Overview

Dataset statistics

Number of variables8
Number of observations4820
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory320.2 KiB
Average record size in memory68.0 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 started2024-05-04 06:50:00.104858
Analysis finished2024-05-04 06:50:07.016282
Duration6.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
영등포구
4820 

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 4820
100.0%

Length

2024-05-04T06:50:07.253965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:50:07.640578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영등포구 4820
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
AF-YDP-2018
4820 

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 4820
100.0%

Length

2024-05-04T06:50:07.984250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:50:08.284788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
af-ydp-2018 4820
100.0%

시리얼
Categorical

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
240AC4207314
342 
240AC4208BA4
342 
240AC4207398
342 
240AC4208BD0
342 
240AC425C9F8
342 
Other values (10)
3110 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row240AC4207314
2nd row240AC4208BA4
3rd row240AC4207398
4th row240AC4208BD0
5th row240AC4208C00

Common Values

ValueCountFrequency (%)
240AC4207314 342
 
7.1%
240AC4208BA4 342
 
7.1%
240AC4207398 342
 
7.1%
240AC4208BD0 342
 
7.1%
240AC425C9F8 342
 
7.1%
240AC425E76C 342
 
7.1%
240AC425E790 342
 
7.1%
240AC42620F0 342
 
7.1%
240AC42651AC 342
 
7.1%
240AC42651E4 342
 
7.1%
Other values (5) 1400
29.0%

Length

2024-05-04T06:50:08.605357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
240ac4207314 342
 
7.1%
240ac4208ba4 342
 
7.1%
240ac4207398 342
 
7.1%
240ac4208bd0 342
 
7.1%
240ac425c9f8 342
 
7.1%
240ac425e76c 342
 
7.1%
240ac425e790 342
 
7.1%
240ac42620f0 342
 
7.1%
240ac42651ac 342
 
7.1%
240ac42651e4 342
 
7.1%
Other values (5) 1400
29.0%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.989004
Minimum8
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.5 KiB
2024-05-04T06:50:08.937571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q111
median15
Q320
95-th percentile27
Maximum30
Range22
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7360931
Coefficient of variation (CV)0.35875236
Kurtosis-0.91094176
Mean15.989004
Median Absolute Deviation (MAD)5
Skewness0.54089987
Sum77067
Variance32.902763
MonotonicityNot monotonic
2024-05-04T06:50:09.262507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10 730
15.1%
11 440
 
9.1%
12 432
 
9.0%
9 297
 
6.2%
19 291
 
6.0%
16 270
 
5.6%
20 242
 
5.0%
15 215
 
4.5%
13 210
 
4.4%
18 203
 
4.2%
Other values (13) 1490
30.9%
ValueCountFrequency (%)
8 52
 
1.1%
9 297
6.2%
10 730
15.1%
11 440
9.1%
12 432
9.0%
13 210
 
4.4%
14 142
 
2.9%
15 215
 
4.5%
16 270
 
5.6%
17 183
 
3.8%
ValueCountFrequency (%)
30 7
 
0.1%
29 19
 
0.4%
28 86
1.8%
27 160
3.3%
26 156
3.2%
25 137
2.8%
24 136
2.8%
23 109
2.3%
22 153
3.2%
21 150
3.1%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.357261
Minimum9
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.5 KiB
2024-05-04T06:50:09.594444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile13
Q116
median19
Q322
95-th percentile28
Maximum38
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6274082
Coefficient of variation (CV)0.23905283
Kurtosis1.0186594
Mean19.357261
Median Absolute Deviation (MAD)3
Skewness0.72999193
Sum93302
Variance21.412906
MonotonicityNot monotonic
2024-05-04T06:50:09.967429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
18 487
10.1%
19 456
9.5%
16 406
 
8.4%
22 379
 
7.9%
20 371
 
7.7%
21 367
 
7.6%
17 349
 
7.2%
24 327
 
6.8%
14 305
 
6.3%
15 291
 
6.0%
Other values (20) 1082
22.4%
ValueCountFrequency (%)
9 1
 
< 0.1%
10 59
 
1.2%
11 62
 
1.3%
12 89
 
1.8%
13 156
 
3.2%
14 305
6.3%
15 291
6.0%
16 406
8.4%
17 349
7.2%
18 487
10.1%
ValueCountFrequency (%)
38 3
 
0.1%
37 2
 
< 0.1%
36 6
 
0.1%
35 20
 
0.4%
34 14
 
0.3%
33 31
0.6%
32 28
 
0.6%
31 43
0.9%
30 70
1.5%
29 14
 
0.3%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.719917
Minimum0
Maximum114
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size42.5 KiB
2024-05-04T06:50:10.610709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q116
median26
Q338
95-th percentile52
Maximum114
Range114
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.914739
Coefficient of variation (CV)0.53805137
Kurtosis2.1456551
Mean27.719917
Median Absolute Deviation (MAD)11
Skewness0.8728854
Sum133610
Variance222.44945
MonotonicityNot monotonic
2024-05-04T06:50:11.074959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 157
 
3.3%
19 153
 
3.2%
22 133
 
2.8%
27 133
 
2.8%
23 132
 
2.7%
28 131
 
2.7%
20 128
 
2.7%
32 127
 
2.6%
9 127
 
2.6%
25 124
 
2.6%
Other values (87) 3475
72.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 16
 
