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 = 38.48230644)Skewed
10분우량 has 9888 (98.9%) zerosZeros

Reproduction

Analysis started2023-12-11 08:25:23.154212
Analysis finished2023-12-11 08:25:24.914697
Duration1.76 second
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%
Mean1289.8429
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:25.025136image/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 deviation740.27191
Coefficient of variation (CV)0.57392409
Kurtosis-1.1909999
Mean1289.8429
Median Absolute Deviation (MAD)600
Skewness0.0083043525
Sum12898429
Variance548002.5
MonotonicityNot monotonic
2023-12-11T17:25:25.557815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
102 258
 
2.6%
1002 246
 
2.5%
2401 243
 
2.4%
701 234
 
2.3%
401 234
 
2.3%
2402 233
 
2.3%
1001 231
 
2.3%
2502 229
 
2.3%
601 228
 
2.3%
1301 225
 
2.2%
Other values (37) 7639
76.4%
ValueCountFrequency (%)
101 215
2.1%
102 258
2.6%
103 208
2.1%
201 215
2.1%
202 224
2.2%
301 204
2.0%
401 234
2.3%
402 208
2.1%
501 212
2.1%
601 228
2.3%
ValueCountFrequency (%)
2502 229
2.3%
2501 203
2.0%
2402 233
2.3%
2401 243
2.4%
2302 220
2.2%
2301 217
2.2%
2202 220
2.2%
2201 189
1.9%
2102 210
2.1%
2101 212
2.1%

강우량계명
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
세곡동
 
258
부암동
 
246
서초구청
 
243
노원구청
 
234
중랑구청
 
234
Other values (42)
8785 

Length

Max length5
Median length4
Mean length3.778
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구청
2nd row양천구청
3rd row중랑구청
4th row공항동P
5th row금천구청

Common Values

ValueCountFrequency (%)
세곡동 258
 
2.6%
부암동 246
 
2.5%
서초구청 243
 
2.4%
노원구청 234
 
2.3%
중랑구청 234
 
2.3%
반포P 233
 
2.3%
종로구청 231
 
2.3%
마천2동 229
 
2.3%
성북구청 228
 
2.3%
서대문구청 225
 
2.2%
Other values (37) 7639
76.4%

Length

2023-12-11T17:25:25.747784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
세곡동 258
 
2.6%
부암동 246
 
2.5%
서초구청 243
 
2.4%
노원구청 234
 
2.3%
중랑구청 234
 
2.3%
반포p 233
 
2.3%
종로구청 231
 
2.3%
마천2동 229
 
2.3%
성북구청 228
 
2.3%
서대문구청 225
 
2.2%
Other values (37) 7639
76.4%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.8829
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:25.894196image/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 deviation7.4031256
Coefficient of variation (CV)0.065582347
Kurtosis-1.1908621
Mean112.8829
Median Absolute Deviation (MAD)6
Skewness0.0083138304
Sum1128829
Variance54.806268
MonotonicityNot monotonic
2023-12-11T17:25:26.071272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
101 681
 
6.8%
113 650
 
6.5%
110 477
 
4.8%
124 476
 
4.8%
107 447
 
4.5%
104 442
 
4.4%
102 439
 
4.4%
123 437
 
4.4%
115 433
 
4.3%
125 432
 
4.3%
Other values (15) 5086
50.9%
ValueCountFrequency (%)
101 681
6.8%
102 439
4.4%
103 204
 
2.0%
104 442
4.4%
105 212
 
2.1%
106 405
4.0%
107 447
4.5%
108 417
4.2%
109 404
4.0%
110 477
4.8%
ValueCountFrequency (%)
125 432
4.3%
124 476
4.8%
123 437
4.4%
122 409
4.1%
121 422
4.2%
120 184
 
1.8%
119 390
3.9%
118 409
4.1%
117 347
3.5%
116 417
4.2%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강남구
 
681
은평구
 
650
종로구
 
477
서초구
 
476
중랑구
 
447
Other values (20)
7269 

Length

Max length4
Median length3
Mean length3.0616
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구
2nd row양천구
3rd row중랑구
4th row강서구
5th row금천구

