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 = 67.04385966)Skewed
10분우량 has 9500 (95.0%) zerosZeros

Reproduction

Analysis started2023-12-11 08:24:33.011944
Analysis finished2023-12-11 08:24:35.357902
Duration2.35 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%
Mean1324.643
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:35.470418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile103
Q1702
median1303
Q32001
95-th percentile2402
Maximum2502
Range2401
Interquartile range (IQR)1299

Descriptive statistics

Standard deviation730.1786
Coefficient of variation (CV)0.55122671
Kurtosis-1.1952543
Mean1324.643
Median Absolute Deviation (MAD)601
Skewness-0.063396928
Sum13246430
Variance533160.78
MonotonicityNot monotonic
2023-12-11T17:24:35.998039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2202 249
 
2.5%
1002 246
 
2.5%
2502 245
 
2.5%
2001 238
 
2.4%
1901 233
 
2.3%
602 232
 
2.3%
801 229
 
2.3%
1801 226
 
2.3%
202 223
 
2.2%
2302 223
 
2.2%
Other values (38) 7656
76.6%
ValueCountFrequency (%)
101 188
1.9%
102 199
2.0%
103 185
1.8%
201 198
2.0%
202 223
2.2%
301 210
2.1%
401 164
1.6%
402 184
1.8%
501 215
2.1%
601 221
2.2%
ValueCountFrequency (%)
2502 245
2.5%
2501 192
1.9%
2402 212
2.1%
2401 200
2.0%
2302 223
2.2%
2301 214
2.1%
2202 249
2.5%
2201 188
1.9%
2102 181
1.8%
2101 176
1.8%

강우량계명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가산2P
 
249
부암동
 
246
마천2동
 
245
구로구청
 
238
영등포구청
 
233
Other values (43)
8789 

Length

Max length5
Median length4
Mean length3.793
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row증산P
2nd row도봉구청
3rd row가산2P
4th row목동P
5th row가산2P

Common Values

ValueCountFrequency (%)
가산2P 249
 
2.5%
부암동 246
 
2.5%
마천2동 245
 
2.5%
구로구청 238
 
2.4%
영등포구청 233
 
2.3%
상월곡동 232
 
2.3%
동대문구청 229
 
2.3%
양천구청 226
 
2.3%
고덕2동 223
 
2.2%
신림P 223
 
2.2%
Other values (38) 7656
76.6%

Length

2023-12-11T17:24:36.203517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가산2p 249
 
2.5%
부암동 246
 
2.5%
마천2동 245
 
2.5%
구로구청 238
 
2.4%
영등포구청 233
 
2.3%
상월곡동 232
 
2.3%
동대문구청 229
 
2.3%
양천구청 226
 
2.3%
고덕2동 223
 
2.2%
신림p 223
 
2.2%
Other values (38) 7656
76.6%

구청 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.2307
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:36.387119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1107
median113
Q3120
95-th percentile124
Maximum125
Range24
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.3020825
Coefficient of variation (CV)0.06448854
Kurtosis-1.1951454
Mean113.2307
Median Absolute Deviation (MAD)6
Skewness-0.063361351
Sum1132307
Variance53.32041
MonotonicityNot monotonic
2023-12-11T17:24:36.570898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 618
 
6.2%
101 572
 
5.7%
120 459
 
4.6%
110 458
 
4.6%
106 453
 
4.5%
118 448
 
4.5%
108 442
 
4.4%
122 437
 
4.4%
125 437
 
4.4%
123 437
 
4.4%
Other values (15) 5239
52.4%
ValueCountFrequency (%)
101 572
5.7%
102 421
4.2%
103 210
 
2.1%
104 348
3.5%
105 215
 
2.1%
106 453
4.5%
107 427
4.3%
108 442
4.4%
109 418
4.2%
110 458
4.6%
ValueCountFrequency (%)
125 437
4.4%
124 412
4.1%
123 437
4.4%
122 437
4.4%
121 357
3.6%
120 459
4.6%
119 437
4.4%
118 448
4.5%
117 427
4.3%
116 394
3.9%

구청명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
은평구
 
618
강남구
 
572
구로구
 
459
종로구
 
458
성북구
 
453
Other values (20)
7440 

Length

Max length4
Median length3
Mean length3.0662
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row은평구
2nd row도봉구
3rd row금천구
4th row양천구
5th row금천구

Common Values

ValueCountFrequency (%)
은평구 618
 
6.2%
강남구 572
 
5.7%
구로구 459
 
4.6%
종로구 458
 
4.6%
성북구 453
 
4.5%
양천구 448
 
4.5%
동대문구 442
 
4.4%
금천구 437
 
4.4%
송파구 437
 
4.4%
관악구 437
 
4.4%
Other values (15) 5239
52.4%

Length

2023-12-11T17:24:36.769826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 618
 
