Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.7 KiB
Average record size in memory77.3 B

Variable types

Numeric5
Categorical2
DateTime2

Dataset

Description샘플 데이터
Author서울시
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=41

Alerts

송신지_자료수집_시각(RECEIVE_TIME) has unique valuesUnique
수신서버_저장_시각(LIST_TIME) has unique valuesUnique
시우량(RAINFALLHOUR) has 475 (95.0%) zerosZeros

Reproduction

Analysis started2024-04-21 07:11:11.536735
Analysis finished2024-04-21 07:11:18.008146
Duration6.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강우량_코드(RAINGAUGE_CODE)
Real number (ℝ)

Distinct48
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1297.954
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-21T16:11:18.176076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1701
median1302
Q31902
95-th percentile2501
Maximum2502
Range2401
Interquartile range (IQR)1201

Descriptive statistics

Standard deviation751.31137
Coefficient of variation (CV)0.57884283
Kurtosis-1.2151831
Mean1297.954
Median Absolute Deviation (MAD)601
Skewness-0.0014596142
Sum648977
Variance564468.78
MonotonicityNot monotonic
2024-04-21T16:11:18.630028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
301 17
 
3.4%
701 15
 
3.0%
702 15
 
3.0%
1001 15
 
3.0%
2502 15
 
3.0%
201 15
 
3.0%
1702 15
 
3.0%
802 14
 
2.8%
902 13
 
2.6%
1401 13
 
2.6%
Other values (38) 353
70.6%
ValueCountFrequency (%)
101 13
2.6%
102 13
2.6%
103 8
1.6%
201 15
3.0%
202 10
2.0%
301 17
3.4%
401 7
1.4%
402 4
 
0.8%
501 4
 
0.8%
601 12
2.4%
ValueCountFrequency (%)
2502 15
3.0%
2501 13
2.6%
2402 11
2.2%
2401 11
2.2%
2302 9
1.8%
2301 11
2.2%
2202 9
1.8%
2201 12
2.4%
2102 6
 
1.2%
2101 10
2.0%

강우량계명(RAINGAUGE_NAME)
Categorical

Distinct48
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
금천구청
 
21
부암동
 
19
마포구청
 
16
고덕2동
 
16
서소문
 
15
Other values (43)
413 

Length

Max length5
Median length4
Mean length3.782
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영등포구청
2nd row서초구청
3rd row금천구청
4th row마천2동
5th row은평구청

Common Values

ValueCountFrequency (%)
금천구청 21
 
4.2%
부암동 19
 
3.8%
마포구청 16
 
3.2%
고덕2동 16
 
3.2%
서소문 15
 
3.0%
개봉2동 15
 
3.0%
면목P 15
 
3.0%
강서구청 13
 
2.6%
가산2P 13
 
2.6%
개포2동 13
 
2.6%
Other values (38) 344
68.8%

Length

2024-04-21T16:11:19.070054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금천구청 21
 
4.2%
부암동 19
 
3.8%
마포구청 16
 
3.2%
고덕2동 16
 
3.2%
서소문 15
 
3.0%
개봉2동 15
 
3.0%
면목p 15
 
3.0%
강서구청 13
 
2.6%
가산2p 13
 
2.6%
개포2동 13
 
2.6%
Other values (38) 344
68.8%

구청_코드(GU_CODE)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.05
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-21T16:11:19.459072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1107.75
median113
Q3119
95-th percentile124
Maximum125
Range24
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation7.0417806
Coefficient of variation (CV)0.062289081
Kurtosis-1.0980216
Mean113.05
Median Absolute Deviation (MAD)6
Skewness-0.093744005
Sum56525
Variance49.586673
MonotonicityNot monotonic
2024-04-21T16:11:19.861135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
113 42
 
8.4%
101 31
 
6.2%
111 28
 
5.6%
121 26
 
5.2%
122 25
 
5.0%
120 24
 
4.8%
102 24
 
4.8%
108 24
 
4.8%
106 24
 
4.8%
119 23
 
4.6%
Other values (15) 229
45.8%
ValueCountFrequency (%)
101 31
6.2%
102 24
4.8%
103 7
 
