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 9341 (93.4%) zerosZeros

Reproduction

Analysis started2023-12-11 08:26:22.799075
Analysis finished2023-12-11 08:26:24.404827
Duration1.61 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%
Mean1307.0205
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:26:24.488518image/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 deviation727.70639
Coefficient of variation (CV)0.55676739
Kurtosis-1.1758509
Mean1307.0205
Median Absolute Deviation (MAD)600
Skewness-0.060341339
Sum13070205
Variance529556.59
MonotonicityNot monotonic
2023-12-11T17:26:24.642623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1302 236
 
2.4%
2302 235
 
2.4%
2002 227
 
2.3%
2202 226
 
2.3%
1303 225
 
2.2%
1301 224
 
2.2%
2402 223
 
2.2%
1401 222
 
2.2%
1901 222
 
2.2%
1101 222
 
2.2%
Other values (38) 7738
77.4%
ValueCountFrequency (%)
101 205
2.1%
102 212
2.1%
103 214
2.1%
201 214
2.1%
202 206
2.1%
301 205
2.1%
401 221
2.2%
402 197
2.0%
501 207
2.1%
601 212
2.1%
ValueCountFrequency (%)
2502 190
1.9%
2501 187
1.9%
2402 223
2.2%
2401 198
2.0%
2302 235
2.4%
2301 176
1.8%
2202 226
2.3%
2201 176
1.8%
2102 191
1.9%
2101 204
2.0%

강우량계명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
증산P
 
236
신림P
 
235
개봉2동
 
227
가산2P
 
226
갈현1동
 
225
Other values (43)
8851 

Length

Max length5
Median length4
Mean length3.7859
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구청
2nd row양천구청
3rd row금천구청
4th row은평구청
5th row고덕2동

Common Values

ValueCountFrequency (%)
증산P 236
 
2.4%
신림P 235
 
2.4%
개봉2동 227
 
2.3%
가산2P 226
 
2.3%
갈현1동 225
 
2.2%
은평구청 224
 
2.2%
반포P 223
 
2.2%
서대문구청 222
 
2.2%
영등포구청 222
 
2.2%
중구청 222
 
2.2%
Other values (38) 7738
77.4%

Length

2023-12-11T17:26:24.781924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
증산p 236
 
2.4%
신림p 235
 
2.4%
개봉2동 227
 
2.3%
가산2p 226
 
2.3%
갈현1동 225
 
2.2%
은평구청 224
 
2.2%
반포p 223
 
2.2%
서대문구청 222
 
2.2%
영등포구청 222
 
2.2%
중구청 222
 
2.2%
Other values (38) 7738
77.4%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.0545
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:26:24.914048image/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 deviation7.2773364
Coefficient of variation (CV)0.064370162
Kurtosis-1.1756777
Mean113.0545
Median Absolute Deviation (MAD)6
Skewness-0.060331
Sum1130545
Variance52.959626
MonotonicityNot monotonic
2023-12-11T17:26:25.052359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 685
 
6.9%
101 631
 
6.3%
106 430
 
4.3%
116 425
 
4.2%
118 424
 
4.2%
109 424
 
4.2%
115 422
 
4.2%
111 421
 
4.2%
124 421
 
4.2%
102 420
 
4.2%
Other values (15) 5297
53.0%
ValueCountFrequency (%)
101 631
6.3%
102 420
4.2%
103 205
 
2.1%
104 418
4.2%
105 207
 
2.1%
106 430
4.3%
107 396
4.0%
108 408
4.1%
109 424
4.2%
110 401
4.0%
ValueCountFrequency (%)
125 377
3.8%
124 421
4.2%
123 411
4.1%
122 402
4.0%
121 395
4.0%
120 413
4.1%
119 417
4.2%
118 424
4.2%
117 420
4.2%
116 425
4.2%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구
 
685
강남구
 
631
성북구
 
430
용산구
 
425
성동구
 
424
Other values (20)
7405 

Length

Max length4
Median length3
Mean length3.0626
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row양천구
3rd row금천구
4th row은평구
5th row강동구

