Overview

Dataset statistics

Number of variables5
Number of observations424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.5 KiB
Average record size in memory42.3 B

Variable types

Numeric2
Categorical2
Text1

Alerts

DO_NM(시도명) has constant value ""Constant
H_SDNG_CD(통계청행정동코드) is highly overall correlated with H_DNG_CD(행자부행정동코드) and 1 other fieldsHigh correlation
H_DNG_CD(행자부행정동코드) is highly overall correlated with H_SDNG_CD(통계청행정동코드) and 1 other fieldsHigh correlation
CT_NM(시군구명) is highly overall correlated with H_SDNG_CD(통계청행정동코드) and 1 other fieldsHigh correlation
H_SDNG_CD(통계청행정동코드) has unique valuesUnique
H_DNG_CD(행자부행정동코드) has unique valuesUnique

Reproduction

Analysis started2024-01-14 06:49:08.061699
Analysis finished2024-01-14 06:49:08.821442
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

H_SDNG_CD(통계청행정동코드)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct424
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1113663.9
Minimum1101053
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-01-14T15:49:08.916598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102058.1
Q11107069.8
median1114068.5
Q31120317.8
95-th percentile1124078.9
Maximum1125074
Range24021
Interquartile range (IQR)13248

Descriptive statistics

Standard deviation7411.4876
Coefficient of variation (CV)0.006655049
Kurtosis-1.2527027
Mean1113663.9
Median Absolute Deviation (MAD)6991
Skewness-0.084974899
Sum4.7219348 × 108
Variance54930149
MonotonicityStrictly increasing
2024-01-14T15:49:09.094314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101053 1
 
0.2%
1119066 1
 
0.2%
1119063 1
 
0.2%
1119062 1
 
0.2%
1119061 1
 
0.2%
1119056 1
 
0.2%
1119055 1
 
0.2%
1119054 1
 
0.2%
1118061 1
 
0.2%
1118060 1
 
0.2%
Other values (414) 414
97.6%
ValueCountFrequency (%)
1101053 1
0.2%
1101054 1
0.2%
1101055 1
0.2%
1101056 1
0.2%
1101057 1
0.2%
1101058 1
0.2%
1101060 1
0.2%
1101061 1
0.2%
1101063 1
0.2%
1101064 1
0.2%
ValueCountFrequency (%)
1125074 1
0.2%
1125073 1
0.2%
1125072 1
0.2%
1125071 1
0.2%
1125070 1
0.2%
1125067 1
0.2%
1125066 1
0.2%
1125065 1
0.2%
1125063 1
0.2%
1125061 1
0.2%

H_DNG_CD(행자부행정동코드)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct424
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11433195
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-01-14T15:49:09.238845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140582
Q111260649
median11440620
Q311598141
95-th percentile11710678
Maximum11740700
Range630185
Interquartile range (IQR)337492.5

Descriptive statistics

Standard deviation191894.28
Coefficient of variation (CV)0.016783959
Kurtosis-1.2660373
Mean11433195
Median Absolute Deviation (MAD)179942.5
Skewness-0.011417148
Sum4.8476747 × 109
Variance3.6823416 × 1010
MonotonicityNot monotonic
2024-01-14T15:49:09.390093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11110530 1
 
0.2%
11560660 1
 
0.2%
11560630 1
 
0.2%
11560620 1
 
0.2%
11560610 1
 
0.2%
11560560 1
 
0.2%
11560550 1
 
0.2%
11560540 1
 
0.2%
11545710 1
 
0.2%
11545700 1
 
0.2%
Other values (414) 414
97.6%
ValueCountFrequency (%)
11110515 1
0.2%
11110530 1
0.2%
11110540 1
0.2%
11110550 1
0.2%
11110560 1
0.2%
11110570 1
0.2%
11110580 1
0.2%
11110600 1
0.2%
11110615 1
0.2%
11110630 1
0.2%
ValueCountFrequency (%)
11740700 1
0.2%
11740690 1
0.2%
11740685 1
0.2%
11740660 1
0.2%
11740650 1
0.2%
11740640 1
0.2%
11740620 1
0.2%
11740610 1
0.2%
11740600 1
0.2%
11740590 1
0.2%

DO_NM(시도명)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
서울
424 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 424
100.0%

Length

2024-01-14T15:49:09.514805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:49:09.606305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 424
100.0%

CT_NM(시군구명)
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
송파구
 
27
강남구
 
22
관악구
 
21
성북구
 
20
강서구
 
20
Other values (20)
314 

Length

Max length4
Median length3
Mean length3.0731132
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송파구 27
 
6.4%
강남구 22
 
5.2%
관악구 21
 
5.0%
성북구 20
 
4.7%
강서구 20
 
4.7%
노원구 19
 
4.5%
강동구 18
 
4.2%
서초구 18
 
4.2%
영등포구 18
 
4.2%
양천구 18
 
4.2%
Other values (15) 223
52.6%

Length

2024-01-14T15:49:09.715098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 27
 