1.4%
104 9
 
1.8%
105 10
 
2.0%
106 24
4.8%
107 20
4.0%
108 24
4.8%
109 23
4.6%
110 19
3.8%
ValueCountFrequency (%)
125 14
2.8%
124 19
3.8%
123 11
2.2%
122 25
5.0%
121 26
5.2%
120 24
4.8%
119 23
4.6%
118 16
3.2%
117 22
4.4%
116 22
4.4%

구청명(GU_NAME)
Categorical

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
은평구
42 
금천구
 
30
강남구
 
29
성북구
 
28
동대문구
 
25
Other values (20)
346 

Length

Max length4
Median length3
Mean length3.082
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양천구
2nd row동대문구
3rd row구로구
4th row강동구
5th row송파구

Common Values

ValueCountFrequency (%)
은평구 42
 
8.4%
금천구 30
 
6.0%
강남구 29
 
5.8%
성북구 28
 
5.6%
동대문구 25
 
5.0%
관악구 25
 
5.0%
영등포구 25
 
5.0%
강서구 22
 
4.4%
성동구 22
 
4.4%
양천구 20
 
4.0%
Other values (15) 232
46.4%

Length

2024-04-21T16:11:20.283608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
은평구 42
 
8.4%
금천구 30
 
6.0%
강남구 29
 
5.8%
성북구 28
 
5.6%
동대문구 25
 
5.0%
관악구 25
 
5.0%
영등포구 25
 
5.0%
강서구 22
 
4.4%
성동구 22
 
4.4%
종로구 20
 
4.0%
Other values (15) 232
46.4%

시우량(RAINFALLHOUR)
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.116
Minimum0
Maximum10
Zeros475
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-21T16:11:20.655073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.025
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.79738048
Coefficient of variation (CV)6.8739697
Kurtosis93.663835
Mean0.116
Median Absolute Deviation (MAD)0
Skewness9.2124735
Sum58
Variance0.63581563
MonotonicityNot monotonic
2024-04-21T16:11:21.020057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 475
95.0%
0.5 13
 
2.6%
1.0 2
 
0.4%
2.0 2
 
0.4%
6.0 2
 
0.4%
3.0 1
 
0.2%
9.0 1
 
0.2%
3.5 1
 
0.2%
2.5 1
 
0.2%
10.0 1
 
0.2%
ValueCountFrequency (%)
0.0 475
95.0%
0.5 13
 
2.6%
1.0 2
 
0.4%
2.0 2
 
0.4%
2.5 1
 
0.2%
3.0 1
 
0.2%
3.5 1
 
0.2%
5.5 1
 
0.2%
6.0 2
 
0.4%
9.0 1
 
0.2%
ValueCountFrequency (%)
10.0 1
 
0.2%
9.0 1
 
0.2%
6.0 2
 
0.4%
5.5 1
 
0.2%
3.5 1
 
0.2%
3.0 1
 
0.2%
2.5 1
 
0.2%
2.0 2
 
0.4%
1.0 2
 
0.4%
0.5 13
2.6%

일년누계(RAINFALLACCU)
Real number (ℝ)

Distinct155
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.482
Minimum171
Maximum2625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-21T16:11:21.396423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile188
Q1225.5
median250
Q3325
95-th percentile493.025
Maximum2625
Range2454
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation338.841
Coefficient of variation (CV)1.0252934
Kurtosis39.138461
Mean330.482
Median Absolute Deviation (MAD)32
Skewness6.1819644
Sum165241
Variance114813.23
MonotonicityNot monotonic
2024-04-21T16:11:21.819126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235.5 18
 
