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 9196 (92.0%) zerosZeros

Reproduction

Analysis started2023-12-11 08:26:10.552512
Analysis finished2023-12-11 08:26:12.903931
Duration2.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강우량계 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1316.7211
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:26:13.012108image/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 deviation728.89495
Coefficient of variation (CV)0.55356822
Kurtosis-1.1820856
Mean1316.7211
Median Absolute Deviation (MAD)601
Skewness-0.06957054
Sum13167211
Variance531287.85
MonotonicityNot monotonic
2023-12-11T17:26:13.242354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1702 233
 
2.3%
2301 228
 
2.3%
1303 226
 
2.3%
902 225
 
2.2%
702 225
 
2.2%
1502 221
 
2.2%
202 220
 
2.2%
402 219
 
2.2%
1001 218
 
2.2%
1501 217
 
2.2%
Other values (38) 7768
77.7%
ValueCountFrequency (%)
101 202
2.0%
102 212
2.1%
103 185
1.8%
201 204
2.0%
202 220
2.2%
301 201
2.0%
401 208
2.1%
402 219
2.2%
501 203
2.0%
601 214
2.1%
ValueCountFrequency (%)
2502 215
2.1%
2501 208
2.1%
2402 190
1.9%
2401 198
2.0%
2302 201
2.0%
2301 228
2.3%
2202 206
2.1%
2201 199
2.0%
2102 209
2.1%
2101 208
2.1%

강우량계명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공항동P
 
233
관악구청
 
228
갈현1동
 
226
뚝섬P
 
225
면목P
 
225
Other values (43)
8863 

Length

Max length5
Median length4
Mean length3.7891
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노원구청
2nd row면목P
3rd row양천구청
4th row도림2동P
5th row부암동

Common Values

ValueCountFrequency (%)
공항동P 233
 
2.3%
관악구청 228
 
2.3%
갈현1동 226
 
2.3%
뚝섬P 225
 
2.2%
면목P 225
 
2.2%
봉원P 221
 
2.2%
고덕2동 220
 
2.2%
상계1동 219
 
2.2%
종로구청 218
 
2.2%
마포구청 217
 
2.2%
Other values (38) 7768
77.7%

Length

2023-12-11T17:26:13.439059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공항동p 233
 
2.3%
관악구청 228
 
2.3%
갈현1동 226
 
2.3%
뚝섬p 225
 
2.2%
면목p 225
 
2.2%
봉원p 221
 
2.2%
고덕2동 220
 
2.2%
상계1동 219
 
2.2%
종로구청 218
 
2.2%
마포구청 217
 
2.2%
Other values (38) 7768
77.7%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.1516
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:26:13.614200image/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 deviation7.2893162
Coefficient of variation (CV)0.064420797
Kurtosis-1.1819474
Mean113.1516
Median Absolute Deviation (MAD)6
Skewness-0.069511873
Sum1131516
Variance53.134131
MonotonicityNot monotonic
2023-12-11T17:26:13.816102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 636
 
6.4%
101 599
 
6.0%
117 449
 
4.5%
115 438
 
4.4%
107 434
 
4.3%
110 430
 
4.3%
109 430
 
4.3%
123 429
 
4.3%
104 427
 
4.3%
118 426
 
4.3%
Other values (15) 5302
53.0%
ValueCountFrequency (%)
101 599
6.0%
102 424
4.2%
103 201
 
2.0%
104 427
4.3%
105 203
 
2.0%
106 398
4.0%
107 434
4.3%
108 398
4.0%
109 430
4.3%
110 430
4.3%
ValueCountFrequency (%)
125 423
4.2%
124 388
3.9%
123 429
4.3%
122 405
4.0%
121 417
4.2%
120 400
4.0%
119 422
4.2%
118 426
4.3%
117 449
4.5%
116 426
4.3%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구
 
636
강남구
 
599
강서구
 
449
마포구
 
438
중랑구
 
434
Other values (20)
7444 

Length

Max length4
Median length3
Mean length3.0624
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노원구
2nd row중랑구
3rd row양천구
4th row영등포구
5th row종로구

