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 9304 (93.0%) zerosZeros

Reproduction

Analysis started2023-12-11 08:24:27.418148
Analysis finished2023-12-11 08:24:29.510774
Duration2.09 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%
Mean1312.4857
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:29.594722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1701
median1303
Q31902
95-th percentile2402
Maximum2502
Range2401
Interquartile range (IQR)1201

Descriptive statistics

Standard deviation733.24812
Coefficient of variation (CV)0.55867132
Kurtosis-1.2041767
Mean1312.4857
Median Absolute Deviation (MAD)601
Skewness-0.092504072
Sum13124857
Variance537652.8
MonotonicityNot monotonic
2023-12-11T17:24:29.808078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1802 236
 
2.4%
2202 234
 
2.3%
1901 232
 
2.3%
1801 232
 
2.3%
1302 232
 
2.3%
2302 231
 
2.3%
1702 229
 
2.3%
1902 229
 
2.3%
401 228
 
2.3%
1501 226
 
2.3%
Other values (38) 7691
76.9%
ValueCountFrequency (%)
101 220
2.2%
102 209
2.1%
103 220
2.2%
201 206
2.1%
202 222
2.2%
301 209
2.1%
401 228
2.3%
402 212
2.1%
501 216
2.2%
601 214
2.1%
ValueCountFrequency (%)
2502 208
2.1%
2501 206
2.1%
2402 176
1.8%
2401 147
1.5%
2302 231
2.3%
2301 199
2.0%
2202 234
2.3%
2201 213
2.1%
2102 200
2.0%
2101 225
2.2%

강우량계명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
목동P
 
236
가산2P
 
234
영등포구청
 
232
증산P
 
232
양천구청
 
232
Other values (43)
8834 

Length

Max length5
Median length4
Mean length3.8052
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서초구청
2nd row중랑구청
3rd row면목P
4th row송파구청
5th row노원구청

Common Values

ValueCountFrequency (%)
목동P 236
 
2.4%
가산2P 234
 
2.3%
영등포구청 232
 
2.3%
증산P 232
 
2.3%
양천구청 232
 
2.3%
신림P 231
 
2.3%
공항동P 229
 
2.3%
도림2동P 229
 
2.3%
노원구청 228
 
2.3%
마포구청 226
 
2.3%
Other values (38) 7691
76.9%

Length

2023-12-11T17:24:29.973129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목동p 236
 
2.4%
가산2p 234
 
2.3%
영등포구청 232
 
2.3%
증산p 232
 
2.3%
양천구청 232
 
2.3%
신림p 231
 
2.3%
공항동p 229
 
2.3%
도림2동p 229
 
2.3%
노원구청 228
 
2.3%
마포구청 226
 
2.3%
Other values (38) 7691
76.9%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.1093
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:30.151810image/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 deviation7.3328255
Coefficient of variation (CV)0.064829555
Kurtosis-1.2040107
Mean113.1093
Median Absolute Deviation (MAD)6
Skewness-0.092523829
Sum1131093
Variance53.770331
MonotonicityNot monotonic
2023-12-11T17:24:30.319357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
101 649
 
6.5%
113 646
 
6.5%
118 468
 
4.7%
119 461
 
4.6%
122 447
 
4.5%
115 447
 
4.5%
104 440
 
4.4%
117 436
 
4.4%
123 430
 
4.3%
102 428
 
4.3%
Other values (15) 5148
51.5%
ValueCountFrequency (%)
101 649
6.5%
102 428
4.3%
103 209
 
2.1%
104 440
4.4%
105 216
 
2.2%
106 412
4.1%
107 386
3.9%
108 425
4.2%
109 424
4.2%
110 337
3.4%
ValueCountFrequency (%)
125 414
4.1%
124 323
3.2%
123 430
4.3%
122 447
4.5%
121 425
4.2%
120 410
4.1%
119 461
4.6%
118 468
4.7%
117 436
4.4%
116 402
4.0%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강남구
 
649
은평구
 
646
양천구
 
468
영등포구
 
461
금천구
 
447
Other values (20)
7329 

Length

Max length4
Median length3
Mean length3.077
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서초구
2nd row중랑구
3rd row중랑구
4th row송파구
5th row노원구

