Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows774
Duplicate rows (%)7.7%
Total size in memory742.2 KiB
Average record size in memory76.0 B

Variable types

Categorical3
Numeric4
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15971/S/1/datasetView.do

Alerts

기관 명 has constant value ""Constant
모델명 has constant value ""Constant
Dataset has 774 (7.7%) duplicate rowsDuplicates
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지(㎍/㎥)High correlation
미세먼지(㎍/㎥) is highly overall correlated with 초미세먼지(㎍/㎥)High correlation
초미세먼지(㎍/㎥) has 968 (9.7%) zerosZeros
미세먼지(㎍/㎥) has 788 (7.9%) zerosZeros

Reproduction

Analysis started2024-05-04 06:50:32.595670
Analysis finished2024-05-04 06:50:39.288562
Duration6.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영등포구 10000
100.0%

Length

2024-05-04T06:50:39.513726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:50:39.773963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영등포구 10000
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AF-YDP-2018
10000 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAF-YDP-2018
2nd rowAF-YDP-2018
3rd rowAF-YDP-2018
4th rowAF-YDP-2018
5th rowAF-YDP-2018

Common Values

ValueCountFrequency (%)
AF-YDP-2018 10000
100.0%

Length

2024-05-04T06:50:40.049443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:50:40.311363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
af-ydp-2018 10000
100.0%

시리얼
Categorical

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
240AC425E790
 
388
240AC425E7F4
 
384
240AC42073B8
 
384
240AC425CACC
 
382
240AC420739C
 
378
Other values (24)
8084 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row240AC4208BD0
2nd row240AC4207398
3rd row240AC425C9F8
4th row240AC4208BA4
5th row240AC425CA98

Common Values

ValueCountFrequency (%)
240AC425E790 388
 
3.9%
240AC425E7F4 384
 
3.8%
240AC42073B8 384
 
3.8%
240AC425CACC 382
 
3.8%
240AC420739C 378
 
3.8%
240AC4207314 376
 
3.8%
240AC42651CC 376
 
3.8%
240AC425C9EC 373
 
3.7%
240AC42651E4 373
 
3.7%
240AC4208BF4 370
 
3.7%
Other values (19) 6216
62.2%

Length

2024-05-04T06:50:40.615954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
240ac425e790 388
 
3.9%
240ac425e7f4 384
 
3.8%
240ac42073b8 384
 
3.8%
240ac425cacc 382
 
3.8%
240ac420739c 378
 
3.8%
240ac4207314 376
 
3.8%
240ac42651cc 376
 
3.8%
240ac425c9ec 373
 
3.7%
240ac42651e4 373
 
3.7%
240ac4208bf4 370
 
3.7%
Other values (19) 6216
62.2%

온도(℃)
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.952
Minimum8
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:50:40.940312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q117
median19
Q323
95-th percentile27
Maximum31
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2700257
Coefficient of variation (CV)0.21401492
Kurtosis-0.54897798
Mean19.952
Median Absolute Deviation (MAD)3
Skewness0.1189068
Sum199520
Variance18.233119
MonotonicityNot monotonic
2024-05-04T06:50:41.294232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 1020
 
10.2%
19 971
 
9.7%
20 857
 
8.6%
17 824
 
8.2%
16 751
 
7.5%
25 628
 
6.3%
23 594
 
5.9%
21 592
 
5.9%
15 584
 
5.8%
24 583
 
5.8%
Other values (14) 2596
26.0%
ValueCountFrequency (%)
8 5
 
0.1%
9 34
 
0.3%
10 82
 
0.8%
11 76
 
0.8%
12 57
 
0.6%
13 237
 
2.4%
14 389
3.9%
15 584
5.8%
16 751
7.5%
17 824
8.2%
ValueCountFrequency (%)
31 13
 
0.1%
30 54
 
0.5%
29 96
 
1.0%
28 228
 
2.3%
27 323
3.2%
26 458
4.6%
25 628
6.3%
24 583
5.8%
23 594
5.9%
22 544
5.4%

습도(%)
Real number (ℝ)

