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

Reproduction

Analysis started2024-05-11 15:53:46.467493
Analysis finished2024-05-11 15:53:51.313870
Duration4.85 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

2024-05-12T00:53:51.422160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T00:53:51.575819image/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

2024-05-12T00:53:51.737904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T00:53:51.897582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
af-ydp-2018 10000
100.0%

시리얼
Categorical

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
240AC42651B4
 
407
240AC4207314
 
406
240AC4208BD0
 
400
240AC42651E4
 
400
240AC425C9F8
 
398
Other values (22)
7989 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row240AC425E79C
2nd row240AC4208C00
3rd row240AC42651AC
4th row240AC4208BE4
5th row240AC425E79C

Common Values

ValueCountFrequency (%)
240AC42651B4 407
 
4.1%
240AC4207314 406
 
4.1%
240AC4208BD0 400
 
4.0%
240AC42651E4 400
 
4.0%
240AC425C9F8 398
 
4.0%
240AC4207398 397
 
4.0%
240AC425E8F4 390
 
3.9%
240AC425CA98 390
 
3.9%
240AC42073B8 389
 
3.9%
240AC425E790 388
 
3.9%
Other values (17) 6035
60.4%

Length

2024-05-12T00:53:52.059964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
240ac42651b4 407
 
4.1%
240ac4207314 406
 
4.1%
240ac4208bd0 400
 
4.0%
240ac42651e4 400
 
4.0%
240ac425c9f8 398
 
4.0%
240ac4207398 397
 
4.0%
240ac425e8f4 390
 
3.9%
240ac425ca98 390
 
3.9%
240ac42073b8 389
 
3.9%
240ac425e790 388
 
3.9%
Other values (17) 6035
60.4%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.7483
Minimum10
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-12T00:53:52.254154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q117
median19
Q322
95-th percentile27
Maximum32
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0082607
Coefficient of variation (CV)0.20296738
Kurtosis-0.35898331
Mean19.7483
Median Absolute Deviation (MAD)3
Skewness0.43297989
Sum197483
Variance16.066154
MonotonicityNot monotonic
2024-05-12T00:53:52.457143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
19 1205
12.0%
18 1155
11.6%
17 978
9.8%
20 817
 
8.2%
15 723
 
7.2%
16 696
 
7.0%
21 673
 
6.7%
22 548
 
5.5%
23 489
 
4.9%
24 478
 
4.8%
Other values (13) 2238
22.4%
ValueCountFrequency (%)
10 26
 
0.3%
11 44
 
0.4%
12 63
 
0.6%
13 115
 
1.1%
14 475
 
4.8%
15 723
7.2%
16 696
7.0%
17 978
9.8%
18 1155
11.6%
19 1205
12.0%
ValueCountFrequency (%)
32 8
 
0.1%
31 27
 
0.3%
30 55
 
0.5%
29 89
 
0.9%
28 188
 
1.9%
27 313
3.1%
26 384
3.8%
25 451
4.5%
24 478
4.8%
23 489
4.9%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.4525
Minimum5
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-12T00:53:52.679783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11
Q115
median17
Q319
95-th percentile24
Maximum46
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8581997
Coefficient of variation (CV)0.27836698
Kurtosis7.0345258
Mean17.4525
Median Absolute Deviation (MAD)2
Skewness1.9603847
Sum174525
Variance23.602104
MonotonicityNot monotonic
2024-05-12T00:53:52.907119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
16 1337
13.4%
17 1116
11.2%
15 1042
10.4%
18 1000
10.0%
19 910
9.1%
20 829
8.3%
14 774
7.7%
21 476
 
4.8%
13 462
 
4.6%
22 425
 
4.2%
Other values (32) 1629
16.3%
ValueCountFrequency (%)
5 3
 
< 0.1%
6 10
 
0.1%
7 19
 
0.2%
8 61
 
0.6%
9 94
 
0.9%
10 165
 
1.7%
11 240
 
2.4%
12 371
3.7%
13 462
4.6%
14 774
7.7%
ValueCountFrequency (%)
46 1
 
< 0.1%
45 9
 
0.1%
44 13
 
0.1%
43 2
 
< 0.1%
42 3
 
< 0.1%
41 5
 
0.1%
40 17
0.2%
39 20
0.2%
38 34
0.3%
37 39
0.4%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.9218
Minimum0
Maximum104
Zeros72
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-12T00:53:53.282869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median27
Q339
95-th percentile61
Maximum104
Range104
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.476041
Coefficient of variation (CV)0.60425152
Kurtosis0.47811253
Mean28.9218
Median Absolute Deviation (MAD)12
Skewness0.75659514
Sum289218
Variance305.41203
MonotonicityNot monotonic
2024-05-12T00:53:53.537900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 261
 
