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-14978/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

Reproduction

Analysis started2024-04-29 17:00:50.673137
Analysis finished2024-04-29 17:00:54.674863
Duration4 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
20240422
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20240422
2nd row20240422
3rd row20240422
4th row20240422
5th row20240422

Common Values

ValueCountFrequency (%)
20240422 10000
100.0%

Length

2024-04-30T02:00:54.751026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T02:00:54.845052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20240422 10000
100.0%

시간대구분
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5802
Minimum18
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T02:00:54.933095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q119
median21
Q322
95-th percentile23
Maximum23
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6747682
Coefficient of variation (CV)0.081377643
Kurtosis-1.2259537
Mean20.5802
Median Absolute Deviation (MAD)1
Skewness-0.040703234
Sum205802
Variance2.8048484
MonotonicityNot monotonic
2024-04-30T02:00:55.104844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
21 1739
17.4%
20 1736
17.4%
23 1722
17.2%
22 1706
17.1%
19 1679
16.8%
18 1418
14.2%
ValueCountFrequency (%)
18 1418
14.2%
19 1679
16.8%
20 1736
17.4%
21 1739
17.4%
22 1706
17.1%
23 1722
17.2%
ValueCountFrequency (%)
23 1722
17.2%
22 1706
17.1%
21 1739
17.4%
20 1736
17.4%
19 1679
16.8%
18 1418
14.2%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct422
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11450534
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T02:00:55.241131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11170685
Q111305534
median11470540
Q311590660
95-th percentile11710650
Maximum11740700
Range630185
Interquartile range (IQR)285126

Descriptive statistics

Standard deviation175802.39
Coefficient of variation (CV)0.015353204
Kurtosis-1.1700616
Mean11450534
Median Absolute Deviation (MAD)150090
Skewness-0.048466284
Sum1.1450534 × 1011
Variance3.0906479 × 1010
MonotonicityNot monotonic
2024-04-30T02:00:55.373085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11380690 58
 
0.6%
11500540 54
 
0.5%
11350600 53
 
0.5%
11470550 52
 
0.5%
11290660 52
 
0.5%
11500615 52
 
0.5%
11530780 52
 
0.5%
11410720 52
 
0.5%
11350580 51
 
0.5%
11500630 50
 
0.5%
Other values (412) 9474
94.7%
ValueCountFrequency (%)
11110515 10
0.1%
11110530 9
0.1%
11110540 5
 
0.1%
11110550 10
0.1%
11110560 17
0.2%
11110570 11
0.1%
11110580 7
0.1%
11110615 5
 
0.1%
11110630 3
 
< 0.1%
11110640 6
 
0.1%
ValueCountFrequency (%)
11740700 29
0.3%
11740690 10
 
0.1%
11740685 36
0.4%
11740660 19
0.2%
11740650 26
0.3%
11740640 20
0.2%
11740620 25
0.2%
11740610 31
0.3%
11740600 29
0.3%
11740590 15
0.1%

집계구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7946
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1143685 × 1012
Minimum1.101053 × 1012
Maximum1.125074 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T02:00:55.517160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.101053 × 1012
5-th percentile1.103073 × 1012
Q11.10906 × 1012
median1.115054 × 1012
Q31.120072 × 1012
95-th percentile1.124077 × 1012
Maximum1.125074 × 1012
Range2.402102 × 1010
Interquartile range (IQR)1.1012011 × 1010

Descriptive statistics

Standard deviation6.7551621 × 109
Coefficient of variation (CV)0.0060618746
Kurtosis-1.1251125
Mean1.1143685 × 1012
Median Absolute Deviation (MAD)5.9909901 × 109
Skewness-0.12740102
Sum1.1143685 × 1016
Variance4.5632215 × 1019
MonotonicityNot monotonic
2024-04-30T02:00:55.669129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114073030011 4
 
< 0.1%
1124075010120 4
 
< 0.1%
1114073010801 4
 
< 0.1%
1108085060014 4
 
< 0.1%
1111056010301 4
 
< 0.1%
1124065010014 4
 
< 0.1%
1115053030001 4
 
< 0.1%
1123063020002 4
 
< 0.1%
1105067020011 4
 
< 0.1%
1111079040101 4
 
< 0.1%
Other values (7936) 9960
99.6%
ValueCountFrequency (%)
1101053010004 2
< 0.1%
1101053020002 1
 
