Overview

Dataset statistics

Number of variables14
Number of observations130
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory117.0 B

Variable types

Text4
Categorical6
Numeric4

Alerts

X has constant value ""Constant
X.1 has constant value ""Constant
A04010600 is highly overall correlated with A0401 and 2 other fieldsHigh correlation
전문상가 is highly overall correlated with A0401 and 2 other fieldsHigh correlation
A0401 is highly overall correlated with 쇼핑 and 2 other fieldsHigh correlation
쇼핑 is highly overall correlated with A0401 and 2 other fieldsHigh correlation
315321 is highly overall correlated with 546617High correlation
546617 is highly overall correlated with 315321High correlation
C00936 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:18:12.466386
Analysis finished2023-12-10 06:18:16.429096
Duration3.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

C00936
Text

UNIQUE 

Distinct130
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-10T15:18:16.889112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters780
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)100.0%

Sample

1st rowC00466
2nd rowC00144
3rd rowC00456
4th rowC00811
5th rowC00441
ValueCountFrequency (%)
c00466 1
 
0.8%
c00184 1
 
0.8%
c01256 1
 
0.8%
c00501 1
 
0.8%
c00044 1
 
0.8%
c01325 1
 
0.8%
c00935 1
 
0.8%
c00305 1
 
0.8%
c00939 1
 
0.8%
c00304 1
 
0.8%
Other values (120) 120
92.3%
2023-12-10T15:18:17.717769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 274
35.1%
C 130
16.7%
1 77
 
9.9%
4 50
 
6.4%
3 48
 
6.2%
2 46
 
5.9%
9 36
 
4.6%
6 34
 
4.4%
7 31
 
4.0%
5 29
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 650
83.3%
Uppercase Letter 130
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274
42.2%
1 77
 
11.8%
4 50
 
7.7%
3 48
 
7.4%
2 46
 
7.1%
9 36
 
5.5%
6 34
 
5.2%
7 31
 
4.8%
5 29
 
4.5%
8 25
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
C 130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 650
83.3%
Latin 130
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274
42.2%
1 77
 
11.8%
4 50
 
7.7%
3 48
 
7.4%
2 46
 
7.1%
9 36
 
5.5%
6 34
 
5.2%
7 31
 
4.8%
5 29
 
4.5%
8 25
 
3.8%
Latin
ValueCountFrequency (%)
C 130
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 274
35.1%
C 130
16.7%
1 77
 
9.9%
4 50
 
6.4%
3 48
 
6.2%
2 46
 
5.9%
9 36
 
4.6%
6 34
 
4.4%
7 31
 
4.0%
5 29
 
3.7%

오르시아
Text

Distinct129
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-10T15:18:18.088783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.3615385
Min length2

Characters and Unicode

Total characters827
Distinct characters261
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)98.5%

Sample

1st row뱅앤올룹슨
2nd row국기원
3rd row백련사
4th row신검사
5th row박여숙화랑
ValueCountFrequency (%)
갤러리 9
 
5.1%
현대백화점 2
 
1.1%
강남 2
 
1.1%
꿈의숲 2
 
1.1%
서울특별시교육청 2
 
1.1%
코엑스 2
 
1.1%
서울 2
 
1.1%
클루 1
 
0.6%
도산안창호기념관 1
 
0.6%
오휘스파논현 1
 
0.6%
Other values (154) 154
86.5%
2023-12-10T15:18:18.708857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48
 
5.8%
스 29
 
3.5%
관 24
 
2.9%
리 23
 
2.8%
화 20
 
2.4%
서 16
 
1.9%
원 16
 
1.9%
아 14
 
1.7%
갤 13
 
1.6%
러 13
 
1.6%
Other values (251) 611
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 754
91.2%
Space Separator 48
 
5.8%
Uppercase Letter 17
 
2.1%
Decimal Number 7
 
0.8%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 29
 
3.8%
관 24
 
3.2%
리 23
 
3.1%
화 20
 
2.7%
서 16
 
2.1%
원 16
 
2.1%
아 14
 
1.9%
갤 13
 
1.7%
러 13
 
1.7%
사 13
 
1.7%
Other values (232) 573
76.0%
Uppercase Letter
ValueCountFrequency (%)
J 3
17.6%
S 2
11.8%
A 2
11.8%
V 1
 
