Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
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
온도(℃) is highly overall correlated with 습도(%)High correlation
습도(%) is highly overall correlated with 온도(℃)High correlation
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지(㎍/㎥)High correlation
미세먼지(㎍/㎥) is highly overall correlated with 초미세먼지(㎍/㎥)High correlation
초미세먼지(㎍/㎥) has 1040 (10.4%) zerosZeros
미세먼지(㎍/㎥) has 906 (9.1%) zerosZeros

Reproduction

Analysis started2024-05-04 06:51:22.414278
Analysis finished2024-05-04 06:51:30.115892
Duration7.7 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

2024-05-04T06:51:30.459623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:51:30.864226image/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

2024-05-04T06:51:31.246609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:51:31.767704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
af-ydp-2018 10000
100.0%

시리얼
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
240AC4208BF4
 
424
240AC425C9EC
 
422
240AC42620F0
 
421
240AC4208BD0
 
420
240AC42073B8
 
419
Other values (20)
7894 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row240AC425E8F4
2nd row240AC42651E4
3rd row240AC4207398
4th row240AC4207414
5th row240AC425E79C

Common Values

ValueCountFrequency (%)
240AC4208BF4 424
 
4.2%
240AC425C9EC 422
 
4.2%
240AC42620F0 421
 
4.2%
240AC4208BD0 420
 
4.2%
240AC42073B8 419
 
4.2%
240AC4207314 419
 
4.2%
240AC425E790 419
 
4.2%
240AC425E8F4 417
 
4.2%
240AC425E79C 417
 
4.2%
240AC42651CC 416
 
4.2%
Other values (15) 5806
58.1%

Length

2024-05-04T06:51:32.115941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
240ac4208bf4 424
 
4.2%
240ac425c9ec 422
 
4.2%
240ac42620f0 421
 
4.2%
240ac4208bd0 420
 
4.2%
240ac42073b8 419
 
4.2%
240ac4207314 419
 
4.2%
240ac425e790 419
 
4.2%
240ac425e8f4 417
 
4.2%
240ac425e79c 417
 
4.2%
240ac42651e4 416
 
4.2%
Other values (15) 5806
58.1%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.6778
Minimum13
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:51:32.516243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q118
median20
Q324
95-th percentile27
Maximum32
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.030262
Coefficient of variation (CV)0.19490768
Kurtosis-0.87241228
Mean20.6778
Median Absolute Deviation (MAD)3
Skewness0.24656477
Sum206778
Variance16.243011
MonotonicityNot monotonic
2024-05-04T06:51:32.960543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
18 1214
12.1%
19 1189
11.9%
24 842
 
8.4%
17 778
 
7.8%
25 752
 
7.5%
16 602
 
6.0%
20 581
 
5.8%
22 580
 
5.8%
21 576
 
5.8%
23 543
 
5.4%
Other values (10) 2343
23.4%
ValueCountFrequency (%)
13 67
 
0.7%
14 414
 
4.1%
15 477
 
4.8%
16 602
6.0%
17 778
7.8%
18 1214
12.1%
19 1189
11.9%
20 581
5.8%
21 576
5.8%
22 580
5.8%
ValueCountFrequency (%)
32 3
 
< 0.1%
31 29
 
0.3%
30 50
 
0.5%
29 123
 
1.2%
28 279
 
2.8%
27 382
3.8%
26 519
5.2%
25 752
7.5%
24 842
8.4%
23 543
5.4%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.6956
Minimum9
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:51:33.387296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile13
Q117
median19
Q322
95-th percentile26
Maximum44
Range35
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.9500698
Coefficient of variation (CV)0.25132871
Kurtosis4.695983
Mean19.6956
Median Absolute Deviation (MAD)2
Skewness1.6278344
Sum196956
Variance24.503191
MonotonicityNot monotonic
2024-05-04T06:51:33.801715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
19 1247
12.5%
20 1192
11.9%
18 1032
10.3%
21 928
9.3%
17 828
8.3%
16 692
 
6.9%
22 626
 
6.3%
15 602
 
6.0%
24 511
 
5.1%
23 399
 
4.0%
Other values (26) 1943
19.4%
ValueCountFrequency (%)
9 34
 
0.3%
10 25
 
0.2%
11 79
 
0.8%
12 213
 
2.1%
13 292
 
2.9%
14 324
 
3.2%
15 602
6.0%
16 692
6.9%
17 828
8.3%
18 1032
10.3%
ValueCountFrequency (%)
44 7
 
0.1%
43 7
 
0.1%
42 8
 
0.1%
41 23
 
0.2%
40 21
 
0.2%
39 42
0.4%
38 58
0.6%
37 66
0.7%
36 38
0.4%
35 61
0.6%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.4765
Minimum0
Maximum80
Zeros1040
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:51:34.206111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q323
95-th percentile38
Maximum80
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.521329
Coefficient of variation (CV)1.0033264
Kurtosis1.199262
Mean13.4765
Median Absolute Deviation (MAD)7
Skewness1.1858237
Sum134765
Variance182.82633
MonotonicityNot monotonic
2024-05-04T06:51:34.643783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1040
 