2.6%
19 255
 
2.5%
15 245
 
2.5%
23 238
 
2.4%
12 237
 
2.4%
32 232
 
2.3%
24 229
 
2.3%
21 224
 
2.2%
31 223
 
2.2%
22 222
 
2.2%
Other values (92) 7634
76.3%
ValueCountFrequency (%)
0 72
0.7%
1 43
 
0.4%
2 62
 
0.6%
3 111
1.1%
4 153
1.5%
5 176
1.8%
6 141
1.4%
7 159
1.6%
8 164
1.6%
9 152
1.5%
ValueCountFrequency (%)
104 1
 
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%
100 1
 
< 0.1%
99 3
< 0.1%
98 3
< 0.1%
96 3
< 0.1%
95 4
< 0.1%
94 4
< 0.1%
93 3
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.1334
Minimum0
Maximum121
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-12T00:53:53.788005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q118
median31
Q346
95-th percentile73
Maximum121
Range121
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.884344
Coefficient of variation (CV)0.61184482
Kurtosis0.57522694
Mean34.1334
Median Absolute Deviation (MAD)14
Skewness0.79544099
Sum341334
Variance436.15582
MonotonicityNot monotonic
2024-05-12T00:53:54.049486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 221
 
2.2%
21 215
 
2.1%
26 209
 
2.1%
24 209
 
2.1%
17 206
 
2.1%
18 200
 
2.0%
38 199
 
2.0%
20 195
 
1.9%
30 195
 
1.9%
29 191
 
1.9%
Other values (110) 7960
79.6%
ValueCountFrequency (%)
0 63
0.6%
1 36
 
0.4%
2 52
 
0.5%
3 77
0.8%
4 127
1.3%
5 122
1.2%
6 145
1.5%
7 124
1.2%
8 150
1.5%
9 135
1.4%
ValueCountFrequency (%)
121 4
< 0.1%
120 2
 
< 0.1%
119 1
 
< 0.1%
117 3
< 0.1%
115 2
 
< 0.1%
114 5
0.1%
113 2
 
< 0.1%
112 7
0.1%
111 3
< 0.1%
110 2
 
< 0.1%

등록일시
Date

Distinct4780
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-01-29 00:01:04
Maximum2024-02-04 23:51:09
2024-05-12T00:53:54.292356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:54.529049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-12T00:53:50.303019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:47.322697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:48.316923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:49.295504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:50.458918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:47.566791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:48.558077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:49.544170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:50.607680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:47.807060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:48.794323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:49.788747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:50.766217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:48.057636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:49.040653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T00:53:50.040892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-12T00:53:54.694122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)
시리얼1.0000.6700.7700.5130.505
온도(℃)0.6701.0000.6690.2660.276
습도(%)0.7700.6691.0000.3550.347
초미세먼지(㎍/㎥)0.5130.2660.3551.0000.986
미세먼지(㎍/㎥)0.5050.2760.3470.9861.000
2024-05-12T00:53:54.862981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)시리얼
온도(℃)1.000-0.5320.1030.0920.316
습도(%)-0.5321.000-0.237-0.2270.406
초미세먼지(㎍/㎥)0.103-0.2371.0000.9930.212
미세먼지(㎍/㎥)0.092-0.2270.9931.0000.208
시리얼0.3160.4060.2120.2081.000

Missing values

2024-05-12T00:53:50.991474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T00:53:51.215674image/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
5997영등포구AF-YDP-2018240AC425E79C261037442024-01-30 14:11:10
4490영등포구AF-YDP-2018240AC4208C00211429332024-01-30 04:41:06
12631영등포구AF-YDP-2018240AC42651AC151980922024-02-01 07:11:08
18315영등포구AF-YDP-2018240AC4208BE4201864782024-02-02 19:31:06
1447영등포구AF-YDP-2018240AC425E79C181634412024-01-29 08:51:07
9854영등포구AF-YDP-2018240AC42620F0251221262024-01-31 14:01:14
18087영등포구AF-YDP-2018240AC425E79C251557702024-02-02 18:01:11
3898영등포구AF-YDP-2018240AC42620F0151530412024-01-30 00:41:08
22281영등포구AF-YDP-2018240AC4207414171826292024-02-03 21:01:05
5921영등포구AF-YDP-2018240AC425E79C261034392024-01-30 13:41:10
기관 명모델명시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)등록일시
5353영등포구AF-YDP-2018240AC42073B8241728292024-01-30 10:11:09
2731영등포구AF-YDP-2018240AC420741427939452024-01-29 17:01:09
19800영등포구AF-YDP-2018240AC4208C00231543502024-02-03 05:01:07
3853영등포구AF-YDP-2018240AC42651E4211717192024-01-30 00:21:09
5837영등포구AF-YDP-2018240AC425C9EC241422232024-01-30 13:11:09
7669영등포구AF-YDP-2018240AC4265A9C181720242024-01-31 00:31:09
23100영등포구AF-YDP-2018240AC425C9F8171817172024-02-04 02:11:07
538영등포구AF-YDP-2018240AC42651E4171612132024-01-29 03:11:09
9230영등포구AF-YDP-2018240AC42651AC211545532024-01-31 10:11:12
23513영등포구AF-YDP-2018240AC425C9EC182022232024-02-04 04:51:29