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 has constant value ""Constant
측정항목 is highly overall correlated with 평균값High correlation
평균값 is highly overall correlated with 측정항목High correlation
국가 기준초과 구분 is highly imbalanced (99.5%)Imbalance
평균값 is highly skewed (γ1 = -22.51214637)Skewed
측정기 상태 has 9819 (98.2%) zerosZeros

Reproduction

Analysis started2024-05-04 03:57:20.646209
Analysis finished2024-05-04 03:57:30.982943
Duration10.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Real number (ℝ)

Distinct438
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.013011 × 109
Minimum2.0130101 × 109
Maximum2.0130119 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:57:31.228430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130101 × 109
5-th percentile2.0130101 × 109
Q12.0130105 × 109
median2.013011 × 109
Q32.0130114 × 109
95-th percentile2.0130118 × 109
Maximum2.0130119 × 109
Range1805
Interquartile range (IQR)901

Descriptive statistics

Standard deviation524.79996
Coefficient of variation (CV)2.6070398 × 10-7
Kurtosis-1.1955111
Mean2.013011 × 109
Median Absolute Deviation (MAD)481
Skewness0.0089265445
Sum2.013011 × 1013
Variance275415
MonotonicityNot monotonic
2024-05-04T03:57:31.777846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2013010720 36
 
0.4%
2013011121 35
 
0.4%
2013010401 35
 
0.4%
2013011813 34
 
0.3%
2013011102 34
 
0.3%
2013011002 34
 
0.3%
2013010204 33
 
0.3%
2013010819 33
 
0.3%
2013011611 33
 
0.3%
2013011311 32
 
0.3%
Other values (428) 9661
96.6%
ValueCountFrequency (%)
2013010100 19
0.2%
2013010101 23
0.2%
2013010102 21
0.2%
2013010103 25
0.2%
2013010104 28
0.3%
2013010105 22
0.2%
2013010106 21
0.2%
2013010107 22
0.2%
2013010108 22
0.2%
2013010109 19
0.2%
ValueCountFrequency (%)
2013011905 8
 
0.1%
2013011904 23
0.2%
2013011903 21
0.2%
2013011902 17
0.2%
2013011901 24
0.2%
2013011900 28
0.3%
2013011823 21
0.2%
2013011822 29
0.3%
2013011821 18
0.2%
2013011820 20
0.2%

측정소 코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.0588
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:57:32.456120image/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 deviation7.2508758
Coefficient of variation (CV)0.06413367
Kurtosis-1.2230307
Mean113.0588
Median Absolute Deviation (MAD)6
Skewness0.000579527
Sum1130588
Variance52.5752
MonotonicityNot monotonic
2024-05-04T03:57:33.102507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
124 440
 
4.4%
105 422
 
4.2%
107 414
 
4.1%
108 414
 
4.1%
121 413
 
4.1%
122 411
 
4.1%
125 411
 
4.1%
120 407
 
4.1%
103 407
 
4.1%
117 407
 
4.1%
Other values (15) 5854
58.5%
ValueCountFrequency (%)
101 391
3.9%
102 382
3.8%
103 407
4.1%
104 395
4.0%
105 422
4.2%
106 406
4.1%
107 414
4.1%
108 414
4.1%
109 379
3.8%
110 406
4.1%
ValueCountFrequency (%)
125 411
4.1%
124 440
4.4%
123 391
3.9%
122 411
4.1%
121 413
4.1%
120 407
4.1%
119 384
3.8%
118 388
3.9%
117 407
4.1%
116 393
3.9%

측정항목
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3262
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:57:33.705667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7700832
Coefficient of variation (CV)0.52008621
Kurtosis-1.2282088
Mean5.3262
Median Absolute Deviation (MAD)3
Skewness-0.20432084
Sum53262
Variance7.6733609
MonotonicityNot monotonic
2024-05-04T03:57:34.144170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1717
17.2%
8 1688
16.9%
9 1685
16.9%
5 1664
16.6%
3 1640
16.4%
6 1606
16.1%
ValueCountFrequency (%)
1 1717
17.2%
3 1640
16.4%
5 1664
16.6%
6 1606
16.1%
8 1688
16.9%
9 1685
16.9%
ValueCountFrequency (%)
9 1685
16.9%
8 1688
16.9%
6 1606
16.1%
5 1664
16.6%
3 1640
16.4%
1 1717
17.2%

평균값
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct325
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1038639
Minimum-9999
Maximum375
Zeros46
Zeros (%)0.5%
Negative40
Negative (%)0.4%
Memory size166.0 KiB
2024-05-04T03:57:34.776737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.003
Q10.012
median0.1
Q329
95-th percentile101
Maximum375
Range10374
Interquartile range (IQR)28.988