5.9%
M 1
 
5.9%
R 1
 
5.9%
U 1
 
5.9%
P 1
 
5.9%
W 1
 
5.9%
H 1
 
5.9%
Other values (3) 3
17.6%
Decimal Number
ValueCountFrequency (%)
9 3
42.9%
1 2
28.6%
4 1
 
14.3%
8 1
 
14.3%
Space Separator
ValueCountFrequency (%)
48
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 754
91.2%
Common 56
 
6.8%
Latin 17
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 29
 
3.8%
관 24
 
3.2%
리 23
 
3.1%
화 20
 
2.7%
서 16
 
2.1%
원 16
 
2.1%
아 14
 
1.9%
갤 13
 
1.7%
러 13
 
1.7%
사 13
 
1.7%
Other values (232) 573
76.0%
Latin
ValueCountFrequency (%)
J 3
17.6%
S 2
11.8%
A 2
11.8%
V 1
 
5.9%
M 1
 
5.9%
R 1
 
5.9%
U 1
 
5.9%
P 1
 
5.9%
W 1
 
5.9%
H 1
 
5.9%
Other values (3) 3
17.6%
Common
ValueCountFrequency (%)
48
85.7%
9 3
 
5.4%
1 2
 
3.6%
4 1
 
1.8%
. 1
 
1.8%
8 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 754
91.2%
ASCII 73
 
8.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48
65.8%
9 3
 
4.1%
J 3
 
4.1%
S 2
 
2.7%
1 2
 
2.7%
A 2
 
2.7%
V 1
 
1.4%
M 1
 
1.4%
R 1
 
1.4%
U 1
 
1.4%
Other values (9) 9
 
12.3%
Hangul
ValueCountFrequency (%)
스 29
 
3.8%
관 24
 
3.2%
리 23
 
3.1%
화 20
 
2.7%
서 16
 
2.1%
원 16
 
2.1%
아 14
 
1.9%
갤 13
 
1.7%
러 13
 
1.7%
사 13
 
1.7%
Other values (232) 573
76.0%

X
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
X
130 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
X 130
100.0%

Length

2023-12-10T15:18:18.937884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:18:19.124348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 130
100.0%

315321
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313681.85
Minimum294497
Maximum320852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-10T15:18:19.354231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294497
5-th percentile297656
Q1313808.5
median314980
Q3316746.5
95-th percentile317890.55
Maximum320852
Range26355
Interquartile range (IQR)2938

Descriptive statistics

Standard deviation5683.6596
Coefficient of variation (CV)0.018119186
Kurtosis5.024933
Mean313681.85
Median Absolute Deviation (MAD)1255.5
Skewness-2.4220496
Sum40778640
Variance32303986
MonotonicityNot monotonic
2023-12-10T15:18:19.690154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316991 13
 
10.0%
315771 3
 
2.3%
314921 2
 
1.5%
311330 2
 
1.5%
315981 2
 
1.5%
315865 2
 
1.5%
319041 2
 
1.5%
294497 2
 
1.5%
314739 1
 
0.8%
314776 1
 
0.8%
Other values (100) 100
76.9%
ValueCountFrequency (%)
294497 2
1.5%
294717 1
0.8%
294920 1
0.8%
295704 1
0.8%
297369 1
0.8%
297611 1
0.8%
297711 1
0.8%
298017 1
0.8%
298705 1
0.8%
298833 1
0.8%
ValueCountFrequency (%)
320852 1
0.8%
319913 1
0.8%
319041 2
1.5%
319040 1
0.8%
318380 1
0.8%
318116 1
0.8%
317615 1
0.8%
317612 1
0.8%
317595 1
0.8%
317581 1
0.8%

546617
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548584.41
Minimum541488
Maximum563361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-10T15:18:19.916766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum541488
5-th percentile543018
Q1545838.75
median547133
Q3547729.75
95-th percentile560916.8
Maximum563361
Range21873
Interquartile range (IQR)1891

Descriptive statistics

Standard deviation5211.6624
Coefficient of variation (CV)0.0095002015
Kurtosis1.3222853
Mean548584.41
Median Absolute Deviation (MAD)1135
Skewness1.5239407
Sum71315973
Variance27161425
MonotonicityNot monotonic
2023-12-10T15:18:20.164061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
545998 13
 
