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 = 35.92787987)Skewed
10분우량 has 9777 (97.8%) zerosZeros

Reproduction

Analysis started2023-12-11 08:25:12.303269
Analysis finished2023-12-11 08:25:14.640806
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강우량계 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1273.8938
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:14.757217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1701
median1301
Q31901
95-th percentile2402
Maximum2502
Range2401
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation727.36351
Coefficient of variation (CV)0.57097657
Kurtosis-1.1658556
Mean1273.8938
Median Absolute Deviation (MAD)600
Skewness0.020265299
Sum12738938
Variance529057.68
MonotonicityNot monotonic
2023-12-11T17:25:14.962239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
301 248
 
2.5%
901 246
 
2.5%
401 241
 
2.4%
1801 236
 
2.4%
1104 233
 
2.3%
1702 227
 
2.3%
1001 226
 
2.3%
102 225
 
2.2%
2001 224
 
2.2%
602 224
 
2.2%
Other values (37) 7670
76.7%
ValueCountFrequency (%)
101 215
2.1%
102 225
2.2%
103 211
2.1%
201 201
2.0%
202 212
2.1%
301 248
2.5%
401 241
2.4%
402 222
2.2%
501 219
2.2%
601 189
1.9%
ValueCountFrequency (%)
2502 185
1.8%
2501 202
2.0%
2402 208
2.1%
2401 206
2.1%
2302 208
2.1%
2301 217
2.2%
2202 159
1.6%
2201 143
1.4%
2102 217
2.2%
2101 201
2.0%

강우량계명
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
도봉구청
 
248
성동구청
 
246
노원구청
 
241
양천구청
 
236
서소문
 
233
Other values (42)
8796 

Length

Max length5
Median length4
Mean length3.7842
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서대문구청
2nd row영등포구청
3rd row성북구청
4th row세곡동
5th row서소문

Common Values

ValueCountFrequency (%)
도봉구청 248
 
2.5%
성동구청 246
 
2.5%
노원구청 241
 
2.4%
양천구청 236
 
2.4%
서소문 233
 
2.3%
공항동P 227
 
2.3%
종로구청 226
 
2.3%
세곡동 225
 
2.2%
상월곡동 224
 
2.2%
구로구청 224
 
2.2%
Other values (37) 7670
76.7%

Length

2023-12-11T17:25:15.174253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도봉구청 248
 
2.5%
성동구청 246
 
2.5%
노원구청 241
 
2.4%
양천구청 236
 
2.4%
서소문 233
 
2.3%
공항동p 227
 
2.3%
종로구청 226
 
2.3%
세곡동 225
 
2.2%
상월곡동 224
 
2.2%
구로구청 224
 
2.2%
Other values (37) 7670
76.7%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.7234
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:15.370292image/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 deviation7.2741036
Coefficient of variation (CV)0.064530556
Kurtosis-1.1657329
Mean112.7234
Median Absolute Deviation (MAD)6
Skewness0.020321512
Sum1127234
Variance52.912584
MonotonicityNot monotonic
2023-12-11T17:25:15.564334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
101 651
 
6.5%
113 620
 
6.2%
109 466
 
4.7%
104 463
 
4.6%
110 444
 
4.4%
118 441
 
4.4%
111 439
 
4.4%
108 434
 
4.3%
107 434
 
4.3%
115 433
 
4.3%
Other values (15) 5175
51.7%
ValueCountFrequency (%)
101 651
6.5%
102 413
4.1%
103 248
 
2.5%
104 463
4.6%
105 219
 
2.2%
106 413
4.1%
107 434
4.3%
108 434
4.3%
109 466
4.7%
110 444
4.4%
ValueCountFrequency (%)
125 387
3.9%
124 414
4.1%
123 425
4.2%
122 302
3.0%
121 418
4.2%
120 224
2.2%
119 430
4.3%
118 441
4.4%
117 428
4.3%
116 429
4.3%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강남구
 
651
은평구
 
620
성동구
 
466
노원구
 
463
종로구
 
444
Other values (20)
7356 

Length

Max length4
Median length3
Mean length3.0637
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서대문구
2nd row영등포구
3rd row성북구
4th row강남구
5th row중구