Descriptive statistics

Standard deviation438.6867
Coefficient of variation (CV)-4223.6687
Kurtosis510.17572
Mean-0.1038639
Median Absolute Deviation (MAD)0.2
Skewness-22.512146
Sum-1038.639
Variance192446.02
MonotonicityNot monotonic
2024-05-04T03:57:35.511753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.007 321
 
3.2%
0.008 278
 
2.8%
0.006 268
 
2.7%
0.002 237
 
2.4%
0.009 225
 
2.2%
0.005 201
 
2.0%
0.01 185
 
1.8%
0.7 167
 
1.7%
0.003 161
 
1.6%
0.001 145
 
1.5%
Other values (315) 7812
78.1%
ValueCountFrequency (%)
-9999.0 19
 
0.2%
-999.9 8
 
0.1%
-9.999 13
 
0.1%
0.0 46
 
0.5%
0.001 145
1.5%
0.002 237
2.4%
0.003 161
1.6%
0.004 145
1.5%
0.005 201
2.0%
0.006 268
2.7%
ValueCountFrequency (%)
375.0 1
< 0.1%
265.0 1
< 0.1%
237.0 1
< 0.1%
233.0 1
< 0.1%
210.0 1
< 0.1%
202.0 1
< 0.1%
197.0 1
< 0.1%
195.0 2
< 0.1%
192.0 1
< 0.1%
191.0 1
< 0.1%

측정기 상태
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0733
Minimum0
Maximum9
Zeros9819
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T03:57:36.213888image/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 deviation0.71258535
Coefficient of variation (CV)9.7214919
Kurtosis138.64793
Mean0.0733
Median Absolute Deviation (MAD)0
Skewness11.595104
Sum733
Variance0.50777789
MonotonicityNot monotonic
2024-05-04T03:57:36.768773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9819
98.2%
2 65
 
0.7%
1 52
 
0.5%
9 51
 
0.5%
8 10
 
0.1%
4 3
 
< 0.1%
ValueCountFrequency (%)
0 9819
98.2%
1 52
 
0.5%
2 65
 
0.7%
4 3
 
< 0.1%
8 10
 
0.1%
9 51
 
0.5%
ValueCountFrequency (%)
9 51
 
0.5%
8 10
 
0.1%
4 3
 
< 0.1%
2 65
 
0.7%
1 52
 
0.5%
0 9819
98.2%

국가 기준초과 구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9996 
1
 
4

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 9996
> 99.9%
1 4
 
< 0.1%

Length

2024-05-04T03:57:37.432376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:57:37.979837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

지자체 기준초과 구분
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

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 10000
100.0%

Length

2024-05-04T03:57:38.523367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T03:57:38.798883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Interactions

2024-05-04T03:57:28.621736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:22.957976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:24.498867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:25.674030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:27.080224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:28.965983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:23.381437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:24.792331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:25.953320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:27.392579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:29.315242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:23.679922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:25.006157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:26.243031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:27.730816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:29.662695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:23.992834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:25.267289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:26.542392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:28.093263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:30.021166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:24.268673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:25.483561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:26.801745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T03:57:28.331557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T03:57:39.315174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소 코드측정항목평균값측정기 상태국가 기준초과 구분
측정일시1.0000.0000.0130.0590.0740.000
측정소 코드0.0001.0000.0000.0760.1100.023
측정항목0.0130.0001.0000.0820.1850.055
평균값0.0590.0760.0821.0000.4830.000
측정기 상태0.0740.1100.1850.4831.0000.000
국가 기준초과 구분0.0000.0230.0550.0000.0001.000
2024-05-04T03:57:39.675673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시측정소 코드측정항목평균값측정기 상태국가 기준초과 구분
측정일시1.000-0.0090.0060.088-0.0040.000
측정소 코드-0.0091.0000.0030.009-0.0110.018
측정항목0.0060.0031.0000.7000.0880.039
평균값0.0880.0090.7001.0000.0080.000
측정기 상태-0.004-0.0110.0880.0081.0000.000
국가 기준초과 구분0.0000.0180.0390.0000.0001.000

Missing values

2024-05-04T03:57:30.433469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T03:57:30.813736image/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
44952201301131111810.018000
48576201301141112210.007000
38539201301111612430.06000
78222013010304104827.0000
31039201301091412430.022000
1586201301011011550.5000
2301201301011510960.032000
12327201301041010560.01000
36944201301110610850.9000
56479201301161611430.032000
측정일시측정소 코드측정항목평균값측정기 상태국가 기준초과 구분지자체 기준초과 구분
27504201301081511010.014000
26340201301080711610.008000
2664420130108091168139.0000
390292013011120105936.0000
15942201301051010810.007000
12842201301041311650.3000
131802013010415122849.0000
19705201301061111030.04000
34105201301101111030.035000
635742013011815121838.0000