Overview

Dataset statistics

Number of variables11
Number of observations58
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory94.3 B

Variable types

Text5
Numeric5
Categorical1

Alerts

4148025324004870004 is highly overall correlated with 4148025324105530003023186 and 2 other fieldsHigh correlation
4148025324105530003023186 is highly overall correlated with 4148025324004870004 and 1 other fieldsHigh correlation
293887 is highly overall correlated with 4148025324004870004 and 1 other fieldsHigh correlation
579866 is highly overall correlated with 경의선High correlation
경의선 is highly overall correlated with 4148025324004870004 and 1 other fieldsHigh correlation
D00097 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:41:59.684491
Analysis finished2023-12-10 06:42:07.942342
Duration8.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

D00097
Text

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-10T15:42:08.179660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters348
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

Unique58 ?
Unique (%)100.0%

Sample

1st rowD00041
2nd rowD00277
3rd rowD00130
4th rowD00289
5th rowD00268
ValueCountFrequency (%)
d00041 1
 
1.7%
d00287 1
 
1.7%
d00002 1
 
1.7%
d00051 1
 
1.7%
d00223 1
 
1.7%
d00272 1
 
1.7%
d00006 1
 
1.7%
d00081 1
 
1.7%
d00274 1
 
1.7%
d00276 1
 
1.7%
Other values (48) 48
82.8%
2023-12-10T15:42:08.740941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 146
42.0%
D 58
 
16.7%
2 40
 
11.5%
1 21
 
6.0%
7 17
 
4.9%
6 14
 
4.0%
3 13
 
3.7%
8 13
 
3.7%
5 11
 
3.2%
9 8
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 290
83.3%
Uppercase Letter 58
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146
50.3%
2 40
 
13.8%
1 21
 
7.2%
7 17
 
5.9%
6 14
 
4.8%
3 13
 
4.5%
8 13
 
4.5%
5 11
 
3.8%
9 8
 
2.8%
4 7
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
D 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 290
83.3%
Latin 58
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146
50.3%
2 40
 
13.8%
1 21
 
7.2%
7 17
 
5.9%
6 14
 
4.8%
3 13
 
4.5%
8 13
 
4.5%
5 11
 
3.8%
9 8
 
2.8%
4 7
 
2.4%
Latin
ValueCountFrequency (%)
D 58
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146
42.0%
D 58
 
16.7%
2 40
 
11.5%
1 21
 
6.0%
7 17
 
4.9%
6 14
 
4.0%
3 13
 
3.7%
8 13
 
3.7%
5 11
 
3.2%
9 8
 
2.3%

파주
Text

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-10T15:42:09.117303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2413793
Min length2

Characters and Unicode

Total characters130
Distinct characters66
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

Unique52 ?
Unique (%)89.7%

Sample

1st row양평
2nd row곡산
3rd row수원
4th row마석
5th row임진강
ValueCountFrequency (%)
양평 2
 
3.4%
수원 2
 
3.4%
행신 2
 
3.4%
전곡 1
 
1.7%
양동 1
 
1.7%
사릉 1
 
1.7%
한탄강 1
 
1.7%
송추 1
 
1.7%
아신 1
 
1.7%
능곡 1
 
1.7%
Other values (45) 45
77.6%
2023-12-10T15:42:09.664212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
평 8
 
6.2%
산 7
 
5.4%
신 6
 
4.6%
원 6
 
4.6%
곡 5
 
3.8%
리 5
 
3.8%
양 4
 
3.1%
수 4
 
3.1%
탄 3
 
2.3%
릉 3
 
2.3%
Other values (56) 79
60.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 130
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 8
 
6.2%
산 7
 
5.4%
신 6
 
4.6%
원 6
 
4.6%
곡 5
 
3.8%
리 5
 
3.8%
양 4
 
3.1%
수 4
 
3.1%
탄 3
 
2.3%
릉 3
 
2.3%
Other values (56) 79
60.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 130
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 8
 
