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 = 61.57676447)Skewed
10분우량 has 9606 (96.1%) zerosZeros

Reproduction

Analysis started2023-12-11 08:24:14.796447
Analysis finished2023-12-11 08:24:17.331915
Duration2.54 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%
Mean1310.8163
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:17.433474image/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 deviation732.60955
Coefficient of variation (CV)0.55889567
Kurtosis-1.1821638
Mean1310.8163
Median Absolute Deviation (MAD)600
Skewness-0.029828419
Sum13108163
Variance536716.75
MonotonicityNot monotonic
2023-12-11T17:24:17.640090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2301 242
 
2.4%
1502 241
 
2.4%
702 239
 
2.4%
1104 231
 
2.3%
1001 231
 
2.3%
2102 231
 
2.3%
2501 230
 
2.3%
2401 228
 
2.3%
401 224
 
2.2%
1501 223
 
2.2%
Other values (37) 7680
76.8%
ValueCountFrequency (%)
101 212
2.1%
102 199
2.0%
103 205
2.1%
201 207
2.1%
202 196
2.0%
301 187
1.9%
401 224
2.2%
402 206
2.1%
501 217
2.2%
601 221
2.2%
ValueCountFrequency (%)
2502 210
2.1%
2501 230
2.3%
2402 209
2.1%
2401 228
2.3%
2302 212
2.1%
2301 242
2.4%
2202 221
2.2%
2201 218
2.2%
2102 231
2.3%
2101 183
1.8%

강우량계명
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
관악구청
 
242
봉원P
 
241
면목P
 
239
종로구청
 
231
흑석P
 
231
Other values (42)
8816 

Length

Max length5
Median length4
Mean length3.7802
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부암동
2nd row동작구청
3rd row서소문
4th row서초구청
5th row한남P

Common Values

ValueCountFrequency (%)
관악구청 242
 
2.4%
봉원P 241
 
2.4%
면목P 239
 
2.4%
종로구청 231
 
2.3%
흑석P 231
 
2.3%
서소문 231
 
2.3%
송파구청 230
 
2.3%
서초구청 228
 
2.3%
노원구청 224
 
2.2%
마포구청 223
 
2.2%
Other values (37) 7680
76.8%

Length

2023-12-11T17:24:17.848610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관악구청 242
 
2.4%
봉원p 241
 
2.4%
면목p 239
 
2.4%
흑석p 231
 
2.3%
서소문 231
 
2.3%
종로구청 231
 
2.3%
송파구청 230
 
2.3%
서초구청 228
 
2.3%
노원구청 224
 
2.2%
마포구청 223
 
2.2%
Other values (37) 7680
76.8%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.0926
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:18.034078image/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 deviation7.3265813
Coefficient of variation (CV)0.064783914
Kurtosis-1.1820541
Mean113.0926
Median Absolute Deviation (MAD)6
Skewness-0.029749086
Sum1130926
Variance53.678793
MonotonicityNot monotonic
2023-12-11T17:24:18.197611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 626
 
6.3%
101 616
 
6.2%
115 464
 
4.6%
123 454
 
4.5%
111 448
 
4.5%
107 447
 
4.5%
125 440
 
4.4%
122 439
 
4.4%
124 437
 
4.4%
117 435
 
4.3%
Other values (15) 5194
51.9%
ValueCountFrequency (%)
101 616
6.2%
102 403
4.0%
103 187
 
1.9%
104 430
4.3%
105 217
 
2.2%
106 431
4.3%
107 447
4.5%
108 420
4.2%
109 408
4.1%
110 426
4.3%
ValueCountFrequency (%)
125 440
4.4%
124 437
4.4%
123 454
4.5%
122 439
4.4%
121 414
4.1%
120 206
2.1%
119 408
4.1%
118 424
4.2%
117 435
4.3%
116 420
4.2%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구
 
626
강남구
 
616
마포구
 
464
관악구
 
454
중구
 
448
Other values (20)
7392 

Length

Max length4
Median length3
Mean length3.0582
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row동작구
3rd row중구
4th row서초구
5th row용산구

