Overview

Dataset statistics

Number of variables8
Number of observations3613
Missing cells24
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory240.1 KiB
Average record size in memory68.0 B

Variable types

Numeric4
Categorical2
Text2

Dataset

Description행정동(읍면동)별 성별 출생등록자수입니다.행정동은 주민들이 거주하는 지역을 행정능률과 주민편의를 위하여 구분한 행정구역 단위를 말합니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15099155/fileData.do

Alerts

통계년월 has constant value ""Constant
행정기관코드 is highly overall correlated with 시도명High correlation
계 is highly overall correlated with 남자 and 1 other fieldsHigh correlation
남자 is highly overall correlated with 계 and 1 other fieldsHigh correlation
여자 is highly overall correlated with 계 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 행정기관코드High correlation
행정기관코드 has unique valuesUnique
계 has 1013 (28.0%) zerosZeros
남자 has 1369 (37.9%) zerosZeros
여자 has 1414 (39.1%) zerosZeros

Reproduction

Analysis started2024-04-06 08:46:37.400374
Analysis finished2024-04-06 08:46:45.197709
Duration7.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3613
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8327467 × 109
Minimum1.1110515 × 109
Maximum5.280042 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:46:45.414715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110515 × 109
5-th percentile1.1380557 × 109
Q12.8260544 × 109
median4.311135 × 109
Q34.773033 × 109
95-th percentile5.2134532 × 109
Maximum5.280042 × 109
Range4.1689905 × 109
Interquartile range (IQR)1.9469786 × 109

Descriptive statistics

Standard deviation1.2616173 × 109
Coefficient of variation (CV)0.32916794
Kurtosis-0.20220735
Mean3.8327467 × 109
Median Absolute Deviation (MAD)5.21921 × 108
Skewness-0.98268915
Sum1.3847714 × 1013
Variance1.5916783 × 1018
MonotonicityStrictly increasing
2024-04-06T17:46:45.937774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111051500 1
 
< 0.1%
4681040000 1
 
< 0.1%
4684025800 1
 
< 0.1%
4684032000 1
 
< 0.1%
4684033000 1
 
< 0.1%
4684034000 1
 
< 0.1%
4684035000 1
 
< 0.1%
4684036000 1
 
< 0.1%
4684037000 1
 
< 0.1%
4686025000 1
 
< 0.1%
Other values (3603) 3603
99.7%
ValueCountFrequency (%)
1111051500 1
< 0.1%
1111053000 1
< 0.1%
1111054000 1
< 0.1%
1111055000 1
< 0.1%
1111056000 1
< 0.1%
1111057000 1
< 0.1%
1111058000 1
< 0.1%
1111060000 1
< 0.1%
1111061500 1
< 0.1%
1111063000 1
< 0.1%
ValueCountFrequency (%)
5280042000 1
< 0.1%
5280041000 1
< 0.1%
5280040000 1
< 0.1%
5280039000 1
< 0.1%
5280038000 1
< 0.1%
5280037000 1
< 0.1%
5280036000 1
< 0.1%
5280035000 1
< 0.1%
5280034000 1
< 0.1%
5280033000 1
< 0.1%

통계년월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-03-31
3613 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-31
2nd row2024-03-31
3rd row2024-03-31
4th row2024-03-31
5th row2024-03-31

Common Values

ValueCountFrequency (%)
2024-03-31 3613
100.0%

Length

2024-04-06T17:46:46.323123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:46:46.624413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-31 3613
100.0%

시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
경기도
602 
서울특별시
426 
경상북도
335 
전라남도
323 
경상남도
310 
Other values (12)
1617 

Length

Max length7
Median length5
Mean length4.5773595
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 602
16.7%
서울특별시 426
11.8%
경상북도 335
9.3%
전라남도 323
8.9%
경상남도 310
8.6%
전북특별자치도 243
6.7%
충청남도 210
 
