Overview

Dataset statistics

Number of variables12
Number of observations1026
Missing cells9
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.3 KiB
Average record size in memory101.1 B

Variable types

Categorical5
Text3
Numeric4

Dataset

Description항공기소음 자동측정망을 통해 전송한 데이터를 공항, 지점, 주소 등의 형태로 월별 평균값(wecpnl)을 집계하여 제공합니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15065195/fileData.do

Alerts

측정년도 has constant value ""Constant
공항영문명 is highly overall correlated with 측정기코드 and 4 other fieldsHigh correlation
지역코드 is highly overall correlated with 측정기코드 and 4 other fieldsHigh correlation
공항명 is highly overall correlated with 측정기코드 and 4 other fieldsHigh correlation
공항코드 is highly overall correlated with 측정기코드 and 4 other fieldsHigh correlation
측정기코드 is highly overall correlated with 우편번호 and 4 other fieldsHigh correlation
우편번호 is highly overall correlated with 측정기코드 and 4 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 11:41:49.178188
Analysis finished2023-12-12 11:41:53.602461
Duration4.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공항명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
김포공항
129 
광주공항
84 
김해공항
84 
사천공항
84 
대구공항
81 
Other values (9)
564 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김포공항 129
12.6%
광주공항 84
 
8.2%
김해공항 84
 
8.2%
사천공항 84
 
8.2%
대구공항 81
 
7.9%
제주공항 74
 
7.2%
울산공항 72
 
7.0%
원주공항 72
 
7.0%
포항공항 72
 
7.0%
군산공항 64
 
6.2%
Other values (4) 210
20.5%

Length

2023-12-12T20:41:53.776931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김포공항 129
12.6%
광주공항 84
 
8.2%
김해공항 84
 
8.2%
사천공항 84
 
8.2%
대구공항 81
 
7.9%
제주공항 74
 
7.2%
울산공항 72
 
7.0%
원주공항 72
 
7.0%
포항공항 72
 
7.0%
군산공항 64
 
6.2%
Other values (4) 210
20.5%

공항코드
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
RKSS
129 
RKJJ
84 
RKPK
84 
RKPS
84 
RKTN
81 
Other values (9)
564 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
RKSS 129
12.6%
RKJJ 84
 
8.2%
RKPK 84
 
8.2%
RKPS 84
 
8.2%
RKTN 81
 
7.9%
RKPC 74
 
7.2%
RKPU 72
 
7.0%
RKNW 72
 
7.0%
RKTH 72
 
7.0%
RKJK 64
 
6.2%
Other values (4) 210
20.5%

Length

2023-12-12T20:41:53.968084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rkss 129
12.6%
rkjj 84
 
8.2%
rkpk 84
 
8.2%
rkps 84
 
8.2%
rktn 81
 
7.9%
rkpc 74
 
7.2%
rkpu 72
 
7.0%
rknw 72
 
7.0%
rkth 72
 
7.0%
rkjk 64
 
6.2%
Other values (4) 210
20.5%

지역코드
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
GI
129 
GJ
84 
BS
84 
JI
84 
DG
81 
Other values (9)
564 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
GI 129
12.6%
GJ 84
 
8.2%
BS 84
 
8.2%
JI 84
 
8.2%
DG 81
 
7.9%
JE 74
 
7.2%
WS 72
 
7.0%
WJ 72
 
7.0%
PH 72
 
7.0%
KS 64
 
6.2%
Other values (4) 210
20.5%

Length

2023-12-12T20:41:54.138161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gi 129
12.6%
gj 84
 
8.2%
bs 84
 
8.2%
ji 84
 
8.2%
dg 81
 
7.9%
je 74
 
7.2%
ws 72
 
7.0%
wj 72
 
7.0%
ph 72
 
7.0%
ks 64
 
6.2%
Other values (4) 210
20.5%

공항영문명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
Gimpo
129 
Gwangju
84 
Gimhae
84 
Sacheon
84 
Daegu
81 
Other values (9)
564 