10.0%
557940 3
 
2.3%
547338 2
 
1.5%
562710 2
 
1.5%
547442 2
 
1.5%
547687 2
 
1.5%
543018 2
 
1.5%
553811 2
 
1.5%
547723 1
 
0.8%
559405 1
 
0.8%
Other values (100) 100
76.9%
ValueCountFrequency (%)
541488 1
0.8%
542102 1
0.8%
542247 1
0.8%
542435 1
0.8%
542684 1
0.8%
542889 1
0.8%
543018 2
1.5%
543136 1
0.8%
543319 1
0.8%
544132 1
0.8%
ValueCountFrequency (%)
563361 1
0.8%
562710 2
1.5%
561938 1
0.8%
561717 1
0.8%
561364 1
0.8%
561185 1
0.8%
560589 1
0.8%
560279 1
0.8%
559920 1
0.8%
559893 1
0.8%

282692
Real number (ℝ)

Distinct101
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170875.02
Minimum3639
Maximum509392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-10T15:18:20.462624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3639
5-th percentile14908.7
Q124918
median179891.5
Q3270459
95-th percentile419491.45
Maximum509392
Range505753
Interquartile range (IQR)245541

Descriptive statistics

Standard deviation154058.48
Coefficient of variation (CV)0.90158577
Kurtosis-1.0216793
Mean170875.02
Median Absolute Deviation (MAD)148940.5
Skewness0.47364348
Sum22213752
Variance2.3734016 × 1010
MonotonicityNot monotonic
2023-12-10T15:18:20.710655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270459 13
 
10.0%
416984 3
 
2.3%
3639 3
 
2.3%
500322 3
 
2.3%
24500 2
 
1.5%
33665 2
 
1.5%
24481 2
 
1.5%
16876 2
 
1.5%
270733 2
 
1.5%
269037 2
 
1.5%
Other values (91) 96
73.8%
ValueCountFrequency (%)
3639 3
2.3%
5696 1
 
0.8%
11636 1
 
0.8%
13610 1
 
0.8%
13781 1
 
0.8%
16287 1
 
0.8%
16292 1
 
0.8%
16588 1
 
0.8%
16876 2
1.5%
17446 1
 
0.8%
ValueCountFrequency (%)
509392 1
 
0.8%
508825 1
 
0.8%
501930 1
 
0.8%
500322 3
2.3%
421543 1
 
0.8%
416984 3
2.3%
416920 1
 
0.8%
416866 1
 
0.8%
414149 1
 
0.8%
413414 1
 
0.8%

A0401
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
A0206
63 
A0401
30 
A0201
16 
A0202
13 
A0203
 
4
Other values (2)
 
4

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowA0401
2nd rowA0206
3rd rowA0201
4th rowA0201
5th rowA0206

Common Values

ValueCountFrequency (%)
A0206 63
48.5%
A0401 30
23.1%
A0201 16
 
12.3%
A0202 13
 
10.0%
A0203 4
 
3.1%
A0101 3
 
2.3%
A0205 1
 
0.8%

Length

2023-12-10T15:18:20.925710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:18:21.477853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a0206 63
48.5%
a0401 30
23.1%
a0201 16
 
12.3%
a0202 13
 
10.0%
a0203 4
 
3.1%
a0101 3
 
2.3%
a0205 1
 
0.8%

쇼핑
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
문화예술
63 
쇼핑
30 
역사관광지
16 
휴양관광지
13 
체험관광지
 
4
Other values (2)
 
4

Length

Max length7
Median length6
Mean length3.8769231
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row쇼핑
2nd row문화예술
3rd row역사관광지
4th row역사관광지
5th row문화예술

Common Values

ValueCountFrequency (%)
문화예술 63
48.5%
쇼핑 30
23.1%
역사관광지 16
 
12.3%
휴양관광지 13
 
10.0%
체험관광지 4
 
3.1%
자연생태관광지 3
 
2.3%
건축/조형물 1
 
0.8%

Length

2023-12-10T15:18:21.681337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:18:21.878690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화예술 63
48.5%
쇼핑 30
23.1%
역사관광지 16
 
12.3%
휴양관광지 13
 
10.0%
체험관광지 4
 
3.1%
자연생태관광지 3
 
2.3%
건축/조형물 1
 
0.8%

A04010600
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
A04010600
26 
A02060500
25 
A02010800
12 
A02060100
10 
A02060600
9 
Other values (19)
48 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique8 ?
Unique (%)6.2%

