Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory576.2 KiB
Average record size in memory59.0 B

Variable types

Numeric3
Categorical2
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1168/S/1/datasetView.do

Alerts

강우량계명 is highly overall correlated with 강우량계 코드 and 2 other fieldsHigh correlation
구청명 is highly overall correlated with 강우량계 코드 and 2 other fieldsHigh correlation
강우량계 코드 is highly overall correlated with 구청 코드 and 2 other fieldsHigh correlation
구청 코드 is highly overall correlated with 강우량계 코드 and 2 other fieldsHigh correlation
10분우량 has 9780 (97.8%) zerosZeros

Reproduction

Analysis started2023-12-11 08:25:44.823729
Analysis finished2023-12-11 08:25:47.122324
Duration2.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강우량계 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1320.3598
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:47.209480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1702
median1303
Q31902
95-th percentile2402
Maximum2502
Range2401
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation729.72363
Coefficient of variation (CV)0.55267029
Kurtosis-1.1640907
Mean1320.3598
Median Absolute Deviation (MAD)601
Skewness-0.048584863
Sum13203598
Variance532496.58
MonotonicityNot monotonic
2023-12-11T17:25:47.377298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1801 265
 
2.6%
2501 245
 
2.5%
1502 242
 
2.4%
1002 233
 
2.3%
2502 228
 
2.3%
701 228
 
2.3%
1602 225
 
2.2%
2301 225
 
2.2%
2202 225
 
2.2%
1101 223
 
2.2%
Other values (37) 7661
76.6%
ValueCountFrequency (%)
101 176
1.8%
102 203
2.0%
103 221
2.2%
201 198
2.0%
202 203
2.0%
301 205
2.1%
401 218
2.2%
402 217
2.2%
501 121
1.2%
601 215
2.1%
ValueCountFrequency (%)
2502 228
2.3%
2501 245
2.5%
2402 208
2.1%
2401 204
2.0%
2302 205
2.1%
2301 225
2.2%
2202 225
2.2%
2201 221
2.2%
2102 214
2.1%
2101 218
2.2%

강우량계명
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
양천구청
 
265
송파구청
 
245
봉원P
 
242
부암동
 
233
중랑구청
 
228
Other values (42)
8787 

Length

Max length5
Median length4
Mean length3.7813
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row면목P
2nd row동대문구청
3rd row신림P
4th row부암동
5th row흑석P

Common Values

ValueCountFrequency (%)
양천구청 265
 
2.6%
송파구청 245
 
2.5%
봉원P 242
 
2.4%
부암동 233
 
2.3%
중랑구청 228
 
2.3%
마천2동 228
 
2.3%
가산2P 225
 
2.2%
관악구청 225
 
2.2%
한남P 225
 
2.2%
종로구청 223
 
2.2%
Other values (37) 7661
76.6%

Length

2023-12-11T17:25:47.519949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양천구청 265
 
2.6%
송파구청 245
 
2.5%
봉원p 242
 
2.4%
부암동 233
 
2.3%
중랑구청 228
 
2.3%
마천2동 228
 
2.3%
가산2p 225
 
2.2%
관악구청 225
 
2.2%
한남p 225
 
2.2%
종로구청 223
 
2.2%
Other values (37) 7661
76.6%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.188
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:47.629839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1107
median113
Q3119
95-th percentile124
Maximum125
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2978478
Coefficient of variation (CV)0.064475455
Kurtosis-1.1639608
Mean113.188
Median Absolute Deviation (MAD)6
Skewness-0.048567157
Sum1131880
Variance53.258582
MonotonicityNot monotonic
2023-12-11T17:25:47.808362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 634
 
6.3%
101 600
 
6.0%
125 473
 
4.7%
118 462
 
4.6%
110 456
 
4.6%
107 448
 
4.5%
122 446
 
4.5%
115 444
 
4.4%
108 438
 
4.4%
104 435
 
4.3%
Other values (15) 5164
51.6%
ValueCountFrequency (%)
101 600
6.0%
102 401
4.0%
103 205
 
2.1%
104 435
4.3%
105 121
 
1.2%
106 417
4.2%
107 448
4.5%
108 438
4.4%
109 425
4.2%
110 456
4.6%
ValueCountFrequency (%)
125 473
4.7%
124 412
4.1%
123 430
4.3%
122 446
4.5%
121 432
4.3%
120 213
2.1%
119 410
4.1%
118 462
4.6%
117 423
4.2%
116 435
4.3%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구
 