6.2%
산 7
 
5.4%
신 6
 
4.6%
원 6
 
4.6%
곡 5
 
3.8%
리 5
 
3.8%
양 4
 
3.1%
수 4
 
3.1%
탄 3
 
2.3%
릉 3
 
2.3%
Other values (56) 79
60.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 130
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
평 8
 
6.2%
산 7
 
5.4%
신 6
 
4.6%
원 6
 
4.6%
곡 5
 
3.8%
리 5
 
3.8%
양 4
 
3.1%
수 4
 
3.1%
탄 3
 
2.3%
릉 3
 
2.3%
Other values (56) 79
60.8%

174231
Real number (ℝ)

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259316.34
Minimum3270
Maximum424327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-10T15:42:09.893287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3270
5-th percentile58932.4
Q1147113.25
median293473
Q3303762
95-th percentile408664.55
Maximum424327
Range421057
Interquartile range (IQR)156648.75

Descriptive statistics

Standard deviation109883.18
Coefficient of variation (CV)0.42374181
Kurtosis-0.33034115
Mean259316.34
Median Absolute Deviation (MAD)70649
Skewness-0.58776212
Sum15040348
Variance1.2074313 × 1010
MonotonicityNot monotonic
2023-12-10T15:42:10.464237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
303612 2
 
3.4%
128117 2
 
3.4%
291686 2
 
3.4%
400827 1
 
1.7%
301018 1
 
1.7%
303791 1
 
1.7%
291347 1
 
1.7%
292832 1
 
1.7%
408341 1
 
1.7%
301011 1
 
1.7%
Other values (45) 45
77.6%
ValueCountFrequency (%)
3270 1
1.7%
7567 1
1.7%
7572 1
1.7%
67996 1
1.7%
128117 2
3.4%
141932 1
1.7%
142008 1
1.7%
142444 1
1.7%
143874 1
1.7%
145091 1
1.7%
ValueCountFrequency (%)
424327 1
1.7%
413156 1
1.7%
410498 1
1.7%
408341 1
1.7%
408198 1
1.7%
403109 1
1.7%
402201 1
1.7%
401901 1
1.7%
401822 1
1.7%
400827 1
1.7%

경의선
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size596.0 B
중앙선
16 
경의선
12 
경원선
9 
경춘선
8 
경부선
7 
Other values (3)
6 

Length

Max length4
Median length3
Mean length3.0172414
Min length3

Unique

Unique2 ?
Unique (%)3.4%

Sample

1st row중앙선
2nd row경의선
3rd row경부선
4th row경춘선
5th row경의선

Common Values

ValueCountFrequency (%)
중앙선 16
27.6%
경의선 12
20.7%
경원선 9
15.5%
경춘선 8
13.8%
경부선 7
12.1%
교외선 4
 
6.9%
일산선 1
 
1.7%
경부고속 1
 
1.7%

Length

2023-12-10T15:42:10.658709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:10.825821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중앙선 16
27.6%
경의선 12
20.7%
경원선 9
15.5%
경춘선 8
13.8%
경부선 7
12.1%
교외선 4
 
6.9%
일산선 1
 
1.7%
경부고속 1
 
1.7%

경기도 파주시 파주읍 주라위길 38
Text

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-10T15:42:11.231328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length33
Mean length23.517241
Min length13

Characters and Unicode

Total characters1364
Distinct characters118
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)89.7%

Sample

1st row경기도 양평군 양평읍 역전길 30
2nd row경기도 고양시 일산동구 경의로 160 곡산역
3rd row경기도 수원시 팔달구 덕영대로 924 수원역
4th row경기도 남양주시 화도읍 마석중앙로 107
5th row경기도 파주시 문산읍 마정리 1253-3 임진강역
ValueCountFrequency (%)
경기도 58
 
18.3%
양평군 13
 
4.1%
고양시 11
 
3.5%
연천군 9
 
2.8%
남양주시 8
 
2.5%
덕양구 8
 
2.5%
경기 4
 
1.3%
파주시 4
 
1.3%
전곡읍 4
 
1.3%
신서면 3
 
0.9%
Other values (155) 195
61.5%
2023-12-10T15:42:11.873969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270
19.8%
경 69
 
