Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory693.4 KiB
Average record size in memory71.0 B

Variable types

Numeric5
Categorical2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15526/S/1/datasetView.do

Alerts

지자체 기준초과 구분 is highly overall correlated with 국가 기준초과 구분High correlation
국가 기준초과 구분 is highly overall correlated with 지자체 기준초과 구분High correlation
측정항목 is highly overall correlated with 평균값High correlation
평균값 is highly overall correlated with 측정항목High correlation
국가 기준초과 구분 is highly imbalanced (69.0%)Imbalance
지자체 기준초과 구분 is highly imbalanced (69.0%)Imbalance
평균값 has 212 (2.1%) zerosZeros
측정기 상태 has 9622 (96.2%) zerosZeros

Reproduction

Analysis started2024-05-04 03:56:00.574412
Analysis finished2024-05-04 03:56:11.520194
Duration10.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct470
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.018011 × 109
Minimum2.0180101 × 109
Maximum2.018012 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:56:11.754437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0180101 × 109
5-th percentile2.0180102 × 109
Q12.0180105 × 109
median2.018011 × 109
Q32.0180115 × 109
95-th percentile2.0180119 × 109
Maximum2.018012 × 109
Range1913
Interquartile range (IQR)994

Descriptive statistics

Standard deviation563.51008
Coefficient of variation (CV)2.7924034 × 10-7
Kurtosis-1.1912847
Mean2.018011 × 109
Median Absolute Deviation (MAD)497
Skewness-0.00048395523
Sum2.018011 × 1013
Variance317543.62
MonotonicityNot monotonic
2024-05-04T03:56:12.347460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2018010321 38
 
0.4%
2018010811 36
 
0.4%
2018010712 34
 
0.3%
2018010410 32
 
0.3%
2018010607 32
 
0.3%
2018011503 32
 
0.3%
2018012010 31
 
0.3%
2018010807 31
 
0.3%
2018011213 31
 
0.3%
2018012011 30
 
0.3%
Other values (460) 9673
96.7%
ValueCountFrequency (%)
2018010100 27
0.3%
2018010101 19
0.2%
2018010102 17
0.2%
2018010103 17
0.2%
2018010104 20
0.2%
2018010105 16
0.2%
2018010106 22
0.2%
2018010107 29
0.3%
2018010108 24
0.2%
2018010109 25
0.2%
ValueCountFrequency (%)
2018012013 14
0.1%
2018012012 18
0.2%
2018012011 30
0.3%
2018012010 31
0.3%
2018012009 27
0.3%
2018012008 23
0.2%
2018012007 15
0.1%
2018012006 19
0.2%
2018012005 25
0.2%
2018012004 25
0.2%

측정소 코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.0226
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:56:12.756429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1107
median113
Q3119
95-th percentile124
Maximum125
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2036295
Coefficient of variation (CV)0.063736187
Kurtosis-1.203123
Mean113.0226
Median Absolute Deviation (MAD)6
Skewness-0.0023322311
Sum1130226
Variance51.892278
MonotonicityNot monotonic
2024-05-04T03:56:13.276420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
107 424
 
4.2%
114 421
 
4.2%
113 417
 
4.2%
119 416
 
4.2%
123 416
 
4.2%
116 410
 
4.1%
104 409
 
4.1%
124 409
 
4.1%
105 408
 
4.1%
108 407
 
4.1%
Other values (15) 5863
58.6%
ValueCountFrequency (%)
101 397
4.0%
102 392
3.9%
103 381
3.8%
104 409
4.1%
105 408
4.1%
106 398
4.0%
107 424
4.2%
108 407
4.1%
109 380
3.8%
110 379
3.8%
ValueCountFrequency (%)
125 394
3.9%
124 409
4.1%
123 416
4.2%
122 389
3.9%
121 389
3.9%
120 401
4.0%
119 416
4.2%
118 385
3.9%
117 394
3.9%
116 410
4.1%

측정항목
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3592
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:56:13.738507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7586476
Coefficient of variation (CV)0.51474988
Kurtosis-1.2072375
Mean5.3592
Median Absolute Deviation (MAD)3
Skewness-0.22050428
Sum53592
Variance7.6101364
MonotonicityNot monotonic
2024-05-04T03:56:14.177783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9 1717
17.2%
6 1684
16.8%
1 1676
16.8%
8 1660
16.6%
5 1645
16.4%
3 1618
16.2%
ValueCountFrequency (%)
1 1676
16.8%
3 1618
16.2%
5 1645
16.4%
6 1684
16.8%
8 1660
16.6%
9 1717
17.2%
ValueCountFrequency (%)
9 1717
17.2%
8 1660
16.6%
6 1684
16.8%
5 1645
16.4%
3 1618
16.2%
1 1676
16.8%

평균값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct265
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-18.48203
Minimum-9999
Maximum3487
Zeros212
Zeros (%)2.1%
Negative36
Negative (%)0.4%
Memory size166.0 KiB
2024-05-04T03:56:14.624154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.002
Q10.007
median0.066
Q326
95-th percentile78
Maximum3487
Range13486
Interquartile range (IQR)25.993

Descriptive statistics

Standard deviation594.15545
Coefficient of variation (CV)-32.147737
Kurtosis276.09082
Mean-18.48203
Median Absolute Deviation (MAD)0.066
Skewness-16.553609
Sum-184820.3
Variance353020.7
MonotonicityNot monotonic
2024-05-04T03:56:15.181988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.005 489
 
