Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

Categorical1
Numeric4
Text2

Dataset

Description기준일ID,시간대구분,행정동코드,집계구코드,총생활인구수,중국인체류인구수,기타체류인구수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-14980/S/1/datasetView.do

Alerts

기준일ID has constant value ""Constant
행정동코드 is highly overall correlated with 집계구코드High correlation
집계구코드 is highly overall correlated with 행정동코드High correlation
총생활인구수 is highly skewed (γ1 = 52.85403413)Skewed

Reproduction

Analysis started2024-05-11 07:01:10.680837
Analysis finished2024-05-11 07:01:20.264508
Duration9.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일ID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20240506
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20240506
2nd row20240506
3rd row20240506
4th row20240506
5th row20240506

Common Values

ValueCountFrequency (%)
20240506 10000
100.0%

Length

2024-05-11T07:01:20.566518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:01:20.949565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20240506 10000
100.0%

시간대구분
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.4314
Minimum15
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:01:21.341897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16
Q117
median19
Q321
95-th percentile23
Maximum23
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3445349
Coefficient of variation (CV)0.12065702
Kurtosis-1.1864289
Mean19.4314
Median Absolute Deviation (MAD)2
Skewness-0.024091716
Sum194314
Variance5.4968437
MonotonicityNot monotonic
2024-05-11T07:01:21.969225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
19 1322
13.2%
23 1248
12.5%
22 1225
12.2%
18 1221
12.2%
21 1218
12.2%
17 1207
12.1%
16 1197
12.0%
20 1177
11.8%
15 185
 
1.8%
ValueCountFrequency (%)
15 185
 
1.8%
16 1197
12.0%
17 1207
12.1%
18 1221
12.2%
19 1322
13.2%
20 1177
11.8%
21 1218
12.2%
22 1225
12.2%
23 1248
12.5%
ValueCountFrequency (%)
23 1248
12.5%
22 1225
12.2%
21 1218
12.2%
20 1177
11.8%
19 1322
13.2%
18 1221
12.2%
17 1207
12.1%
16 1197
12.0%
15 185
 
1.8%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct423
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11451469
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:01:22.685907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11170560
Q111290705
median11470550
Q311620595
95-th percentile11710670
Maximum11740700
Range630185
Interquartile range (IQR)329890

Descriptive statistics

Standard deviation182676.7
Coefficient of variation (CV)0.015952251
Kurtosis-1.2235904
Mean11451469
Median Absolute Deviation (MAD)164930
Skewness-0.099992194
Sum1.1451469 × 1011
Variance3.3370778 × 1010
MonotonicityNot monotonic
2024-05-11T07:01:23.265131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11590530 66
 
0.7%
11500535 57
 
0.6%
11680610 54
 
0.5%
11545670 53
 
0.5%
11350595 50
 
0.5%
11440565 49
 
0.5%
11350600 48
 
0.5%
11500540 47
 
0.5%
11650560 47
 
0.5%
11650540 46
 
0.5%
Other values (413) 9483
94.8%
ValueCountFrequency (%)
11110515 14
0.1%
11110530 16
0.2%
11110540 5
 
0.1%
11110550 11
0.1%
11110560 20
0.2%
11110570 9
0.1%
11110580 8
 
0.1%
11110600 3
 
< 0.1%
11110615 16
0.2%
11110630 3
 
< 0.1%
ValueCountFrequency (%)
11740700 27
0.3%
11740690 4
 
< 0.1%
11740685 41
0.4%
11740660 33
0.3%
11740650 22
0.2%
11740640 19
0.2%
11740620 14
 
0.1%
11740610 35
0.4%
11740600 28
0.3%
11740590 29
0.3%

집계구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7231
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.114378 × 1012
Minimum1.101053 × 1012
Maximum1.125074 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:01:24.028321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.101053 × 1012
5-th percentile1.103059 × 1012
Q11.108081 × 1012
median1.115058 × 1012
Q31.121063 × 1012
95-th percentile1.124078 × 1012
Maximum1.125074 × 1012
Range2.402102 × 1010
Interquartile range (IQR)1.298201 × 1010

Descriptive statistics

Standard deviation7.0374806 × 109
Coefficient of variation (CV)0.006315165
Kurtosis-1.1849868
Mean1.114378 × 1012
Median Absolute Deviation (MAD)6.011015 × 109
Skewness-0.17307688
Sum1.114378 × 1016
Variance4.9526134 × 1019
MonotonicityNot monotonic
2024-05-11T07:01:24.598126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103057010011 6
 