5.1%
기 62
 
4.5%
도 61
 
4.5%
양 51
 
3.7%
로 44
 
3.2%
1 39
 
2.9%
평 37
 
2.7%
3 36
 
2.6%
시 36
 
2.6%
Other values (108) 659
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 827
60.6%
Space Separator 270
 
19.8%
Decimal Number 214
 
15.7%
Dash Punctuation 18
 
1.3%
Close Punctuation 18
 
1.3%
Open Punctuation 17
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 69
 
8.3%
기 62
 
7.5%
도 61
 
7.4%
양 51
 
6.2%
로 44
 
5.3%
평 37
 
4.5%
시 36
 
4.4%
군 25
 
3.0%
역 24
 
2.9%
면 20
 
2.4%
Other values (94) 398
48.1%
Decimal Number
ValueCountFrequency (%)
1 39
18.2%
3 36
16.8%
5 26
12.1%
2 26
12.1%
4 23
10.7%
7 16
7.5%
0 14
 
6.5%
6 12
 
5.6%
9 11
 
5.1%
8 11
 
5.1%
Space Separator
ValueCountFrequency (%)
270
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 827
60.6%
Common 537
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 69
 
8.3%
기 62
 
7.5%
도 61
 
7.4%
양 51
 
6.2%
로 44
 
5.3%
평 37
 
4.5%
시 36
 
4.4%
군 25
 
3.0%
역 24
 
2.9%
면 20
 
2.4%
Other values (94) 398
48.1%
Common
ValueCountFrequency (%)
270
50.3%
1 39
 
7.3%
3 36
 
6.7%
5 26
 
4.8%
2 26
 
4.8%
4 23
 
4.3%
- 18
 
3.4%
) 18
 
3.4%
( 17
 
3.2%
7 16
 
3.0%
Other values (4) 48
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 827
60.6%
ASCII 537
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
270
50.3%
1 39
 
7.3%
3 36
 
6.7%
5 26
 
4.8%
2 26
 
4.8%
4 23
 
4.3%
- 18
 
3.4%
) 18
 
3.4%
( 17
 
3.2%
7 16
 
3.0%
Other values (4) 48
 
8.9%
Hangul
ValueCountFrequency (%)
경 69
 
8.3%
기 62
 
7.5%
도 61
 
7.4%
양 51
 
6.2%
로 44
 
5.3%
평 37
 
4.5%
시 36
 
4.4%
군 25
 
3.0%
역 24
 
2.9%
면 20
 
2.4%
Other values (94) 398
48.1%

4148025324004870004
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1513412 × 1018
Minimum4.1115134 × 1018
Maximum4.18304 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-10T15:42:12.104050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1115134 × 1018
5-th percentile4.1201105 × 1018
Q14.1281128 × 1018
median4.1400102 × 1018
Q34.1820306 × 1018
95-th percentile4.1830395 × 1018
Maximum4.18304 × 1018
Range7.1526624 × 1016
Interquartile range (IQR)5.3917846 × 1016

Descriptive statistics

Standard deviation2.6003606 × 1016
Coefficient of variation (CV)0.0062639048
Kurtosis-1.7022078
Mean4.1513412 × 1018
Median Absolute Deviation (MAD)1.799955 × 1016
Skewness0.16833185
Sum9.7011638 × 1017
Variance6.7618752 × 1032
MonotonicityNot monotonic
2023-12-10T15:42:12.335145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4183025021001300037 2
 
