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 1571 (15.7%) zerosZeros

Reproduction

Analysis started2024-05-11 06:51:30.424377
Analysis finished2024-05-11 06:51:34.338207
Duration3.91 seconds
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

2024-05-11T15:51:34.469899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:34.657200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sdot001 10000
100.0%

시리얼
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4030.3438
Minimum4001
Maximum4064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:51:34.849610image/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 deviation17.145798
Coefficient of variation (CV)0.0042541776
Kurtosis-0.95161416
Mean4030.3438
Median Absolute Deviation (MAD)13
Skewness0.11741299
Sum40303438
Variance293.9784
MonotonicityNot monotonic
2024-05-11T15:51:35.113618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4039 218
 
2.2%
4009 211
 
2.1%
4041 207
 
2.1%
4046 206
 
2.1%
4028 205
 
2.1%
4010 205
 
2.1%
4002 205
 
2.1%
4064 204
 
2.0%
4026 204
 
2.0%
4035 202
 
2.0%
Other values (44) 7933
79.3%
ValueCountFrequency (%)
4001 178
1.8%
4002 205
2.1%
4004 187
1.9%
4005 190
1.9%
4006 193
1.9%
4007 197
2.0%
4008 200
2.0%
4009 211
2.1%
4010 205
2.1%
4013 173
1.7%
ValueCountFrequency (%)
4064 204
2.0%
4063 28
 
0.3%
4062 181
1.8%
4061 166
1.7%
4060 164
1.6%
4054 196
2.0%
4053 198
2.0%
4051 181
1.8%
4050 185
1.8%
4049 201
2.0%

측정시간
Text

Distinct5084
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T15:51:35.483251image/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

Unique2395 ?
Unique (%)23.9%

Sample

1st row2023-12-31_08:27:00
2nd row2023-12-30_08:15:00
3rd row2023-12-28_20:37:00
4th row2023-12-29_08:45:00
5th row2023-12-29_01:14:00
ValueCountFrequency (%)
2023-12-26_10:30:00 9
 
0.1%
2023-12-27_17:10:00 8
 
0.1%
2023-12-31_09:20:00 8
 
0.1%
2023-12-31_05:30:00 8
 
0.1%
2023-12-29_12:00:00 8
 
0.1%
2023-12-26_21:20:00 8
 
0.1%
2023-12-27_16:10:00 8
 
0.1%
2023-12-29_18:30:00 7
 
0.1%
2023-12-29_05:30:00 7
 
0.1%
2023-12-30_15:30:00 7
 
0.1%
Other values (5074) 9922
99.2%
2024-05-11T15:51:35.984641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 42411
22.3%
0 41632
21.9%
- 20000
10.5%
: 20000
10.5%
1 19261
10.1%
3 15935
 
8.4%
_ 10000
 
5.3%
5 5545
 
2.9%
6 4260
 
2.2%
7 3534
 
1.9%
Other values (3) 7422
 
3.9%

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 (%)
2 42411
30.3%
0 41632
29.7%
1 19261
13.8%
3 15935
 
11.4%
5 5545
 
4.0%
6 4260
 
3.0%
7 3534
 
2.5%
4 2877
 
2.1%
9 2306
 
1.6%
8 2239
 
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 (%)
2 42411
22.3%
0 41632
21.9%
- 20000
10.5%
: 20000
10.5%
1 19261
10.1%
3 15935
 
8.4%
_ 10000
 
5.3%
5 5545
 
2.9%
6 4260
 
2.2%
7 3534
 
1.9%
Other values (3) 7422
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 42411
22.3%
0 41632
21.9%
- 20000
10.5%
: 20000
10.5%
1 19261
10.1%
3 15935
 
8.4%
_ 10000
 
5.3%
5 5545
 
2.9%
6 4260
 
2.2%
7 3534
 
1.9%
Other values (3) 7422
 
3.9%

지역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
main_street
6458 
parks
2072 
traditional_markets
906 
residential_area
 
198
public_facilities
 
185

Length

Max length19
Median length11
Mean length10.764
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
main_street 6458
64.6%
parks 2072
 