Sample

1st rowA04010600
2nd rowA02061100
3rd rowA02010800
4th rowA02010800
5th rowA02060500

Common Values

ValueCountFrequency (%)
A04010600 26
20.0%
A02060500 25
19.2%
A02010800 12
9.2%
A02060100 10
 
7.7%
A02060600 9
 
6.9%
A02020700 6
 
4.6%
A02020300 5
 
3.8%
A02060300 4
 
3.1%
A02060900 4
 
3.1%
A02010700 4
 
3.1%
Other values (14) 25
19.2%

Length

2023-12-10T15:18:22.172062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a04010600 26
20.0%
a02060500 25
19.2%
a02010800 12
9.2%
a02060100 10
 
7.7%
a02060600 9
 
6.9%
a02020700 6
 
4.6%
a02020300 5
 
3.8%
a02060300 4
 
3.1%
a02060900 4
 
3.1%
a02010700 4
 
3.1%
Other values (14) 25
19.2%

전문상가
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
전문상가
26 
미술관
25 
사찰
12 
박물관
10 
공연장
9 
Other values (19)
48 

Length

Max length11
Median length8
Mean length3.6692308
Min length1

Unique

Unique8 ?
Unique (%)6.2%

Sample

1st row전문상가
2nd row문화전수시설
3rd row사찰
4th row사찰
5th row미술관

Common Values

ValueCountFrequency (%)
전문상가 26
20.0%
미술관 25
19.2%
사찰 12
9.2%
박물관 10
 
7.7%
공연장 9
 
6.9%
공원 6
 
4.6%
온천/욕장/스파 5
 
3.8%
전시장 4
 
3.1%
도서관 4
 
3.1%
유적지/사적지 4
 
3.1%
Other values (14) 25
19.2%

Length

2023-12-10T15:18:22.409984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전문상가 26
19.8%
미술관 25
19.1%
사찰 12
9.2%
박물관 10
 
7.6%
공연장 9
 
6.9%
공원 6
 
4.6%
온천/욕장/스파 5
 
3.8%
전시장 4
 
3.1%
도서관 4
 
3.1%
유적지/사적지 4
 
3.1%
Other values (15) 26
19.8%

서울특별시 강남구 선릉로131길 12
Text

Distinct112
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-10T15:18:22.941388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length18.907692
Min length16

Characters and Unicode

Total characters2458
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)80.0%

Sample

1st row서울특별시 강남구 언주로 865
2nd row서울특별시 강남구 테헤란로7길 32
3rd row서울특별시 강북구 4.19로20길 142
4th row서울특별시 강북구 삼양로179길 209-44
5th row서울특별시 강남구 압구정로 461
ValueCountFrequency (%)
서울특별시 130
25.0%
강남구 101
19.4%
강북구 17
 
3.3%
영동대로 12
 
2.3%
강서구 12
 
2.3%
압구정로 11
 
2.1%
513 9
 
1.7%
봉은사로 6
 
1.2%
언주로 5
 
1.0%
테헤란로 5
 
1.0%
Other values (170) 212
40.8%
2023-12-10T15:18:23.741242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
390
15.9%
구 148
 
6.0%
서 142
 
5.8%
강 135
 
5.5%
특 130
 
5.3%
별 130
 
5.3%
시 130
 
5.3%
울 130
 
5.3%
로 128
 
5.2%
1 121
 
4.9%
Other values (66) 874
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1563
63.6%
Decimal Number 493
 
20.1%
Space Separator 390
 
15.9%
Dash Punctuation 9
 
0.4%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 148
9.5%
서 142
9.1%
강 135
8.6%
특 130
8.3%
별 130
8.3%
시 130
8.3%
울 130
8.3%
로 128
8.2%
남 107
 
6.8%
길 65
 
4.2%
Other values (53) 318
20.3%
Decimal Number
ValueCountFrequency (%)
1 121
24.5%
3 64
13.0%
4 60
12.2%
5 54
11.0%
2 53
10.8%
7 40
 
8.1%
6 34
 
6.9%
0 28
 
5.7%
9 20
 
4.1%
8 19
 
3.9%
Space Separator
ValueCountFrequency (%)
390
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1563
63.6%
Common 895
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 148
9.5%
서 142
9.1%
강 135
8.6%
특 130
8.3%
별 130
8.3%
시 130
8.3%
울 130
8.3%
로 128
8.2%
남 107
 