3.4%
4111513400000180000 2
 
3.4%
4128112800005190003 2
 
3.4%
4180025321001200004 1
 
1.7%
4163034023005160002 1
 
1.7%
4183034024101120002 1
 
1.7%
4128112000004540003 1
 
1.7%
4128510600000920000 1
 
1.7%
4136010200006600000 1
 
1.7%
4163034026002910003 1
 
1.7%
Other values (45) 45
77.6%
ValueCountFrequency (%)
4111513400000180000 2
3.4%
4115010100001680054 1
1.7%
4121010600002760001 1
1.7%
4122010100004270053 1
1.7%
4122011200002570005 1
1.7%
4122011300001850579 1
1.7%
4125010700002450210 1
1.7%
4125010900001260003 1
1.7%
4128110200006030001 1
1.7%
4128110600005390000 1
1.7%
ValueCountFrequency (%)
4183040024007360013 1
1.7%
4183039526013360012 1
1.7%
4183039524100750001 1
1.7%
4183039521011200010 1
1.7%
4183038027100860004 1
1.7%
4183038021002230002 1
1.7%
4183034024101120002 1
1.7%
4183033028002580025 1
1.7%
4183033025004800029 1
1.7%
4183033022002550002 1
1.7%

4148025324105530003023186
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1513414 × 1024
Minimum4.1115134 × 1024
Maximum4.18304 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-10T15:42:12.560021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1115134 × 1024
5-th percentile4.1201105 × 1024
Q14.1281128 × 1024
median4.1400102 × 1024
Q34.1820306 × 1024
95-th percentile4.1830395 × 1024
Maximum4.18304 × 1024
Range7.1526624 × 1022
Interquartile range (IQR)5.3917846 × 1022

Descriptive statistics

Standard deviation2.60035 × 1022
Coefficient of variation (CV)0.0062638789
Kurtosis-1.7021951
Mean4.1513414 × 1024
Median Absolute Deviation (MAD)1.799955 × 1022
Skewness0.16832018
Sum2.407778 × 1026
Variance6.7618199 × 1044
MonotonicityNot monotonic
2023-12-10T15:42:12.817740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1830250211013e+24 2
 
3.4%
4.11151340010018e+24 2
 
3.4%
4.12811280010519e+24 2
 
3.4%
4.1800253211012e+24 1
 
1.7%
4.16303402310516e+24 1
 
1.7%
4.18303402420112e+24 1
 
1.7%
4.12811200010454e+24 1
 
1.7%
4.12851060010092e+24 1
 
1.7%
4.1360102001016096e+24 1
 
1.7%
4.16303402610291e+24 1
 
1.7%
Other values (45) 45
77.6%
ValueCountFrequency (%)
4.11151340010018e+24 2
3.4%
4.11501010010168e+24 1
1.7%
4.12101060010276e+24 1
1.7%
4.1220101001042705e+24 1
1.7%
4.1220112001025696e+24 1
1.7%
4.1220113001018497e+24 1
1.7%
4.1250107001024497e+24 1
1.7%
4.12501090010126e+24 1
1.7%
4.1281102001060305e+24 1
1.7%
4.12811060010539e+24 1
1.7%
ValueCountFrequency (%)
4.18304002410748e+24 1
1.7%
4.18303952611336e+24 1
1.7%
4.1830395242007503e+24 1
1.7%
4.1830395211112e+24 1
1.7%
4.18303802720086e+24 1
1.7%
4.1830380211022303e+24 1
1.7%
4.18303402420112e+24 1
1.7%
4.18303302810258e+24 1
1.7%
4.1830330251048e+24 1
1.7%
4.1830330221025503e+24 1
1.7%

경기도 파주시 파주읍 봉암리 487-4번지
Text

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-10T15:42:13.173666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length22.672414
Min length16

Characters and Unicode

Total characters1315
Distinct characters101
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

Unique52 ?
Unique (%)89.7%

Sample

1st row경기도 양평군 양평읍 양근리 130-37번지
2nd row경기도 고양시 일산동구 백석동 1185-1번지
3rd row경기도 수원시 팔달구 매산로1가 18번지
4th row경기도 남양주시 화도읍 마석우리 222-2번지
5th row경기도 파주시 문산읍 마정리 1253-3번지
ValueCountFrequency (%)
경기도 58
 
20.9%
양평군 13
 
4.7%
고양시 11
 
4.0%
덕양구 8
 
2.9%
남양주시 8
 
2.9%
연천군 6
 
2.2%
파주시 4
 
1.4%
행신동 3
 
1.1%
가평군 3
 
1.1%
양평읍 3
 
1.1%
Other values (135) 160
57.8%
2023-12-10T15:42:13.712078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
219
 