5.8%
부산광역시 205
 
5.7%
강원특별자치도 194
 
5.4%
인천광역시 160
 
4.4%
Other values (7) 605
16.7%

Length

2024-04-06T17:46:46.935431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 602
16.7%
서울특별시 426
11.8%
경상북도 335
9.3%
전라남도 323
8.9%
경상남도 310
8.6%
전북특별자치도 243
6.7%
충청남도 210
 
5.8%
부산광역시 205
 
5.7%
강원특별자치도 194
 
5.4%
인천광역시 160
 
4.4%
Other values (7) 605
16.7%

시군구명
Text

Distinct229
Distinct (%)6.4%
Missing24
Missing (%)0.7%
Memory size28.4 KiB
2024-04-06T17:46:47.783027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4664252
Min length2

Characters and Unicode

Total characters12441
Distinct characters143
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

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row종로구
3rd row종로구
4th row종로구
5th row종로구
ValueCountFrequency (%)
서구 95
 
2.3%
북구 87
 
2.1%
동구 83
 
2.0%
중구 77
 
1.9%
남구 75
 
1.9%
창원시 55
 
1.4%
성남시 50
 
1.2%
수원시 44
 
1.1%
고양시 44
 
1.1%
청주시 43
 
1.1%
Other values (229) 3399
83.9%
2024-04-06T17:46:49.021163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 1641
 
13.2%
시 1632
 
13.1%
군 908
 
7.3%
463
 
3.7%
주 409
 
3.3%
천 343
 
2.8%
산 342
 
2.7%
성 297
 
2.4%
동 293
 
2.4%
양 274
 
2.2%
Other values (133) 5839
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11978
96.3%
Space Separator 463
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1641
 
13.7%
시 1632
 
13.6%
군 908
 
7.6%
주 409
 
3.4%
천 343
 
2.9%
산 342
 
2.9%
성 297
 
2.5%
동 293
 
2.4%
양 274
 
2.3%
남 263
 
2.2%
Other values (132) 5576
46.6%
Space Separator
ValueCountFrequency (%)
463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11978
96.3%
Common 463
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1641
 
13.7%
시 1632
 
13.6%
군 908
 
7.6%
주 409
 
3.4%
천 343
 
2.9%
산 342
 
2.9%
성 297
 
2.5%
동 293
 
2.4%
양 274
 
2.3%
남 263
 
2.2%
Other values (132) 5576
46.6%
Common
ValueCountFrequency (%)
463
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11978
96.3%
ASCII 463
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 1641
 
13.7%
시 1632
 
13.6%
군 908
 
7.6%
주 409
 
3.4%
천 343
 
2.9%
산 342
 
2.9%
성 297
 
2.5%
동 293
 
2.4%
양 274
 
2.3%
남 263
 
2.2%
Other values (132) 5576
46.6%
ASCII
ValueCountFrequency (%)
463
100.0%

읍면동명
Text

Distinct3277
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-04-06T17:46:49.818426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.5203432
Min length2

Characters and Unicode

Total characters12719
Distinct characters348
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

Unique3056 ?
Unique (%)84.6%

Sample

1st row청운효자동
2nd row사직동
3rd row삼청동
4th row부암동
5th row평창동
ValueCountFrequency (%)
중앙동 31
 
0.9%
남면 12
 
0.3%
서면 9
 
0.2%
북면 8
 
0.2%
송정동 7
 
0.2%
금성면 5
 
0.1%
신흥동 5
 
0.1%
동면 5
 
0.1%
교동 5
 
0.1%
성산면 4
 
0.1%
Other values (3267) 3522
97.5%
2024-04-06T17:46:51.140555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2293
 
18.0%
면 1212
 
9.5%
1 399
 
3.1%
2 388
 
3.1%
제 354
 
2.8%
산 298
 
2.3%
읍 258
 
2.0%
3 169
 
1.3%
신 159
 
1.3%
성 158
 
1.2%
Other values (338) 7031
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11568
91.0%
Decimal Number 1118
 