6.8%
길 65
 
4.2%
Other values (53) 318
20.3%
Common
ValueCountFrequency (%)
390
43.6%
1 121
 
13.5%
3 64
 
7.2%
4 60
 
6.7%
5 54
 
6.0%
2 53
 
5.9%
7 40
 
4.5%
6 34
 
3.8%
0 28
 
3.1%
9 20
 
2.2%
Other values (3) 31
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1563
63.6%
ASCII 895
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
390
43.6%
1 121
 
13.5%
3 64
 
7.2%
4 60
 
6.7%
5 54
 
6.0%
2 53
 
5.9%
7 40
 
4.5%
6 34
 
3.8%
0 28
 
3.1%
9 20
 
2.2%
Other values (3) 31
 
3.5%
Hangul
ValueCountFrequency (%)
구 148
9.5%
서 142
9.1%
강 135
8.6%
특 130
8.3%
별 130
8.3%
시 130
8.3%
울 130
8.3%
로 128
8.2%
남 107
 
6.8%
길 65
 
4.2%
Other values (53) 318
20.3%

1168010800001120021
Real number (ℝ)

Distinct110
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1614452 × 1018
Minimum1.1305102 × 1018
Maximum1.1680118 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-10T15:18:24.028642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305102 × 1018
5-th percentile1.1305103 × 1018
Q11.1680101 × 1018
median1.1680105 × 1018
Q31.1680107 × 1018
95-th percentile1.1680111 × 1018
Maximum1.1680118 × 1018
Range3.75016 × 1016
Interquartile range (IQR)5.9999891 × 1011

Descriptive statistics

Standard deviation1.3115767 × 1016
Coefficient of variation (CV)0.011292627
Kurtosis1.3092194
Mean1.1614452 × 1018
Median Absolute Deviation (MAD)2.0000479 × 1011
Skewness-1.7189942
Sum3.4139224 × 1018
Variance1.7202334 × 1032
MonotonicityNot monotonic
2023-12-10T15:18:24.313447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1168010500001590000 13
 
10.0%
1130510200000900000 3
 
2.3%
1168010700006490009 2
 
1.5%
1130510400100680001 2
 
1.5%
1168010400001180017 2
 
1.5%
1168010400001010005 2
 
1.5%
1168011400007130000 2
 
1.5%
1150011000100810024 2
 
1.5%
1168010700006180004 1
 
0.8%
1130510200100170011 1
 
0.8%
Other values (100) 100
76.9%
ValueCountFrequency (%)
1130510200000900000 3
2.3%
1130510200100170011 1
 
0.8%
1130510300003600010 1
 
0.8%
1130510300004870000 1
 
0.8%
1130510300100070001 1
 
0.8%
1130510300100730023 1
 
0.8%
1130510300101250000 1
 
0.8%
1130510300101270001 1
 
0.8%
1130510300101640005 1
 
0.8%
1130510400000760024 1
 
0.8%
ValueCountFrequency (%)
1168011800005140002 1
0.8%
1168011500100100001 1
0.8%
1168011400007390000 1
0.8%
1168011400007130000 2
1.5%
1168011400004420000 1
0.8%
1168011200002850000 1
0.8%
1168011000004940000 1
0.8%
1168011000004290000 1
0.8%
1168011000004280000 1
0.8%
1168010800001920004 1
0.8%

X.1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
X
130 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
X 130
100.0%

Length

2023-12-10T15:18:24.568561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:18:24.727907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
x 130
100.0%

서울특별시 강남구 논현동 112-21번지
Text

Distinct110
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-10T15:18:25.176654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length20.353846
Min length17

Characters and Unicode

Total characters2646
Distinct characters56
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

Unique102 ?
Unique (%)78.5%

Sample

1st row서울특별시 강남구 신사동 618-4번지
2nd row서울특별시 강남구 역삼동 635번지
3rd row서울특별시 강북구 수유동 산127-1번지
4th row서울특별시 강북구 우이동 산68-1번지
5th row서울특별시 강남구 청담동 118-17번지
ValueCountFrequency (%)
서울특별시 130
25.0%
강남구 101
19.4%
신사동 26
 
5.0%
삼성동 22
 
4.2%
강북구 17
 
3.3%
청담동 15
 
2.9%
159번지 13
 
2.5%
강서구 12
 
2.3%
논현동 10
 
1.9%
역삼동 8
 
1.5%
Other values (125) 166
31.9%
2023-12-10T15:18:25.952931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
390
 