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.7616
Minimum4
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:50:41.671259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q113
median17
Q320
95-th percentile25
Maximum44
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6876885
Coefficient of variation (CV)0.33932849
Kurtosis1.9503607
Mean16.7616
Median Absolute Deviation (MAD)4
Skewness0.76202954
Sum167616
Variance32.3498
MonotonicityNot monotonic
2024-05-04T06:50:42.101655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
19 775
 
7.8%
17 744
 
7.4%
18 733
 
7.3%
20 725
 
7.2%
14 675
 
6.8%
16 648
 
6.5%
15 606
 
6.1%
13 595
 
5.9%
21 581
 
5.8%
12 488
 
4.9%
Other values (31) 3430
34.3%
ValueCountFrequency (%)
4 3
 
< 0.1%
5 74
 
0.7%
6 74
 
0.7%
7 179
 
1.8%
8 327
3.3%
9 331
3.3%
10 345
3.5%
11 461
4.6%
12 488
4.9%
13 595
5.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
43 2
 
< 0.1%
42 8
 
0.1%
41 13
 
0.1%
40 16
0.2%
39 34
0.3%
38 13
 
0.1%
37 19
0.2%
36 10
 
0.1%
35 13
 
0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.0052
Minimum0
Maximum81
Zeros968
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:50:42.551069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q313
95-th percentile29
Maximum81
Range81
Interquartile range (IQR)9

Descriptive statistics

Standard deviation9.6269539
Coefficient of variation (CV)0.96219505
Kurtosis6.0780812
Mean10.0052
Median Absolute Deviation (MAD)5
Skewness2.019997
Sum100052
Variance92.678241
MonotonicityNot monotonic
2024-05-04T06:50:43.048409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 968
 
9.7%
6 664
 
6.6%
7 638
 
6.4%
8 632
 
6.3%
5 626
 
6.3%
9 582
 
5.8%
2 517
 
5.2%
4 508
 
5.1%
1 498
 
5.0%
3 497
 
5.0%
Other values (64) 3870
38.7%
ValueCountFrequency (%)
0 968
9.7%
1 498
5.0%
2 517
5.2%
3 497
5.0%
4 508
5.1%
5 626
6.3%
6 664
6.6%
7 638
6.4%
8 632
6.3%
9 582
5.8%
ValueCountFrequency (%)
81 1
 
< 0.1%
80 1
 
< 0.1%
75 1
 
< 0.1%
70 1
 
< 0.1%
69 1
 
< 0.1%
68 3
< 0.1%
67 2
< 0.1%
66 2
< 0.1%
65 3
< 0.1%
64 1
 
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.9376
Minimum0
Maximum92
Zeros788
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:50:43.479036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q316
95-th percentile33
Maximum92
Range92
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.141415
Coefficient of variation (CV)0.9333044
Kurtosis6.0406357
Mean11.9376
Median Absolute Deviation (MAD)6
Skewness1.9929635
Sum119376
Variance124.13112
MonotonicityNot monotonic
2024-05-04T06:50:43.920969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 788
 
7.9%
8 529
 
5.3%
7 516
 
5.2%
10 511
 
5.1%
9 506
 
5.1%
6 499
 
5.0%
1 478
 
4.8%
11 477
 
4.8%
5 445
 
4.5%
4 444
 
4.4%
Other values (76) 4807
48.1%
ValueCountFrequency (%)
0 788
7.9%
1 478
4.8%
2 431
4.3%
3 426
4.3%
4 444
4.4%
5 445
4.5%
6 499
5.0%
7 516
5.2%
8 529
5.3%
9 506
5.1%
ValueCountFrequency (%)
92 1
< 0.1%
90 1
< 0.1%
88 1
< 0.1%
85 1
< 0.1%
84 2
< 0.1%
82 1
< 0.1%
81 1
< 0.1%
80 2
< 0.1%
79 1
< 0.1%
76 1
< 0.1%

