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 is highly skewed (γ1 = 20.38715422)Skewed
10분우량 has 9815 (98.2%) zerosZeros

Reproduction

Analysis started2023-12-11 08:25:28.883111
Analysis finished2023-12-11 08:25:31.271345
Duration2.39 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%
Mean1310.3381
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:31.381711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1701
median1302
Q31902
95-th percentile2402
Maximum2502
Range2401
Interquartile range (IQR)1201

Descriptive statistics

Standard deviation731.41902
Coefficient of variation (CV)0.55819107
Kurtosis-1.1896496
Mean1310.3381
Median Absolute Deviation (MAD)600
Skewness-0.047124323
Sum13103381
Variance534973.79
MonotonicityNot monotonic
2023-12-11T17:25:31.594975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1301 253
 
2.5%
1801 237
 
2.4%
401 235
 
2.4%
2001 232
 
2.3%
202 231
 
2.3%
601 230
 
2.3%
2401 227
 
2.3%
402 223
 
2.2%
2101 220
 
2.2%
902 220
 
2.2%
Other values (38) 7692
76.9%
ValueCountFrequency (%)
101 194
1.9%
102 193
1.9%
103 209
2.1%
201 207
2.1%
202 231
2.3%
301 197
2.0%
401 235
2.4%
402 223
2.2%
501 197
2.0%
601 230
2.3%
ValueCountFrequency (%)
2502 201
2.0%
2501 210
2.1%
2402 208
2.1%
2401 227
2.3%
2302 216
2.2%
2301 209
2.1%
2202 179
1.8%
2201 210
2.1%
2102 210
2.1%
2101 220
2.2%

강우량계명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구청
 
253
양천구청
 
237
노원구청
 
235
구로구청
 
232
고덕2동
 
231
Other values (43)
8812 

Length

Max length5
Median length4
Mean length3.7921
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구청
2nd row휘경P
3rd row공항동P
4th row고덕2동
5th row은평구청

Common Values

ValueCountFrequency (%)
은평구청 253
 
2.5%
양천구청 237
 
2.4%
노원구청 235
 
2.4%
구로구청 232
 
2.3%
고덕2동 231
 
2.3%
성북구청 230
 
2.3%
서초구청 227
 
2.3%
상계1동 223
 
2.2%
동작구청 220
 
2.2%
뚝섬P 220
 
2.2%
Other values (38) 7692
76.9%

Length

2023-12-11T17:25:31.779823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구청 253
 
2.5%
양천구청 237
 
2.4%
노원구청 235
 
2.4%
구로구청 232
 
2.3%
고덕2동 231
 
2.3%
성북구청 230
 
2.3%
서초구청 227
 
2.3%
상계1동 223
 
2.2%
동작구청 220
 
2.2%
뚝섬p 220
 
2.2%
Other values (38) 7692
76.9%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.0879
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:31.975058image/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 deviation7.3146377
Coefficient of variation (CV)0.064680993
Kurtosis-1.1894784
Mean113.0879
Median Absolute Deviation (MAD)6
Skewness-0.047110073
Sum1130879
Variance53.503924
MonotonicityNot monotonic
2023-12-11T17:25:32.151456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 669
 
6.7%
101 596
 
6.0%
104 458
 
4.6%
118 446
 
4.5%
102 438
 
4.4%
124 435
 
4.3%
121 430
 
4.3%
109 427
 
4.3%
123 425
 
4.2%
107 418
 
4.2%
Other values (15) 5258
52.6%
ValueCountFrequency (%)
101 596
6.0%
102 438
4.4%
103 197
 
2.0%
104 458
4.6%
105 197
 
2.0%
106 416
4.2%
107 418
4.2%
108 403
4.0%
109 427
4.3%
110 417
4.2%
ValueCountFrequency (%)
125 411
4.1%
124 435
4.3%
123 425
4.2%
122 389
3.9%
121 430
4.3%
120 374
3.7%
119 394
3.9%
118 446
4.5%
117 393
3.9%
116 415
4.2%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구
 
669
강남구
 
596
노원구
 
458
양천구
 
446
강동구
 
438
Other values (20)
7393 

Length

Max length4
Median length3
Mean length3.0597
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구
2nd row동대문구
3rd row강서구
4th row강동구
5th row은평구