3.6%
225.5 12
 
2.4%
227.0 11
 
2.2%
263.5 10
 
2.0%
240.5 9
 
1.8%
269.0 8
 
1.6%
244.5 8
 
1.6%
205.5 8
 
1.6%
428.5 8
 
1.6%
247.0 7
 
1.4%
Other values (145) 401
80.2%
ValueCountFrequency (%)
171.0 7
1.4%
174.5 1
 
0.2%
175.5 4
0.8%
182.0 6
1.2%
185.0 3
0.6%
186.0 3
0.6%
188.0 3
0.6%
196.0 2
 
0.4%
197.5 3
0.6%
200.0 6
1.2%
ValueCountFrequency (%)
2625.0 1
 
0.2%
2619.0 2
 
0.4%
2617.0 7
1.4%
528.0 3
0.6%
499.0 4
0.8%
495.0 3
0.6%
493.5 5
1.0%
493.0 1
 
0.2%
487.0 1
 
0.2%
484.5 1
 
0.2%

최대우량(RAINFALLMAX)
Real number (ℝ)

Distinct37
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.236
Minimum3.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-21T16:11:22.201941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile8.5
Q112
median15
Q318
95-th percentile23.55
Maximum27.5
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5693612
Coefficient of variation (CV)0.29990557
Kurtosis0.16729465
Mean15.236
Median Absolute Deviation (MAD)3
Skewness0.26322298
Sum7618
Variance20.879062
MonotonicityNot monotonic
2024-04-21T16:11:22.599276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
16.5 46
 
9.2%
13.5 40
 
8.0%
14.0 34
 
6.8%
10.5 33
 
6.6%
15.5 29
 
5.8%
18.0 26
 
5.2%
20.0 25
 
5.0%
11.5 24
 
4.8%
17.5 24
 
4.8%
13.0 20
 
4.0%
Other values (27) 199
39.8%
ValueCountFrequency (%)
3.5 9
 
1.8%
8.0 1
 
0.2%
8.5 19
3.8%
9.0 12
 
2.4%
9.5 8
 
1.6%
10.0 4
 
0.8%
10.5 33
6.6%
11.0 13
 
2.6%
11.5 24
4.8%
12.0 9
 
1.8%
ValueCountFrequency (%)
27.5 2
 
0.4%
27.0 3
 
0.6%
26.5 2
 
0.4%
26.0 7
1.4%
25.5 5
 
1.0%
25.0 1
 
0.2%
24.5 5
 
1.0%
23.5 2
 
0.4%
22.0 16
3.2%
21.5 11
2.2%

송신지_자료수집_시각(RECEIVE_TIME)
Date

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2012-11-09 05:09:40
Maximum2020-05-26 07:38:41
2024-04-21T16:11:22.992099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:23.444243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수신서버_저장_시각(LIST_TIME)
Date

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2012-11-09 02:40:18
Maximum2020-05-26 08:27:01
2024-04-21T16:11:23.851382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:24.297718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-04-21T16:11:16.696483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:12.122988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:13.376139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:14.671420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:15.724703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:16.849263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:12.370701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:13.633808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:14.900272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:15.979873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:17.023793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:12.630999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:13.895424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:15.052923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:16.198739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:17.196075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:12.888990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:14.156558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:15.219012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:16.380237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:17.367135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:13.141694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:14.414078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:15.470037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T16:11:16.540329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T16:11:24.577028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량_코드(RAINGAUGE_CODE)강우량계명(RAINGAUGE_NAME)구청_코드(GU_CODE)구청명(GU_NAME)시우량(RAINFALLHOUR)일년누계(RAINFALLACCU)최대우량(RAINFALLMAX)
강우량_코드(RAINGAUGE_CODE)1.0000.1090.0000.2200.0000.0000.099
강우량계명(RAINGAUGE_NAME)0.1091.0000.0000.3150.2620.0000.108
구청_코드(GU_CODE)0.0000.0001.0000.0000.0980.1620.000
구청명(GU_NAME)0.2200.3150.0001.0000.2270.3360.000
시우량(RAINFALLHOUR)0.0000.2620.0980.2271.0000.0000.184
일년누계(RAINFALLACCU)0.0000.0000.1620.3360.0001.0000.000
최대우량(RAINFALLMAX)0.0990.1080.0000.0000.1840.0001.000
2024-04-21T16:11:24.860977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량계명(RAINGAUGE_NAME)구청명(GU_NAME)
강우량계명(RAINGAUGE_NAME)1.0000.072
구청명(GU_NAME)0.0721.000
2024-04-21T16:11:25.031276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우량_코드(RAINGAUGE_CODE)구청_코드(GU_CODE)시우량(RAINFALLHOUR)일년누계(RAINFALLACCU)최대우량(RAINFALLMAX)강우량계명(RAINGAUGE_NAME)구청명(GU_NAME)
강우량_코드(RAINGAUGE_CODE)1.0000.0370.0180.008-0.0630.0330.077
구청_코드(GU_CODE)0.0371.000-0.0180.0370.0060.0000.000
시우량(RAINFALLHOUR)0.018-0.0181.0000.0130.0060.0840.123
일년누계(RAINFALLACCU)0.0080.0370.0131.000-0.0150.0000.177
최대우량(RAINFALLMAX)-0.0630.0060.006-0.0151.0000.0510.000
강우량계명(RAINGAUGE_NAME)0.0330.0000.0840.0000.0511.0000.072
구청명(GU_NAME)0.0770.0000.1230.1770.0000.0721.000

