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

Numeric2
Categorical3
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 imbalanced (98.8%)Imbalance

Reproduction

Analysis started2023-12-11 08:25:35.236874
Analysis finished2023-12-11 08:25:36.817380
Duration1.58 second
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%
Mean1308.6066
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:36.915649image/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 deviation733.50017
Coefficient of variation (CV)0.56052
Kurtosis-1.1790949
Mean1308.6066
Median Absolute Deviation (MAD)600
Skewness-0.044814557
Sum13086066
Variance538022.5
MonotonicityNot monotonic
2023-12-11T17:25:37.092308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
202 237
 
2.4%
1302 234
 
2.3%
1602 233
 
2.3%
1501 230
 
2.3%
2301 229
 
2.3%
1702 229
 
2.3%
1701 228
 
2.3%
1502 228
 
2.3%
2202 227
 
2.3%
402 226
 
2.3%
Other values (38) 7699
77.0%
ValueCountFrequency (%)
101 195
1.9%
102 216
2.2%
103 217
2.2%
201 204
2.0%
202 237
2.4%
301 189
1.9%
401 217
2.2%
402 226
2.3%
501 179
1.8%
601 214
2.1%
ValueCountFrequency (%)
2502 206
2.1%
2501 225
2.2%
2402 212
2.1%
2401 224
2.2%
2302 209
2.1%
2301 229
2.3%
2202 227
2.3%
2201 206
2.1%
2102 192
1.9%
2101 204
2.0%

강우량계명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고덕2동
 
237
증산P
 
234
한남P
 
233
마포구청
 
230
관악구청
 
229
Other values (43)
8837 

Length

Max length5
Median length4
Mean length3.787
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성북구청
2nd row마포구청
3rd row구로구청
4th row서대문구청
5th row목동P

Common Values

ValueCountFrequency (%)
고덕2동 237
 
2.4%
증산P 234
 
2.3%
한남P 233
 
2.3%
마포구청 230
 
2.3%
관악구청 229
 
2.3%
공항동P 229
 
2.3%
강서구청 228
 
2.3%
봉원P 228
 
2.3%
가산2P 227
 
2.3%
상계1동 226
 
2.3%
Other values (38) 7699
77.0%

Length

2023-12-11T17:25:37.282106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고덕2동 237
 
2.4%
증산p 234
 
2.3%
한남p 233
 
2.3%
마포구청 230
 
2.3%
관악구청 229
 
2.3%
공항동p 229
 
2.3%
강서구청 228
 
2.3%
봉원p 228
 
2.3%
가산2p 227
 
2.3%
상계1동 226
 
2.3%
Other values (38) 7699
77.0%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.0705
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:37.465444image/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 deviation7.3355921
Coefficient of variation (CV)0.064876268
Kurtosis-1.1789501
Mean113.0705
Median Absolute Deviation (MAD)6
Skewness-0.044802792
Sum1130705
Variance53.810911
MonotonicityNot monotonic
2023-12-11T17:25:37.621601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 666
 
6.7%
101 628
 
6.3%
115 458
 
4.6%
117 457
 
4.6%
104 443
 
4.4%
116 443
 
4.4%
102 441
 
4.4%
123 438
 
4.4%
107 436
 
4.4%
124 436
 
4.4%
Other values (15) 5154
51.5%
ValueCountFrequency (%)
101 628
6.3%
102 441
4.4%
103 189
 
1.9%
104 443
4.4%
105 179
 
1.8%
106 435
4.3%
107 436
4.4%
108 406
4.1%
109 407
4.1%
110 404
4.0%
ValueCountFrequency (%)
125 431
4.3%
124 436
4.4%
123 438
4.4%
122 433
4.3%
121 396
4.0%
120 225
2.2%
119 418
4.2%
118 424
4.2%
117 457
4.6%
116 443
4.4%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구
 
666
강남구
 
628
마포구
 
458
강서구
 
457
용산구
 
443
Other values (20)
7348 

Length

Max length4
Median length3
Mean length3.0663
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성북구
2nd row마포구
3rd row구로구
4th row서대문구
5th row양천구

Common Values

ValueCountFrequency (%)
은평구 666
 
6.7%
강남구 628
 
6.3%
마포구 458
 
4.6%
강서구 457
 
4.6%
용산구 443
 
4.4%
노원구 443
 
4.4%
강동구 441
 
4.4%
관악구 438
 
4.4%
중랑구 436
 
4.4%
서초구 436
 
4.4%
Other values (15) 5154
51.5%

Length

2023-12-11T17:25:37.801384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 666
 
6.7%
강남구 628
 
6.3%
마포구 458
 
4.6%
강서구 457
 
4.6%
용산구 443
 
4.4%
노원구 443
 
4.4%
강동구 441
 
4.4%
관악구 438
 
4.4%
중랑구 436
 
4.4%
서초구 436
 
4.4%
Other values (15) 5154
51.5%

10분우량
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0.0
9982 
0.5
 
17
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9982
99.8%
0.5 17
 
0.2%
1.0 1
 
< 0.1%

Length

2023-12-11T17:25:37.943726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:25:38.092217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9982
99.8%
0.5 17
 
0.2%
1.0 1
 
< 0.1%

자료수집 시각
Date

Distinct2170
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-05-01 00:09:00
Maximum2022-05-16 00:59:00
2023-12-11T17:25:38.224843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:38.422936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:25:35.971598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:35.759998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:36.458146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:35.864338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:25:38.563819image/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
2023-12-11T17:25:38.675510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10분우량강우량계명구청명
10분우량1.0000.0000.000
강우량계명0.0001.0000.999
구청명0.0000.9991.000
2023-12-11T17:25:38.786305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드강우량계명구청명10분우량
강우량계 코드1.0000.9990.9980.9820.000
구청 코드0.9991.0000.9980.9990.000
강우량계명0.9980.9981.0000.9990.000
구청명0.9820.9990.9991.0000.000
10분우량0.0000.0000.0000.0001.000

Missing values

2023-12-11T17:25:36.621687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:25:36.761909image/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
6009601성북구청106성북구0.02022-05-01 21:39
202941501마포구청115마포구0.02022-05-04 00:49
875722001구로구청120구로구0.02022-05-14 02:59
31271401서대문구청114서대문구0.02022-05-01 11:19
744941802목동P118양천구0.02022-05-12 04:29
881201801양천구청118양천구0.02022-05-14 04:59
462681201광진구청112광진구0.02022-05-07 23:39
522442001구로구청120구로구0.02022-05-08 21:19
955981902도림2동P119영등포구0.02022-05-15 08:39
486822001구로구청120구로구0.02022-05-08 08:29
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
27064602상월곡동106성북구0.02022-05-05 01:59
594911801양천구청118양천구0.02022-05-09 22:59
54038401노원구청104노원구0.02022-05-09 03:39
95976401노원구청104노원구0.02022-05-15 10:09
89420102세곡동101강남구0.02022-05-14 09:49
11091303갈현1동113은평구0.02022-05-01 03:59
78338103개포2동101강남구0.02022-05-12 18:09
142721802목동P118양천구0.02022-05-03 03:29
148262202가산2P122금천구0.02022-05-03 05:19
328472401서초구청124서초구0.02022-05-05 22:49