Common Values

ValueCountFrequency (%)
은평구 669
 
6.7%
강남구 596
 
6.0%
노원구 458
 
4.6%
양천구 446
 
4.5%
강동구 438
 
4.4%
서초구 435
 
4.3%
동작구 430
 
4.3%
성동구 427
 
4.3%
관악구 425
 
4.2%
중랑구 418
 
4.2%
Other values (15) 5258
52.6%

Length

2023-12-11T17:25:32.334122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 669
 
6.7%
강남구 596
 
6.0%
노원구 458
 
4.6%
양천구 446
 
4.5%
강동구 438
 
4.4%
서초구 435
 
4.3%
동작구 430
 
4.3%
성동구 427
 
4.3%
관악구 425
 
4.2%
중랑구 418
 
4.2%
Other values (15) 5258
52.6%

10분우량
Real number (ℝ)

SKEWED  ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019
Minimum0
Maximum7.5
Zeros9815
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:32.527393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7.5
Range7.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20867044
Coefficient of variation (CV)10.982655
Kurtosis547.15645
Mean0.019
Median Absolute Deviation (MAD)0
Skewness20.387154
Sum190
Variance0.043543354
MonotonicityNot monotonic
2023-12-11T17:25:32.715940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 9815
98.2%
0.5 123
 
1.2%
1.0 24
 
0.2%
1.5 15
 
0.1%
2.5 5
 
0.1%
3.5 5
 
0.1%
2.0 4
 
< 0.1%
3.0 3
 
< 0.1%
7.5 2
 
< 0.1%
4.0 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0.0 9815
98.2%
0.5 123
 
1.2%
1.0 24
 
0.2%
1.5 15
 
0.1%
2.0 4
 
< 0.1%
2.5 5
 
0.1%
3.0 3
 
< 0.1%
3.5 5
 
0.1%
4.0 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
7.5 2
 
< 0.1%
6.0 1
 
< 0.1%
5.5 1
 
< 0.1%
4.5 1
 
< 0.1%
4.0 1
 
< 0.1%
3.5 5
 
0.1%
3.0 3
 
< 0.1%
2.5 5
 
0.1%
2.0 4
 
< 0.1%
1.5 15
0.1%

자료수집 시각
Date

Distinct2199
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-06-01 00:09:00
Maximum2022-06-15 14:19:00
2023-12-11T17:25:32.935489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:33.186252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:25:30.528586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:29.611439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:30.086852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:30.691664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:29.743336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:30.217594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:30.844556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:29.919093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:30.370281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:25:33.380907image/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
2023-12-11T17:25:33.515904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:25:33.633282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.999-0.0270.9980.981
구청 코드0.9991.000-0.0270.9980.999
10분우량-0.027-0.0271.0000.0000.000
강우량계명0.9980.9980.0001.0000.999
구청명0.9810.9990.0000.9991.000

Missing values

2023-12-11T17:25:31.034617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:25:31.202577image/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
313221701강서구청117강서구0.02022-06-05 15:29
74466802휘경P108동대문구0.02022-06-11 21:39
649401702공항동P117강서구0.02022-06-10 12:39
54035202고덕2동102강동구0.02022-06-08 22:49
845531301은평구청113은평구0.02022-06-13 08:49
86723701중랑구청107중랑구0.02022-06-13 16:19
50391101강남구청101강남구0.02022-06-08 10:08
498311801양천구청118양천구0.02022-06-08 08:09
97971101중구청111중구0.02022-06-02 10:49
278602502마천2동125송파구0.02022-06-05 03:09
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
71153401노원구청104노원구0.02022-06-11 10:09
282351902도림2동P119영등포구0.02022-06-05 04:29
338081701강서구청117강서구0.02022-06-06 00:19
283611701강서구청117강서구0.02022-06-05 04:59
761432401서초구청124서초구0.02022-06-12 03:29
783532401서초구청124서초구0.02022-06-12 11:09
760302202가산2P122금천구0.02022-06-12 03:09
428212502마천2동125송파구0.02022-06-07 07:49
492632201금천구청122금천구0.02022-06-08 06:09
385342101동작구청121동작구0.02022-06-06 16:59