Overview

Dataset statistics

Number of variables7
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory62.4 B

Variable types

Categorical2
Text2
Numeric2
DateTime1

Dataset

Description샘플 데이터
Author전자가족관계등록시스템(대법원)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=60

Alerts

시도명(ctprvn_nm) has constant value ""Constant
조회기간(inqire_ym) is highly overall correlated with 건수(managt_cnt)High correlation
건수(managt_cnt) is highly overall correlated with 조회기간(inqire_ym)High correlation

Reproduction

Analysis started2024-01-14 06:48:59.857354
Analysis finished2024-01-14 06:49:02.364511
Duration2.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

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

2024-01-14T15:49:02.438861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:49:02.554251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 30
100.0%
Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-14T15:49:02.699170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.1666667
Min length8

Characters and Unicode

Total characters275
Distinct characters32
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 관악구
2nd row서울특별시 영등포구
3rd row서울특별시 도봉구
4th row서울특별시 성동구
5th row서울특별시 광진구
ValueCountFrequency (%)
서울특별시 30
50.0%
영등포구 5
 
8.3%
성북구 3
 
5.0%
용산구 3
 
5.0%
중구 2
 
3.3%
종로구 2
 
3.3%
은평구 2
 
3.3%
동대문구 2
 
3.3%
광진구 2
 
3.3%
도봉구 2
 
3.3%
Other values (7) 7
 
11.7%
2024-01-14T15:49:03.073102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
11.3%
31
11.3%
30
10.9%
30
10.9%
30
10.9%
30
10.9%
30
10.9%
6
 
2.2%
5
 
1.8%
5
 
1.8%
Other values (22) 47
17.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 245
89.1%
Space Separator 30
 
10.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
31
12.7%
31
12.7%
30
12.2%
30
12.2%
30
12.2%
30
12.2%
6
 
2.4%
5
 
2.0%
5
 
2.0%
5
 
2.0%
Other values (21) 42
17.1%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 245
89.1%
Common 30
 
10.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
31
12.7%
31
12.7%
30
12.2%
30
12.2%
30
12.2%
30
12.2%
6
 
2.4%
5
 
2.0%
5
 
2.0%
5
 
2.0%
Other values (21) 42
17.1%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 245
89.1%
ASCII 30
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
31
12.7%
31
12.7%
30
12.2%
30
12.2%
30
12.2%
30
12.2%
6
 
2.4%
5
 
2.0%
5
 
2.0%
5
 
2.0%
Other values (21) 42
17.1%
ASCII
ValueCountFrequency (%)
30
100.0%
Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-14T15:49:03.285154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length10.3
Min length9

Characters and Unicode

Total characters309
Distinct characters42
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

Unique13 ?
Unique (%)43.3%

Sample

1st row서울특별시 성북구청
2nd row서울특별시 은평구청
3rd row서울특별시 금천구청
4th row서울특별시 중랑구청
5th row서울특별시 서초구청
ValueCountFrequency (%)
서울특별시 28
46.7%
금천구청 5
 
8.3%
은평구청 3
 
5.0%
강서구청 3
 
5.0%
종로구청 2
 
3.3%
중랑구청 2
 
3.3%
재외국민 2
 
3.3%
가족관계등록사무소 2
 
3.3%
서대문구청 1
 
1.7%
성북구청 1
 
1.7%
Other values (11) 11
 
18.3%
2024-01-14T15:49:03.575069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33
10.7%
30
9.7%
29
9.4%
28
9.1%
28
9.1%
28
9.1%
28
9.1%
28
9.1%
6
 
1.9%
5
 
1.6%
Other values (32) 66
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 279
90.3%
Space Separator 30
 
9.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
33
11.8%
29
10.4%
28
10.0%
28
10.0%
28
10.0%
28
10.0%
28
10.0%
6
 
2.2%
5
 
1.8%
5
 
1.8%
Other values (31) 61
21.9%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 279
90.3%
Common 30
 