14.7%
서 143
 
5.4%
번 134
 
5.1%
구 133
 
5.0%
특 130
 
4.9%
별 130
 
4.9%
시 130
 
4.9%
강 130
 
4.9%
울 130
 
4.9%
동 130
 
4.9%
Other values (46) 1066
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1701
64.3%
Decimal Number 475
 
18.0%
Space Separator 390
 
14.7%
Dash Punctuation 80
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 143
 
8.4%
번 134
 
7.9%
구 133
 
7.8%
특 130
 
7.6%
별 130
 
7.6%
시 130
 
7.6%
강 130
 
7.6%
울 130
 
7.6%
동 130
 
7.6%
지 130
 
7.6%
Other values (34) 381
22.4%
Decimal Number
ValueCountFrequency (%)
1 101
21.3%
2 59
12.4%
5 57
12.0%
4 48
10.1%
9 46
9.7%
6 40
 
8.4%
7 38
 
8.0%
3 32
 
6.7%
8 29
 
6.1%
0 25
 
5.3%
Space Separator
ValueCountFrequency (%)
390
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1701
64.3%
Common 945
35.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 143
 
8.4%
번 134
 
7.9%
구 133
 
7.8%
특 130
 
7.6%
별 130
 
7.6%
시 130
 
7.6%
강 130
 
7.6%
울 130
 
7.6%
동 130
 
7.6%
지 130
 
7.6%
Other values (34) 381
22.4%
Common
ValueCountFrequency (%)
390
41.3%
1 101
 
10.7%
- 80
 
8.5%
2 59
 
6.2%
5 57
 
6.0%
4 48
 
5.1%
9 46
 
4.9%
6 40
 
4.2%
7 38
 
4.0%
3 32
 
3.4%
Other values (2) 54
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1701
64.3%
ASCII 945
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
390
41.3%
1 101
 
10.7%
- 80
 
8.5%
2 59
 
6.2%
5 57
 
6.0%
4 48
 
5.1%
9 46
 
4.9%
6 40
 
4.2%
7 38
 
4.0%
3 32
 
3.4%
Other values (2) 54
 
5.7%
Hangul
ValueCountFrequency (%)
서 143
 
8.4%
번 134
 
7.9%
구 133
 
7.8%
특 130
 
7.6%
별 130
 
7.6%
시 130
 
7.6%
강 130
 
7.6%
울 130
 
7.6%
동 130
 
7.6%
지 130
 
7.6%
Other values (34) 381
22.4%

Interactions

2023-12-10T15:18:15.297166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:13.500967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.062999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.663208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.452272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:13.631685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.199910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.819239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.589262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:13.767246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.319575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.977346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.744966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:13.896688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:14.458099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:18:15.116609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:18:26.186614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
315321546617282692A0401쇼핑A04010600전문상가1168010800001120021
3153211.0000.8980.7240.6610.6610.6720.6720.999
5466170.8981.0000.8500.5040.5040.6910.6911.000
2826920.7240.8501.0000.2340.2340.4410.4410.923
A04010.6610.5040.2341.0001.0001.0001.0000.566
쇼핑0.6610.5040.2341.0001.0001.0001.0000.566
A040106000.6720.6910.4411.0001.0001.0001.0000.789
전문상가0.6720.6910.4411.0001.0001.0001.0000.789
11680108000011200210.9991.0000.9230.5660.5660.7890.7891.000
2023-12-10T15:18:26.398234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A04010600전문상가A0401쇼핑
A040106001.0001.0000.9280.928
전문상가1.0001.0000.9280.928
A04010.9280.9281.0001.000
쇼핑0.9280.9281.0001.000
2023-12-10T15:18:26.582784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
3153215466172826921168010800001120021A0401쇼핑A04010600전문상가
3153211.000-0.6870.4290.3210.3310.3310.3610.361
546617-0.6871.000-0.157-0.4720.2910.2910.3180.318
2826920.429-0.1571.000-0.1810.0890.0890.1400.140
11680108000011200210.321-0.472-0.1811.0000.4220.4220.4410.441
A04010.3310.2910.0890.4221.0001.0000.9280.928
쇼핑0.3310.2910.0890.4221.0001.0000.9280.928
A040106000.3610.3180.1400.4410.9280.9281.0001.000
전문상가0.3610.3180.1400.4410.9280.9281.0001.000