4.9%
0.006 447
 
4.5%
0.004 424
 
4.2%
0.007 330
 
3.3%
0.002 270
 
2.7%
0.003 262
 
2.6%
0.5 256
 
2.6%
0.4 234
 
2.3%
0.6 217
 
2.2%
0.7 213
 
2.1%
Other values (255) 6858
68.6%
ValueCountFrequency (%)
-9999.0 35
 
0.4%
-0.1 1
 
< 0.1%
0.0 212
2.1%
0.001 54
 
0.5%
0.002 270
2.7%
0.003 262
2.6%
0.004 424
4.2%
0.005 489
4.9%
0.006 447
4.5%
0.007 330
3.3%
ValueCountFrequency (%)
3487.0 1
 
< 0.1%
3416.0 1
 
< 0.1%
161.0 1
 
< 0.1%
160.0 1
 
< 0.1%
157.0 1
 
< 0.1%
155.0 4
< 0.1%
154.0 1
 
< 0.1%
153.0 1
 
< 0.1%
151.0 2
< 0.1%
150.0 1
 
< 0.1%

측정기 상태
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2505
Minimum0
Maximum9
Zeros9622
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:56:15.709458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3725663
Coefficient of variation (CV)5.4793064
Kurtosis29.147384
Mean0.2505
Median Absolute Deviation (MAD)0
Skewness5.5333242
Sum2505
Variance1.8839381
MonotonicityNot monotonic
2024-05-04T03:56:16.071236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9622
96.2%
8 218
 
2.2%
9 59
 
0.6%
1 54
 
0.5%
4 41
 
0.4%
2 6
 
0.1%
ValueCountFrequency (%)
0 9622
96.2%
1 54
 
0.5%
2 6
 
0.1%
4 41
 
0.4%
8 218
 
2.2%
9 59
 
0.6%
ValueCountFrequency (%)
9 59
 
0.6%
8 218
 
2.2%
4 41
 
0.4%
2 6
 
0.1%
1 54
 
0.5%
0 9622
96.2%

국가 기준초과 구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9443 
1
 
557

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9443
94.4%
1 557
 
5.6%

Length

2024-05-04T03:56:16.494896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:56:16.808331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9443
94.4%
1 557
 
5.6%

지자체 기준초과 구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9443 
1
 
557

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9443
94.4%
1 557
 
5.6%

Length

2024-05-04T03:56:17.143855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:56:17.417633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9443
94.4%
1 557
 
5.6%

Interactions

2024-05-04T03:56:08.721262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:02.291807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:03.667305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:05.089444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:06.841489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:09.051926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:02.553564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:03.941988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:05.383319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:07.230154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:09.410229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:02.840505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:04.198646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:05.670860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:07.615441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:10.129082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:03.166696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:04.480788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:06.019503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:08.006016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:10.558997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:03.429544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:04.780766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:06.501963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:56:08.363463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:56:17.602046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소 코드측정항목평균값측정기 상태국가 기준초과 구분지자체 기준초과 구분
측정일시1.0000.0000.0000.0400.1490.4770.477
측정소 코드0.0001.0000.0000.0490.4380.0380.038
측정항목0.0000.0001.0000.0310.0920.5210.521
평균값0.0400.0490.0311.0000.2530.0650.065
측정기 상태0.1490.4380.0920.2531.0000.0550.055
국가 기준초과 구분0.4770.0380.5210.0650.0551.0001.000
지자체 기준초과 구분0.4770.0380.5210.0650.0551.0001.000
2024-05-04T03:56:17.845338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지자체 기준초과 구분국가 기준초과 구분
지자체 기준초과 구분1.0000.999
국가 기준초과 구분0.9991.000
2024-05-04T03:56:18.028563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소 코드측정항목평균값측정기 상태국가 기준초과 구분지자체 기준초과 구분
측정일시1.000-0.002-0.0030.087-0.0700.3670.367
측정소 코드-0.0021.000-0.0010.052-0.1580.0290.029
측정항목-0.003-0.0011.0000.6840.0050.3770.377
평균값0.0870.0520.6841.000-0.2090.0580.058
측정기 상태-0.070-0.1580.005-0.2091.0000.0400.040
국가 기준초과 구분0.3670.0290.3770.0580.0401.0000.999
지자체 기준초과 구분0.3670.0290.3770.0580.0400.9991.000

Missing values

2024-05-04T03:56:10.967925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:56:11.355143image/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

측정일시측정소 코드측정항목평균값측정기 상태국가 기준초과 구분지자체 기준초과 구분
1881201801011211460.024000
49119201801141511260.016000
505902018011501107857.0000
42721201801122012130.061000
3500201801012310950.8000
16902201801051611810.006000
1548201801011010910.006000
41332018010203114926.0000
39120201801112012110.006000
29604201801090511010.006000
측정일시측정소 코드측정항목평균값측정기 상태국가 기준초과 구분지자체 기준초과 구분
55122018010212119858.0000
2562201801011710310.006000
238902018010715107868.0000
18673201801060411330.033000
43003201801122211830.053000
14617201801050111230.056000
25650201801080310110.006000
56180201801161411451.0000
116692018010405120916.0000
14328201801042311410.008000