6.2%
강남구 572
 
5.7%
구로구 459
 
4.6%
종로구 458
 
4.6%
성북구 453
 
4.5%
양천구 448
 
4.5%
동대문구 442
 
4.4%
금천구 437
 
4.4%
송파구 437
 
4.4%
관악구 437
 
4.4%
Other values (15) 5239
52.4%

10분우량
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04255
Minimum0
Maximum38.5
Zeros9500
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T17:24:36.933077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.025
Maximum38.5
Range38.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44377268
Coefficient of variation (CV)10.42944
Kurtosis5674.7798
Mean0.04255
Median Absolute Deviation (MAD)0
Skewness67.04386
Sum425.5
Variance0.19693419
MonotonicityNot monotonic
2023-12-11T17:24:37.080826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 9500
95.0%
0.5 332
 
3.3%
1.0 120
 
1.2%
1.5 21
 
0.2%
2.0 12
 
0.1%
2.5 10
 
0.1%
3.0 2
 
< 0.1%
38.5 1
 
< 0.1%
4.0 1
 
< 0.1%
10.5 1
 
< 0.1%
ValueCountFrequency (%)
0.0 9500
95.0%
0.5 332
 
3.3%
1.0 120
 
1.2%
1.5 21
 
0.2%
2.0 12
 
0.1%
2.5 10
 
0.1%
3.0 2
 
< 0.1%
4.0 1
 
< 0.1%
10.5 1
 
< 0.1%
38.5 1
 
< 0.1%
ValueCountFrequency (%)
38.5 1
 
< 0.1%
10.5 1
 
< 0.1%
4.0 1
 
< 0.1%
3.0 2
 
< 0.1%
2.5 10
 
0.1%
2.0 12
 
0.1%
1.5 21
 
0.2%
1.0 120
 
1.2%
0.5 332
 
3.3%
0.0 9500
95.0%

자료수집 시각
Date

Distinct2150
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-04-01 00:09:00
Maximum2021-04-16 16:29:00
2023-12-11T17:24:37.293269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:37.546524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T17:24:34.628078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:33.666835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:34.140322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:34.786335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:33.830518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:34.288496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:34.920987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:33.979744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:24:34.438330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:24:37.682449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드강우량계명구청 코드구청명10분우량
강우량계 코드1.0001.0001.0001.0000.027
강우량계명1.0001.0001.0001.0000.000
구청 코드1.0001.0001.0001.0000.018
구청명1.0001.0001.0001.0000.000
10분우량0.0270.0000.0180.0001.000
2023-12-11T17:24:37.814683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명구청명
강우량계명1.0000.999
구청명0.9991.000
2023-12-11T17:24:37.946826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계 코드구청 코드10분우량강우량계명구청명
강우량계 코드1.0000.999-0.0060.9980.982
구청 코드0.9991.000-0.0060.9980.999
10분우량-0.006-0.0061.0000.0000.000
강우량계명0.9980.9980.0001.0000.999
구청명0.9820.9990.0000.9991.000

Missing values

2023-12-11T17:24:35.117201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:24:35.289191image/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
570151302증산P113은평구0.02021-04-10 05:19
4484301도봉구청103도봉구0.02021-04-01 16:19
398302202가산2P122금천구0.02021-04-07 16:49
877691802목동P118양천구0.02021-04-14 21:49
618792202가산2P122금천구0.02021-04-10 23:09
171211701강서구청117강서구0.02021-04-04 07:59
81158702면목P107중랑구0.02021-04-13 22:39
219321101중구청111중구0.02021-04-05 00:49
42721301도봉구청103도봉구0.02021-04-08 02:59
66202201금천구청122금천구0.02021-04-02 19:19
강우량계 코드강우량계명구청 코드구청명10분우량자료수집 시각
768892502마천2동125송파구0.02021-04-13 07:29
518502001구로구청120구로구0.02021-04-09 10:49
24442202고덕2동102강동구0.02021-04-05 09:47
272932002개봉2동120구로구0.02021-04-05 19:39
776971302증산P113은평구0.02021-04-13 10:19
778172101동작구청121동작구0.02021-04-13 10:49
690311104서소문111중구0.02021-04-12 02:59
97794102세곡동101강남구0.02021-04-16 08:39
934211601용산구청116용산구0.02021-04-15 17:29
503021802목동P118양천구0.02021-04-09 05:29