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 9742 (97.4%) zerosZeros

Reproduction

Analysis started2023-12-11 08:24:20.787893
Analysis finished2023-12-11 08:24:23.068034
Duration2.28 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%
Mean1308.7032
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:23.186917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1702
median1302
Q31902
95-th percentile2402
Maximum2502
Range2401
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation732.69796
Coefficient of variation (CV)0.55986564
Kurtosis-1.1890824
Mean1308.7032
Median Absolute Deviation (MAD)600
Skewness-0.05676663
Sum13087032
Variance536846.3
MonotonicityNot monotonic
2023-12-11T17:24:23.435180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1001 250
 
2.5%
1301 240
 
2.4%
103 234
 
2.3%
1502 226
 
2.3%
1602 225
 
2.2%
401 225
 
2.2%
802 221
 
2.2%
901 221
 
2.2%
201 219
 
2.2%
2401 217
 
2.2%
Other values (38) 7722
77.2%
ValueCountFrequency (%)
101 203
2.0%
102 214
2.1%
103 234
2.3%
201 219
2.2%
202 194
1.9%
301 211
2.1%
401 225
2.2%
402 213
2.1%
501 172
1.7%
601 194
1.9%
ValueCountFrequency (%)
2502 209
2.1%
2501 198
2.0%
2402 200
2.0%
2401 217
2.2%
2302 217
2.2%
2301 197
2.0%
2202 205
2.1%
2201 205
2.1%
2102 203
2.0%
2101 215
2.1%

강우량계명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
종로구청
 
250
은평구청
 
240
개포2동
 
234
봉원P
 
226
한남P
 
225
Other values (43)
8825 

Length

Max length5
Median length4
Mean length3.79
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양천구청
2nd row도림2동P
3rd row광진구청
4th row마포구청
5th row성동구청

Common Values

ValueCountFrequency (%)
종로구청 250
 
2.5%
은평구청 240
 
2.4%
개포2동 234
 
2.3%
봉원P 226
 
2.3%
한남P 225
 
2.2%
노원구청 225
 
2.2%
휘경P 221
 
2.2%
성동구청 221
 
2.2%
강동구청 219
 
2.2%
서초구청 217
 
2.2%
Other values (38) 7722
77.2%

Length

2023-12-11T17:24:23.673144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구청 250
 
2.5%
은평구청 240
 
2.4%
개포2동 234
 
2.3%
봉원p 226
 
2.3%
한남p 225
 
2.2%
노원구청 225
 
2.2%
휘경p 221
 
2.2%
성동구청 221
 
2.2%
강동구청 219
 
2.2%
서초구청 217
 
2.2%
Other values (38) 7722
77.2%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.0714
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:23.875275image/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 deviation7.3274055
Coefficient of variation (CV)0.06480335
Kurtosis-1.1888952
Mean113.0714
Median Absolute Deviation (MAD)6
Skewness-0.056768325
Sum1130714
Variance53.690871
MonotonicityNot monotonic
2023-12-11T17:24:24.086289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
101 651
 
6.5%
113 625
 
6.2%
110 447
 
4.5%
104 438
 
4.4%
115 437
 
4.4%
119 430
 
4.3%
109 429
 
4.3%
111 424
 
4.2%
108 421
 
4.2%
121 418
 
4.2%
Other values (15) 5280
52.8%
ValueCountFrequency (%)
101 651
6.5%
102 413
4.1%
103 211
 
2.1%
104 438
4.4%
105 172
 
1.7%
106 401
4.0%
107 412
4.1%
108 421
4.2%
109 429
4.3%
110 447
4.5%
ValueCountFrequency (%)
125 407
4.1%
124 417
4.2%
123 414
4.1%
122 410
4.1%
121 418
4.2%
120 400
4.0%
119 430
4.3%
118 408
4.1%
117 408
4.1%
116 403
4.0%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강남구
 
651
은평구
 
625
종로구
 
447
노원구
 
438
마포구
 
437
Other values (20)
7402 

Length

Max length4
Median length3
Mean length3.0632
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양천구
2nd row영등포구
3rd row광진구
4th row마포구
5th row성동구

