Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory54.4 B

Variable types

Numeric2
Categorical2
Text2

Dataset

Description샘플 데이터
Author전자가족관계등록시스템(대법원)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=62

Alerts

시도명(ctprvn_nm) has constant value ""Constant

Reproduction

Analysis started2024-01-14 06:50:03.975117
Analysis finished2024-01-14 06:50:06.339367
Duration2.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조회기간(inqire_ym)
Real number (ℝ)

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201807.5
Minimum201504
Maximum202104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:06.396182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201504
5-th percentile201509.25
Q1201707.5
median201810.5
Q3201911.5
95-th percentile202011.55
Maximum202104
Range600
Interquartile range (IQR)204

Descriptive statistics

Standard deviation172.46304
Coefficient of variation (CV)0.00085459182
Kurtosis-0.96333472
Mean201807.5
Median Absolute Deviation (MAD)102.5
Skewness-0.26573558
Sum6054225
Variance29743.5
MonotonicityNot monotonic
2024-01-14T15:50:06.518947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
201906 3
 
10.0%
202011 3
 
10.0%
201709 2
 
6.7%
201804 1
 
3.3%
202012 1
 
3.3%
201910 1
 
3.3%
202104 1
 
3.3%
201912 1
 
3.3%
201806 1
 
3.3%
202004 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
201504 1
3.3%
201507 1
3.3%
201512 1
3.3%
201601 1
3.3%
201603 1
3.3%
201608 1
3.3%
201609 1
3.3%
201707 1
3.3%
201709 2
6.7%
201712 1
3.3%
ValueCountFrequency (%)
202104 1
 
3.3%
202012 1
 
3.3%
202011 3
10.0%
202004 1
 
3.3%
202003 1
 
3.3%
201912 1
 
3.3%
201910 1
 
3.3%
201906 3
10.0%
201903 1
 
3.3%
201812 1
 
3.3%

시도명(ctprvn_nm)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
서울특별시
30 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 30
100.0%

Length

2024-01-14T15:50:06.658827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:50:06.763896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 30
100.0%

시군구(signgu_nm)
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-14T15:50:06.896783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length9.3333333
Min length8

Characters and Unicode

Total characters280
Distinct characters41
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

Unique7 ?
Unique (%)23.3%

Sample

1st row서울특별시 중구
2nd row서울특별시 서대문구
3rd row서울특별시 동작구
4th row서울특별시 강남구
5th row서울특별시 관악구
ValueCountFrequency (%)
서울특별시 29
48.3%
영등포구 4
 
6.7%
성북구 4
 
6.7%
은평구 3
 
5.0%
마포구 2
 
3.3%
종로구 2
 
3.3%
성동구 2
 
3.3%
관악구 2
 
3.3%
동작구 2
 
3.3%
서대문구 2
 
3.3%
Other values (8) 8
 
13.3%
2024-01-14T15:50:07.176573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 33
11.8%
30
10.7%
울 29
10.4%
특 29
10.4%
별 29
10.4%
시 29
10.4%
구 29
10.4%
성 6
 
2.1%
포 6
 
2.1%
등 5
 
1.8%
Other values (31) 55
19.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 250
89.3%
Space Separator 30
 
10.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 33
13.2%
울 29
11.6%
특 29
11.6%
별 29
11.6%
시 29
11.6%
구 29
11.6%
성 6
 
2.4%
포 6
 
2.4%
등 5
 
2.0%
영 4
 
1.6%
Other values (30) 51
20.4%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 250
89.3%
Common 30
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 33
13.2%
울 29
11.6%
특 29
11.6%
별 29
11.6%
시 29
11.6%
구 29
11.6%
성 6
 
2.4%
포 6
 
2.4%
등 5
 
2.0%
영 4
 
1.6%
Other values (30) 51
20.4%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
89.3%
ASCII 30
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 33
13.2%
울 29
11.6%
특 29
11.6%
별 29
11.6%
시 29
11.6%
구 29
11.6%
성 6
 
2.4%
포 6
 
2.4%
등 5
 
2.0%
영 4
 
1.6%
Other values (30) 51
20.4%
ASCII
ValueCountFrequency (%)
30
100.0%

구청명(emd_nm)
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-14T15:50:07.350976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.066667
Min length9