Common Values

ValueCountFrequency (%)
강남구 681
 
6.8%
은평구 650
 
6.5%
종로구 477
 
4.8%
서초구 476
 
4.8%
중랑구 447
 
4.5%
노원구 442
 
4.4%
강동구 439
 
4.4%
관악구 437
 
4.4%
마포구 433
 
4.3%
송파구 432
 
4.3%
Other values (15) 5086
50.9%

Length

2023-12-11T17:25:26.268053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 681
 
6.8%
은평구 650
 
6.5%
종로구 477
 
4.8%
서초구 476
 
4.8%
중랑구 447
 
4.5%
노원구 442
 
4.4%
강동구 439
 
4.4%
관악구 437
 
4.4%
마포구 433
 
4.3%
송파구 432
 
4.3%
Other values (15) 5086
50.9%

10분우량
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0119
Minimum0
Maximum12.5
Zeros9888
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:25:26.421782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12.5
Range12.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18617695
Coefficient of variation (CV)15.645122
Kurtosis2176.8431
Mean0.0119
Median Absolute Deviation (MAD)0
Skewness38.482306
Sum119
Variance0.034661856
MonotonicityNot monotonic
2023-12-11T17:25:26.583120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 9888
98.9%
0.5 75
 
0.8%
1.0 14
 
0.1%
1.5 9
 
0.1%
3.0 4
 
< 0.1%
2.5 4
 
< 0.1%
3.5 3
 
< 0.1%
4.0 1
 
< 0.1%
12.5 1
 
< 0.1%
5.0 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9888
98.9%
0.5 75
 
0.8%
1.0 14
 
0.1%
1.5 9
 
0.1%
2.5 4
 
< 0.1%
3.0 4
 
< 0.1%
3.5 3
 
< 0.1%
4.0 1
 
< 0.1%
5.0 1
 
< 0.1%
12.5 1
 
< 0.1%
ValueCountFrequency (%)
12.5 1
 
< 0.1%
5.0 1
 
< 0.1%
4.0 1
 
< 0.1%
3.5 3
 
< 0.1%
3.0 4
 
< 0.1%
2.5 4
 
< 0.1%
1.5 9
 
0.1%
1.0 14
 
0.1%
0.5 75
 
0.8%
0.0 9888
98.9%

자료수집 시각
Date

Distinct2228
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-08-01 00:09:00
Maximum2021-08-16 13:29:00
2023-12-11T17:25:26.792305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:27.016499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:25:24.308717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:23.680365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:23.988183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:24.469128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:23.777438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:24.086035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:24.595565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:23.885600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:25:24.194425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:25:27.130360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.031
강우량계명1.0001.0001.0001.0000.000
구청 코드1.0001.0001.0001.0000.025
구청명1.0001.0001.0001.0000.000
10분우량0.0310.0000.0250.0001.000
2023-12-11T17:25:27.254828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:25:27.359418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.9990.0050.9980.982
구청 코드0.9991.0000.0040.9980.999
10분우량0.0050.0041.0000.0090.000
강우량계명0.9980.9980.0091.0000.999
구청명0.9820.9990.0000.9991.000

Missing values

2023-12-11T17:25:24.747020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:25:24.863711image/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
365782501송파구청125송파구0.02021-08-06 11:29
651571801양천구청118양천구0.02021-08-11 06:09
59923701중랑구청107중랑구0.02021-08-10 11:39
875681702공항동P117강서구0.02021-08-14 13:59
677112201금천구청122금천구0.02021-08-11 15:29
860781104서소문111중구0.02021-08-14 08:39
329001401서대문구청114서대문구0.02021-08-05 22:29
36779602상월곡동106성북구0.02021-08-06 12:19
31475901성동구청109성동구0.02021-08-05 17:29
959781902도림2동P119영등포구0.02021-08-15 21:49
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
484532201금천구청122금천구0.02021-08-08 06:19
200521001종로구청110종로구0.02021-08-04 00:49
20375102세곡동101강남구0.02021-08-04 01:59
699451001종로구청110종로구0.02021-08-11 23:19
834861602한남P116용산구0.02021-08-13 23:19
243441701강서구청117강서구0.02021-08-04 15:59
93634102세곡동101강남구0.02021-08-15 12:59
930571201광진구청112광진구0.02021-08-15 10:49
131301은평구청113은평구0.02021-08-01 00:09
72409202고덕2동102강동구0.02021-08-12 08:09