Common Values

ValueCountFrequency (%)
강남구 651
 
6.5%
은평구 620
 
6.2%
성동구 466
 
4.7%
노원구 463
 
4.6%
종로구 444
 
4.4%
양천구 441
 
4.4%
중구 439
 
4.4%
중랑구 434
 
4.3%
동대문구 434
 
4.3%
마포구 433
 
4.3%
Other values (15) 5175
51.7%

Length

2023-12-11T17:25:15.765671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 651
 
6.5%
은평구 620
 
6.2%
성동구 466
 
4.7%
노원구 463
 
4.6%
종로구 444
 
4.4%
양천구 441
 
4.4%
중구 439
 
4.4%
중랑구 434
 
4.3%
동대문구 434
 
4.3%
마포구 433
 
4.3%
Other values (15) 5175
51.7%

10분우량
Real number (ℝ)

SKEWED  ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0193
Minimum0
Maximum13
Zeros9777
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:16.252109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25765121
Coefficient of variation (CV)13.349804
Kurtosis1595.3481
Mean0.0193
Median Absolute Deviation (MAD)0
Skewness35.92788
Sum193
Variance0.066384148
MonotonicityNot monotonic
2023-12-11T17:25:16.401946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 9777
97.8%
0.5 185
 
1.8%
1.0 18
 
0.2%
1.5 5
 
0.1%
2.0 4
 
< 0.1%
2.5 3
 
< 0.1%
4.5 2
 
< 0.1%
4.0 1
 
< 0.1%
12.5 1
 
< 0.1%
3.5 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0.0 9777
97.8%
0.5 185
 
1.8%
1.0 18
 
0.2%
1.5 5
 
0.1%
2.0 4
 
< 0.1%
2.5 3
 
< 0.1%
3.5 1
 
< 0.1%
4.0 1
 
< 0.1%
4.5 2
 
< 0.1%
6.5 1
 
< 0.1%
ValueCountFrequency (%)
13.0 1
 
< 0.1%
12.5 1
 
< 0.1%
11.0 1
 
< 0.1%
6.5 1
 
< 0.1%
4.5 2
 
< 0.1%
4.0 1
 
< 0.1%
3.5 1
 
< 0.1%
2.5 3
< 0.1%
2.0 4
< 0.1%
1.5 5
0.1%

자료수집 시각
Date

Distinct2160
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-10-01 00:09:00
Maximum2021-10-15 23:39:00
2023-12-11T17:25:16.571650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:16.746310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:25:13.963066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:13.000413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:13.490133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:14.094655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:13.165819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:13.640470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:14.237460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:13.325847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:13.795229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:25:16.856911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.032
강우량계명1.0001.0001.0001.0000.000
구청 코드1.0001.0001.0001.0000.042
구청명1.0001.0001.0001.0000.047
10분우량0.0320.0000.0420.0471.000
2023-12-11T17:25:16.984446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:25:17.087343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.999-0.0150.9980.983
구청 코드0.9991.000-0.0150.9980.999
10분우량-0.015-0.0151.0000.0000.020
강우량계명0.9980.9980.0001.0000.999
구청명0.9830.9990.0200.9991.000

Missing values

2023-12-11T17:25:14.410806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:25:14.561491image/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
279911401서대문구청114서대문구0.02021-10-05 05:39
386091901영등포구청119영등포구0.02021-10-06 19:29
63111601성북구청106성북구0.02021-10-10 10:49
72284102세곡동101강남구0.02021-10-11 20:49
727391104서소문111중구0.02021-10-11 22:29
988581502봉원P115마포구0.02021-10-15 19:29
908701중랑구청107중랑구0.02021-10-01 03:19
340192501송파구청125송파구0.02021-10-06 03:19
556971901영등포구청119영등포구0.02021-10-09 08:19
976472302신림P123관악구0.02021-10-15 15:19
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
30595602상월곡동106성북구0.02021-10-05 15:09
33781401노원구청104노원구0.02021-10-06 02:29
624981002부암동110종로구0.02021-10-10 08:39
871391301은평구청113은평구0.02021-10-14 01:59
437731701강서구청117강서구0.02021-10-07 13:49
624421702공항동P117강서구0.02021-10-10 08:29
871461701강서구청117강서구0.02021-10-14 01:59
3816601성북구청106성북구0.02021-10-01 13:39
983131901영등포구청119영등포구0.02021-10-15 17:39
968271502봉원P115마포구0.02021-10-15 12:19