Common Values

ValueCountFrequency (%)
은평구 626
 
6.3%
강남구 616
 
6.2%
마포구 464
 
4.6%
관악구 454
 
4.5%
중구 448
 
4.5%
중랑구 447
 
4.5%
송파구 440
 
4.4%
금천구 439
 
4.4%
서초구 437
 
4.4%
강서구 435
 
4.3%
Other values (15) 5194
51.9%

Length

2023-12-11T17:24:18.378252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 626
 
6.3%
강남구 616
 
6.2%
마포구 464
 
4.6%
관악구 454
 
4.5%
중구 448
 
4.5%
중랑구 447
 
4.5%
송파구 440
 
4.4%
금천구 439
 
4.4%
서초구 437
 
4.4%
강서구 435
 
4.3%
Other values (15) 5194
51.9%

10분우량
Real number (ℝ)

SKEWED  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04685
Minimum0
Maximum45.5
Zeros9606
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:18.533782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum45.5
Range45.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54116482
Coefficient of variation (CV)11.55101
Kurtosis5009.7625
Mean0.04685
Median Absolute Deviation (MAD)0
Skewness61.576764
Sum468.5
Variance0.29285936
MonotonicityNot monotonic
2023-12-11T17:24:18.684337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 9606
96.1%
0.5 227
 
2.3%
1.0 57
 
0.6%
1.5 38
 
0.4%
2.0 34
 
0.3%
3.0 15
 
0.1%
2.5 10
 
0.1%
3.5 7
 
0.1%
12.0 1
 
< 0.1%
7.5 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0.0 9606
96.1%
0.5 227
 
2.3%
1.0 57
 
0.6%
1.5 38
 
0.4%
2.0 34
 
0.3%
2.5 10
 
0.1%
3.0 15
 
0.1%
3.5 7
 
0.1%
4.0 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
45.5 1
 
< 0.1%
12.0 1
 
< 0.1%
7.5 1
 
< 0.1%
5.0 1
 
< 0.1%
4.5 1
 
< 0.1%
4.0 1
 
< 0.1%
3.5 7
 
0.1%
3.0 15
0.1%
2.5 10
 
0.1%
2.0 34
0.3%

자료수집 시각
Date

Distinct2134
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-07-01 00:09:00
Maximum2021-07-15 21:29:00
2023-12-11T17:24:18.854341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:19.025973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:24:16.565292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:15.370474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:15.784283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:16.726811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:15.499532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:15.939599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:16.882744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:15.631645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:16.420641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:24:19.136473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.027
강우량계명1.0001.0001.0001.0000.018
구청 코드1.0001.0001.0001.0000.027
구청명1.0001.0001.0001.0000.000
10분우량0.0270.0180.0270.0001.000
2023-12-11T17:24:19.271287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:24:19.376593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.999-0.0060.9980.983
구청 코드0.9991.000-0.0070.9980.999
10분우량-0.006-0.0071.0000.0090.000
강우량계명0.9980.9980.0091.0000.999
구청명0.9830.9990.0000.9991.000

Missing values

2023-12-11T17:24:17.054691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:24:17.244263image/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
465981002부암동110종로구0.02021-07-07 22:49
767282101동작구청121동작구0.02021-07-12 10:29
73661104서소문111중구0.02021-07-02 02:29
86972401서초구청124서초구0.02021-07-02 07:29
944271602한남P116용산구0.02021-07-15 01:29
864991702공항동P117강서구0.02021-07-13 21:29
41813801동대문구청108동대문구0.02021-07-07 05:49
80396701중랑구청107중랑구0.02021-07-12 23:29
26447301도봉구청103도봉구0.02021-07-04 22:59
636122401서초구청124서초구0.02021-07-10 11:29
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
95441301은평구청113은평구0.02021-07-02 10:49
759581702공항동P117강서구0.02021-07-12 07:39
46756601성북구청106성북구0.02021-07-07 23:19
62008202고덕2동102강동구0.02021-07-10 05:39
79572301도봉구청103도봉구0.02021-07-12 20:29
47943702면목P107중랑구0.02021-07-08 03:39
840781901영등포구청119영등포구0.02021-07-13 12:49
71567402상계1동104노원구0.02021-07-11 15:49
97897401노원구청104노원구0.02021-07-15 13:59
59477701중랑구청107중랑구0.02021-07-09 20:29