Length

Max length8
Median length7
Mean length5.7358674
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Gimpo 129
12.6%
Gwangju 84
 
8.2%
Gimhae 84
 
8.2%
Sacheon 84
 
8.2%
Daegu 81
 
7.9%
Jeju 74
 
7.2%
Ulsan 72
 
7.0%
Wonju 72
 
7.0%
Pohang 72
 
7.0%
Gunsan 64
 
6.2%
Other values (4) 210
20.5%

Length

2023-12-12T20:41:54.352840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gimpo 129
12.6%
gwangju 84
 
8.2%
gimhae 84
 
8.2%
sacheon 84
 
8.2%
daegu 81
 
7.9%
jeju 74
 
7.2%
ulsan 72
 
7.0%
wonju 72
 
7.0%
pohang 72
 
7.0%
gunsan 64
 
6.2%
Other values (4) 210
20.5%

지점명
Text

Distinct91
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2023-12-12T20:41:54.804048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.4688109
Min length2

Characters and Unicode

Total characters3559
Distinct characters134
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row덕흥동
2nd row덕흥동
3rd row덕흥동
4th row덕흥동
5th row덕흥동
ValueCountFrequency (%)
덕흥동 12
 
1.1%
상운 12
 
1.1%
명촌 12
 
1.1%
상안 12
 
1.1%
농소 12
 
1.1%
반구 12
 
1.1%
학서 12
 
1.1%
수문포 12
 
1.1%
가평리 12
 
1.1%
중광정 12
 
1.1%
Other values (83) 930
88.6%
2023-12-12T20:41:55.521109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 304
 
8.5%
리 118
 
3.3%
마 98
 
2.8%
덕 92
 
2.6%
서 76
 
2.1%
을 74
 
2.1%
도 72
 
2.0%
장 72
 
2.0%
교 72
 
2.0%
산 71
 
2.0%
Other values (124) 2510
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3413
95.9%
Uppercase Letter 63
 
1.8%
Decimal Number 59
 
1.7%
Space Separator 24
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 304
 
8.9%
리 118
 
3.5%
마 98
 
2.9%
덕 92
 
2.7%
서 76
 
2.2%
을 74
 
2.2%
도 72
 
2.1%
장 72
 
2.1%
교 72
 
2.1%
산 71
 
2.1%
Other values (118) 2364
69.3%
Uppercase Letter
ValueCountFrequency (%)
P 21
33.3%
T 21
33.3%
A 21
33.3%
Decimal Number
ValueCountFrequency (%)
1 35
59.3%
2 24
40.7%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3413
95.9%
Common 83
 
2.3%
Latin 63
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 304
 
8.9%
리 118
 
3.5%
마 98
 
2.9%
덕 92
 
2.7%
서 76
 
2.2%
을 74
 
2.2%
도 72
 
2.1%
장 72
 
2.1%
교 72
 
2.1%
산 71
 
2.1%
Other values (118) 2364
69.3%
Common
ValueCountFrequency (%)
1 35
42.2%
2 24
28.9%
24
28.9%
Latin
ValueCountFrequency (%)
P 21
33.3%
T 21
33.3%
A 21
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3413
95.9%
ASCII 146
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 304
 
8.9%
리 118
 
3.5%
마 98
 
2.9%
덕 92
 
2.7%
서 76
 
2.2%
을 74
 
2.2%
도 72
 
2.1%
장 72
 
2.1%
교 72
 
2.1%
산 71
 
2.1%
Other values (118) 2364
69.3%
ASCII
ValueCountFrequency (%)
1 35
24.0%
2 24
16.4%
24
16.4%
P 21
14.4%
T 21
14.4%
A 21
14.4%

지점번호
Text

Distinct89
Distinct (%)8.8%
Missing9
Missing (%)0.9%
Memory size8.1 KiB
2023-12-12T20:41:55.982765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0511308
Min length5

