Overview

Dataset statistics

Number of variables15
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory132.1 B

Variable types

Numeric9
Categorical6

Dataset

Description소음유형 중 철도소음수동측정망을 통해 수집한 데이터를 권역, 대표노선, 지점, 용도구분 등의 형태로 반기별 데이터를 제공합니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15065383/fileData.do

Alerts

연도 has constant value ""Constant
순번 is highly overall correlated with 권역 and 1 other fieldsHigh correlation
지면위 낮 11시 is highly overall correlated with 지면위 낮 18시 and 6 other fieldsHigh correlation
지면위 낮 18시 is highly overall correlated with 지면위 낮 11시 and 5 other fieldsHigh correlation
지면위 낮 평균 is highly overall correlated with 지면위 낮 11시 and 6 other fieldsHigh correlation
지면위 밤22시 is highly overall correlated with 지면위 낮 11시 and 6 other fieldsHigh correlation
최고소음예상층 낮 11시 is highly overall correlated with 지면위 낮 11시 and 6 other fieldsHigh correlation
최고소음예상층 낮 18시 is highly overall correlated with 지면위 낮 11시 and 6 other fieldsHigh correlation
최고소음예상층 낮 평균 is highly overall correlated with 지면위 낮 11시 and 8 other fieldsHigh correlation
최고소음도예상층 밤 22시 is highly overall correlated with 지면위 낮 11시 and 6 other fieldsHigh correlation
권역 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
대표노선 is highly overall correlated with 권역 and 2 other fieldsHigh correlation
용도구분 is highly overall correlated with 최고소음예상층 낮 평균 and 2 other fieldsHigh correlation
측정지점 is highly overall correlated with 최고소음예상층 낮 11시 and 4 other fieldsHigh correlation
반기 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:52:44.445891
Analysis finished2023-12-12 00:52:55.195316
Duration10.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:55.283019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.1
Q116.5
median32
Q347.5
95-th percentile59.9
Maximum63
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.330303
Coefficient of variation (CV)0.57282196
Kurtosis-1.2
Mean32
Median Absolute Deviation (MAD)16
Skewness0
Sum2016
Variance336
MonotonicityStrictly increasing
2023-12-12T09:52:55.433727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
2 1
 
1.6%
35 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
Other values (53) 53
84.1%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%
54 1
1.6%

권역
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size636.0 B
호남
18 
수도권
16 
영남
14 
대전
8 
강원
7 

Length

Max length3
Median length2
Mean length2.2539683
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수도권
2nd row영남
3rd row영남
4th row영남
5th row수도권

Common Values

ValueCountFrequency (%)
호남 18
28.6%
수도권 16
25.4%
영남 14
22.2%
대전 8
12.7%
강원 7
 
11.1%

Length

2023-12-12T09:52:55.596561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:52:55.751261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
호남 18
28.6%
수도권 16
25.4%
영남 14
22.2%
대전 8
12.7%
강원 7
 
11.1%

대표노선
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size636.0 B
경부선
12 
호남선
12 
혼합선(수도권)
10 
경부(KTX전용)
4 
경춘선
3 
Other values (11)
22 

Length

Max length9
Median length3
Mean length4.4920635
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row혼합선(수도권)
2nd row경부선
3rd row동해남부선
4th row경부선
5th row공항철도(KTX)

Common Values

ValueCountFrequency (%)
경부선 12
19.0%
호남선 12
19.0%
혼합선(수도권) 10
15.9%
경부(KTX전용) 4
 
6.3%
경춘선 3
 
4.8%
동해남부선 2
 
3.2%
공항철도(KTX) 2
 
3.2%
광주선 2
 
3.2%
경의선 2
 
3.2%
중앙선 2
 
3.2%
Other values (6) 12
19.0%

Length

2023-12-12T09:52:55.911489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경부선 12
19.0%
호남선 12
19.0%
혼합선(수도권 10
15.9%
경부(ktx전용 4
 
6.3%
경춘선 3
 
4.8%
동해남부선 2
 
3.2%
공항철도(ktx 2
 
3.2%
광주선 2
 
3.2%
경의선 2
 
3.2%
중앙선 2
 
3.2%
Other values (6) 12
19.0%

용도구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size636.0 B
주거지역
38 
녹지지역
15 
일반주거지역
6 
준공업지역
 
2
관리지역
 
2

Length

Max length6
Median length4
Mean length4.2222222
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row준공업지역
2nd row주거지역
3rd row주거지역
4th row주거지역
5th row녹지지역

