Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory722.7 KiB
Average record size in memory74.0 B

Variable types

Categorical4
Numeric2
Text1
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15964/S/1/datasetView.do

Alerts

모델번호 has constant value ""Constant
지역 is highly overall correlated with 시리얼 and 2 other fieldsHigh correlation
자치구 is highly overall correlated with 시리얼 and 2 other fieldsHigh correlation
행정동 is highly overall correlated with 시리얼 and 2 other fieldsHigh correlation
시리얼 is highly overall correlated with 지역 and 2 other fieldsHigh correlation
방문자수 has 1542 (15.4%) zerosZeros

Reproduction

Analysis started2024-05-11 06:51:49.463756
Analysis finished2024-05-11 06:51:51.166316
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

모델번호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
SDOT001
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSDOT001
2nd rowSDOT001
3rd rowSDOT001
4th rowSDOT001
5th rowSDOT001

Common Values

ValueCountFrequency (%)
SDOT001 10000
100.0%

Length

2024-05-11T15:51:51.289721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:51.441122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sdot001 10000
100.0%

시리얼
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4030.3849
Minimum4001
Maximum4064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:51:51.589616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4001
5-th percentile4004
Q14017
median4030
Q34043
95-th percentile4061
Maximum4064
Range63
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.19862
Coefficient of variation (CV)0.0042672401
Kurtosis-0.94302193
Mean4030.3849
Median Absolute Deviation (MAD)13
Skewness0.11146715
Sum40303849
Variance295.79253
MonotonicityNot monotonic
2024-05-11T15:51:51.822854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4002 231
 
2.3%
4062 212
 
2.1%
4008 206
 
2.1%
4047 206
 
2.1%
4020 206
 
2.1%
4039 202
 
2.0%
4034 200
 
2.0%
4040 199
 
2.0%
4024 198
 
2.0%
4001 198
 
2.0%
Other values (43) 7942
79.4%
ValueCountFrequency (%)
4001 198
2.0%
4002 231
2.3%
4004 171
1.7%
4005 195
1.9%
4006 186
1.9%
4007 189
1.9%
4008 206
2.1%
4009 186
1.9%
4010 185
1.8%
4013 192
1.9%
ValueCountFrequency (%)
4064 189
1.9%
4062 212
2.1%
4061 191
1.9%
4060 193
1.9%
4054 189
1.9%
4053 174
1.7%
4051 190
1.9%
4050 192
1.9%
4049 179
1.8%
4048 195
1.9%

측정시간
Text

Distinct5018
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:51:52.167238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2352 ?
Unique (%)23.5%

Sample

1st row2024-01-18_04:12:00
2nd row2024-01-17_05:11:00
3rd row2024-01-21_11:17:00
4th row2024-01-21_14:41:00
5th row2024-01-20_08:21:00
ValueCountFrequency (%)
2024-01-15_14:12:00 15
 
0.1%
2024-01-16_22:30:00 10
 
0.1%
2024-01-18_16:20:00 9
 
0.1%
2024-01-20_06:20:00 8
 
0.1%
2024-01-15_19:30:00 8
 
0.1%
2024-01-15_23:40:00 8
 
0.1%
2024-01-16_15:20:00 7
 
0.1%
2024-01-16_04:20:00 7
 
0.1%
2024-01-21_13:00:00 7
 
0.1%
2024-01-17_20:26:00 7
 
0.1%
Other values (5008) 9914
99.1%
2024-05-11T15:51:52.671642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 51133
26.9%
2 28158
14.8%
1 26663
14.0%
- 20000
 
10.5%
: 20000
 
10.5%
4 12924
 
6.8%
_ 10000
 
5.3%
5 5549
 
2.9%
6 4369
 
2.3%
7 3517
 
1.9%
Other values (3) 7687
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Connector Punctuation 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51133
36.5%
2 28158
20.1%
1 26663
19.0%
4 12924
 
9.2%
5 5549
 
4.0%
6 4369
 
3.1%
7 3517
 
2.5%
3 3147
 
2.2%
9 2285
 
1.6%
8 2255
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51133
26.9%
2 28158
14.8%
1 26663
14.0%
- 20000
 
10.5%
: 20000
 
10.5%
4 12924
 
6.8%
_ 10000
 
5.3%
5 5549
 
2.9%
6 4369
 
2.3%
7 3517
 
1.9%
Other values (3) 7687
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51133
26.9%
2 28158
14.8%
1 26663
14.0%
- 20000
 