20.7%
traditional_markets 906
 
9.1%
residential_area 198
 
2.0%
public_facilities 185
 
1.8%
commercial_area 181
 
1.8%

Length

2024-05-11T15:51:36.194454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:51:36.380124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
main_street 6458
64.6%
parks 2072
 
20.7%
traditional_markets 906
 
9.1%
residential_area 198
 
2.0%
public_facilities 185
 
1.8%
commercial_area 181
 
1.8%

자치구
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Seoul_Grand_Park
1128 
Jung-gu
1119 
Gangnam-gu
995 
Jongno-gu
974 
Gangseo-gu
919 
Other values (13)
4865 

Length

Max length16
Median length11
Mean length10.4831
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeongdong-gu
2nd rowGwanak-gu
3rd rowJongno-gu
4th rowSeoul_Grand_Park
5th rowGwangjin-gu

Common Values

ValueCountFrequency (%)
Seoul_Grand_Park 1128
11.3%
Jung-gu 1119
11.2%
Gangnam-gu 995
10.0%
Jongno-gu 974
9.7%
Gangseo-gu 919
9.2%
Seocho-gu 760
7.6%
Gangdong-gu 735
7.3%
Gwangjin-gu 582
 
5.8%
Seodaemun-gu 572
 
5.7%
Eunpyeong-gu 390
 
3.9%
Other values (8) 1826
18.3%

Length

2024-05-11T15:51:36.582772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seoul_grand_park 1128
11.3%
jung-gu 1119
11.2%
gangnam-gu 995
10.0%
jongno-gu 974
9.7%
gangseo-gu 919
9.2%
seocho-gu 760
7.6%
gangdong-gu 735
7.3%
gwangjin-gu 582
 
5.8%
seodaemun-gu 572
 
5.7%
eunpyeong-gu 390
 
3.9%
Other values (8) 1826
18.3%

행정동
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Daechi4-dong
 
411
Hongje3-dong
 
387
Ihwa-dong
 
385
Gahoe-dong
 
371
Buam-dong
 
371
Other values (43)
8075 

Length

Max length16
Median length14
Mean length12.2121
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeongsu1ga2-dong
2nd rowNakseongdae-dong
3rd rowIhwa-dong
4th rowmeeting_bridge1
5th rowGuui1-dong

Common Values

ValueCountFrequency (%)
Daechi4-dong 411
 
4.1%
Hongje3-dong 387
 
3.9%
Ihwa-dong 385
 
3.9%
Gahoe-dong 371
 
3.7%
Buam-dong 371
 
3.7%
Myeong-dong 364
 
3.6%
Samcheong-dong 218
 
2.2%
Sinsa-dong 206
 
2.1%
Chang1-dong 205
 
2.1%
Yeoksam1-dong 205
 
2.1%
Other values (38) 6877
68.8%

Length

2024-05-11T15:51:36.744210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
daechi4-dong 411
 
4.1%
hongje3-dong 387
 
3.9%
ihwa-dong 385
 
3.9%
gahoe-dong 371
 
3.7%
buam-dong 371
 
3.7%
myeong-dong 364
 
3.6%
samcheong-dong 218
 
2.2%
sinsa-dong 206
 
2.1%
chang1-dong 205
 
2.1%
yeoksam1-dong 205
 
2.1%
Other values (38) 6877
68.8%

방문자수
Real number (ℝ)

ZEROS 

Distinct440
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.6612
Minimum0
Maximum917
Zeros1571
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T15:51:36.957609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q346
95-th percentile176
Maximum917
Range917
Interquartile range (IQR)44

Descriptive statistics

Standard deviation78.236143
Coefficient of variation (CV)1.9240982
Kurtosis25.95712
Mean40.6612
Median Absolute Deviation (MAD)13
Skewness4.4259512
Sum406612
Variance6120.8941
MonotonicityNot monotonic
2024-05-11T15:51:37.134051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1571
 
