Overview

Dataset statistics

Number of variables8
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory70.5 B

Variable types

Categorical5
Numeric2
Text1

Dataset

Description샘플 데이터
Author빅밸류
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=47

Alerts

시도명(SI_NM) has constant value ""Constant
시군구명(GU_NM) has constant value ""Constant
시도코드(SI_CD) has constant value ""Constant
시군구코드(GU_CD) has constant value ""Constant
행정동코드(HJ_CD) is highly overall correlated with 법정동코드(BJ_CD) and 1 other fieldsHigh correlation
법정동코드(BJ_CD) is highly overall correlated with 행정동코드(HJ_CD) and 1 other fieldsHigh correlation
행정동명(HJ_NM) is highly overall correlated with 행정동코드(HJ_CD) and 1 other fieldsHigh correlation
행정동코드(HJ_CD) has 1 (1.9%) zerosZeros
법정동코드(BJ_CD) has 1 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-10 14:58:20.434636
Analysis finished2023-12-10 14:58:22.617156
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명(SI_NM)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
서울
52 

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 52
100.0%

Length

2023-12-10T23:58:22.981951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:23.599662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 52
100.0%

시군구명(GU_NM)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
마포구
52 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마포구 52
100.0%

Length

2023-12-10T23:58:24.215612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:24.489263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 52
100.0%

시도코드(SI_CD)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
11
52 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 52
100.0%

Length

2023-12-10T23:58:24.739972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:24.914128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 52
100.0%

시군구코드(GU_CD)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
440
52 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row440
2nd row440
3rd row440
4th row440
5th row440

Common Values

ValueCountFrequency (%)
440 52
100.0%

Length

2023-12-10T23:58:25.146858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:58:25.403107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
440 52
100.0%

행정동코드(HJ_CD)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60596.154
Minimum0
Maximum74000
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-10T23:58:25.603350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53100
Q156500
median60000
Q366000
95-th percentile72450
Maximum74000
Range74000
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation10363.037
Coefficient of variation (CV)0.17101806
Kurtosis22.900992
Mean60596.154
Median Absolute Deviation (MAD)4500
Skewness-3.9043176
Sum3151000
Variance1.0739253 × 108
MonotonicityNot monotonic
2023-12-10T23:58:25.873054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
59000 6
 
11.5%
65500 5
 
9.6%
63000 5
 
9.6%
56500 4
 
7.7%
67000 3
 
5.8%
66000 3
 
5.8%
60000 3
 
5.8%
55500 3
 
5.8%
56000 3
 
5.8%
58500 2
 
3.8%
Other values (13) 15
28.8%
ValueCountFrequency (%)
0 1
 
1.9%
51000 1
 
1.9%
52000 1
 
1.9%
54000 1
 
1.9%
55000 2
 
3.8%
55500 3
5.8%
56000 3
5.8%
56500 4
7.7%
58500 2
 
3.8%
59000 6
11.5%
ValueCountFrequency (%)
74000 1
 
1.9%
73000 2
 
3.8%
72000 1
 
1.9%
71000 1
 
1.9%
70000 1
 
1.9%
69000 1
 
1.9%
68000 1
 
1.9%
67000 3
5.8%
66000 3
5.8%
65500 5
9.6%

행정동명(HJ_NM)
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Memory size548.0 B
용강동
6 
신수동
5 
서강동
5 
공덕동
4 
신공덕동
3 
Other values (18)
29 

Length

Max length5
Median length3
Mean length3.4615385
Min length3

Unique

Unique11 ?
Unique (%)21.2%

Sample

1st row서강동
2nd row<NA>
3rd row아현제1동
4th row아현제2동
5th row공덕제1동

Common Values

ValueCountFrequency (%)
용강동 6
 
11.5%
신수동 5
 
9.6%
서강동 5
 
9.6%
공덕동 4
 
7.7%
신공덕동 3
 
5.8%
대흥동 3
 
5.8%
아현동 3
 
5.8%
서교동 3
 
5.8%
동교동 3
 
5.8%
도화동 2
 
3.8%
Other values (13) 15
28.8%

Length

2023-12-10T23:58:26.179327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용강동 6
 
11.5%
서강동 5
 
9.6%
신수동 5
 
9.6%
공덕동 4
 
7.7%
신공덕동 3
 
5.8%
대흥동 3
 
5.8%
아현동 3
 
5.8%
서교동 3
 
5.8%
동교동 3
 
5.8%
성산제2동 2
 
3.8%
Other values (13) 15
28.8%

법정동코드(BJ_CD)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10907.692
Minimum0
Maximum12700
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-10T23:58:26.473402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10100
Q110400
median10900
Q311725
95-th percentile12500
Maximum12700
Range12700
Interquartile range (IQR)1325

Descriptive statistics

Standard deviation1736.8945
Coefficient of variation (CV)0.15923574
Kurtosis31.264774
Mean10907.692
Median Absolute Deviation (MAD)650
Skewness-4.9219606
Sum567200
Variance3016802.4
MonotonicityNot monotonic
2023-12-10T23:58:26.749099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
10200 5
 