8.8%
Other Punctuation 33
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2293
 
19.8%
면 1212
 
10.5%
제 354
 
3.1%
산 298
 
2.6%
읍 258
 
2.2%
신 159
 
1.4%
성 158
 
1.4%
천 153
 
1.3%
장 148
 
1.3%
대 136
 
1.2%
Other values (327) 6399
55.3%
Decimal Number
ValueCountFrequency (%)
1 399
35.7%
2 388
34.7%
3 169
15.1%
4 80
 
7.2%
5 35
 
3.1%
6 22
 
2.0%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%
0 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11568
91.0%
Common 1151
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2293
 
19.8%
면 1212
 
10.5%
제 354
 
3.1%
산 298
 
2.6%
읍 258
 
2.2%
신 159
 
1.4%
성 158
 
1.4%
천 153
 
1.3%
장 148
 
1.3%
대 136
 
1.2%
Other values (327) 6399
55.3%
Common
ValueCountFrequency (%)
1 399
34.7%
2 388
33.7%
3 169
14.7%
4 80
 
7.0%
5 35
 
3.0%
. 33
 
2.9%
6 22
 
1.9%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11568
91.0%
ASCII 1151
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2293
 
19.8%
면 1212
 
10.5%
제 354
 
3.1%
산 298
 
2.6%
읍 258
 
2.2%
신 159
 
1.4%
성 158
 
1.4%
천 153
 
1.3%
장 148
 
1.3%
대 136
 
1.2%
Other values (327) 6399
55.3%
ASCII
ValueCountFrequency (%)
1 399
34.7%
2 388
33.7%
3 169
14.7%
4 80
 
7.0%
5 35
 
3.0%
. 33
 
2.9%
6 22
 
1.9%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%

계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2687517
Minimum0
Maximum69
Zeros1013
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:46:51.639866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile19
Maximum69
Range69
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1959607
Coefficient of variation (CV)1.3657809
Kurtosis10.052661
Mean5.2687517
Median Absolute Deviation (MAD)2
Skewness2.5736557
Sum19036
Variance51.78185
MonotonicityNot monotonic
2024-04-06T17:46:52.062916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1013
28.0%
1 489
13.5%
2 307
 
8.5%
3 237
 
6.6%
4 172
 
4.8%
6 172
 
4.8%
5 155
 
4.3%
7 151
 
4.2%
9 116
 
3.2%
8 113
 
3.1%
Other values (42) 688
19.0%
ValueCountFrequency (%)
0 1013
28.0%
1 489
13.5%
2 307
 
8.5%
3 237
 
6.6%
4 172
 
4.8%
5 155
 
4.3%
6 172
 
4.8%
7 151
 
4.2%
8 113
 
3.1%
9 116
 
3.2%
ValueCountFrequency (%)
69 1
< 0.1%
66 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
52 2
0.1%
51 2
0.1%
49 1
< 0.1%
48 1
< 0.1%
46 1
< 0.1%
44 2
0.1%

남자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.704124
Minimum0
Maximum32
Zeros1369
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:46:52.478693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum32
Range32
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.84222
Coefficient of variation (CV)1.4208742
Kurtosis8.7730118
Mean2.704124
Median Absolute Deviation (MAD)1
Skewness2.4721772
Sum9770
Variance14.762655
MonotonicityNot monotonic
2024-04-06T17:46:52.943384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 1369
37.9%
1 579
16.0%
2 376
 