16.7%
지 62
 
4.7%
도 60
 
4.6%
경 58
 
4.4%
기 58
 
4.4%
번 58
 
4.4%
1 55
 
4.2%
양 52
 
4.0%
- 48
 
3.7%
3 36
 
2.7%
Other values (91) 609
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 797
60.6%
Decimal Number 251
 
19.1%
Space Separator 219
 
16.7%
Dash Punctuation 48
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 62
 
7.8%
도 60
 
7.5%
경 58
 
7.3%
기 58
 
7.3%
번 58
 
7.3%
양 52
 
6.5%
시 36
 
4.5%
리 32
 
4.0%
평 32
 
4.0%
동 31
 
3.9%
Other values (79) 318
39.9%
Decimal Number
ValueCountFrequency (%)
1 55
21.9%
3 36
14.3%
2 35
13.9%
5 30
12.0%
6 21
 
8.4%
4 19
 
7.6%
0 18
 
7.2%
9 14
 
5.6%
7 12
 
4.8%
8 11
 
4.4%
Space Separator
ValueCountFrequency (%)
219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 797
60.6%
Common 518
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 62
 
7.8%
도 60
 
7.5%
경 58
 
7.3%
기 58
 
7.3%
번 58
 
7.3%
양 52
 
6.5%
시 36
 
4.5%
리 32
 
4.0%
평 32
 
4.0%
동 31
 
3.9%
Other values (79) 318
39.9%
Common
ValueCountFrequency (%)
219
42.3%
1 55
 
10.6%
- 48
 
9.3%
3 36
 
6.9%
2 35
 
6.8%
5 30
 
5.8%
6 21
 
4.1%
4 19
 
3.7%
0 18
 
3.5%
9 14
 
2.7%
Other values (2) 23
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 797
60.6%
ASCII 518
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
219
42.3%
1 55
 
10.6%
- 48
 
9.3%
3 36
 
6.9%
2 35
 
6.8%
5 30
 
5.8%
6 21
 
4.1%
4 19
 
3.7%
0 18
 
3.5%
9 14
 
2.7%
Other values (2) 23
 
4.4%
Hangul
ValueCountFrequency (%)
지 62
 
7.8%
도 60
 
7.5%
경 58
 
7.3%
기 58
 
7.3%
번 58
 
7.3%
양 52
 
6.5%
시 36
 
4.5%
리 32
 
4.0%
평 32
 
4.0%
동 31
 
3.9%
Other values (79) 318
39.9%

경기도 파주시 파주읍 주라위길 38.1
Text

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-10T15:42:14.104721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length19.12069
Min length14

Characters and Unicode

Total characters1109
Distinct characters108
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

Unique52 ?
Unique (%)89.7%

Sample

1st row경기도 양평군 양평읍 역전길 30
2nd row경기도 고양시 일산동구 경의로 160
3rd row경기도 수원시 팔달구 덕영대로 924
4th row경기도 남양주시 화도읍 마석중앙로 107
5th row경기도 파주시 문산읍 임진각로 115
ValueCountFrequency (%)
경기도 58
 
20.9%
양평군 13
 
4.7%
고양시 11
 
4.0%
남양주시 8
 
2.9%
덕양구 8
 
2.9%
연천군 6
 
2.2%
파주시 4
 
1.4%
가평군 3
 
1.1%
경춘로 3
 
1.1%
평화로 3
 
1.1%
Other values (126) 160
57.8%
2023-12-10T15:42:14.671369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
219
19.7%
경 65
 
5.9%
도 60
 
5.4%
기 58
 
5.2%
양 51
 
4.6%
로 46
 
4.1%
시 36
 
3.2%
평 35
 
3.2%
1 29
 
2.6%
3 26
 
2.3%
Other values (98) 484
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 714
64.4%
Space Separator 219
 
