NA><NA><NA>0.00.0
9ON서울코레일관광개발<NA>여행사<NA><NA><NA><NA><NA><NA>0.00.0
mnuri_card_mrhst_cdarea_lcmrhst_nmaddrclctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmlo_valla_val
90ON대구(주)삼성여행사대구 중구 국채보상로 515 갑을빌딩 2층여행사22대구광역시22010중구2201061성내3동128.58698535.870413
91ON서울김해롯데워터파크 온라인 입장권(카바나,티키아일랜드스파)<NA>관광지<NA><NA><NA><NA><NA><NA>0.00.0
92ON울산울산현대 축구단<NA>스포츠관람<NA><NA><NA><NA><NA><NA>0.00.0
93ON서울롯데시네마<NA>영상<NA><NA><NA><NA><NA><NA>0.00.0
94ON서울정보화마을 주말농장<NA>관광지<NA><NA><NA><NA><NA><NA>0.00.0
95ON서울아르코예술극장<NA>공연<NA><NA><NA><NA><NA><NA>0.00.0
96OFF경남브룩스호텔경남 진주시 순환로 567숙박38경상남도38030진주시3803067평거동128.05884335.174273
97OFF전북명문사전북 남원시 광한북로 51도서35전라북도35050남원시3505052죽항동127.37978535.407567
98ON서울재단법인인천광역시부평구문화재단<NA>공연<NA><NA><NA><NA><NA><NA>0.00.0
99OFF인천붐인천 서구 연희동 738-1미술23인천광역시23080서구2308053연희동126.67276837.549404