6.4%
강남구 22
 
5.2%
관악구 21
 
5.0%
성북구 20
 
4.7%
강서구 20
 
4.7%
노원구 19
 
4.5%
강동구 18
 
4.2%
서초구 18
 
4.2%
영등포구 18
 
4.2%
양천구 18
 
4.2%
Other values (15) 223
52.6%

H_DNG_NM(행정동명)
Text

Distinct423
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-01-14T15:49:10.050941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7924528
Min length2

Characters and Unicode

Total characters1608
Distinct characters188
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)99.5%

Sample

1st row사직동
2nd row삼청동
3rd row부암동
4th row평창동
5th row무악동
ValueCountFrequency (%)
신사동 2
 
0.5%
노량진1동 1
 
0.2%
독산3동 1
 
0.2%
양평2동 1
 
0.2%
양평1동 1
 
0.2%
당산2동 1
 
0.2%
당산1동 1
 
0.2%
여의동 1
 
0.2%
시흥5동 1
 
0.2%
시흥4동 1
 
0.2%
Other values (413) 413
97.4%
2024-01-14T15:49:10.550548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 426
26.5%
2 97
 
6.0%
1 97
 
6.0%
3 43
 
2.7%
신 38
 
2.4%
4 26
 
1.6%
가 23
 
1.4%
곡 18
 
1.1%
계 17
 
1.1%
화 17
 
1.1%
Other values (178) 806
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1307
81.3%
Decimal Number 292
 
18.2%
Other Punctuation 9
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 426
32.6%
신 38
 
2.9%
가 23
 
1.8%
곡 18
 
1.4%
계 17
 
1.3%
화 17
 
1.3%
산 16
 
1.2%
방 16
 
1.2%
성 16
 
1.2%
상 16
 
1.2%
Other values (167) 704
53.9%
Decimal Number
ValueCountFrequency (%)
2 97
33.2%
1 97
33.2%
3 43
14.7%
4 26
 
8.9%
5 11
 
3.8%
6 7
 
2.4%
7 6
 
2.1%
8 3
 
1.0%
0 1
 
0.3%
9 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1307
81.3%
Common 301
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 426
32.6%
신 38
 
2.9%
가 23
 
1.8%
곡 18
 
1.4%
계 17
 
1.3%
화 17
 
1.3%
산 16
 
1.2%
방 16
 
1.2%
성 16
 
1.2%
상 16
 
1.2%
Other values (167) 704
53.9%
Common
ValueCountFrequency (%)
2 97
32.2%
1 97
32.2%
3 43
14.3%
4 26
 
8.6%
5 11
 
3.7%
. 9
 
3.0%
6 7
 
2.3%
7 6
 
2.0%
8 3
 
1.0%
0 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1307
81.3%
ASCII 301
 
18.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 426
32.6%
신 38
 
2.9%
가 23
 
1.8%
곡 18
 
1.4%
계 17
 
1.3%
화 17
 
1.3%
산 16
 
1.2%
방 16
 
1.2%
성 16
 
1.2%
상 16
 
1.2%
Other values (167) 704
53.9%
ASCII
ValueCountFrequency (%)
2 97
32.2%
1 97
32.2%
3 43
14.3%
4 26
 
8.6%
5 11
 
3.7%
. 9
 
3.0%
6 7
 
2.3%
7 6
 
2.0%
8 3
 
1.0%
0 1
 
0.3%

Interactions

2024-01-14T15:49:08.459604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:08.272789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:08.554625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:08.365088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:49:10.652909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
H_SDNG_CD(통계청행정동코드)H_DNG_CD(행자부행정동코드)CT_NM(시군구명)
H_SDNG_CD(통계청행정동코드)1.0000.9961.000
H_DNG_CD(행자부행정동코드)0.9961.0001.000
CT_NM(시군구명)1.0001.0001.000
2024-01-14T15:49:10.747596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
H_SDNG_CD(통계청행정동코드)H_DNG_CD(행자부행정동코드)CT_NM(시군구명)
H_SDNG_CD(통계청행정동코드)1.0000.9990.977
H_DNG_CD(행자부행정동코드)0.9991.0000.982
CT_NM(시군구명)0.9770.9821.000

Missing values

2024-01-14T15:49:08.678611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:49:08.775752image/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

H_SDNG_CD(통계청행정동코드)H_DNG_CD(행자부행정동코드)DO_NM(시도명)CT_NM(시군구명)H_DNG_NM(행정동명)
0110105311110530서울종로구사직동
1110105411110540서울종로구삼청동
2110105511110550서울종로구부암동
3110105611110560서울종로구평창동
4110105711110570서울종로구무악동
5110105811110580서울종로구교남동
6110106011110600서울종로구가회동
7110106111110615서울종로구종로1.2.3.4가동
8110106311110630서울종로구종로5.6가동
9110106411110640서울종로구이화동
H_SDNG_CD(통계청행정동코드)H_DNG_CD(행자부행정동코드)DO_NM(시도명)CT_NM(시군구명)H_DNG_NM(행정동명)
414112506111740600서울강동구천호1동
415112506311740620서울강동구천호3동
416112506511740640서울강동구성내1동
417112506611740650서울강동구성내2동
418112506711740660서울강동구성내3동
419112507011740690서울강동구둔촌1동
420112507111740700서울강동구둔촌2동
421112507211740570서울강동구암사1동
422112507311740610서울강동구천호2동
423112507411740685서울강동구길동