0.3%
2 13
 
0.3%
3 17
 
0.4%
4 22
 
0.5%
5 37
 
0.8%
6 88
1.8%
7 91
1.9%
8 99
2.1%
9 127
2.6%
ValueCountFrequency (%)
114 1
 
< 0.1%
113 1
 
< 0.1%
112 3
0.1%
111 2
< 0.1%
110 1
 
< 0.1%
109 1
 
< 0.1%
107 1
 
< 0.1%
106 2
< 0.1%
104 1
 
< 0.1%
101 1
 
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.289004
Minimum1
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.5 KiB
2024-05-04T06:50:11.456694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q119
median30
Q344
95-th percentile62
Maximum139
Range138
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.658378
Coefficient of variation (CV)0.54688518
Kurtosis2.8779845
Mean32.289004
Median Absolute Deviation (MAD)12
Skewness1.0158368
Sum155633
Variance311.81831
MonotonicityNot monotonic
2024-05-04T06:50:11.916783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 126
 
2.6%
27 121
 
2.5%
29 119
 
2.5%
16 119
 
2.5%
26 118
 
2.4%
33 114
 
2.4%
23 113
 
2.3%
34 111
 
2.3%
28 108
 
2.2%
15 108
 
2.2%
Other values (96) 3663
76.0%
ValueCountFrequency (%)
1 13
 
0.3%
2 17
 
0.4%
3 7
 
0.1%
4 15
 
0.3%
5 25
 
0.5%
6 54
1.1%
7 58
1.2%
8 78
1.6%
9 72
1.5%
10 94
2.0%
ValueCountFrequency (%)
139 3
0.1%
138 1
 
< 0.1%
137 2
< 0.1%
135 1
 
< 0.1%
133 2
< 0.1%
132 1
 
< 0.1%
130 1
 
< 0.1%
128 1
 
< 0.1%
125 1
 
< 0.1%
120 1
 
< 0.1%

등록일시
Date

Distinct1257
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
Minimum2023-12-25 00:01:04
Maximum2023-12-27 08:51:07
2024-05-04T06:50:12.322903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:12.711985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-04T06:50:04.912239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:01.063400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:02.419436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:03.627727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:05.205127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:01.442246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:02.680109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:03.969766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:05.470571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:01.806758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:03.021304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:04.273876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:05.841230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:02.108615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:03.290079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:50:04.647298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:50:12.980502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)
시리얼1.0000.7560.8160.7250.713
온도(℃)0.7561.0000.7830.6150.583
습도(%)0.8160.7831.0000.5390.548
초미세먼지(㎍/㎥)0.7250.6150.5391.0000.990
미세먼지(㎍/㎥)0.7130.5830.5480.9901.000
2024-05-04T06:50:13.260127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)시리얼
온도(℃)1.000-0.648-0.426-0.4250.390
습도(%)-0.6481.0000.1640.1820.470
초미세먼지(㎍/㎥)-0.4260.1641.0000.9900.368
미세먼지(㎍/㎥)-0.4250.1820.9901.0000.357
시리얼0.3900.4700.3680.3571.000

Missing values

2024-05-04T06:50:06.414839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:50:06.832005image/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
0영등포구AF-YDP-2018240AC4207314261018202023-12-25 00:01:04
1영등포구AF-YDP-2018240AC4208BA4143117182023-12-25 00:01:05
2영등포구AF-YDP-2018240AC420739892116182023-12-25 00:01:05
3영등포구AF-YDP-2018240AC4208BD0121638432023-12-25 00:01:05
4영등포구AF-YDP-2018240AC4208C00191417182023-12-25 00:01:05
5영등포구AF-YDP-2018240AC4207414101942482023-12-25 00:01:05
6영등포구AF-YDP-2018240AC425C9F8111526302023-12-25 00:01:05
7영등포구AF-YDP-2018240AC425E76C1119662023-12-25 00:01:06
8영등포구AF-YDP-2018240AC425E790112024322023-12-25 00:01:06
9영등포구AF-YDP-2018240AC425E79C102042462023-12-25 00:01:06
기관 명모델명시리얼온도(℃)습도(%)초미세먼지(㎍/㎥)미세먼지(㎍/㎥)등록일시
4810영등포구AF-YDP-2018240AC4208BD0151961712023-12-27 08:51:05
4811영등포구AF-YDP-2018240AC4208C00231721252023-12-27 08:51:05
4812영등포구AF-YDP-2018240AC425C9F8141943492023-12-27 08:51:05
4813영등포구AF-YDP-2018240AC425CAE4172061692023-12-27 08:51:06
4814영등포구AF-YDP-2018240AC425E76C122414172023-12-27 08:51:06
4815영등포구AF-YDP-2018240AC425E790251629332023-12-27 08:51:06
4816영등포구AF-YDP-2018240AC425E79C181853652023-12-27 08:51:06
4817영등포구AF-YDP-2018240AC42620F0122152722023-12-27 08:51:06
4818영등포구AF-YDP-2018240AC42651AC122345512023-12-27 08:51:06
4819영등포구AF-YDP-2018240AC42651E4231745552023-12-27 08:51:07