19.7%
Decimal Number 169
 
15.2%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 65
 
9.1%
도 60
 
8.4%
기 58
 
8.1%
양 51
 
7.1%
로 46
 
6.4%
시 36
 
5.0%
평 35
 
4.9%
군 22
 
3.1%
면 18
 
2.5%
길 17
 
2.4%
Other values (86) 306
42.9%
Decimal Number
ValueCountFrequency (%)
1 29
17.2%
3 26
15.4%
5 22
13.0%
2 20
11.8%
7 17
10.1%
4 17
10.1%
9 11
 
6.5%
0 10
 
5.9%
6 10
 
5.9%
8 7
 
4.1%
Space Separator
ValueCountFrequency (%)
219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 714
64.4%
Common 395
35.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 65
 
9.1%
도 60
 
8.4%
기 58
 
8.1%
양 51
 
7.1%
로 46
 
6.4%
시 36
 
5.0%
평 35
 
4.9%
군 22
 
3.1%
면 18
 
2.5%
길 17
 
2.4%
Other values (86) 306
42.9%
Common
ValueCountFrequency (%)
219
55.4%
1 29
 
7.3%
3 26
 
6.6%
5 22
 
5.6%
2 20
 
5.1%
7 17
 
4.3%
4 17
 
4.3%
9 11
 
2.8%
0 10
 
2.5%
6 10
 
2.5%
Other values (2) 14
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 714
64.4%
ASCII 395
35.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
219
55.4%
1 29
 
7.3%
3 26
 
6.6%
5 22
 
5.6%
2 20
 
5.1%
7 17
 
4.3%
4 17
 
4.3%
9 11
 
2.8%
0 10
 
2.5%
6 10
 
2.5%
Other values (2) 14
 
3.5%
Hangul
ValueCountFrequency (%)
경 65
 
9.1%
도 60
 
8.4%
기 58
 
8.1%
양 51
 
7.1%
로 46
 
6.4%
시 36
 
5.0%
평 35
 
4.9%
군 22
 
3.1%
면 18
 
2.5%
길 17
 
2.4%
Other values (86) 306
42.9%

293887
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324121.17
Minimum286709
Maximum380874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-10T15:42:14.871074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum286709
5-th percentile292734.55
Q1300578.5
median318472.5
Q3343807
95-th percentile370292.4
Maximum380874
Range94165
Interquartile range (IQR)43228.5

Descriptive statistics

Standard deviation25529.309
Coefficient of variation (CV)0.078764707
Kurtosis-0.68597384
Mean324121.17
Median Absolute Deviation (MAD)20425.5
Skewness0.55441371
Sum18799028
Variance6.5174562 × 108
MonotonicityNot monotonic
2023-12-10T15:42:15.122620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355253 2
 
3.4%
311509 2
 
3.4%
297388 2
 
3.4%
318114 1
 
1.7%
307985 1
 
1.7%
353036 1
 
1.7%
296077 1
 
1.7%
293826 1
 
1.7%
333525 1
 
1.7%
305753 1
 
1.7%
Other values (45) 45
77.6%
ValueCountFrequency (%)
286709 1
1.7%
291657 1
1.7%
292171 1
1.7%
292834 1
1.7%
293497 1
1.7%
293826 1
1.7%
294424 1
1.7%
295167 1
1.7%
296077 1
1.7%
297388 2
3.4%
ValueCountFrequency (%)
380874 1
1.7%
378463 1
1.7%
372584 1
1.7%
369888 1
1.7%
367473 1
1.7%
364332 1
1.7%
360130 1
1.7%
357140 1
1.7%
355253 2
3.4%
353036 1
1.7%

579866
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean557883.43
Minimum488210
Maximum623663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-10T15:42:15.374080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum488210
5-th percentile504140.25
Q1542636.75
median558757.5
Q3570019.75
95-th percentile604111.8
Maximum623663
Range135453
Interquartile range (IQR)27383

Descriptive statistics

Standard deviation28579.677
Coefficient of variation (CV)0.05122876
Kurtosis0.49389983
Mean557883.43
Median Absolute Deviation (MAD)15209.5
Skewness-0.062950291
Sum32357239
Variance8.1679792 × 108
MonotonicityNot monotonic
2023-12-10T15:42:15.616168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
543548 2
 