Characters and Unicode

Total characters302
Distinct characters35
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

Unique8 ?
Unique (%)26.7%

Sample

1st row서울특별시 중구청
2nd row서울특별시 강서구청
3rd row서울특별시 동작구청
4th row서울특별시 성동구청
5th row서울특별시 서대문구청
ValueCountFrequency (%)
서울특별시 30
50.0%
도봉구청 2
 
3.3%
동작구청 2
 
3.3%
강남구청 2
 
3.3%
서대문구청 2
 
3.3%
금천구청 2
 
3.3%
성동구청 2
 
3.3%
은평구청 2
 
3.3%
강서구청 2
 
3.3%
관악구청 2
 
3.3%
Other values (10) 12
 
20.0%
2024-01-14T15:50:07.629426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 35
11.6%
구 32
10.6%
시 30
9.9%
30
9.9%
청 30
9.9%
별 30
9.9%
특 30
9.9%
울 30
9.9%
강 6
 
2.0%
동 5
 
1.7%
Other values (25) 44
14.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 272
90.1%
Space Separator 30
 
9.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 35
12.9%
구 32
11.8%
시 30
11.0%
청 30
11.0%
별 30
11.0%
특 30
11.0%
울 30
11.0%
강 6
 
2.2%
동 5
 
1.8%
중 3
 
1.1%
Other values (24) 41
15.1%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 272
90.1%
Common 30
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 35
12.9%
구 32
11.8%
시 30
11.0%
청 30
11.0%
별 30
11.0%
특 30
11.0%
울 30
11.0%
강 6
 
2.2%
동 5
 
1.8%
중 3
 
1.1%
Other values (24) 41
15.1%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 272
90.1%
ASCII 30
 
9.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 35
12.9%
구 32
11.8%
시 30
11.0%
청 30
11.0%
별 30
11.0%
특 30
11.0%
울 30
11.0%
강 6
 
2.2%
동 5
 
1.8%
중 3
 
1.1%
Other values (24) 41
15.1%
ASCII
ValueCountFrequency (%)
30
100.0%

건수(managt_cnt)
Real number (ℝ)

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.166667
Minimum2
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:07.729992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.45
Q17
median10
Q312.75
95-th percentile25.4
Maximum374
Range372
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation66.676824
Coefficient of variation (CV)3.007977
Kurtosis29.551373
Mean22.166667
Median Absolute Deviation (MAD)3
Skewness5.4185685
Sum665
Variance4445.7989
MonotonicityNot monotonic
2024-01-14T15:50:07.829080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10 6
20.0%
7 4
13.3%
6 3
10.0%
12 2
 
6.7%
14 2
 
6.7%
15 2
 
6.7%
2 2
 
6.7%
8 1
 
3.3%
3 1
 
3.3%
5 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
2 2
 
6.7%
3 1
 
3.3%
5 1
 
3.3%
6 3
10.0%
7 4
13.3%
8 1
 
3.3%
9 1
 
3.3%
10 6
20.0%
11 1
 
3.3%
12 2
 
6.7%
ValueCountFrequency (%)
374 1
 
3.3%
29 1
 
3.3%
21 1
 
3.3%
15 2
 
6.7%
14 2
 
6.7%
13 1
 
3.3%
12 2
 
6.7%
11 1
 
3.3%
10 6
20.0%
9 1
 
3.3%

적재일시(ldadng_dt)
Categorical

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2017-11-21 16:45:55
19 
2020-12-02 10:37:33
 
1
2019-12-04 16:02:31
 
1
2019-05-07 14:08:38
 
1
2019-09-05 13:52:51
 
1
Other values (7)
7 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique11 ?
Unique (%)36.7%

Sample

1st row2020-12-02 10:37:33
2nd row2019-12-04 16:02:31
3rd row2017-11-21 16:45:55
4th row2017-11-21 16:45:55
5th row2019-05-07 14:08:38