< 0.1%
1101053020003 1
 
< 0.1%
1101053020301 1
 
< 0.1%
1101053020302 3
< 0.1%
1101053020303 1
 
< 0.1%
1101054010001 1
 
< 0.1%
1101054010003 1
 
< 0.1%
1101054010004 1
 
< 0.1%
1101054010005 2
< 0.1%
ValueCountFrequency (%)
1125074030012 1
 
< 0.1%
1125074030009 1
 
< 0.1%
1125074030002 1
 
< 0.1%
1125074023901 3
< 0.1%
1125074023601 1
 
< 0.1%
1125074021302 1
 
< 0.1%
1125074021301 1
 
< 0.1%
1125074020102 2
< 0.1%
1125074020101 2
< 0.1%
1125074020031 1
 
< 0.1%

총생활인구수
Real number (ℝ)

Distinct9673
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.735771
Minimum0
Maximum1403.088
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-30T02:00:55.806308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.199225
Q11.938575
median6.05575
Q316.93365
95-th percentile87.291805
Maximum1403.088
Range1403.088
Interquartile range (IQR)14.995075

Descriptive statistics

Standard deviation61.382963
Coefficient of variation (CV)2.8240527
Kurtosis129.5796
Mean21.735771
Median Absolute Deviation (MAD)5.02485
Skewness9.32804
Sum217357.71
Variance3767.8681
MonotonicityNot monotonic
2024-04-30T02:00:55.929412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
0.1%
0.0007 6
 
0.1%
0.0006 5
 
0.1%
0.0002 5
 
0.1%
0.0253 5
 
0.1%
0.0003 4
 
< 0.1%
0.1813 3
 
< 0.1%
0.0414 3
 
< 0.1%
0.0427 3
 
< 0.1%
1.5464 3
 
< 0.1%
Other values (9663) 9953
99.5%
ValueCountFrequency (%)
0.0 10
0.1%
0.0001 3
 
< 0.1%
0.0002 5
0.1%
0.0003 4
 
< 0.1%
0.0004 2
 
< 0.1%
0.0005 3
 
< 0.1%
0.0006 5
0.1%
0.0007 6
0.1%
0.0008 2
 
< 0.1%
0.0009 1
 
< 0.1%
ValueCountFrequency (%)
1403.088 1
< 0.1%
1328.0321 1
< 0.1%
1285.2905 1
< 0.1%
1025.0011 1
< 0.1%
991.1458 1
< 0.1%
969.6653 1
< 0.1%
963.2346 1
< 0.1%
927.0795 1
< 0.1%
843.6502 1
< 0.1%
810.4583 1
< 0.1%

중국인체류인구수
Text

Distinct4437
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-30T02:00:56.272899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length1
Mean length3.4765
Min length1

Characters and Unicode

Total characters34765
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

Unique4387 ?
Unique (%)43.9%

Sample

1st row*
2nd row*
3rd row*
4th row25.9595
5th row12.5863
ValueCountFrequency (%)
5513
55.1%
6.2935 3
 
< 0.1%
5.181 3
 
< 0.1%
4.5274 2
 
< 0.1%
6.2161 2
 
< 0.1%
10.9286 2
 
< 0.1%
11.2528 2
 
< 0.1%
5.6211 2
 
< 0.1%
4.4371 2
 
< 0.1%
7.7606 2
 
< 0.1%
Other values (4427) 4467
44.7%
2024-04-30T02:00:56.958043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 5513
15.9%
. 4487
12.9%
1 3389
9.7%
4 2703
7.8%
2 2654
7.6%
5 2586
7.4%
6 2434
7.0%
7 2408
6.9%
3 2400
6.9%
8 2283
6.6%
Other values (2) 3908
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24765
71.2%
Other Punctuation 10000
28.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3389
13.7%
4 2703
10.9%
2 2654
10.7%
5 2586
10.4%
6 2434
9.8%
7 2408
9.7%
3 2400
9.7%
8 2283
9.2%
9 2154
8.7%
0 1754
7.1%
Other Punctuation
ValueCountFrequency (%)
* 5513
55.1%
. 4487
44.9%

Most occurring scripts

ValueCountFrequency (%)
Common 34765
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 5513
15.9%
. 4487
12.9%
1 3389
9.7%
4 2703
7.8%
2 2654
7.6%
5 2586
7.4%
6 2434
7.0%
7 2408
6.9%
3 2400
6.9%
8 2283
6.6%
Other values (2) 3908
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 5513
15.9%
. 4487
12.9%
1 3389
9.7%
4 2703
7.8%
2 2654
7.6%
5 2586
7.4%
6 2434
7.0%
7 2408
6.9%
3 2400
6.9%
8 2283
6.6%
Other values (2) 3908
11.2%