Characters and Unicode

Total characters5137
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRKJJ2
2nd rowRKJJ2
3rd rowRKJJ2
4th rowRKJJ2
5th rowRKJJ2
ValueCountFrequency (%)
rkss10 14
 
1.4%
rkny1 12
 
1.2%
rkpu5 12
 
1.2%
rkpu3 12
 
1.2%
rkpu2 12
 
1.2%
rkpu1 12
 
1.2%
rkpu6 12
 
1.2%
rkjy4 12
 
1.2%
rkjy1 12
 
1.2%
rkny2 12
 
1.2%
Other values (79) 895
88.0%
2023-12-12T20:41:56.618350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 1165
22.7%
R 1017
19.8%
S 342
 
6.7%
J 335
 
6.5%
P 314
 
6.1%
1 240
 
4.7%
T 204
 
4.0%
N 200
 
3.9%
2 165
 
3.2%
5 141
 
2.7%
Other values (13) 1014
19.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4068
79.2%
Decimal Number 1069
 
20.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 1165
28.6%
R 1017
25.0%
S 342
 
8.4%
J 335
 
8.2%
P 314
 
7.7%
T 204
 
5.0%
N 200
 
4.9%
U 123
 
3.0%
Y 102
 
2.5%
C 74
 
1.8%
Other values (3) 192
 
4.7%
Decimal Number
ValueCountFrequency (%)
1 240
22.5%
2 165
15.4%
5 141
13.2%
4 128
12.0%
3 123
11.5%
6 97
9.1%
7 82
 
7.7%
9 48
 
4.5%
8 29
 
2.7%
0 16
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4068
79.2%
Common 1069
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 1165
28.6%
R 1017
25.0%
S 342
 
8.4%
J 335
 
8.2%
P 314
 
7.7%
T 204
 
5.0%
N 200
 
4.9%
U 123
 
3.0%
Y 102
 
2.5%
C 74
 
1.8%
Other values (3) 192
 
4.7%
Common
ValueCountFrequency (%)
1 240
22.5%
2 165
15.4%
5 141
13.2%
4 128
12.0%
3 123
11.5%
6 97
9.1%
7 82
 
7.7%
9 48
 
4.5%
8 29
 
2.7%
0 16
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 1165
22.7%
R 1017
19.8%
S 342
 
6.7%
J 335
 
6.5%
P 314
 
6.1%
1 240
 
4.7%
T 204
 
4.0%
N 200
 
3.9%
2 165
 
3.2%
5 141
 
2.7%
Other values (13) 1014
19.7%

측정기코드
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3821354.2
Minimum1147501
Maximum5011514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2023-12-12T20:41:56.872357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1147501
5-th percentile1150503
Q12914507
median4283501
Q34684503
95-th percentile5011505
Maximum5011514
Range3864013
Interquartile range (IQR)1769996

Descriptive statistics

Standard deviation1076877.3
Coefficient of variation (CV)0.28180515
Kurtosis-0.010448734
Mean3821354.2
Median Absolute Deviation (MAD)541001
Skewness-0.95404868
Sum3.9207094 × 109
Variance1.1596647 × 1012
MonotonicityNot monotonic
2023-12-12T20:41:57.133478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2914501 12
 
1.2%
4684502 12
 
1.2%
3120504 12
 
1.2%
3120503 12
 
1.2%
3120502 12
 
1.2%
3120501 12
 
1.2%
3111503 12
 
1.2%
4613504 12
 
1.2%
4613501 12
 
1.2%
4283508 12
 
1.2%
Other values (81) 906
88.3%
ValueCountFrequency (%)
1147501 7
0.7%
1147502 12
1.2%
1150501 12
1.2%
1150502 12
1.2%
1150503 12
1.2%
1153501 12
1.2%
2644501 12
1.2%
2644502 12
1.2%
2644503 12
1.2%
2644504 12
1.2%
ValueCountFrequency (%)
5011514 12
1.2%
5011512 3
 
0.3%
5011511 12
1.2%
5011509 12
1.2%
5011507 11
1.1%
5011505 12
1.2%
5011502 12
1.2%
4825502 12
1.2%
4824506 12
1.2%
4824505 12
1.2%