등록일시
Date

Distinct4473
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-01-15 00:01:05
Maximum2024-01-21 23:51:09
2024-05-04T06:50:44.301704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:44.713371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-04T06:50:37.455835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:33.839215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:34.904175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:36.294818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:37.769880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:34.090020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:35.293123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:36.569994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:38.066570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:34.370330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:35.590394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:36.867531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:38.378460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:34.650652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:35.890230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:37.169494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:50:44.994049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)
시리얼1.0000.6860.7610.4480.456
온도(℃)0.6861.0000.5470.2150.245
습도(%)0.7610.5471.0000.2550.264
초미세먼지(㎍/㎥)0.4480.2150.2551.0000.980
미세먼지(㎍/㎥)0.4560.2450.2640.9801.000
2024-05-04T06:50:45.336278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)시리얼
온도(℃)1.000-0.4220.0120.0010.320
습도(%)-0.4221.0000.000-0.0040.379
초미세먼지(㎍/㎥)0.0120.0001.0000.9850.175
미세먼지(㎍/㎥)0.001-0.0040.9851.0000.179
시리얼0.3200.3790.1750.1791.000

Missing values

2024-05-04T06:50:38.724861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:50:39.108609image/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

기관 명모델명시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)등록일시
20408영등포구AF-YDP-2018240AC4208BD0181925272024-01-18 02:01:06
10034영등포구AF-YDP-2018240AC4207398131825302024-01-15 19:51:05
18203영등포구AF-YDP-2018240AC425C9F8261117202024-01-17 12:51:10
15230영등포구AF-YDP-2018240AC4208BA420288122024-01-16 19:11:06
25389영등포구AF-YDP-2018240AC425CA9822188112024-01-19 09:11:10
32059영등포구AF-YDP-2018240AC4208C002217002024-01-21 03:21:12
7955영등포구AF-YDP-2018240AC4208C0028117102024-01-15 15:41:09
19607영등포구AF-YDP-2018240AC425E790202118182024-01-17 21:11:07
23539영등포구AF-YDP-2018240AC4207398152619222024-01-18 21:21:06
777영등포구AF-YDP-2018240AC4208C00201411162024-01-15 01:31:06
기관 명모델명시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)등록일시
19820영등포구AF-YDP-2018240AC4208BA4193515212024-01-17 22:31:06
7323영등포구AF-YDP-2018240AC4265A9C211612152024-01-15 14:21:13
5178영등포구AF-YDP-2018240AC425E8F4268682024-01-15 10:11:12
33638영등포구AF-YDP-2018240AC420739C2416002024-01-21 13:31:05
8148영등포구AF-YDP-2018240AC42620F0288332024-01-15 16:01:11
26585영등포구AF-YDP-2018240AC425E7F42120222024-01-19 16:41:13
32437영등포구AF-YDP-2018240AC42651E42019002024-01-21 05:42:27
17711영등포구AF-YDP-2018240AC425E8F4231213152024-01-17 09:51:12
5580영등포구AF-YDP-2018240AC425E78C2512452024-01-15 11:01:11
9854영등포구AF-YDP-2018240AC42651E42512672024-01-15 19:21:10

Duplicate rows

Most frequently occurring

기관 명모델명시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)등록일시# duplicates
13영등포구AF-YDP-2018240AC4207314191013152024-01-15 22:51:053
18영등포구AF-YDP-2018240AC4207314269582024-01-15 09:11:083
58영등포구AF-YDP-2018240AC42073989229112024-01-15 07:21:063
63영등포구AF-YDP-2018240AC4207398111810112024-01-15 23:31:053
76영등포구AF-YDP-2018240AC4207398161736442024-01-15 18:41:053
83영등포구AF-YDP-2018240AC4207398241230402024-01-15 11:51:083
88영등포구AF-YDP-2018240AC420739C1618672024-01-15 04:01:053
89영등포구AF-YDP-2018240AC420739C1618772024-01-15 03:41:053
93영등포구AF-YDP-2018240AC420739C19136112024-01-15 08:11:053
98영등포구AF-YDP-2018240AC420739C251412162024-01-15 15:31:083