10.5%
: 20000
 
10.5%
4 12924
 
6.8%
_ 10000
 
5.3%
5 5549
 
2.9%
6 4369
 
2.3%
7 3517
 
1.9%
Other values (3) 7687
 
4.0%

지역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
main_street
6412 
parks
2060 
traditional_markets
972 
public_facilities
 
192
commercial_area
 
190

Length

Max length19
Median length11
Mean length10.8198
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowparks
2nd rowparks
3rd rowmain_street
4th rowcommercial_area
5th rowmain_street

Common Values

ValueCountFrequency (%)
main_street 6412
64.1%
parks 2060
 
20.6%
traditional_markets 972
 
9.7%
public_facilities 192
 
1.9%
commercial_area 190
 
1.9%
residential_area 174
 
1.7%

Length

2024-05-11T15:51:52.883130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:53.078407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
main_street 6412
64.1%
parks 2060
 
20.6%
traditional_markets 972
 
9.7%
public_facilities 192
 
1.9%
commercial_area 190
 
1.9%
residential_area 174
 
1.7%

자치구
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Seoul_Grand_Park
1149 
Jung-gu
1089 
Jongno-gu
981 
Gangnam-gu
963 
Gangseo-gu
943 
Other values (13)
4875 

Length

Max length16
Median length11
Mean length10.4865
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeoul_Grand_Park
2nd rowSeoul_Grand_Park
3rd rowGangdong-gu
4th rowGangdong-gu
5th rowJongno-gu

Common Values

ValueCountFrequency (%)
Seoul_Grand_Park 1149
11.5%
Jung-gu 1089
10.9%
Jongno-gu 981
9.8%
Gangnam-gu 963
9.6%
Gangseo-gu 943
9.4%
Seocho-gu 795
8.0%
Gangdong-gu 736
7.4%
Gwangjin-gu 575
 
5.8%
Seodaemun-gu 567
 
5.7%
Gangbuk-gu 364
 
3.6%
Other values (8) 1838
18.4%

Length

2024-05-11T15:51:53.282719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seoul_grand_park 1149
11.5%
jung-gu 1089
10.9%
jongno-gu 981
9.8%
gangnam-gu 963
9.6%
gangseo-gu 943
9.4%
seocho-gu 795
8.0%
gangdong-gu 736
7.4%
gwangjin-gu 575
 
5.8%
seodaemun-gu 567
 
5.7%
gangbuk-gu 364
 
3.6%
Other values (8) 1838
18.4%

행정동
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Ihwa-dong
 
395
Daechi4-dong
 
392
Gahoe-dong
 
384
Buam-dong
 
379
Hongje3-dong
 
375
Other values (42)
8075 

Length

Max length16
Median length14
Mean length12.2225
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvalet_parking1
2nd rowwomen_parking1
3rd rowSeongnae1-dong
4th rowBuam-dong
5th rowSamcheong-dong

Common Values

ValueCountFrequency (%)
Ihwa-dong 395
 
4.0%
Daechi4-dong 392
 
3.9%
Gahoe-dong 384
 
3.8%
Buam-dong 379
 
3.8%
Hongje3-dong 375
 
3.8%
Myeong-dong 355
 
3.5%
Seocho4-dong 231
 
2.3%
meeting_bridge1 212
 
2.1%
Hwayang-dong 206
 
2.1%
Itaewon2-dong 206
 
2.1%
Other values (37) 6865
68.7%

Length

2024-05-11T15:51:53.486764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ihwa-dong 395
 
4.0%
daechi4-dong 392
 
3.9%
gahoe-dong 384
 
3.8%
buam-dong 379
 
3.8%
hongje3-dong 375
 
3.8%
myeong-dong 355
 
3.5%
seocho4-dong 231
 
2.3%
meeting_bridge1 212
 
2.1%
hwayang-dong 206
 
2.1%
itaewon2-dong 206
 
2.1%
Other values (37) 6865
68.7%

방문자수
Real number (ℝ)

ZEROS 

Distinct383
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.6239
Minimum0
Maximum550
Zeros1542
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:51:53.715647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q344
95-th percentile168.05
Maximum550
Range550
Interquartile range (IQR)42

Descriptive statistics

Standard deviation64.654031
Coefficient of variation (CV)1.7184298
Kurtosis15.443914
Mean37.6239
Median Absolute Deviation (MAD)13
Skewness3.4681217
Sum376239
Variance4180.1437
MonotonicityNot monotonic
2024-05-11T15:51:54.269538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1542
 