9.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
33
11.8%
29
10.4%
28
10.0%
28
10.0%
28
10.0%
28
10.0%
28
10.0%
6
 
2.2%
5
 
1.8%
5
 
1.8%
Other values (31) 61
21.9%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 279
90.3%
ASCII 30
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
33
11.8%
29
10.4%
28
10.0%
28
10.0%
28
10.0%
28
10.0%
28
10.0%
6
 
2.2%
5
 
1.8%
5
 
1.8%
Other values (31) 61
21.9%
ASCII
ValueCountFrequency (%)
30
100.0%
Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
사망
이혼
개명
혼인
출생

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개명
2nd row출생
3rd row사망
4th row개명
5th row사망

Common Values

ValueCountFrequency (%)
사망 9
30.0%
이혼 8
26.7%
개명 5
16.7%
혼인 5
16.7%
출생 3
 
10.0%

Length

2024-01-14T15:49:03.698331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:49:03.808162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사망 9
30.0%
이혼 8
26.7%
개명 5
16.7%
혼인 5
16.7%
출생 3
 
10.0%

조회기간(inqire_ym)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201751.9
Minimum201501
Maximum202007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:49:03.934278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201501
5-th percentile201504.35
Q1201608.5
median201706
Q3201903.5
95-th percentile202005.55
Maximum202007
Range506
Interquartile range (IQR)295

Descriptive statistics

Standard deviation167.32486
Coefficient of variation (CV)0.00082935955
Kurtosis-1.2819824
Mean201751.9
Median Absolute Deviation (MAD)148.5
Skewness0.092406089
Sum6052557
Variance27997.61
MonotonicityNot monotonic
2024-01-14T15:49:04.090452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
201901 2
 
6.7%
201702 2
 
6.7%
202004 1
 
3.3%
201501 1
 
3.3%
201605 1
 
3.3%
201610 1
 
3.3%
201801 1
 
3.3%
201608 1
 
3.3%
201709 1
 
3.3%
201902 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
201501 1
3.3%
201503 1
3.3%
201506 1
3.3%
201510 1
3.3%
201604 1
3.3%
201605 1
3.3%
201607 1
3.3%
201608 1
3.3%
201610 1
3.3%
201611 1
3.3%
ValueCountFrequency (%)
202007 1
3.3%
202006 1
3.3%
202005 1
3.3%
202004 1
3.3%
201911 1
3.3%
201910 1
3.3%
201908 1
3.3%
201904 1
3.3%
201902 1
3.3%
201901 2
6.7%

건수(managt_cnt)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.83333
Minimum1
Maximum384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:49:04.241287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.9
Q149.75
median123.5
Q3184
95-th percentile292.8
Maximum384
Range383
Interquartile range (IQR)134.25

Descriptive statistics

Standard deviation92.64321
Coefficient of variation (CV)0.72471872
Kurtosis1.0511617
Mean127.83333
Median Absolute Deviation (MAD)65.5
Skewness1.0153201
Sum3835
Variance8582.7644
MonotonicityNot monotonic
2024-01-14T15:49:04.410299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
184 2
 
6.7%
33 2
 
6.7%
42 1
 
3.3%
108 1
 
3.3%
225 1
 
3.3%
146 1
 
3.3%
48 1
 
3.3%
55 1
 
3.3%
171 1
 
3.3%
76 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
1 1
3.3%
31 1
3.3%
33 2
6.7%
35 1
3.3%
39 1
3.3%
42 1
3.3%
48 1
3.3%
55 1
3.3%
62 1
3.3%
68 1
3.3%
ValueCountFrequency (%)
384 1
3.3%
345 1
3.3%
229 1
3.3%
225 1
3.3%
201 1
3.3%
193 1
3.3%
186 1
3.3%
184 2
6.7%
171 1
3.3%
153 1
3.3%
Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2017-11-21 16:58:47
Maximum2021-05-14 09:37:01
2024-01-14T15:49:04.581442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:04.736792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