3.4%
518667 2
 
3.4%
557309 2
 
3.4%
600874 1
 
1.7%
568588 1
 
1.7%
547935 1
 
1.7%
558015 1
 
1.7%
562456 1
 
1.7%
561477 1
 
1.7%
566973 1
 
1.7%
Other values (45) 45
77.6%
ValueCountFrequency (%)
488210 1
1.7%
495461 1
1.7%
497489 1
1.7%
505314 1
1.7%
518667 2
3.4%
524869 1
1.7%
532445 1
1.7%
535387 1
1.7%
535504 1
1.7%
537243 1
1.7%
ValueCountFrequency (%)
623663 1
1.7%
620578 1
1.7%
611364 1
1.7%
602832 1
1.7%
600874 1
1.7%
598023 1
1.7%
594333 1
1.7%
592104 1
1.7%
589252 1
1.7%
586134 1
1.7%

Interactions

2023-12-10T15:42:06.366887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.545343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.554236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.917050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:05.264368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:06.531632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.649212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.701870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:03.305722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:05.385765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:06.679826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.792670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.852433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:03.764675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:05.537820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:07.203007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.294847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.642397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:04.469287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:06.090329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:07.332769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.423771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.767698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:04.867018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:06.213273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:42:15.784859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
D00097파주174231경의선경기도 파주시 파주읍 주라위길 3841480253240048700044148025324105530003023186경기도 파주시 파주읍 봉암리 487-4번지경기도 파주시 파주읍 주라위길 38.1293887579866
D000971.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
파주1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1742311.0001.0001.0000.7361.0000.7460.7461.0001.0000.6880.661
경의선1.0001.0000.7361.0001.0000.8950.8951.0001.0000.7690.794
경기도 파주시 파주읍 주라위길 381.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
41480253240048700041.0001.0000.7460.8951.0001.0001.0001.0001.0000.8220.825
41480253241055300030231861.0001.0000.7460.8951.0001.0001.0001.0001.0000.8220.825
경기도 파주시 파주읍 봉암리 487-4번지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경기도 파주시 파주읍 주라위길 38.11.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2938871.0001.0000.6880.7691.0000.8220.8221.0001.0001.0000.826
5798661.0001.0000.6610.7941.0000.8250.8251.0001.0000.8261.000
2023-12-10T15:42:15.999959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
17423141480253240048700044148025324105530003023186293887579866경의선
1742311.0000.2050.2050.0280.2600.328
41480253240048700040.2051.0001.0000.6830.0140.511
41480253241055300030231860.2051.0001.0000.6830.0140.360
2938870.0280.6830.6831.000-0.3090.499
5798660.2600.0140.014-0.3091.0000.530
경의선0.3280.5110.3600.4990.5301.000

Missing values

2023-12-10T15:42:07.537171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:42:07.837011image/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