10.4%
3 288
 
8.0%
4 216
 
6.0%
5 191
 
5.3%
6 135
 
3.7%
7 97
 
2.7%
8 76
 
2.1%
10 55
 
1.5%
Other values (20) 231
 
6.4%
ValueCountFrequency (%)
0 1369
37.9%
1 579
16.0%
2 376
 
10.4%
3 288
 
8.0%
4 216
 
6.0%
5 191
 
5.3%
6 135
 
3.7%
7 97
 
2.7%
8 76
 
2.1%
9 52
 
1.4%
ValueCountFrequency (%)
32 1
 
< 0.1%
30 4
0.1%
27 1
 
< 0.1%
26 3
0.1%
25 1
 
< 0.1%
24 3
0.1%
23 2
0.1%
22 1
 
< 0.1%
21 4
0.1%
20 3
0.1%

여자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5646277
Minimum0
Maximum39
Zeros1414
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:46:53.336644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile10
Maximum39
Range39
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7020152
Coefficient of variation (CV)1.4434903
Kurtosis11.263108
Mean2.5646277
Median Absolute Deviation (MAD)1
Skewness2.6729779
Sum9266
Variance13.704917
MonotonicityNot monotonic
2024-04-06T17:46:53.864706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 1414
39.1%
1 581
16.1%
2 360
 
10.0%
3 289
 
8.0%
4 234
 
6.5%
5 178
 
4.9%
6 132
 
3.7%
7 100
 
2.8%
8 86
 
2.4%
9 55
 
1.5%
Other values (20) 184
 
5.1%
ValueCountFrequency (%)
0 1414
39.1%
1 581
16.1%
2 360
 
10.0%
3 289
 
8.0%
4 234
 
6.5%
5 178
 
4.9%
6 132
 
3.7%
7 100
 
2.8%
8 86
 
2.4%
9 55
 
1.5%
ValueCountFrequency (%)
39 1
 
< 0.1%
34 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 2
0.1%
26 1
 
< 0.1%
25 3
0.1%
23 1
 
< 0.1%
21 3
0.1%
20 3
0.1%

Interactions

2024-04-06T17:46:42.657764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:38.985129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:40.153933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:41.387118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:43.493550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:39.254611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:40.472964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:41.646209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:43.853100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:39.541055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:40.776173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:41.944500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:44.194267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:39.840919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:41.046115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:46:42.309751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:46:54.148452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드시도명계남자여자
행정기관코드1.0000.9920.2800.2580.265
시도명0.9921.0000.3360.2930.301
계0.2800.3361.0000.8530.876
남자0.2580.2930.8531.0000.861
여자0.2650.3010.8760.8611.000
2024-04-06T17:46:54.428436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드계남자여자시도명
행정기관코드1.000-0.414-0.379-0.3840.972
계-0.4141.0000.9350.9270.140
남자-0.3790.9351.0000.7600.118
여자-0.3840.9270.7601.0000.122
시도명0.9720.1400.1180.1221.000

Missing values

2024-04-06T17:46:44.666474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:46:45.041607image/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

행정기관코드통계년월시도명시군구명읍면동명계남자여자
011110515002024-03-31서울특별시종로구청운효자동936
111110530002024-03-31서울특별시종로구사직동000
211110540002024-03-31서울특별시종로구삼청동000
311110550002024-03-31서울특별시종로구부암동532
411110560002024-03-31서울특별시종로구평창동321
511110570002024-03-31서울특별시종로구무악동000
611110580002024-03-31서울특별시종로구교남동312
711110600002024-03-31서울특별시종로구가회동000
811110615002024-03-31서울특별시종로구종로1.2.3.4가동000
911110630002024-03-31서울특별시종로구종로5.6가동211
행정기관코드통계년월시도명시군구명읍면동명계남자여자
360352800330002024-03-31전북특별자치도부안군행안면101
360452800340002024-03-31전북특별자치도부안군계화면202
360552800350002024-03-31전북특별자치도부안군보안면110
360652800360002024-03-31전북특별자치도부안군변산면110
360752800370002024-03-31전북특별자치도부안군진서면000
360852800380002024-03-31전북특별자치도부안군백산면000
360952800390002024-03-31전북특별자치도부안군상서면000
361052800400002024-03-31전북특별자치도부안군하서면000
361152800410002024-03-31전북특별자치도부안군줄포면000
361252800420002024-03-31전북특별자치도부안군위도면000