Overview

Dataset statistics

Number of variables7
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory65.3 B

Variable types

Categorical1
Numeric4
Text1
DateTime1

Dataset

Description샘플 데이터
Author통계청
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=64

Alerts

1인가구남성수(one_psn_hshld_male_co) has unique valuesUnique

Reproduction

Analysis started2024-04-21 16:47:47.204905
Analysis finished2024-04-21 16:47:50.458966
Duration3.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
2016
12 
2015
11 
2017

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2017
3rd row2017
4th row2016
5th row2015

Common Values

ValueCountFrequency (%)
2016 12
40.0%
2015 11
36.7%
2017 7
23.3%

Length

2024-04-22T01:47:50.570064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T01:47:50.742023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 12
40.0%
2015 11
36.7%
2017 7
23.3%
Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11393.5
Minimum11110
Maximum11710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-04-22T01:47:50.907522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11137
Q111267.5
median11380
Q311500
95-th percentile11683
Maximum11710
Range600
Interquartile range (IQR)232.5

Descriptive statistics

Standard deviation172.39765
Coefficient of variation (CV)0.015131228
Kurtosis-0.83522379
Mean11393.5
Median Absolute Deviation (MAD)120
Skewness0.20384221
Sum341805
Variance29720.948
MonotonicityNot monotonic
2024-04-22T01:47:51.095804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11500 3
 
10.0%
11410 3
 
10.0%
11215 2
 
6.7%
11350 2
 
6.7%
11290 2
 
6.7%
11710 2
 
6.7%
11110 2
 
6.7%
11590 2
 
6.7%
11380 2
 
6.7%
11260 2
 
6.7%
Other values (7) 8
26.7%
ValueCountFrequency (%)
11110 2
6.7%
11170 2
6.7%
11215 2
6.7%
11260 2
6.7%
11290 2
6.7%
11305 1
 
3.3%
11320 1
 
3.3%
11350 2
6.7%
11380 2
6.7%
11410 3
10.0%
ValueCountFrequency (%)
11710 2
6.7%
11650 1
 
3.3%
11620 1
 
3.3%
11590 2
6.7%
11560 1
 
3.3%
11500 3
10.0%
11470 1
 
3.3%
11410 3
10.0%
11380 2
6.7%
11350 2
6.7%
Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
2024-04-22T01:47:51.607604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1
Min length2

Characters and Unicode

Total characters93
Distinct characters31
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

Unique9 ?
Unique (%)30.0%

Sample

1st row광진구
2nd row강북구
3rd row성동구
4th row서초구
5th row강동구
ValueCountFrequency (%)
성동구 4
13.3%
송파구 3
 
10.0%
서초구 2
 
6.7%
강동구 2
 
6.7%
용산구 2
 
6.7%
서대문구 2
 
6.7%
영등포구 2
 
6.7%
은평구 2
 
6.7%
도봉구 2
 
6.7%
광진구 1
 
3.3%
Other values (8) 8
26.7%
2024-04-22T01:47:52.355976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
32.3%
7
 
7.5%
5
 
5.4%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (21) 32
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30
32.3%
7
 
7.5%
5
 
5.4%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (21) 32
34.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
30
32.3%
7
 
7.5%
5
 
5.4%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (21) 32
34.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
30
32.3%
7
 
7.5%
5
 
5.4%
4
 
4.3%
3
 
3.2%
3
 
3.2%
3
 
3.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (21) 32
34.4%
Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5172.8333
Minimum2024
Maximum8599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-04-22T01:47:52.545963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2024
5-th percentile2024
Q13539
median4801.5
Q36569
95-th percentile8599
Maximum8599
Range6575
Interquartile range (IQR)3030

Descriptive statistics

Standard deviation2159.2386
Coefficient of variation (CV)0.41741895
Kurtosis-1.1916236
Mean5172.8333
Median Absolute Deviation (MAD)1767.5
Skewness0.19619449
Sum155185
Variance4662311.5
MonotonicityNot monotonic
2024-04-22T01:47:52.737463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6569 4
13.3%
4044 4
13.3%
3539 3
10.0%
2024 3
10.0%
8599 3
10.0%
8084 3
10.0%
4549 2
6.7%
2529 2
6.7%
5054 2
6.7%
7074 1
 
3.3%
Other values (3) 3
10.0%
ValueCountFrequency (%)
2024 3
10.0%
2529 2
6.7%
3034 1
 
3.3%
3539 3
10.0%
4044 4
13.3%
4549 2
6.7%
5054 2
6.7%
5559 1
 
3.3%
6064 1
 
3.3%
6569 4
13.3%
ValueCountFrequency (%)
8599 3
10.0%
8084 3
10.0%
7074 1
 
3.3%
6569 4
13.3%
6064 1
 
3.3%
5559 1
 
3.3%
5054 2
6.7%
4549 2
6.7%
4044 4
13.3%
3539 3
10.0%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2120.1
Minimum95
Maximum13326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-04-22T01:47:52.948943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile116.65
Q11195
median1841
Q32407
95-th percentile3577.95
Maximum13326
Range13231
Interquartile range (IQR)1212

Descriptive statistics

Standard deviation2348.7507
Coefficient of variation (CV)1.107849
Kurtosis18.776475
Mean2120.1
Median Absolute Deviation (MAD)637.5
Skewness3.9078749
Sum63603
Variance5516630
MonotonicityNot monotonic
2024-04-22T01:47:53.184053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3137 1
 