Missing values

2023-12-10T15:18:15.974078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:18:16.303457image/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

C00936오르시아X315321546617282692A0401쇼핑A04010600전문상가서울특별시 강남구 선릉로131길 121168010800001120021X.1서울특별시 강남구 논현동 112-21번지
0C00466뱅앤올룹슨X31473954772331603A0401쇼핑A04010600전문상가서울특별시 강남구 언주로 8651168010700006180004X서울특별시 강남구 신사동 618-4번지
1C00144국기원X31443454489924565A0206문화예술A02061100문화전수시설서울특별시 강남구 테헤란로7길 321168010100006350000X서울특별시 강남구 역삼동 635번지
2C00456백련사X311721559893221511A0201역사관광지A02010800사찰서울특별시 강북구 4.19로20길 1421130510300101270001X서울특별시 강북구 수유동 산127-1번지
3C00811신검사X3113305627103639A0201역사관광지A02010800사찰서울특별시 강북구 삼양로179길 209-441130510400100680001X서울특별시 강북구 우이동 산68-1번지
4C00441박여숙화랑X31598154744229450A0206문화예술A02060500미술관서울특별시 강남구 압구정로 4611168010400001180017X서울특별시 강남구 청담동 118-17번지
5C01304한얼공예X316991545998270459A0401쇼핑A04010700공예 공방서울특별시 강남구 봉은사로 5241168010500001590000X서울특별시 강남구 삼성동 159번지
6C00050갤러리JJX31430654667920534A0206문화예술A02060500미술관서울특별시 강남구 논현로 7451168010800000080013X서울특별시 강남구 논현동 8-13번지
7C00438밀알미술관X319041543018268152A0206문화예술A02060500미술관서울특별시 강남구 일원로 901168011400007130000X서울특별시 강남구 일원동 713번지
8C01400VR스테이션X31436054415233216A0203체험관광지A02030400이색체험서울특별시 강남구 강남대로 3641168010100008260021X서울특별시 강남구 역삼동 826-21번지
9C00064경기여고 경운박물관X31758154313624593A0206문화예술A02060100박물관서울특별시 강남구 삼성로 291168010300001520000X서울특별시 강남구 개포동 152번지
C00936오르시아X315321546617282692A0401쇼핑A04010600전문상가서울특별시 강남구 선릉로131길 121168010800001120021X.1서울특별시 강남구 논현동 112-21번지
120C01146창희보석예술관X31447654771731771A0206문화예술A02060300전시장서울특별시 강남구 압구정로 2101168010700006140003X서울특별시 강남구 신사동 614-3번지
121C00463백암아트홀X317615545781270716A0206문화예술A02060600공연장서울특별시 강남구 테헤란로113길 71168010500001700005X서울특별시 강남구 삼성동 170-5번지
122C00443박을복자수박물관X312719561717221098A0206문화예술A02060100박물관서울특별시 강북구 삼양로149가길 531130510400000860004X서울특별시 강북구 우이동 86-4번지
123C00257다도화랑X31411054719824498A0206문화예술A02060500미술관서울특별시 강남구 논현로159길 241168010700005670036X서울특별시 강남구 신사동 567-36번지
124C00903엘크레X31380254678528934A0203체험관광지A02030400이색체험서울특별시 강남구 강남대로156길 471168010700005350011X서울특별시 강남구 신사동 535-11번지
125C00747세라믹 팔레스 홀X319041543018268152A0206문화예술A02060600공연장서울특별시 강남구 일원로 901168011400007130000X서울특별시 강남구 일원동 713번지
126C00522삼각산X3113305627103639A0101자연생태관광지A01010400산서울특별시 강북구 삼양로173길 4601130510400100680001X서울특별시 강북구 우이동 산68-1번지
127C00480봉은사X316805546306266484A0201역사관광지A02010800사찰서울특별시 강남구 봉은사로 5311168010500000730000X서울특별시 강남구 삼성동 73번지
128C00468보광사 보광선원X312212561364221117A0201역사관광지A02010800사찰서울특별시 강북구 삼양로145길 1761130510400000760024X서울특별시 강북구 우이동 76-24번지
129C01220탄허기념박물관X320852542247151763A0206문화예술A02060100박물관서울특별시 강남구 밤고개로14길 13-511168011200002850000X서울특별시 강남구 자곡동 285번지