Common Values

ValueCountFrequency (%)
은평구 685
 
6.9%
강남구 631
 
6.3%
성북구 430
 
4.3%
용산구 425
 
4.2%
성동구 424
 
4.2%
양천구 424
 
4.2%
마포구 422
 
4.2%
중구 421
 
4.2%
서초구 421
 
4.2%
강서구 420
 
4.2%
Other values (15) 5297
53.0%

Length

2023-12-11T17:26:25.206839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 685
 
6.9%
강남구 631
 
6.3%
성북구 430
 
4.3%
용산구 425
 
4.2%
성동구 424
 
4.2%
양천구 424
 
4.2%
마포구 422
 
4.2%
중구 421
 
4.2%
서초구 421
 
4.2%
강서구 420
 
4.2%
Other values (15) 5297
53.0%

10분우량
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0562
Minimum0
Maximum11
Zeros9341
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:26:25.348404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.5
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30724746
Coefficient of variation (CV)5.4670366
Kurtosis313.44324
Mean0.0562
Median Absolute Deviation (MAD)0
Skewness13.604851
Sum562
Variance0.094401
MonotonicityNot monotonic
2023-12-11T17:26:25.461981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 9341
93.4%
0.5 453
 
4.5%
1.0 112
 
1.1%
1.5 37
 
0.4%
2.0 25
 
0.2%
2.5 16
 
0.2%
3.0 6
 
0.1%
3.5 3
 
< 0.1%
6.5 2
 
< 0.1%
7.5 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0.0 9341
93.4%
0.5 453
 
4.5%
1.0 112
 
1.1%
1.5 37
 
0.4%
2.0 25
 
0.2%
2.5 16
 
0.2%
3.0 6
 
0.1%
3.5 3
 
< 0.1%
4.5 1
 
< 0.1%
5.5 1
 
< 0.1%
ValueCountFrequency (%)
11.0 1
 
< 0.1%
8.0 1
 
< 0.1%
7.5 1
 
< 0.1%
6.5 2
 
< 0.1%
5.5 1
 
< 0.1%
4.5 1
 
< 0.1%
3.5 3
 
< 0.1%
3.0 6
 
0.1%
2.5 16
0.2%
2.0 25
0.2%

자료수집 시각
Date

Distinct2106
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-10-01 00:09:00
Maximum2022-10-15 14:29:00
2023-12-11T17:26:25.588124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:25.778990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:26:23.916761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:23.338582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:23.624892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:24.011025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:23.432258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:23.729210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:24.107271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:23.524904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:26:23.827678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:26:25.898273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.039
강우량계명1.0001.0001.0001.0000.030
구청 코드1.0001.0001.0001.0000.037
구청명1.0001.0001.0001.0000.048
10분우량0.0390.0300.0370.0481.000
2023-12-11T17:26:26.019235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:26:26.134510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.9990.0080.9980.982
구청 코드0.9991.0000.0090.9980.999
10분우량0.0080.0091.0000.0180.017
강우량계명0.9980.9980.0181.0000.999
구청명0.9820.9990.0170.9991.000

Missing values

2023-12-11T17:26:24.244663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:26:24.353910image/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
115891001종로구청110종로구0.02022-10-02 16:29
668931801양천구청118양천구0.02022-10-10 16:59
872712201금천구청122금천구0.02022-10-13 18:19
57381301은평구청113은평구0.02022-10-01 20:09
96603202고덕2동102강동구0.02022-10-15 02:39
41981601용산구청116용산구0.02022-10-01 14:39
55200501강북구청105강북구0.02022-10-09 00:29
62685802휘경P108동대문구0.02022-10-10 02:29
68901301은평구청113은평구0.02022-10-02 00:09
46261901성동구청109성동구0.02022-10-07 16:59
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
298441702공항동P117강서구0.02022-10-05 07:49
76185702면목P107중랑구0.02022-10-12 01:19
7263601성북구청106성북구0.02022-10-02 01:29
443071501마포구청115마포구0.02022-10-07 10:09
25171901성동구청109성동구0.02022-10-04 15:39
503492401서초구청124서초구0.02022-10-08 07:29
867002202가산2P122금천구0.02022-10-13 16:19
61575201강동구청102강동구0.02022-10-09 22:39
835201101중구청111중구0.02022-10-13 04:39
110272202가산2P122금천구0.02022-10-02 14:29