634
강남구
 
600
송파구
 
473
양천구
 
462
종로구
 
456
Other values (20)
7375 

Length

Max length4
Median length3
Mean length3.0636
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중랑구
2nd row동대문구
3rd row관악구
4th row종로구
5th row동작구

Common Values

ValueCountFrequency (%)
은평구 634
 
6.3%
강남구 600
 
6.0%
송파구 473
 
4.7%
양천구 462
 
4.6%
종로구 456
 
4.6%
중랑구 448
 
4.5%
금천구 446
 
4.5%
마포구 444
 
4.4%
동대문구 438
 
4.4%
용산구 435
 
4.3%
Other values (15) 5164
51.6%

Length

2023-12-11T17:25:48.011607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 634
 
6.3%
강남구 600
 
6.0%
송파구 473
 
4.7%
양천구 462
 
4.6%
종로구 456
 
4.6%
중랑구 448
 
4.5%
금천구 446
 
4.5%
마포구 444
 
4.4%
동대문구 438
 
4.4%
용산구 435
 
4.3%
Other values (15) 5164
51.6%

10분우량
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02135
Minimum0
Maximum3.5
Zeros9780
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:48.168086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.5
Range3.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17035868
Coefficient of variation (CV)7.9793293
Kurtosis127.85903
Mean0.02135
Median Absolute Deviation (MAD)0
Skewness10.413195
Sum213.5
Variance0.02902208
MonotonicityNot monotonic
2023-12-11T17:25:48.287241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 9780
97.8%
0.5 116
 
1.2%
1.0 44
 
0.4%
1.5 35
 
0.4%
2.0 11
 
0.1%
2.5 11
 
0.1%
3.0 2
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9780
97.8%
0.5 116
 
1.2%
1.0 44
 
0.4%
1.5 35
 
0.4%
2.0 11
 
0.1%
2.5 11
 
0.1%
3.0 2
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
3.5 1
 
< 0.1%
3.0 2
 
< 0.1%
2.5 11
 
0.1%
2.0 11
 
0.1%
1.5 35
 
0.4%
1.0 44
 
0.4%
0.5 116
 
1.2%
0.0 9780
97.8%

자료수집 시각
Date

Distinct2198
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-03-01 00:09:00
Maximum2022-03-16 03:19:00
2023-12-11T17:25:48.452968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:48.607380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:25:46.559427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:45.488579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:46.207337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:46.676697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:45.915758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:46.362994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:46.781918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:46.047269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:46.453938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:25:48.705888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.000
강우량계명1.0001.0001.0001.0000.000
구청 코드1.0001.0001.0001.0000.000
구청명1.0001.0001.0001.0000.000
10분우량0.0000.0000.0000.0001.000
2023-12-11T17:25:48.811092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:25:48.896142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.999-0.0080.9980.983
구청 코드0.9991.000-0.0090.9980.999
10분우량-0.008-0.0091.0000.0000.000
강우량계명0.9980.9980.0001.0000.999
구청명0.9830.9990.0000.9991.000

Missing values

2023-12-11T17:25:46.924810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:25:47.066634image/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

강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
53983702면목P107중랑구0.02022-03-09 03:29
59816801동대문구청108동대문구0.02022-03-10 00:19
747962302신림P123관악구0.02022-03-12 08:59
927131002부암동110종로구0.02022-03-15 01:29
5312102흑석P121동작구0.02022-03-01 01:59
870441101중구청111중구0.02022-03-14 05:09
109911702공항동P117강서구0.02022-03-02 15:59
51371101중구청111중구0.02022-03-01 18:49
48372301관악구청123관악구0.02022-03-01 17:39
598081101중구청111중구0.02022-03-10 00:19
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
14515801동대문구청108동대문구0.02022-03-03 04:59
44252402상계1동104노원구0.02022-03-07 16:09
979941502봉원P115마포구0.02022-03-15 20:09
719301801양천구청118양천구0.02022-03-11 22:59
200252501송파구청125송파구0.02022-03-04 00:49
20252001구로구청120구로구0.02022-03-01 07:29
20269602상월곡동106성북구0.02022-03-04 01:39
521081401서대문구청114서대문구0.02022-03-08 20:49
22141102세곡동101강남구0.02022-03-04 08:19
631061801양천구청118양천구0.02022-03-10 12:49