Common Values

ValueCountFrequency (%)
은평구 636
 
6.4%
강남구 599
 
6.0%
강서구 449
 
4.5%
마포구 438
 
4.4%
중랑구 434
 
4.3%
종로구 430
 
4.3%
성동구 430
 
4.3%
관악구 429
 
4.3%
노원구 427
 
4.3%
용산구 426
 
4.3%
Other values (15) 5302
53.0%

Length

2023-12-11T17:26:14.016818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 636
 
6.4%
강남구 599
 
6.0%
강서구 449
 
4.5%
마포구 438
 
4.4%
중랑구 434
 
4.3%
종로구 430
 
4.3%
성동구 430
 
4.3%
관악구 429
 
4.3%
노원구 427
 
4.3%
용산구 426
 
4.3%
Other values (15) 5302
53.0%

10분우량
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09725
Minimum0
Maximum5.5
Zeros9196
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:26:14.173134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5.5
Range5.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38785626
Coefficient of variation (CV)3.9882392
Kurtosis29.211008
Mean0.09725
Median Absolute Deviation (MAD)0
Skewness4.9526833
Sum972.5
Variance0.15043248
MonotonicityNot monotonic
2023-12-11T17:26:14.303181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 9196
92.0%
0.5 272
 
2.7%
1.0 207
 
2.1%
1.5 154
 
1.5%
2.0 98
 
1.0%
2.5 52
 
0.5%
3.0 11
 
0.1%
3.5 7
 
0.1%
5.5 1
 
< 0.1%
5.0 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9196
92.0%
0.5 272
 
2.7%
1.0 207
 
2.1%
1.5 154
 
1.5%
2.0 98
 
1.0%
2.5 52
 
0.5%
3.0 11
 
0.1%
3.5 7
 
0.1%
4.5 1
 
< 0.1%
5.0 1
 
< 0.1%
ValueCountFrequency (%)
5.5 1
 
< 0.1%
5.0 1
 
< 0.1%
4.5 1
 
< 0.1%
3.5 7
 
0.1%
3.0 11
 
0.1%
2.5 52
 
0.5%
2.0 98
 
1.0%
1.5 154
1.5%
1.0 207
2.1%
0.5 272
2.7%

자료수집 시각
Date

Distinct2215
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-09-01 00:09:00
Maximum2022-09-15 15:29:00
2023-12-11T17:26:14.484105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:14.729185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:26:11.853443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:11.136922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:11.519973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:11.987245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:11.259608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:11.644556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:12.138827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:11.379284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:11.740148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:26:14.861573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.004
강우량계명1.0001.0001.0001.0000.065
구청 코드1.0001.0001.0001.0000.016
구청명1.0001.0001.0001.0000.051
10분우량0.0040.0650.0160.0511.000
2023-12-11T17:26:14.999460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:26:15.105187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.9990.0050.9980.982
구청 코드0.9991.0000.0050.9980.999
10분우량0.0050.0051.0000.0230.019
강우량계명0.9980.9980.0231.0000.999
구청명0.9820.9990.0190.9991.000

Missing values

2023-12-11T17:26:12.650298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:26:12.829876image/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
28906401노원구청104노원구0.52022-09-05 05:39
85909702면목P107중랑구0.02022-09-13 13:09
187741801양천구청118양천구0.02022-09-03 18:08
373441902도림2동P119영등포구0.02022-09-06 10:49
712701002부암동110종로구0.02022-09-11 10:19
51658101강남구청101강남구0.02022-09-08 13:19
589702201금천구청122금천구0.02022-09-09 15:39
711521001종로구청110종로구0.02022-09-11 09:59
825972201금천구청122금천구0.02022-09-13 01:39
25570402상계1동104노원구0.02022-09-04 17:59
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
74865902뚝섬P109성동구0.02022-09-11 22:49
713661201광진구청112광진구0.02022-09-11 10:39
874142402반포P124서초구0.02022-09-13 18:19
13530402상계1동104노원구0.02022-09-02 23:49
582631501마포구청115마포구0.02022-09-09 13:09
629122201금천구청122금천구0.02022-09-10 05:19
919991701강서구청117강서구0.02022-09-14 10:49
15486402상계1동104노원구0.02022-09-03 06:49
113751302증산P113은평구0.02022-09-02 16:09
66120902뚝섬P109성동구0.02022-09-10 16:29