Common Values

ValueCountFrequency (%)
2017-11-21 16:45:55 19
63.3%
2020-12-02 10:37:33 1
 
3.3%
2019-12-04 16:02:31 1
 
3.3%
2019-05-07 14:08:38 1
 
3.3%
2019-09-05 13:52:51 1
 
3.3%
2020-12-02 10:40:44 1
 
3.3%
2018-07-25 13:28:32 1
 
3.3%
2019-03-12 20:28:15 1
 
3.3%
2020-12-02 10:41:15 1
 
3.3%
2020-12-02 10:39:08 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2024-01-14T15:50:07.933658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-21 19
31.7%
16:45:55 19
31.7%
2020-12-02 5
 
8.3%
2018-07-25 1
 
1.7%
2021-06-21 1
 
1.7%
10:40:10 1
 
1.7%
10:39:08 1
 
1.7%
10:41:15 1
 
1.7%
20:28:15 1
 
1.7%
2019-03-12 1
 
1.7%
Other values (10) 10
16.7%

Interactions

2024-01-14T15:50:05.964737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:05.732657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:06.065814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:05.876605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:50:08.003478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회기간(inqire_ym)시군구(signgu_nm)구청명(emd_nm)건수(managt_cnt)적재일시(ldadng_dt)
조회기간(inqire_ym)1.0000.4620.7920.0000.729
시군구(signgu_nm)0.4621.0000.0001.0000.818
구청명(emd_nm)0.7920.0001.0000.0000.000
건수(managt_cnt)0.0001.0000.0001.0000.000
적재일시(ldadng_dt)0.7290.8180.0000.0001.000
2024-01-14T15:50:08.096828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회기간(inqire_ym)건수(managt_cnt)적재일시(ldadng_dt)
조회기간(inqire_ym)1.000-0.0300.331
건수(managt_cnt)-0.0301.0000.000
적재일시(ldadng_dt)0.3310.0001.000

Missing values

2024-01-14T15:50:06.181300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:50:06.293711image/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

조회기간(inqire_ym)시도명(ctprvn_nm)시군구(signgu_nm)구청명(emd_nm)건수(managt_cnt)적재일시(ldadng_dt)
0201804서울특별시서울특별시 중구서울특별시 중구청62020-12-02 10:37:33
1201811서울특별시서울특별시 서대문구서울특별시 강서구청72019-12-04 16:02:31
2201906서울특별시서울특별시 동작구서울특별시 동작구청102017-11-21 16:45:55
3201609서울특별시서울특별시 강남구서울특별시 성동구청3742017-11-21 16:45:55
4201810서울특별시서울특별시 관악구서울특별시 서대문구청72019-05-07 14:08:38
5201504서울특별시서울특별시 은평구서울특별시 강북구청122017-11-21 16:45:55
6201712서울특별시서울특별시 은평구서울특별시 동작구청142017-11-21 16:45:55
7201603서울특별시서울특별시 관악구서울특별시 금천구청72017-11-21 16:45:55
8202011서울특별시서울특별시 마포구서울특별시 구로구청102019-09-05 13:52:51
9202011서울특별시서울특별시 송파구서울특별시 은평구청152020-12-02 10:40:44
조회기간(inqire_ym)시도명(ctprvn_nm)시군구(signgu_nm)구청명(emd_nm)건수(managt_cnt)적재일시(ldadng_dt)
20202003서울특별시재외국민 가족관계등록사무소서울특별시 관악구청62020-12-02 10:41:15
21201906서울특별시서울특별시 성동구서울특별시 영등포구청212017-11-21 16:45:55
22201812서울특별시서울특별시 성북구서울특별시 마포구청82017-11-21 16:45:55
23201512서울특별시서울특별시 서대문구서울특별시 도봉구청132020-12-02 10:39:08
24201709서울특별시서울특별시 영등포구서울특별시 구로구청22020-12-02 10:40:10
25202004서울특별시서울특별시 영등포구서울특별시 관악구청102017-11-21 16:45:55
26201806서울특별시서울특별시 영등포구서울특별시 성동구청52017-11-21 16:45:55
27201912서울특별시서울특별시 강서구서울특별시 서대문구청72017-11-21 16:45:55
28202104서울특별시서울특별시 은평구서울특별시 용산구청102021-06-21 13:18:17
29201910서울특별시서울특별시 성북구서울특별시 강동구청32017-11-21 16:45:55