Common Values

ValueCountFrequency (%)
강남구 649
 
6.5%
은평구 646
 
6.5%
양천구 468
 
4.7%
영등포구 461
 
4.6%
금천구 447
 
4.5%
마포구 447
 
4.5%
노원구 440
 
4.4%
강서구 436
 
4.4%
관악구 430
 
4.3%
강동구 428
 
4.3%
Other values (15) 5148
51.5%

Length

2023-12-11T17:24:30.493441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 649
 
6.5%
은평구 646
 
6.5%
양천구 468
 
4.7%
영등포구 461
 
4.6%
금천구 447
 
4.5%
마포구 447
 
4.5%
노원구 440
 
4.4%
강서구 436
 
4.4%
관악구 430
 
4.3%
강동구 428
 
4.3%
Other values (15) 5148
51.5%

10분우량
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1734
Minimum0
Maximum26
Zeros9304
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:30.682825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.5
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.782919
Coefficient of variation (CV)10.282117
Kurtosis190.57902
Mean0.1734
Median Absolute Deviation (MAD)0
Skewness13.764992
Sum1734
Variance3.1788003
MonotonicityNot monotonic
2023-12-11T17:24:30.855635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 9304
93.0%
0.5 466
 
4.7%
1.0 110
 
1.1%
1.5 38
 
0.4%
26.0 28
 
0.3%
2.0 15
 
0.1%
2.5 10
 
0.1%
24.0 9
 
0.1%
23.0 5
 
0.1%
25.5 4
 
< 0.1%
Other values (4) 11
 
0.1%
ValueCountFrequency (%)
0.0 9304
93.0%
0.5 466
 
4.7%
1.0 110
 
1.1%
1.5 38
 
0.4%
2.0 15
 
0.1%
2.5 10
 
0.1%
3.0 3
 
< 0.1%
3.5 3
 
< 0.1%
4.5 1
 
< 0.1%
23.0 5
 
0.1%
ValueCountFrequency (%)
26.0 28
0.3%
25.5 4
 
< 0.1%
24.0 9
 
0.1%
23.5 4
 
< 0.1%
23.0 5
 
0.1%
4.5 1
 
< 0.1%
3.5 3
 
< 0.1%
3.0 3
 
< 0.1%
2.5 10
 
0.1%
2.0 15
0.1%

자료수집 시각
Date

Distinct2176
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-05-01 00:09:00
Maximum2021-05-17 13:19:00
2023-12-11T17:24:31.015614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:31.211544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:24:28.924351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:28.086907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:28.518927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:29.055694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:28.208194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:28.656753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:29.168108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:28.359352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:28.791953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:24:31.362726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.182
강우량계명1.0001.0001.0001.0000.502
구청 코드1.0001.0001.0001.0000.182
구청명1.0001.0001.0001.0000.346
10분우량0.1820.5020.1820.3461.000
2023-12-11T17:24:31.490287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:24:31.597862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.9990.0250.9980.982
구청 코드0.9991.0000.0240.9980.999
10분우량0.0250.0241.0000.2600.189
강우량계명0.9980.9980.2601.0000.999
구청명0.9820.9990.1890.9991.000

Missing values

2023-12-11T17:24:29.338393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:24:29.460864image/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
912172401서초구청124서초구0.52021-05-16 06:09
80057701중랑구청107중랑구0.02021-05-14 15:29
52249702면목P107중랑구0.02021-05-10 10:39
876782501송파구청125송파구0.02021-05-15 17:59
69569401노원구청104노원구0.02021-05-13 02:09
847631002부암동110종로구0.02021-05-15 07:49
947861701강서구청117강서구1.02021-05-16 18:49
473821801양천구청118양천구0.02021-05-09 16:29
452562102흑석P121동작구0.02021-05-09 08:59
953491801양천구청118양천구0.02021-05-16 20:49
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
309302001구로구청120구로구0.02021-05-07 07:09
84854702면목P107중랑구0.02021-05-15 08:09
494451802목동P118양천구0.02021-05-09 23:39
7553801동대문구청108동대문구0.02021-05-02 06:49
80350402상계1동104노원구0.02021-05-14 16:29
433391802목동P118양천구0.02021-05-09 02:19
620512201금천구청122금천구0.02021-05-11 22:29
215231001종로구청110종로구0.02021-05-04 11:19
72211301은평구청113은평구0.02021-05-02 05:29
67851401서대문구청114서대문구0.02021-05-02 03:39