15.4%
1 571
 
5.7%
2 469
 
4.7%
3 366
 
3.7%
4 301
 
3.0%
5 272
 
2.7%
6 251
 
2.5%
7 222
 
2.2%
9 198
 
2.0%
8 195
 
1.9%
Other values (373) 5613
56.1%
ValueCountFrequency (%)
0 1542
15.4%
1 571
 
5.7%
2 469
 
4.7%
3 366
 
3.7%
4 301
 
3.0%
5 272
 
2.7%
6 251
 
2.5%
7 222
 
2.2%
8 195
 
1.9%
9 198
 
2.0%
ValueCountFrequency (%)
550 1
< 0.1%
538 1
< 0.1%
537 2
< 0.1%
535 1
< 0.1%
530 1
< 0.1%
523 2
< 0.1%
519 1
< 0.1%
514 1
< 0.1%
512 1
< 0.1%
510 1
< 0.1%

등록일
Date

Distinct2888
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-01-15 00:28:00
Maximum2024-01-21 23:58:03
2024-05-11T15:51:54.493462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:54.729326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-11T15:51:50.495747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:50.229406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:50.646331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:50.361295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:51:54.881105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼지역자치구행정동방문자수
시리얼1.0000.7610.9620.9980.490
지역0.7611.0000.9230.9940.216
자치구0.9620.9231.0001.0000.449
행정동0.9980.9941.0001.0000.680
방문자수0.4900.2160.4490.6801.000
2024-05-11T15:51:55.025881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역자치구행정동
지역1.0000.6440.945
자치구0.6441.0000.992
행정동0.9450.9921.000
2024-05-11T15:51:55.159215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼방문자수지역자치구행정동
시리얼1.000-0.3080.5350.8200.979
방문자수-0.3081.0000.1140.1870.304
지역0.5350.1141.0000.6440.945
자치구0.8200.1870.6441.0000.992
행정동0.9790.3040.9450.9921.000

Missing values

2024-05-11T15:51:50.877180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:51:51.069662image/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

모델번호시리얼측정시간지역자치구행정동방문자수등록일
23331SDOT00140452024-01-18_04:12:00parksSeoul_Grand_Parkvalet_parking102024-01-18 04:28:01
16186SDOT00140602024-01-17_05:11:00parksSeoul_Grand_Parkwomen_parking102024-01-17 05:28:00
47390SDOT00140072024-01-21_11:17:00main_streetGangdong-guSeongnae1-dong312024-01-21 11:28:01
48487SDOT00140512024-01-21_14:41:00commercial_areaGangdong-guBuam-dong352024-01-21 14:58:02
39644SDOT00140392024-01-20_08:21:00main_streetJongno-guSamcheong-dong42024-01-20 08:38:01
10089SDOT00140452024-01-16_08:52:00parksSeoul_Grand_Parkvalet_parking152024-01-16 09:08:03
39319SDOT00140152024-01-20_07:20:00main_streetJung-guGwanghui-dong102024-01-20 07:38:00
51068SDOT00140012024-01-21_22:50:00main_streetSeocho-guSeocho3-dong92024-01-21 23:08:03
22011SDOT00140462024-01-17_23:20:00main_streetGangnam-guSinsa-dong162024-01-17 23:38:03
42719SDOT00140292024-01-20_18:17:00traditional_marketsGangbuk-guSuyu3-dong742024-01-20 18:28:00
모델번호시리얼측정시간지역자치구행정동방문자수등록일
19930SDOT00140542024-01-17_16:50:00parksSeoul_Grand_Parkmeeting_bridge202024-01-17 17:08:02
45841SDOT00140092024-01-21_05:36:00main_streetGangnam-guDaechi4-dong72024-01-21 05:48:14
46419SDOT00140012024-01-21_08:00:00main_streetSeocho-guSeocho3-dong22024-01-21 08:18:02
24991SDOT00140492024-01-18_09:22:00traditional_marketsSeodaemun-guHongje3-dong152024-01-18 09:38:03
5441SDOT00140362024-01-15_17:50:00main_streetYangcheon-guSinjeong4-dong822024-01-15 18:08:01
37088SDOT00140252024-01-19_23:57:00main_streetGangseo-guGonghang-dong72024-01-20 00:08:03
13003SDOT00140062024-01-16_18:10:00parksGangdong-guAmsa3-dong02024-01-16 18:28:02
32086SDOT00140162024-01-19_08:15:00main_streetJung-guMyeong-dong1152024-01-19 08:28:01
13915SDOT00140042024-01-16_21:06:00main_streetSeocho-guBanpo3-dong562024-01-16 21:18:02
14773SDOT00140052024-01-16_23:52:00main_streetSeocho-guYangjae1-dong112024-01-17 00:08:08