Interactions

2024-01-14T15:49:01.959343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:01.711693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:02.045631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:49:01.869224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:49:04.833014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구(signgu_nm)구청명(emd_nm)가족관계등록사건(regist_se)조회기간(inqire_ym)건수(managt_cnt)적재일시(ldadng_dt)
시군구(signgu_nm)1.0000.7530.0000.6170.7930.860
구청명(emd_nm)0.7531.0000.5740.7110.4540.761
가족관계등록사건(regist_se)0.0000.5741.0000.4270.0000.324
조회기간(inqire_ym)0.6170.7110.4271.0000.4350.000
건수(managt_cnt)0.7930.4540.0000.4351.0000.555
적재일시(ldadng_dt)0.8600.7610.3240.0000.5551.000
2024-01-14T15:49:04.957233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회기간(inqire_ym)건수(managt_cnt)가족관계등록사건(regist_se)
조회기간(inqire_ym)1.000-0.5880.248
건수(managt_cnt)-0.5881.0000.000
가족관계등록사건(regist_se)0.2480.0001.000

Missing values

2024-01-14T15:49:02.179990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:49:02.306347image/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

시도명(ctprvn_nm)시군구(signgu_nm)구청명(emd_nm)가족관계등록사건(regist_se)조회기간(inqire_ym)건수(managt_cnt)적재일시(ldadng_dt)
0서울특별시서울특별시 관악구서울특별시 성북구청개명202004422017-11-21 16:58:47
1서울특별시서울특별시 영등포구서울특별시 은평구청출생202007332017-11-21 16:58:47
2서울특별시서울특별시 도봉구서울특별시 금천구청사망2017013452017-11-21 16:58:47
3서울특별시서울특별시 성동구서울특별시 중랑구청개명201904312019-06-07 14:09:16
4서울특별시서울특별시 광진구서울특별시 서초구청사망201704782021-02-22 13:23:08
5서울특별시서울특별시 광진구재외국민 가족관계등록사무소이혼2019101432017-11-21 16:58:47
6서울특별시서울특별시 동작구서울특별시 마포구청이혼201901952018-07-25 13:44:32
7서울특별시서울특별시 구로구서울특별시 구로구청혼인2016041482018-09-04 15:54:29
8서울특별시서울특별시 동대문구서울특별시 동대문구청사망2015102292017-11-21 16:58:47
9서울특별시서울특별시 중구서울특별시 은평구청개명202006352019-06-07 14:09:16
시도명(ctprvn_nm)시군구(signgu_nm)구청명(emd_nm)가족관계등록사건(regist_se)조회기간(inqire_ym)건수(managt_cnt)적재일시(ldadng_dt)
20서울특별시서울특별시 종로구재외국민 가족관계등록사무소개명2015061932017-11-21 16:58:47
21서울특별시서울특별시 영등포구서울특별시 강동구청혼인2015031862017-11-29 17:36:46
22서울특별시서울특별시 중구서울특별시 강서구청이혼201611762017-11-21 16:58:47
23서울특별시서울특별시 영등포구서울특별시 강서구청이혼2019021712018-07-25 13:45:30
24서울특별시서울특별시 강동구서울특별시 중랑구청이혼201709552019-10-04 14:45:54
25서울특별시서울특별시 도봉구서울특별시 금천구청혼인201608482018-07-25 13:45:30
26서울특별시서울특별시 용산구서울특별시 동작구청이혼2018011462020-12-02 10:51:45
27서울특별시서울특별시 성북구서울특별시 광진구청혼인2017022252018-07-25 13:45:30
28서울특별시서울특별시 동대문구서울특별시 금천구청혼인2016101842020-12-02 10:51:45
29서울특별시서울특별시 은평구서울특별시 은평구청출생2016051082017-11-21 16:58:47