Common Values

ValueCountFrequency (%)
주거지역 38
60.3%
녹지지역 15
 
23.8%
일반주거지역 6
 
9.5%
준공업지역 2
 
3.2%
관리지역 2
 
3.2%

Length

2023-12-12T09:52:56.167516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:52:56.310844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주거지역 38
60.3%
녹지지역 15
 
23.8%
일반주거지역 6
 
9.5%
준공업지역 2
 
3.2%
관리지역 2
 
3.2%

측정지점
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Memory size636.0 B
주신빌딩
 
3
한국산업인력공단
 
2
해운대구 삼환아파트
 
2
모라동 동원아파트
 
2
논골경로당
 
2
Other values (26)
52 

Length

Max length12
Median length10
Mean length7.5555556
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영등포 센터플러스
2nd row한국산업인력공단
3rd row해운대구 삼환아파트
4th row모라동 동원아파트
5th row논골경로당

Common Values

ValueCountFrequency (%)
주신빌딩 3
 
4.8%
한국산업인력공단 2
 
3.2%
해운대구 삼환아파트 2
 
3.2%
모라동 동원아파트 2
 
3.2%
논골경로당 2
 
3.2%
운암동 광주성도교회 2
 
3.2%
소촌동 모아아파트 2
 
3.2%
판암동 아름다운교회 2
 
3.2%
도마동 효성타운 2
 
3.2%
읍내동 백송아파트 2
 
3.2%
Other values (21) 42
66.7%

Length

2023-12-12T09:52:56.489823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
읍내동 4
 
3.9%
주신빌딩 3
 
2.9%
오천동 2
 
1.9%
병점 2
 
1.9%
청록빌라트 2
 
1.9%
바다풍경모텔 2
 
1.9%
더조은오피스텔 2
 
1.9%
통사리 2
 
1.9%
평화동 2
 
1.9%
신흥장미아파트 2
 
1.9%
Other values (40) 80
77.7%

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size636.0 B
2022
63 

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 63
100.0%

Length

2023-12-12T09:52:56.689224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:52:56.826934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 63
100.0%

반기
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size636.0 B
상반기
32 
하반기
31 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상반기 32
50.8%
하반기 31
49.2%

Length

2023-12-12T09:52:56.963027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:52:57.090261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상반기 32
50.8%
하반기 31
49.2%

지면위 낮 11시
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.028571
Minimum47.5
Maximum65.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:57.234690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.5
5-th percentile48.88
Q152.45
median56.4
Q358.6
95-th percentile64.5
Maximum65.5
Range18
Interquartile range (IQR)6.15

Descriptive statistics

Standard deviation4.6538028
Coefficient of variation (CV)0.083061243
Kurtosis-0.54460828
Mean56.028571
Median Absolute Deviation (MAD)3.5
Skewness0.19997957
Sum3529.8
Variance21.65788
MonotonicityNot monotonic
2023-12-12T09:52:57.440272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.3 2
 
3.2%
58.1 2
 
3.2%
51.5 2
 
3.2%
57.5 2
 
3.2%
56.4 2
 
3.2%
56.5 2
 
3.2%
64.5 2
 
3.2%
52.8 2
 
3.2%
49.6 2
 
3.2%
52.1 2
 
3.2%
Other values (43) 43
68.3%
ValueCountFrequency (%)
47.5 1
1.6%
47.7 1
1.6%
47.8 1
1.6%
48.8 1
1.6%
49.6 2
3.2%
50.1 1
1.6%
50.6 1
1.6%
50.7 1
1.6%
50.8 1
1.6%
51.1 1
1.6%
ValueCountFrequency (%)
65.5 1
1.6%
65.4 1
1.6%
65.0 1
1.6%
64.5 2
3.2%
62.8 1
1.6%
62.7 1
1.6%
62.6 1
1.6%
62.0 1
1.6%
60.9 1
1.6%
60.7 1
1.6%

지면위 낮 18시
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.04127
Minimum46.4
Maximum66.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:57.681404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46.4
5-th percentile48.71
Q152.75
median55.9
Q359.3
95-th percentile64.04
Maximum66.7
Range20.3
Interquartile range (IQR)6.55