0.1%
1123080040004 5
 
0.1%
1120055020022 5
 
0.1%
1104070020005 5
 
0.1%
1109074010101 5
 
0.1%
1109072030005 4
 
< 0.1%
1108062020001 4
 
< 0.1%
1114072020011 4
 
< 0.1%
1123065031404 4
 
< 0.1%
1125073010015 4
 
< 0.1%
Other values (7221) 9954
99.5%
ValueCountFrequency (%)
1101053010002 2
< 0.1%
1101053010003 1
 
< 0.1%
1101053010004 2
< 0.1%
1101053010006 2
< 0.1%
1101053020001 2
< 0.1%
1101053020301 2
< 0.1%
1101053020304 3
< 0.1%
1101053020701 2
< 0.1%
1101054010001 1
 
< 0.1%
1101054010002 1
 
< 0.1%
ValueCountFrequency (%)
1125074030401 1
< 0.1%
1125074030011 2
< 0.1%
1125074030010 1
< 0.1%
1125074030006 1
< 0.1%
1125074030005 2
< 0.1%
1125074030003 1
< 0.1%
1125074030002 1
< 0.1%
1125074023601 1
< 0.1%
1125074021303 1
< 0.1%
1125074021301 1
< 0.1%

총생활인구수
Real number (ℝ)

SKEWED 

Distinct8603
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.274785
Minimum0
Maximum13931.446
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:01:25.249147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.013195
Q10.357675
median1.23035
Q33.66445
95-th percentile27.802345
Maximum13931.446
Range13931.446
Interquartile range (IQR)3.306775

Descriptive statistics

Standard deviation178.75105
Coefficient of variation (CV)12.522154
Kurtosis3773.4295
Mean14.274785
Median Absolute Deviation (MAD)1.1011
Skewness52.854034
Sum142747.85
Variance31951.937
MonotonicityNot monotonic
2024-05-11T07:01:25.749135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
 
0.2%
0.0003 17
 
0.2%
0.0008 16
 
0.2%
0.0004 13
 
0.1%
0.0005 11
 
0.1%
0.0001 11
 
0.1%
0.0037 10
 
0.1%
0.0006 10
 
0.1%
0.0002 9
 
0.1%
0.0007 9
 
0.1%
Other values (8593) 9869
98.7%
ValueCountFrequency (%)
0.0 25
0.2%
0.0001 11
0.1%
0.0002 9
 
0.1%
0.0003 17
0.2%
0.0004 13
0.1%
0.0005 11
0.1%
0.0006 10
 
0.1%
0.0007 9
 
0.1%
0.0008 16
0.2%
0.0009 7
 
0.1%
ValueCountFrequency (%)
13931.4456 1
< 0.1%
3970.2449 1
< 0.1%
3633.5607 1
< 0.1%
3585.0964 1
< 0.1%
3542.2029 1
< 0.1%
3315.2592 1
< 0.1%
2530.348 1
< 0.1%
2256.8705 1
< 0.1%
2119.2162 1
< 0.1%
2092.8546 1
< 0.1%

중국인체류인구수
Text

Distinct1171
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:01:26.676517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length1
Mean length1.6394
Min length1

Characters and Unicode

Total characters16394
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1161 ?
Unique (%)11.6%

Sample

1st row31.2468
2nd row*
3rd row*
4th row*
5th row*
ValueCountFrequency (%)
8821
88.2%
4.1092 2
 
< 0.1%
6.3956 2
 
< 0.1%
4.1977 2
 
< 0.1%
7.2047 2
 
< 0.1%
5.7129 2
 
< 0.1%
4.135 2
 
< 0.1%
6.727 2
 
< 0.1%
5.4314 2
 
< 0.1%
7.4562 2
 
< 0.1%
Other values (1161) 1161
 
11.6%
2024-05-11T07:01:28.365622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 8821
53.8%
. 1179
 
7.2%
1 865
 
5.3%
4 765
 
4.7%
5 690
 
4.2%
6 674
 
4.1%
2 669
 
4.1%
3 594
 
3.6%
7 580
 
3.5%
8 569
 
3.5%
Other values (2) 988
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 10000
61.0%
Decimal Number 6394
39.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 865
13.5%
4 765
12.0%
5 690
10.8%
6 674
10.5%
2 669
10.5%
3 594
9.3%
7 580
9.1%
8 569
8.9%
9 558
8.7%
0 430
6.7%
Other Punctuation
ValueCountFrequency (%)
* 8821
88.2%
. 1179
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 16394
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8821
53.8%
. 1179
 