Missing values

2024-04-21T16:11:17.592775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T16:11:17.878877image/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

강우량_코드(RAINGAUGE_CODE)강우량계명(RAINGAUGE_NAME)구청_코드(GU_CODE)구청명(GU_NAME)시우량(RAINFALLHOUR)일년누계(RAINFALLACCU)최대우량(RAINFALLMAX)송신지_자료수집_시각(RECEIVE_TIME)수신서버_저장_시각(LIST_TIME)
0202영등포구청113양천구0.0309.08.52015-01-19 23:57:422015-01-19 08:33:02
1301서초구청120동대문구0.0269.013.52019-04-16 06:16:402019-04-16 10:12:01
22402금천구청115구로구0.5263.516.52018-11-18 10:26:412018-11-18 06:04:48
31502마천2동115강동구0.0435.525.52015-06-16 15:18:402015-06-16 17:59:03
42401은평구청106송파구0.0214.511.02015-07-10 00:52:402015-07-10 15:33:08
5301고덕2동112마포구0.0235.516.52017-08-12 01:01:412017-08-12 01:48:26
6402개봉2동124금천구0.0212.013.52018-08-16 12:49:412018-08-16 13:57:18
7601봉원P120영등포구0.0285.09.02012-12-13 05:54:412012-12-13 09:52:12
8702중랑구청118영등포구0.0197.514.02014-02-04 12:26:412014-02-04 13:19:07
9102서대문구청118성동구0.0200.022.02018-11-24 19:47:402018-11-24 10:36:29
강우량_코드(RAINGAUGE_CODE)강우량계명(RAINGAUGE_NAME)구청_코드(GU_CODE)구청명(GU_NAME)시우량(RAINFALLHOUR)일년누계(RAINFALLACCU)최대우량(RAINFALLMAX)송신지_자료수집_시각(RECEIVE_TIME)수신서버_저장_시각(LIST_TIME)
4901702개포2동101금천구0.0200.013.52019-03-12 18:06:412019-03-12 00:57:20
4911702면목P124용산구0.0221.516.02015-09-27 18:23:402015-09-27 08:23:14
492701서초구청113관악구0.0248.510.52014-02-07 16:15:412014-02-07 08:15:08
493101면목P124강동구0.0309.09.02017-10-01 06:28:402017-10-01 09:09:14
4942002뚝섬P120강동구0.0428.510.52015-07-17 10:13:412015-07-17 06:18:25
4952502성북구청111금천구0.0390.516.52017-01-02 08:32:412017-01-02 18:56:59
496602양천구청116마포구0.0240.510.02016-02-14 11:26:402016-02-14 03:15:23
497902개포2동101서초구0.0390.015.52014-12-11 06:49:402014-12-11 21:50:21
4982301동대문구청101광진구0.0209.022.02015-09-23 11:21:412015-09-23 09:12:28
4992401신림P104송파구5.5224.015.52015-08-08 23:05:402015-08-08 01:07:59