주소
Text

Distinct91
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2023-12-12T20:41:57.537332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length27
Mean length19.12963
Min length3

Characters and Unicode

Total characters19627
Distinct characters171
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주 서구 덕흥동 953-2
2nd row광주 서구 덕흥동 953-2
3rd row광주 서구 덕흥동 953-2
4th row광주 서구 덕흥동 953-2
5th row광주 서구 덕흥동 953-2
ValueCountFrequency (%)
강원 119
 
2.6%
강서구 108
 
2.4%
북구 84
 
1.9%
경북 72
 
1.6%
경남 72
 
1.6%
사천시 72
 
1.6%
부산 72
 
1.6%
포항시 72
 
1.6%
남구 72
 
1.6%
대구 70
 
1.5%
Other values (234) 3712
82.0%
2023-12-12T20:41:58.229864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3499
 
17.8%
1 885
 
4.5%
2 738
 
3.8%
동 731
 
3.7%
구 702
 
3.6%
- 663
 
3.4%
3 514
 
2.6%
시 441
 
2.2%
4 395
 
2.0%
리 377
 
1.9%
Other values (161) 10682
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10694
54.5%
Decimal Number 4197
 
21.4%
Space Separator 3499
 
17.8%
Dash Punctuation 663
 
3.4%
Close Punctuation 227
 
1.2%
Open Punctuation 227
 
1.2%
Uppercase Letter 84
 
0.4%
Other Punctuation 36
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 731
 
6.8%
구 702
 
6.6%
시 441
 
4.1%
리 377
 
3.5%
면 377
 
3.5%
산 328
 
3.1%
서 304
 
2.8%
북 301
 
2.8%
강 275
 
2.6%
경 239
 
2.2%
Other values (139) 6619
61.9%
Decimal Number
ValueCountFrequency (%)
1 885
21.1%
2 738
17.6%
3 514
12.2%
4 395
9.4%
6 349
 
8.3%
7 298
 
7.1%
5 277
 
6.6%
9 269
 
6.4%
8 241
 
5.7%
0 231
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 24
28.6%
N 12
14.3%
F 12
14.3%
A 12
14.3%
P 12
14.3%
T 12
14.3%
Other Punctuation
ValueCountFrequency (%)
. 24
66.7%
, 12
33.3%
Space Separator
ValueCountFrequency (%)
3499
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 663
100.0%
Close Punctuation
ValueCountFrequency (%)
) 227
100.0%
Open Punctuation
ValueCountFrequency (%)
( 227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10694
54.5%
Common 8849
45.1%
Latin 84
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 731
 
6.8%
구 702
 
6.6%
시 441
 
4.1%
리 377
 
3.5%
면 377
 
3.5%
산 328
 
3.1%
서 304
 
2.8%
북 301
 
2.8%
강 275
 
2.6%
경 239
 
2.2%
Other values (139) 6619
61.9%
Common
ValueCountFrequency (%)
3499
39.5%
1 885
 
10.0%
2 738
 
8.3%
- 663
 
7.5%
3 514
 
5.8%
4 395
 
4.5%
6 349
 
3.9%
7 298
 
3.4%
5 277
 
3.1%
9 269
 
3.0%
Other values (6) 962
 
10.9%
Latin
ValueCountFrequency (%)
B 24
28.6%
N 12
14.3%
F 12
14.3%
A 12
14.3%
P 12
14.3%
T 12
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10694
54.5%
ASCII 8933
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3499
39.2%
1 885
 