9.6%
10100 4
 
7.7%
10900 4
 
7.7%
10400 4
 
7.7%
10800 3
 
5.8%
11000 3
 
5.8%
12000 2
 
3.8%
11700 2
 
3.8%
10300 2
 
3.8%
10700 2
 
3.8%
Other values (17) 21
40.4%
ValueCountFrequency (%)
0 1
 
1.9%
10100 4
7.7%
10200 5
9.6%
10300 2
 
3.8%
10400 4
7.7%
10500 1
 
1.9%
10600 1
 
1.9%
10700 2
 
3.8%
10800 3
5.8%
10900 4
7.7%
ValueCountFrequency (%)
12700 1
1.9%
12600 1
1.9%
12500 2
3.8%
12400 1
1.9%
12300 2
3.8%
12200 1
1.9%
12100 2
3.8%
12000 2
3.8%
11800 1
1.9%
11700 2
3.8%

법정동명(BJ_NM)
Text

Distinct27
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-10T23:58:27.113964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0769231
Min length2

Characters and Unicode

Total characters160
Distinct characters40
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

Unique13 ?
Unique (%)25.0%

Sample

1st row당인동
2nd row마포구
3rd row아현동
4th row아현동
5th row공덕동
ValueCountFrequency (%)
공덕동 5
 
9.6%
도화동 4
 
7.7%
아현동 4
 
7.7%
염리동 4
 
7.7%
대흥동 3
 
5.8%
노고산동 3
 
5.8%
신수동 2
 
3.8%
성산동 2
 
3.8%
신정동 2
 
3.8%
망원동 2
 
3.8%
Other values (17) 21
40.4%
2023-12-10T23:58:27.804895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 53
33.1%
공 7
 
4.4%
덕 7
 
4.4%
신 6
 
3.8%
현 5
 
3.1%
산 5
 
3.1%
수 4
 
2.5%
교 4
 
2.5%
정 4
 
2.5%
아 4
 
2.5%
Other values (30) 61
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 53
33.1%
공 7
 
4.4%
덕 7
 
4.4%
신 6
 
3.8%
현 5
 
3.1%
산 5
 
3.1%
수 4
 
2.5%
교 4
 
2.5%
정 4
 
2.5%
아 4
 
2.5%
Other values (30) 61
38.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 160
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 53
33.1%
공 7
 
4.4%
덕 7
 
4.4%
신 6
 
3.8%
현 5
 
3.1%
산 5
 
3.1%
수 4
 
2.5%
교 4
 
2.5%
정 4
 
2.5%
아 4
 
2.5%
Other values (30) 61
38.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 53
33.1%
공 7
 
4.4%
덕 7
 
4.4%
신 6
 
3.8%
현 5
 
3.1%
산 5
 
3.1%
수 4
 
2.5%
교 4
 
2.5%
정 4
 
2.5%
아 4
 
2.5%
Other values (30) 61
38.1%

Interactions

2023-12-10T23:58:21.250204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:20.855336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:21.524163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:21.045694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:27.989363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(HJ_CD)행정동명(HJ_NM)법정동코드(BJ_CD)법정동명(BJ_NM)
행정동코드(HJ_CD)1.0001.0000.7950.865
행정동명(HJ_NM)1.0001.0000.8310.000
법정동코드(BJ_CD)0.7950.8311.0001.000
법정동명(BJ_NM)0.8650.0001.0001.000
2023-12-10T23:58:28.169917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(HJ_CD)법정동코드(BJ_CD)행정동명(HJ_NM)
행정동코드(HJ_CD)1.0000.9300.786
법정동코드(BJ_CD)0.9301.0000.504
행정동명(HJ_NM)0.7860.5041.000

Missing values

2023-12-10T23:58:22.102913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:58:22.437706image/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(SI_NM)시군구명(GU_NM)시도코드(SI_CD)시군구코드(GU_CD)행정동코드(HJ_CD)행정동명(HJ_NM)법정동코드(BJ_CD)법정동명(BJ_NM)
0서울마포구1144065500서강동11800당인동
1서울마포구114400<NA>0마포구
2서울마포구1144051000아현제1동10100아현동
3서울마포구1144052000아현제2동10100아현동
4서울마포구1144054000공덕제1동10200공덕동
5서울마포구1144055000공덕제2동10200공덕동
6서울마포구1144055000공덕제2동10900염리동
7서울마포구1144056000신공덕동10200공덕동
8서울마포구1144056000신공덕동10300신공덕동
9서울마포구1144056000신공덕동10400도화동
시도명(SI_NM)시군구명(GU_NM)시도코드(SI_CD)시군구코드(GU_CD)행정동코드(HJ_CD)행정동명(HJ_NM)법정동코드(BJ_CD)법정동명(BJ_NM)
42서울마포구1144067000동교동12000서교동
43서울마포구1144067000동교동12100동교동
44서울마포구1144068000합정동12200합정동
45서울마포구1144069000망원제1동12300망원동
46서울마포구1144070000망원제2동12300망원동
47서울마포구1144071000연남동12400연남동
48서울마포구1144072000성산제1동12500성산동
49서울마포구1144073000성산제2동12500성산동
50서울마포구1144073000성산제2동12600중동
51서울마포구1144074000상암동12700상암동