10.4%
2 742
 
7.4%
3 714
 
7.1%
1 612
 
6.1%
4 600
 
6.0%
5 522
 
5.2%
6 434
 
4.3%
7 301
 
3.0%
13 205
 
2.1%
28 198
 
2.0%
Other values (69) 4632
46.3%
ValueCountFrequency (%)
0 1040
10.4%
1 612
6.1%
2 742
7.4%
3 714
7.1%
4 600
6.0%
5 522
5.2%
6 434
4.3%
7 301
 
3.0%
8 188
 
1.9%
9 148
 
1.5%
ValueCountFrequency (%)
80 2
< 0.1%
79 1
 
< 0.1%
78 1
 
< 0.1%
77 3
< 0.1%
76 2
< 0.1%
73 3
< 0.1%
72 3
< 0.1%
71 4
< 0.1%
70 2
< 0.1%
69 2
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8715
Minimum0
Maximum95
Zeros906
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:51:35.086396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q327
95-th percentile46
Maximum95
Range95
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.844977
Coefficient of variation (CV)0.99832889
Kurtosis1.1507076
Mean15.8715
Median Absolute Deviation (MAD)8
Skewness1.1900366
Sum158715
Variance251.06329
MonotonicityNot monotonic
2024-05-04T06:51:35.545648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 906
 
9.1%
3 648
 
6.5%
2 624
 
6.2%
4 540
 
5.4%
1 517
 
5.2%
5 508
 
5.1%
6 429
 
4.3%
7 390
 
3.9%
8 280
 
2.8%
9 198
 
2.0%
Other values (81) 4960
49.6%
ValueCountFrequency (%)
0 906
9.1%
1 517
5.2%
2 624
6.2%
3 648
6.5%
4 540
5.4%
5 508
5.1%
6 429
4.3%
7 390
3.9%
8 280
 
2.8%
9 198
 
2.0%
ValueCountFrequency (%)
95 1
 
< 0.1%
92 1
 
< 0.1%
90 1
 
< 0.1%
89 1
 
< 0.1%
87 3
< 0.1%
86 2
 
< 0.1%
85 2
 
< 0.1%
84 3
< 0.1%
83 3
< 0.1%
82 6
0.1%

등록일시
Date

Distinct2206
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-02-05 00:01:04
Maximum2024-02-07 23:51:09
2024-05-04T06:51:35.927072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:36.484346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-04T06:51:27.466719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:23.574498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:24.719791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:26.113948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:27.956210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:23.850379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:25.033880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:26.466443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:28.286054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:24.145265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:25.530823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:26.818008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:28.598874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:24.443095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:25.801264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:51:27.183042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:51:36.951530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)
시리얼1.0000.7490.8320.5410.530
온도(℃)0.7491.0000.7770.4040.389
습도(%)0.8320.7771.0000.4670.451
초미세먼지(㎍/㎥)0.5410.4040.4671.0000.986
미세먼지(㎍/㎥)0.5300.3890.4510.9861.000
2024-05-04T06:51:37.273313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)시리얼
온도(℃)1.000-0.6800.0920.0840.375
습도(%)-0.6801.000-0.244-0.2300.474
초미세먼지(㎍/㎥)0.092-0.2441.0000.9930.220
미세먼지(㎍/㎥)0.084-0.2300.9931.0000.216
시리얼0.3750.4740.2200.2161.000

Missing values

2024-05-04T06:51:29.190210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:51:29.854794image/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
544영등포구AF-YDP-2018240AC425E8F41423442024-02-05 03:31:07
6727영등포구AF-YDP-2018240AC42651E4221913152024-02-06 22:01:09
4150영등포구AF-YDP-2018240AC42073981326222024-02-06 04:01:05
7332영등포구AF-YDP-2018240AC4207414181837472024-02-07 02:21:28
5005영등포구AF-YDP-2018240AC425E79C2416552024-02-06 09:51:11
3327영등포구AF-YDP-2018240AC425E8F41621572024-02-05 22:11:08
314영등포구AF-YDP-2018240AC425CACC1823442024-02-05 02:01:19
6305영등포구AF-YDP-2018240AC420739C212011112024-02-06 19:11:05
5290영등포구AF-YDP-2018240AC425CACC2820772024-02-06 11:51:10
3743영등포구AF-YDP-2018240AC420739C1922562024-02-06 01:11:05
기관 명모델명시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)등록일시
1841영등포구AF-YDP-2018240AC42620F02716002024-02-05 12:11:11
632영등포구AF-YDP-2018240AC4208BA416417112024-02-05 04:11:06
9504영등포구AF-YDP-2018240AC42651CC241620242024-02-07 17:41:11
4710영등포구AF-YDP-2018240AC4208C002317562024-02-06 07:51:06
8775영등포구AF-YDP-2018240AC425E8F4251517192024-02-07 12:21:11
862영등포구AF-YDP-2018240AC425C9F81620442024-02-05 05:41:07
8680영등포구AF-YDP-2018240AC42620F0281315172024-02-07 11:41:11
2993영등포구AF-YDP-2018240AC42651CC2416552024-02-05 19:51:08
9694영등포구AF-YDP-2018240AC4207314261441492024-02-07 19:11:05
6997영등포구AF-YDP-2018240AC42073B8202019212024-02-07 00:01:05