7.2%
1 865
 
5.3%
4 765
 
4.7%
5 690
 
4.2%
6 674
 
4.1%
2 669
 
4.1%
3 594
 
3.6%
7 580
 
3.5%
8 569
 
3.5%
Other values (2) 988
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8821
53.8%
. 1179
 
7.2%
1 865
 
5.3%
4 765
 
4.7%
5 690
 
4.2%
6 674
 
4.1%
2 669
 
4.1%
3 594
 
3.6%
7 580
 
3.5%
8 569
 
3.5%
Other values (2) 988
 
6.0%

기타체류인구수
Text

Distinct1555
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:01:29.868067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length1.8549
Min length1

Characters and Unicode

Total characters18549
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1546 ?
Unique (%)15.5%

Sample

1st row54.8647
2nd row*
3rd row15.2969
4th row*
5th row*
ValueCountFrequency (%)
8438
84.4%
4.2018 2
 
< 0.1%
4.0005 2
 
< 0.1%
4.9365 2
 
< 0.1%
4.1389 2
 
< 0.1%
4.5015 2
 
< 0.1%
4.7264 2
 
< 0.1%
6.2599 2
 
< 0.1%
4.8291 2
 
< 0.1%
6.0044 1
 
< 0.1%
Other values (1545) 1545
 
15.4%
2024-05-11T07:01:31.783752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 8438
45.5%
. 1562
 
8.4%
1 1165
 
6.3%
4 1014
 
5.5%
2 901
 
4.9%
5 892
 
4.8%
6 823
 
4.4%
7 821
 
4.4%
3 816
 
4.4%
8 774
 
4.2%
Other values (2) 1343
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 10000
53.9%
Decimal Number 8549
46.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1165
13.6%
4 1014
11.9%
2 901
10.5%
5 892
10.4%
6 823
9.6%
7 821
9.6%
3 816
9.5%
8 774
9.1%
9 712
8.3%
0 631
7.4%
Other Punctuation
ValueCountFrequency (%)
* 8438
84.4%
. 1562
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 18549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 8438
45.5%
. 1562
 
8.4%
1 1165
 
6.3%
4 1014
 
5.5%
2 901
 
4.9%
5 892
 
4.8%
6 823
 
4.4%
7 821
 
4.4%
3 816
 
4.4%
8 774
 
4.2%
Other values (2) 1343
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 8438
45.5%
. 1562
 
8.4%
1 1165
 
6.3%
4 1014
 
5.5%
2 901
 
4.9%
5 892
 
4.8%
6 823
 
4.4%
7 821
 
4.4%
3 816
 
4.4%
8 774
 
4.2%
Other values (2) 1343
 
7.2%

Interactions

2024-05-11T07:01:17.322856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:12.578654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:14.215409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:15.853255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:17.807457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:13.016953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:14.652685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:16.185519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:18.257782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:13.389425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:15.067650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:16.736417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:18.814673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:13.787342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:15.439214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:01:17.013296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T07:01:32.351184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간대구분행정동코드집계구코드총생활인구수
시간대구분1.0000.2210.2260.041
행정동코드0.2211.0000.9960.071
집계구코드0.2260.9961.0000.071
총생활인구수0.0410.0710.0711.000
2024-05-11T07:01:32.967278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간대구분행정동코드집계구코드총생활인구수
시간대구분1.0000.0420.0410.044
행정동코드0.0421.0000.999-0.028
집계구코드0.0410.9991.000-0.026
총생활인구수0.044-0.028-0.0261.000

Missing values

2024-05-11T07:01:19.361249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T07:01:20.039225image/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

기준일ID시간대구분행정동코드집계구코드총생활인구수중국인체류인구수기타체류인구수
35309202405062111680521112305203000686.111631.246854.8647
6318920240506181121574011050550100063.5529**
24274202405062211710680112408001010615.2968*15.2969
7030120240506181159055011200550200010.0094**
2511020240506211111065011010730100082.0036**
5220720240506191126066011070680200171.0499**
5837320240506191159064011200650200010.826**
9775320240506161174053011250530200010.0359**
3640420240506211171060011240630200252.111**
2770120240506211129060011080600300020.4017**
기준일ID시간대구분행정동코드집계구코드총생활인구수중국인체류인구수기타체류인구수
49719202405061911110515110107201000296.139613.244882.8947
7287920240506181171056611240590301041.809**
1128420240506231171056211240580200150.2584**
3283320240506211154568011180580103070.0407**
4773920240506201168056511230780519010.9562**
5540820240506191147051011150510101012.3048**
8582920240506171174061011250730100210.718**
107720240506231120064511040730306031.6672**
8662020240506161117056011030720200027.6434*4.7854
9085020240506161141056511130730204012.6226**