Overview

Dataset statistics

Number of variables6
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.5 KiB
Average record size in memory50.3 B

Variable types

Numeric2
Categorical3
Text1

Dataset

Description샘플 데이터
Authore-나라지표
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=61

Alerts

인구변화량(popltn_chnge_qy) has 14 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-10 14:56:23.624871
Analysis finished2023-12-10 14:56:27.521520
Duration3.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년(year_yy)
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.462
Minimum2010
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:27.642694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12012
median2013
Q32015.25
95-th percentile2017
Maximum2017
Range7
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.290644
Coefficient of variation (CV)0.0011376644
Kurtosis-1.2072411
Mean2013.462
Median Absolute Deviation (MAD)2
Skewness0.080816417
Sum1006731
Variance5.2470501
MonotonicityNot monotonic
2023-12-10T23:56:27.832550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2013 76
15.2%
2017 68
13.6%
2012 65
13.0%
2011 64
12.8%
2010 60
12.0%
2014 59
11.8%
2016 57
11.4%
2015 51
10.2%
ValueCountFrequency (%)
2010 60
12.0%
2011 64
12.8%
2012 65
13.0%
2013 76
15.2%
2014 59
11.8%
2015 51
10.2%
2016 57
11.4%
2017 68
13.6%
ValueCountFrequency (%)
2017 68
13.6%
2016 57
11.4%
2015 51
10.2%
2014 59
11.8%
2013 76
15.2%
2012 65
13.0%
2011 64
12.8%
2010 60
12.0%

자치구(atdrc_nm)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
종로구
 
26
광진구
 
25
중랑구
 
25
마포구
 
24
성북구
 
24
Other values (20)
376 

Length

Max length4
Median length3
Mean length3.102
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row관악구
3rd row노원구
4th row강동구
5th row구로구

Common Values

ValueCountFrequency (%)
종로구 26
 
5.2%
광진구 25
 
5.0%
중랑구 25
 
5.0%
마포구 24
 
4.8%
성북구 24
 
4.8%
노원구 23
 
4.6%
서대문구 23
 
4.6%
강북구 23
 
4.6%
동대문구 22
 
4.4%
영등포구 21
 
4.2%
Other values (15) 264
52.8%

Length

2023-12-10T23:56:28.113671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 26
 
5.2%
광진구 25
 
5.0%
중랑구 25
 
5.0%
마포구 24
 
4.8%
성북구 24
 
4.8%
노원구 23
 
4.6%
서대문구 23
 
4.6%
강북구 23
 
4.6%
동대문구 22
 
4.4%
영등포구 21
 
4.2%
Other values (15) 264
52.8%

연령명(age_co_nm)
Text

Distinct101
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:28.640390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.904
Min length2

Characters and Unicode

Total characters1452
Distinct characters13
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

Unique4 ?
Unique (%)0.8%

Sample

1st row41세
2nd row49세
3rd row58세
4th row50세
5th row70세
ValueCountFrequency (%)
63세 9
 
1.8%
79세 9
 
1.8%
2세 9
 
1.8%
5세 9
 
1.8%
69세 9
 
1.8%
77세 9
 
1.8%
48세 9
 
1.8%
14세 8
 
1.6%
19세 8
 
1.6%
4세 8
 
1.6%
Other values (91) 413
82.6%
2023-12-10T23:56:29.446861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 500
34.4%
9 108
 
7.4%
6 105
 
7.2%
1 104
 
7.2%
3 103
 
7.1%
7 100
 
6.9%
4 98
 
6.7%
5 93
 
6.4%
2 92
 
6.3%
8 88
 
6.1%
Other values (3) 61
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 944
65.0%
Other Letter 508
35.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 108
11.4%
6 105
11.1%
1 104
11.0%
3 103
10.9%
7 100
10.6%
4 98
10.4%
5 93
9.9%
2 92
9.7%
8 88
9.3%
0 53
5.6%
Other Letter
ValueCountFrequency (%)
세 500
98.4%
이 4
 
0.8%
상 4
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 944
65.0%
Hangul 508
35.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 108
11.4%
6 105
11.1%
1 104
11.0%
3 103
10.9%
7 100
10.6%
4 98
10.4%
5 93
9.9%
2 92
9.7%
8 88
9.3%
0 53
5.6%
Hangul
ValueCountFrequency (%)
세 500
98.4%
이 4
 
0.8%
상 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 944
65.0%
Hangul 508
35.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 500
98.4%
이 4
 
0.8%
상 4
 
0.8%
ASCII
ValueCountFrequency (%)
9 108
11.4%
6 105
11.1%
1 104
11.0%
3 103
10.9%
7 100
10.6%
4 98
10.4%
5 93
9.9%
2 92
9.7%
8 88
9.3%
0 53
5.6%

이동구간명(mvmn_sctn_nm)
Categorical

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
시도간전입
98 
시군구간전입
86 
시도간전출
85 
순이동
83 
시군구내
76 

Length

Max length6
Median length5
Mean length4.832
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시군구내
2nd row순이동
3rd row시도간전출
4th row순이동
5th row시군구간전출

Common Values

ValueCountFrequency (%)
시도간전입 98
19.6%
시군구간전입 86
17.2%
시도간전출 85
17.0%
순이동 83
16.6%
시군구내 76
15.2%
시군구간전출 72
14.4%

Length

2023-12-10T23:56:29.721832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:29.961456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시도간전입 98
19.6%
시군구간전입 86
17.2%
시도간전출 85
17.0%
순이동 83
16.6%
시군구내 76
15.2%
시군구간전출 72
14.4%

인구변화량(popltn_chnge_qy)
Real number (ℝ)