9.9%
2 738
 
8.3%
- 663
 
7.4%
3 514
 
5.8%
4 395
 
4.4%
6 349
 
3.9%
7 298
 
3.3%
5 277
 
3.1%
9 269
 
3.0%
Other values (12) 1046
 
11.7%
Hangul
ValueCountFrequency (%)
동 731
 
6.8%
구 702
 
6.6%
시 441
 
4.1%
리 377
 
3.5%
면 377
 
3.5%
산 328
 
3.1%
서 304
 
2.8%
북 301
 
2.8%
강 275
 
2.6%
경 239
 
2.2%
Other values (139) 6619
61.9%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8209016 × 109
Minimum1.1470103 × 109
Maximum5.011014 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2023-12-12T20:41:58.468039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1470103 × 109
5-th percentile1.1500112 × 109
Q12.9140129 × 109
median4.283032 × 109
Q34.684035 × 109
95-th percentile5.0110109 × 109
Maximum5.011014 × 109
Range3.8640037 × 109
Interquartile range (IQR)1.7700221 × 109

Descriptive statistics

Standard deviation1.0768943 × 109
Coefficient of variation (CV)0.28184297
Kurtosis-0.010475268
Mean3.8209016 × 109
Median Absolute Deviation (MAD)5.41002 × 108
Skewness-0.95408024
Sum3.9202451 × 1012
Variance1.1597012 × 1018
MonotonicityNot monotonic
2023-12-12T20:41:58.742851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2644010200 36
 
3.5%
2644010300 36
 
3.5%
4513040021 24
 
2.3%
4213031023 24
 
2.3%
5011010900 24
 
2.3%
4684035022 24
 
2.3%
4513040022 24
 
2.3%
4824034021 24
 
2.3%
4711111200 24
 
2.3%
4824034027 24
 
2.3%
Other values (64) 762
74.3%
ValueCountFrequency (%)
1147010300 19
1.9%
1150010300 12
 
1.2%
1150010800 12
 
1.2%
1150011200 12
 
1.2%
1153010600 12
 
1.2%
2644010200 36
3.5%
2644010300 36
3.5%
2714010800 12
 
1.2%
2714011100 12
 
1.2%
2714011400 10
 
1.0%
ValueCountFrequency (%)
5011014000 12
1.2%
5011012700 12
1.2%
5011012600 3
 
0.3%
5011012300 12
1.2%
5011010900 24
2.3%
5011010800 11
1.1%
4825011800 12
1.2%
4824034027 24
2.3%
4824034026 12
1.2%
4824034021 24
2.3%

측정년도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
2022
1026 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 1026
100.0%

Length

2023-12-12T20:41:59.021940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:41:59.230413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 1026
100.0%

측정월
Real number (ℝ)

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.454191
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2023-12-12T20:41:59.370526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4449464
Coefficient of variation (CV)0.5337534
Kurtosis-1.2071972
Mean6.454191
Median Absolute Deviation (MAD)3
Skewness0.020826811
Sum6622
Variance11.867656
MonotonicityNot monotonic
2023-12-12T20:41:59.554375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 88
8.6%
2 87
8.5%
4 87
8.5%
7 87
8.5%
1 86
8.4%
3 86
8.4%
5 86
8.4%
8 85
8.3%
11 84
8.2%
12 84
8.2%
Other values (2) 166
16.2%
ValueCountFrequency (%)
1 86
8.4%
2 87
8.5%
3 86
8.4%
4 87
8.5%
5 86
8.4%
6 88
8.6%
7 87
8.5%
8 85
8.3%
9 83
8.1%
10 83
8.1%
ValueCountFrequency (%)
12 84
8.2%
11 84
8.2%
10 83
8.1%
9 83
8.1%
8 85
8.3%
7 87
8.5%
6 88
8.6%
5 86
8.4%
4 87
8.5%
3 86
8.4%

측정값
Real number (ℝ)

Distinct46
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.137427
Minimum47
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.1 KiB
2023-12-12T20:41:59.787067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile56
Q166
median72
Q379
95-th percentile86
Maximum93
Range46
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3366115
Coefficient of variation (CV)0.12942812
Kurtosis-0.58659546
Mean72.137427
Median Absolute Deviation (MAD)7
Skewness-0.20461747
Sum74013
Variance87.172315
MonotonicityNot monotonic
2023-12-12T20:42:00.019634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
68 50
 