D00097파주174231경의선경기도 파주시 파주읍 주라위길 3841480253240048700044148025324105530003023186경기도 파주시 파주읍 봉암리 487-4번지경기도 파주시 파주읍 주라위길 38.1293887579866
0D00041양평303612중앙선경기도 양평군 양평읍 역전길 3041830250210013000374183025021101300037000001경기도 양평군 양평읍 양근리 130-37번지경기도 양평군 양평읍 역전길 30355253543548
1D00277곡산289341경의선경기도 고양시 일산동구 경의로 160 곡산역41285106000118500014128510600111850001007542경기도 고양시 일산동구 백석동 1185-1번지경기도 고양시 일산동구 경의로 160294424561029
2D00130수원128117경부선경기도 수원시 팔달구 덕영대로 924 수원역41115134000001800004111513400100180000005527경기도 수원시 팔달구 매산로1가 18번지경기도 수원시 팔달구 덕영대로 924311509518667
3D00289마석142008경춘선경기도 남양주시 화도읍 마석중앙로 10741360256210022200024136025621102220002000001경기도 남양주시 화도읍 마석우리 222-2번지경기도 남양주시 화도읍 마석중앙로 107339471561336
4D00268임진강174010경의선경기도 파주시 문산읍 마정리 1253-3 임진강역41480250270125300034148025027112510002016856경기도 파주시 문산읍 마정리 1253-3번지경기도 파주시 문산읍 임진각로 115292834586134
5D00014일신7572중앙선경기도 양평군 지평면 구둔역길 2-341830395260133600124183039526113360002024946경기도 양평군 지평면 일신리 1336-12번지경기도 양평군 지평면 구둔역길 2-3372584537243
6D00101강매410498경의선경기도 고양시 덕양구 소원로 20241281128000111500014128112800111150001000001경기도 고양시 덕양구 행신동 1115-1번지경기도 고양시 덕양구 소원로 202298257557319
7D00252소요산286892경원선경기 동두천시 평화로 292541250109000012600034125010900101260003003400경기도 동두천시 상봉암동 126-3번지경기도 동두천시 평화로 2925317686594333
8D00285용문302882중앙선경기도 양평군 용문면 용문역길 2141830400240073600134183040024107480001003398경기도 양평군 용문면 다문리 736-13번지경기도 양평군 용문면 용문역길 21364332542333
9D00226도라산401822경의선경기도 파주시 장단면 희망로 30741480390210055600004148039021105550001007062경기도 파주시 장단면 노상리 556번지경기도 파주시 장단면 희망로 307286709589252
D00097파주174231경의선경기도 파주시 파주읍 주라위길 3841480253240048700044148025324105530003023186경기도 파주시 파주읍 봉암리 487-4번지경기도 파주시 파주읍 주라위길 38.1293887579866
48D00329삼산67996중앙선경기도 양평군 양동면 삼산역길 19341830380271008600044183038027200860004022555경기도 양평군 양동면 삼산리 산86-4번지경기도 양평군 양동면 삼산역길 193380874532445
49D00159가평403109경춘선경기도 가평군 가평읍 문화로 13-4241820250230060300024182025023106030002011448경기도 가평군 가평읍 달전리 603-2번지경기도 가평군 가평읍 문화로 13-42357140579191
50D00269문산402201경의선경기도 파주시 문산읍 문산역로 9441480250210001700144148025021100170550011681경기도 파주시 문산읍 문산리 17-14번지경기도 파주시 문산읍 문산역로 94293497584245
51D00290신원327417중앙선경기도 양평군 양서면 신원역길 741830330250048000294183033025104800029000001경기도 양평군 양서면 신원리 480-29번지경기도 양평군 양서면 신원역길 7344780547220
52D00072삼릉290370교외선경기도 고양시 덕양구 원당로 364번길 3841281102000060300014128110200106030001005504경기도 고양시 덕양구 원당동 603-1번지경기도 고양시 덕양구 원당로364번길 38298795564670
53D00278금곡146491경춘선경기도 남양주시 금곡로19번길 4741360103000040402764136010300104040276000001경기도 남양주시 금곡동 404-276번지경기도 남양주시 금곡로19번길 47330289559749
54D00138석불3270중앙선경기도 양평군 지평면 지평의병로434-2541830395241007500014183039524200750001023623경기도 양평군 지평면 망미리 산75-1번지경기도 양평군 지평면 지평의병로 434-25369888539197
55D00266전곡302596경원선경기도 연천군 전곡읍 전곡역로 75 ( 경기도 연천군 전곡읍 전곡리 333-10 전곡역)41800253210033300104180025321103330010002067경기도 연천군 전곡읍 전곡리 333-10번지경기도 연천군 전곡읍 전곡역로 75318660602832
56D00002사릉145487경춘선경기도 남양주시 진건읍 진건우회로 6341360259210060500034136025921106050003000001경기도 남양주시 진건읍 사능리 605-3번지경기도 남양주시 진건읍 진건우회로 63327567561337
57D00160지평303101중앙선경기도 양평군 지평면 지평역길 3241830395210112000104183039521111200010024694경기도 양평군 지평면 지평리 1120-10번지경기도 양평군 지평면 지평역길 32367473541646