3.3%
1686 1
 
3.3%
1424 1
 
3.3%
2862 1
 
3.3%
13326 1
 
3.3%
1368 1
 
3.3%
3267 1
 
3.3%
1829 1
 
3.3%
1853 1
 
3.3%
3518 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
95 1
3.3%
109 1
3.3%
126 1
3.3%
179 1
3.3%
390 1
3.3%
430 1
3.3%
880 1
3.3%
1151 1
3.3%
1327 1
3.3%
1368 1
3.3%
ValueCountFrequency (%)
13326 1
3.3%
3627 1
3.3%
3518 1
3.3%
3267 1
3.3%
3137 1
3.3%
2862 1
3.3%
2774 1
3.3%
2426 1
3.3%
2350 1
3.3%
2183 1
3.3%
Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1056.8667
Minimum77
Maximum2277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-04-22T01:47:53.406895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile92.25
Q1431.25
median956.5
Q31584.75
95-th percentile2063.05
Maximum2277
Range2200
Interquartile range (IQR)1153.5

Descriptive statistics

Standard deviation717.68453
Coefficient of variation (CV)0.67906819
Kurtosis-1.4521021
Mean1056.8667
Median Absolute Deviation (MAD)615.5
Skewness0.15611831
Sum31706
Variance515071.09
MonotonicityNot monotonic
2024-04-22T01:47:53.615621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2015 2
 
6.7%
650 1
 
3.3%
598 1
 
3.3%
418 1
 
3.3%
1504 1
 
3.3%
1595 1
 
3.3%
95 1
 
3.3%
323 1
 
3.3%
90 1
 
3.3%
1548 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
77 1
3.3%
90 1
3.3%
95 1
3.3%
199 1
3.3%
253 1
3.3%
289 1
3.3%
323 1
3.3%
418 1
3.3%
471 1
3.3%
484 1
3.3%
ValueCountFrequency (%)
2277 1
3.3%
2077 1
3.3%
2046 1
3.3%
2015 2
6.7%
1948 1
3.3%
1708 1
3.3%
1595 1
3.3%
1554 1
3.3%
1548 1
3.3%
1516 1
3.3%
Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
Minimum2017-11-27 15:43:19
Maximum2018-10-05 10:18:44
2024-04-22T01:47:53.798295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:53.972960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

Interactions

2024-04-22T01:47:49.520905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:47.503443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:48.314176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:48.918673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:49.671815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:47.656355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:48.472300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:49.075918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:49.819612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:47.810611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:48.622443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:49.230217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:49.968597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:47.968047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:48.778259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T01:47:49.381845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T01:47:54.108814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년(stdr_yy)시군구코드(signgu_code_se)자치구명(atdrc_nm)연령대구분(agrde_five_adult_se)1인가구남성수(one_psn_hshld_male_co)1인가구여성수(one_psn_hshld_female_co)적재일시(ldadng_dt)
기준년(stdr_yy)1.0000.0000.0000.8440.0000.0000.000
시군구코드(signgu_code_se)0.0001.0000.0000.5990.0000.0000.568
자치구명(atdrc_nm)0.0000.0001.0000.0000.2400.0000.672
연령대구분(agrde_five_adult_se)0.8440.5990.0001.0000.0000.0000.177
1인가구남성수(one_psn_hshld_male_co)0.0000.0000.2400.0001.0000.0000.000
1인가구여성수(one_psn_hshld_female_co)0.0000.0000.0000.0000.0001.0000.379
적재일시(ldadng_dt)0.0000.5680.6720.1770.0000.3791.000
2024-04-22T01:47:54.318561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드(signgu_code_se)연령대구분(agrde_five_adult_se)1인가구남성수(one_psn_hshld_male_co)1인가구여성수(one_psn_hshld_female_co)기준년(stdr_yy)
시군구코드(signgu_code_se)1.000-0.3020.088-0.0590.000
연령대구분(agrde_five_adult_se)-0.3021.0000.1730.1250.440
1인가구남성수(one_psn_hshld_male_co)0.0880.1731.000-0.0070.000
1인가구여성수(one_psn_hshld_female_co)-0.0590.125-0.0071.0000.000
기준년(stdr_yy)0.0000.4400.0000.0001.000

Missing values

2024-04-22T01:47:50.150900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T01:47:50.365764image/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

기준년(stdr_yy)시군구코드(signgu_code_se)자치구명(atdrc_nm)연령대구분(agrde_five_adult_se)1인가구남성수(one_psn_hshld_male_co)1인가구여성수(one_psn_hshld_female_co)적재일시(ldadng_dt)
0201511590광진구454931376502017-11-27 15:43:19
1201711620강북구2529242620462017-11-30 11:02:26
2201711590성동구707412620152018-10-05 10:18:44
3201611170서초구35391097712018-10-05 10:18:44
4201511260강동구606427744712017-11-30 11:02:26
5201511650송파구5559235019482017-11-27 15:43:19
6201511380송파구353988017082017-11-30 11:02:26
7201711410용산구2024956752017-11-27 15:43:19
8201611215노원구6569132720772017-11-30 11:02:26
9201511110용산구6569196213182017-11-27 15:43:19
기준년(stdr_yy)시군구코드(signgu_code_se)자치구명(atdrc_nm)연령대구분(agrde_five_adult_se)1인가구남성수(one_psn_hshld_male_co)1인가구여성수(one_psn_hshld_female_co)적재일시(ldadng_dt)
20201711500강동구30341794842017-11-30 11:02:26
21201511350서초구4044163822772017-11-30 11:02:26
22201711110서대문구5054351815482018-10-05 10:18:44
23201511320성동구45491853902017-11-30 11:02:26
24201611215은평구8084182920152017-11-30 11:02:26
25201711500양천구252932673232017-11-27 15:43:19
26201611350중랑구40441368952017-11-27 15:43:19
27201511410도봉구40441332615952017-11-27 15:43:19
28201511560종로구8599286215042018-10-05 10:18:44
29201611710서대문구353914244182018-10-05 10:18:44