ZEROS 

Distinct335
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.868
Minimum-347
Maximum2537
Zeros14
Zeros (%)2.8%
Negative60
Negative (%)12.0%
Memory size4.5 KiB
2023-12-10T23:56:30.202595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-347
5-th percentile-58.25
Q119
median118.5
Q3281.75
95-th percentile608
Maximum2537
Range2884
Interquartile range (IQR)262.75

Descriptive statistics

Standard deviation253.74602
Coefficient of variation (CV)1.3952208
Kurtosis20.310659
Mean181.868
Median Absolute Deviation (MAD)113.5
Skewness3.0509691
Sum90934
Variance64387.045
MonotonicityNot monotonic
2023-12-10T23:56:30.460927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
2.8%
5 8
 
1.6%
2 6
 
1.2%
4 6
 
1.2%
-1 4
 
0.8%
22 4
 
0.8%
73 4
 
0.8%
25 4
 
0.8%
10 4
 
0.8%
1 4
 
0.8%
Other values (325) 442
88.4%
ValueCountFrequency (%)
-347 1
0.2%
-338 1
0.2%
-322 1
0.2%
-312 1
0.2%
-236 1
0.2%
-207 1
0.2%
-200 1
0.2%
-167 1
0.2%
-164 1
0.2%
-154 1
0.2%
ValueCountFrequency (%)
2537 1
0.2%
2021 1
0.2%
1256 1
0.2%
1022 1
0.2%
995 1
0.2%
843 1
0.2%
840 1
0.2%
816 1
0.2%
813 1
0.2%
801 1
0.2%

적재일시(ldadng_ct)
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2017-11-23 13:33:11
373 
2017-11-29 17:55:05
70 
2018-07-25 16:04:31
57 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-11-29 17:55:05
2nd row2017-11-23 13:33:11
3rd row2017-11-23 13:33:11
4th row2017-11-23 13:33:11
5th row2017-11-23 13:33:11

Common Values

ValueCountFrequency (%)
2017-11-23 13:33:11 373
74.6%
2017-11-29 17:55:05 70
 
14.0%
2018-07-25 16:04:31 57
 
11.4%

Length

2023-12-10T23:56:30.700119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:30.871662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-11-23 373
37.3%
13:33:11 373
37.3%
2017-11-29 70
 
7.0%
17:55:05 70
 
7.0%
2018-07-25 57
 
5.7%
16:04:31 57
 
5.7%

Interactions

2023-12-10T23:56:26.629554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:26.250942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:26.830379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:56:26.471172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:56:31.011964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년(year_yy)자치구(atdrc_nm)이동구간명(mvmn_sctn_nm)인구변화량(popltn_chnge_qy)적재일시(ldadng_ct)
년(year_yy)1.0000.0000.0000.0000.000
자치구(atdrc_nm)0.0001.0000.0000.0000.000
이동구간명(mvmn_sctn_nm)0.0000.0001.0000.0000.000
인구변화량(popltn_chnge_qy)0.0000.0000.0001.0000.039
적재일시(ldadng_ct)0.0000.0000.0000.0391.000
2023-12-10T23:56:31.208385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이동구간명(mvmn_sctn_nm)적재일시(ldadng_ct)자치구(atdrc_nm)
이동구간명(mvmn_sctn_nm)1.0000.0000.000
적재일시(ldadng_ct)0.0001.0000.000
자치구(atdrc_nm)0.0000.0001.000
2023-12-10T23:56:31.372246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년(year_yy)인구변화량(popltn_chnge_qy)자치구(atdrc_nm)이동구간명(mvmn_sctn_nm)적재일시(ldadng_ct)
년(year_yy)1.0000.0130.0000.0000.034
인구변화량(popltn_chnge_qy)0.0131.0000.0000.0000.029
자치구(atdrc_nm)0.0000.0001.0000.0000.000
이동구간명(mvmn_sctn_nm)0.0000.0000.0001.0000.000
적재일시(ldadng_ct)0.0340.0290.0000.0001.000

Missing values

2023-12-10T23:56:27.155891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:56:27.432250image/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

년(year_yy)자치구(atdrc_nm)연령명(age_co_nm)이동구간명(mvmn_sctn_nm)인구변화량(popltn_chnge_qy)적재일시(ldadng_ct)
02017강동구41세시군구내72017-11-29 17:55:05
12011관악구49세순이동3732017-11-23 13:33:11
22012노원구58세시도간전출12017-11-23 13:33:11
32016강동구50세순이동102017-11-23 13:33:11
42010구로구70세시군구간전출7992017-11-23 13:33:11
52011강남구11세시도간전입272017-11-29 17:55:05
62017노원구73세시군구간전출1312017-11-23 13:33:11
72012종로구87세시도간전입-362017-11-29 17:55:05
82016광진구96세시도간전출1052017-11-23 13:33:11
92015종로구66세시도간전입2312017-11-23 13:33:11
년(year_yy)자치구(atdrc_nm)연령명(age_co_nm)이동구간명(mvmn_sctn_nm)인구변화량(popltn_chnge_qy)적재일시(ldadng_ct)
4902015종로구77세시군구간전출3762017-11-23 13:33:11
4912014종로구22세시군구간전입4712017-11-23 13:33:11
4922017강동구4세시군구간전입5312017-11-23 13:33:11
4932010강북구87세시군구내4962017-11-23 13:33:11
4942012노원구9세순이동1342017-11-23 13:33:11
4952010관악구59세시군구간전출5012017-11-29 17:55:05
4962016동대문구19세시군구내12017-11-23 13:33:11
4972015양천구14세시도간전입-422017-11-23 13:33:11
4982014양천구55세순이동02017-11-23 13:33:11
4992013송파구76세시군구간전입1712017-11-23 13:33:11