Common Values

ValueCountFrequency (%)
강남구 651
 
6.5%
은평구 625
 
6.2%
종로구 447
 
4.5%
노원구 438
 
4.4%
마포구 437
 
4.4%
영등포구 430
 
4.3%
성동구 429
 
4.3%
중구 424
 
4.2%
동대문구 421
 
4.2%
동작구 418
 
4.2%
Other values (15) 5280
52.8%

Length

2023-12-11T17:24:24.313943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 651
 
6.5%
은평구 625
 
6.2%
종로구 447
 
4.5%
노원구 438
 
4.4%
마포구 437
 
4.4%
영등포구 430
 
4.3%
성동구 429
 
4.3%
중구 424
 
4.2%
동대문구 421
 
4.2%
동작구 418
 
4.2%
Other values (15) 5280
52.8%

10분우량
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0171
Minimum0
Maximum5.5
Zeros9742
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:24.486050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.13213378
Coefficient of variation (CV)7.7271217
Kurtosis430.91847
Mean0.0171
Median Absolute Deviation (MAD)0
Skewness15.869491
Sum171
Variance0.017459336
MonotonicityNot monotonic
2023-12-11T17:24:24.641813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 9742
97.4%
0.5 213
 
2.1%
1.0 29
 
0.3%
1.5 7
 
0.1%
2.5 4
 
< 0.1%
2.0 3
 
< 0.1%
5.5 1
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9742
97.4%
0.5 213
 
2.1%
1.0 29
 
0.3%
1.5 7
 
0.1%
2.0 3
 
< 0.1%
2.5 4
 
< 0.1%
3.5 1
 
< 0.1%
5.5 1
 
< 0.1%
ValueCountFrequency (%)
5.5 1
 
< 0.1%
3.5 1
 
< 0.1%
2.5 4
 
< 0.1%
2.0 3
 
< 0.1%
1.5 7
 
0.1%
1.0 29
 
0.3%
0.5 213
 
2.1%
0.0 9742
97.4%

자료수집 시각
Date

Distinct2115
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-06-01 00:09:00
Maximum2021-06-15 15:09:00
2023-12-11T17:24:24.852700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:25.080527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:24:22.337548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:21.455294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:21.888477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:22.484178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:21.605530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:22.035059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:22.616079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:21.750430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:22.198199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:24:25.608905image/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
2023-12-11T17:24:25.762500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:24:25.918266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.999-0.0210.9980.982
구청 코드0.9991.000-0.0210.9980.999
10분우량-0.021-0.0211.0000.0000.000
강우량계명0.9980.9980.0001.0000.999
구청명0.9820.9990.0000.9991.000

Missing values

2023-12-11T17:24:22.825170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:24:22.995626image/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
234191801양천구청118양천구0.02021-06-04 11:29
928501902도림2동P119영등포구0.02021-06-14 13:49
387631201광진구청112광진구0.02021-06-06 16:59
416911501마포구청115마포구0.02021-06-07 03:29
82775901성동구청109성동구0.02021-06-13 02:29
49820702면목P107중랑구0.02021-06-08 07:49
433071501마포구청115마포구0.02021-06-07 09:09
208372301관악구청123관악구0.02021-06-04 02:29
156402402반포P124서초구0.02021-06-03 08:19
292281802목동P118양천구0.02021-06-05 07:39
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
51198401노원구청104노원구0.02021-06-08 12:39
79860301도봉구청103도봉구0.02021-06-12 16:19
80682102흑석P121동작구0.02021-06-02 04:19
242181501마포구청115마포구0.02021-06-04 14:19
32509102세곡동101강남구0.02021-06-05 19:09
13644901성동구청109성동구0.02021-06-03 01:29
943011201광진구청112광진구0.02021-06-14 18:59
459582502마천2동125송파구0.02021-06-07 18:19
303852101동작구청121동작구0.02021-06-05 11:39
15501103개포2동101강남구0.02021-06-03 07:59