4.9%
79 47
 
4.6%
69 46
 
4.5%
70 44
 
4.3%
77 43
 
4.2%
67 43
 
4.2%
83 42
 
4.1%
72 41
 
4.0%
71 38
 
3.7%
75 34
 
3.3%
Other values (36) 598
58.3%
ValueCountFrequency (%)
47 1
 
0.1%
49 5
 
0.5%
50 5
 
0.5%
51 6
 
0.6%
52 4
 
0.4%
53 4
 
0.4%
54 13
1.3%
55 8
0.8%
56 11
1.1%
57 16
1.6%
ValueCountFrequency (%)
93 1
 
0.1%
92 1
 
0.1%
91 4
 
0.4%
90 13
 
1.3%
89 9
 
0.9%
88 1
 
0.1%
87 13
 
1.3%
86 22
2.1%
85 33
3.2%
84 28
2.7%

Interactions

2023-12-12T20:41:52.598255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:50.467840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:51.238279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:51.979973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:52.740468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:50.652447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:51.444617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:52.149904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:52.917761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:50.865294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:51.643413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:52.324062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:53.048293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:51.053124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:51.811625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:41:52.455245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:42:00.707829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공항명공항코드지역코드공항영문명지점명지점번호측정기코드주소우편번호측정월측정값
공항명1.0001.0001.0001.0001.0001.0000.9951.0000.9950.0000.699
공항코드1.0001.0001.0001.0001.0001.0000.9951.0000.9950.0000.699
지역코드1.0001.0001.0001.0001.0001.0000.9951.0000.9950.0000.699
공항영문명1.0001.0001.0001.0001.0001.0000.9951.0000.9950.0000.699
지점명1.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.930
지점번호1.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.930
측정기코드0.9950.9950.9950.9951.0001.0001.0001.0001.0000.0000.541
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.930
우편번호0.9950.9950.9950.9951.0001.0001.0001.0001.0000.0000.544
측정월0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정값0.6990.6990.6990.6990.9300.9300.5410.9300.5440.0001.000
2023-12-12T20:42:00.922798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공항영문명지역코드공항명공항코드
공항영문명1.0001.0001.0001.000
지역코드1.0001.0001.0001.000
공항명1.0001.0001.0001.000
공항코드1.0001.0001.0001.000
2023-12-12T20:42:01.075373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정기코드우편번호측정월측정값공항명공항코드지역코드공항영문명
측정기코드1.0000.9970.008-0.2370.8840.8840.8840.884
우편번호0.9971.0000.008-0.2230.8840.8840.8840.884
측정월0.0080.0081.0000.0160.0000.0000.0000.000
측정값-0.237-0.2230.0161.0000.3710.3710.3710.371
공항명0.8840.8840.0000.3711.0001.0001.0001.000
공항코드0.8840.8840.0000.3711.0001.0001.0001.000
지역코드0.8840.8840.0000.3711.0001.0001.0001.000
공항영문명0.8840.8840.0000.3711.0001.0001.0001.000

Missing values

2023-12-12T20:41:53.235787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:41:53.501926image/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

공항명공항코드지역코드공항영문명지점명지점번호측정기코드주소우편번호측정년도측정월측정값
0광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022177
1광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022277
2광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022377
3광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022478
4광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022579
5광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022678
6광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022777
7광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022879
8광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-229140117002022978
9광주공항RKJJGJGwangju덕흥동RKJJ22914501광주 서구 덕흥동 953-2291401170020221078
공항명공항코드지역코드공항영문명지점명지점번호측정기코드주소우편번호측정년도측정월측정값
1016포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-747111320252022367
1017포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-747111320252022469
1018포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-747111320252022569
1019포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-747111320252022668
1020포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-747111320252022766
1021포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-747111320252022869
1022포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-747111320252022967
1023포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-7471113202520221070
1024포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-7471113202520221168
1025포항공항RKTHPHPohang석리RKTH64711506경북 포항시 남구 동해면 석리 18-7471113202520221270