기타체류인구수
Text

Distinct3290
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-30T02:00:57.247612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length1
Mean length2.7907
Min length1

Characters and Unicode

Total characters27907
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

Unique3249 ?
Unique (%)32.5%

Sample

1st row*
2nd row*
3rd row*
4th row12.1771
5th row15.1762
ValueCountFrequency (%)
6671
66.7%
7.0773 2
 
< 0.1%
5.8136 2
 
< 0.1%
6.6988 2
 
< 0.1%
4.157 2
 
< 0.1%
9.0719 2
 
< 0.1%
7.2972 2
 
< 0.1%
5.3841 2
 
< 0.1%
4.9678 2
 
< 0.1%
4.7419 2
 
< 0.1%
Other values (3280) 3311
33.1%
2024-04-30T02:00:57.662863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 6671
23.9%
. 3329
11.9%
1 2462
 
8.8%
4 2032
 
7.3%
5 1973
 
7.1%
2 1856
 
6.7%
6 1830
 
6.6%
7 1705
 
6.1%
8 1630
 
5.8%
3 1615
 
5.8%
Other values (2) 2804
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17907
64.2%
Other Punctuation 10000
35.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2462
13.7%
4 2032
11.3%
5 1973
11.0%
2 1856
10.4%
6 1830
10.2%
7 1705
9.5%
8 1630
9.1%
3 1615
9.0%
9 1585
8.9%
0 1219
6.8%
Other Punctuation
ValueCountFrequency (%)
* 6671
66.7%
. 3329
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 27907
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 6671
23.9%
. 3329
11.9%
1 2462
 
8.8%
4 2032
 
7.3%
5 1973
 
7.1%
2 1856
 
6.7%
6 1830
 
6.6%
7 1705
 
6.1%
8 1630
 
5.8%
3 1615
 
5.8%
Other values (2) 2804
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27907
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 6671
23.9%
. 3329
11.9%
1 2462
 
8.8%
4 2032
 
7.3%
5 1973
 
7.1%
2 1856
 
6.7%
6 1830
 
6.6%
7 1705
 
6.1%
8 1630
 
5.8%
3 1615
 
5.8%
Other values (2) 2804
10.0%

Interactions

2024-04-30T02:00:54.024050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:52.777680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.244043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.667012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:54.123855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:52.919487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.359678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.744589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:54.221845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.030278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.480443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.828305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:54.331712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.124451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.576769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T02:00:53.918532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T02:00:57.748635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간대구분행정동코드집계구코드총생활인구수
시간대구분1.0000.1450.1440.000
행정동코드0.1451.0000.9960.104
집계구코드0.1440.9961.0000.100
총생활인구수0.0000.1040.1001.000
2024-04-30T02:00:57.830580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간대구분행정동코드집계구코드총생활인구수
시간대구분1.0000.0710.0710.032
행정동코드0.0711.0000.999-0.055
집계구코드0.0710.9991.000-0.052
총생활인구수0.032-0.055-0.0521.000

Missing values

2024-04-30T02:00:54.482542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T02:00:54.605967image/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
4639820240422211159056011200560300083.1544**
3987820240422211135061911110600110070.5732**
3085420240422221165058011220590302012.8636**
53453202404222011230536110608110000538.136725.959512.1771
2519202404222311230730110607302000127.762612.586315.1762
6199120240422201153074011170720604100.2413**
1961920240422221126054011070540200034.061**
953620240422231150060411160650201061.8965**
790342024042219115305501117055010005164.2212160.7138*
48599202404222111680531112305303000123.22795.516417.7115
기준일ID시간대구분행정동코드집계구코드총생활인구수중국인체류인구수기타체류인구수
1569320240422231171063211240670200110.7582**
8392020240422191168070011230760101040.6858**
7292320240422191130553511090700100057.15045.2236*
18395202404222211215740110505501000224.895714.206810.6888
529082024042220112157301105054030011100.370939.718860.6522
82757202404221911650590112206103000426.47835.18121.2974
99761202404221811650531112205402010115.85735.274310.5831
24050202404222211410565111307301000642.399432.053510.3456
97620202404221811560605111907603030414.216210.4756*
26022024042223112307401106089060002547.0277309.2446237.7831