15.7%
1 575
 
5.8%
2 471
 
4.7%
3 394
 
3.9%
4 342
 
3.4%
5 244
 
2.4%
6 235
 
2.4%
7 215
 
2.1%
8 208
 
2.1%
11 193
 
1.9%
Other values (430) 5552
55.5%
ValueCountFrequency (%)
0 1571
15.7%
1 575
 
5.8%
2 471
 
4.7%
3 394
 
3.9%
4 342
 
3.4%
5 244
 
2.4%
6 235
 
2.4%
7 215
 
2.1%
8 208
 
2.1%
9 177
 
1.8%
ValueCountFrequency (%)
917 1
< 0.1%
839 1
< 0.1%
831 1
< 0.1%
798 1
< 0.1%
783 1
< 0.1%
772 1
< 0.1%
767 1
< 0.1%
753 1
< 0.1%
729 1
< 0.1%
717 1
< 0.1%

등록일
Date

Distinct4353
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-12-25 00:28:01
Maximum2023-12-31 23:58:02
2024-05-11T15:51:37.329567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:37.537635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-11T15:51:33.634853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:33.308045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:33.785238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:51:33.481193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:51:37.655195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼지역자치구행정동방문자수
시리얼1.0000.7590.9610.9980.474
지역0.7591.0000.9230.9990.163
자치구0.9610.9231.0001.0000.412
행정동0.9980.9991.0001.0000.654
방문자수0.4740.1630.4120.6541.000
2024-05-11T15:51:37.771389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역자치구행정동
지역1.0000.6450.944
자치구0.6451.0000.992
행정동0.9440.9921.000
2024-05-11T15:51:37.894969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼방문자수지역자치구행정동
시리얼1.000-0.2880.5330.8190.979
방문자수-0.2881.0000.0860.1700.286
지역0.5330.0861.0000.6450.944
자치구0.8190.1700.6451.0000.992
행정동0.9790.2860.9440.9921.000

Missing values

2024-05-11T15:51:33.993583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:51:34.238225image/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
50275SDOT00140422023-12-31_08:27:00main_streetSeongdong-guSeongsu1ga2-dong02023-12-31 08:27:00
42341SDOT00140352023-12-30_08:15:00main_streetGwanak-guNakseongdae-dong232023-12-30 08:15:00
24860SDOT00140402023-12-28_20:37:00main_streetJongno-guIhwa-dong62023-12-28 20:37:00
30552SDOT00140622023-12-29_08:45:00parksSeoul_Grand_Parkmeeting_bridge182023-12-29 08:45:00
32234SDOT00140482023-12-29_01:14:00main_streetGwangjin-guGuui1-dong182023-12-29 01:14:00
46068SDOT00140022023-12-31_21:16:00main_streetSeocho-guSeocho4-dong572023-12-31 21:28:02
4560SDOT00140012023-12-25_14:30:00main_streetSeocho-guSeocho3-dong112023-12-25 14:48:03
39335SDOT00140142023-12-30_07:16:00main_streetJung-guSindang-dong02023-12-30 07:16:00
8701SDOT00140192023-12-26_04:10:00main_streetJung-guHoehyeon-dong162023-12-26 04:28:00
3467SDOT00140432023-12-25_11:10:00parksSeongdong-guSeongsu1ga1-dong92023-12-25 11:28:01
모델번호시리얼측정시간지역자치구행정동방문자수등록일
25594SDOT00140272023-12-28_09:10:00main_streetGangseo-guDeungchon-dong1632023-12-28 09:10:00
47490SDOT00140162023-12-31_10:15:00main_streetJung-guMyeong-dong1432023-12-31 10:15:00
38192SDOT00140042023-12-30_07:46:00main_streetSeocho-guBanpo3-dong132023-12-30 07:46:00
28469SDOT00140502023-12-28_20:36:00public_facilitiesSeodaemun-guCheonyeon-dong02023-12-28 20:36:00
3005SDOT00140302023-12-25_09:57:00parksGangbuk-guBeon3-dong132023-12-25 10:08:01
13451SDOT00140072023-12-26_19:07:00main_streetGangdong-guSeongnae1-dong202023-12-26 19:18:02
15663SDOT00140302023-12-27_02:47:00parksGangbuk-guBeon3-dong02023-12-27 02:58:00
48821SDOT00140402023-12-31_00:37:00main_streetJongno-guIhwa-dong02023-12-31 00:37:00
5644SDOT00140602023-12-25_18:01:00parksSeoul_Grand_Parkwomen_parking132023-12-25 18:18:01
18957SDOT00140162023-12-27_19:35:00main_streetJung-guMyeong-dong3762023-12-27 19:35:00