Descriptive statistics

Standard deviation4.6765246
Coefficient of variation (CV)0.08344787
Kurtosis-0.40492801
Mean56.04127
Median Absolute Deviation (MAD)3.4
Skewness0.17159531
Sum3530.6
Variance21.869882
MonotonicityNot monotonic
2023-12-12T09:52:57.877616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.3 3
 
4.8%
57.8 2
 
3.2%
52.5 2
 
3.2%
57.3 2
 
3.2%
48.3 2
 
3.2%
57.9 2
 
3.2%
56.7 2
 
3.2%
54.3 2
 
3.2%
57.4 2
 
3.2%
54.6 2
 
3.2%
Other values (40) 42
66.7%
ValueCountFrequency (%)
46.4 1
1.6%
48.3 2
3.2%
48.7 1
1.6%
48.8 1
1.6%
49.3 1
1.6%
49.6 1
1.6%
50.6 2
3.2%
50.7 1
1.6%
50.9 1
1.6%
51.7 1
1.6%
ValueCountFrequency (%)
66.7 1
1.6%
66.3 1
1.6%
64.5 1
1.6%
64.1 1
1.6%
63.5 1
1.6%
62.6 1
1.6%
62.5 1
1.6%
61.8 1
1.6%
61.5 1
1.6%
60.7 1
1.6%

지면위 낮 평균
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.095238
Minimum47
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:58.068196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile49
Q152
median56
Q359
95-th percentile63
Maximum66
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4891061
Coefficient of variation (CV)0.08002651
Kurtosis-0.49649707
Mean56.095238
Median Absolute Deviation (MAD)3
Skewness0.19720371
Sum3534
Variance20.152074
MonotonicityNot monotonic
2023-12-12T09:52:58.231677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
58 8
12.7%
51 7
11.1%
57 6
9.5%
55 5
 
7.9%
59 5
 
7.9%
52 5
 
7.9%
49 4
 
6.3%
56 4
 
6.3%
53 3
 
4.8%
63 3
 
4.8%
Other values (7) 13
20.6%
ValueCountFrequency (%)
47 1
 
1.6%
49 4
6.3%
51 7
11.1%
52 5
7.9%
53 3
 
4.8%
54 3
 
4.8%
55 5
7.9%
56 4
6.3%
57 6
9.5%
58 8
12.7%
ValueCountFrequency (%)
66 2
 
3.2%
65 1
 
1.6%
63 3
 
4.8%
62 2
 
3.2%
61 3
 
4.8%
60 1
 
1.6%
59 5
7.9%
58 8
12.7%
57 6
9.5%
56 4
6.3%

지면위 밤22시
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.920635
Minimum35.3
Maximum65.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:58.403010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.3
5-th percentile43.82
Q148.1
median53
Q357
95-th percentile63.9
Maximum65.7
Range30.4
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation6.3983153
Coefficient of variation (CV)0.12090398
Kurtosis-0.19020594
Mean52.920635
Median Absolute Deviation (MAD)4.7
Skewness-0.10042198
Sum3334
Variance40.938438
MonotonicityNot monotonic
2023-12-12T09:52:58.891846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
48.0 4
 
6.3%
55.0 4
 
6.3%
57.0 4
 
6.3%
52.0 3
 
4.8%
50.0 2
 
3.2%
42.0 2
 
3.2%
58.3 2
 
3.2%
46.0 2
 
3.2%
58.0 2
 
3.2%
63.0 2
 
3.2%
Other values (35) 36
57.1%
ValueCountFrequency (%)
35.3 1
1.6%
42.0 2
3.2%
43.8 1
1.6%
44.0 1
1.6%
45.0 1
1.6%
45.1 1
1.6%
45.2 1
1.6%
46.0 2
3.2%
46.2 1
1.6%
47.2 1
1.6%
ValueCountFrequency (%)
65.7 1
1.6%
65.0 1
1.6%
64.1 1
1.6%
64.0 1
1.6%
63.0 2
3.2%
61.7 1
1.6%
61.0 1
1.6%
60.1 1
1.6%
58.3 2
3.2%
58.0 2
3.2%

최고소음예상층 낮 11시
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.038095
Minimum48.4
Maximum72.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:59.097934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48.4
5-th percentile50.81
Q155.8
median58.7
Q362.4
95-th percentile66.25
Maximum72.8
Range24.4
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation5.0040444
Coefficient of variation (CV)0.084759585
Kurtosis0.53412368
Mean59.038095
Median Absolute Deviation (MAD)3.4
Skewness0.35888591
Sum3719.4
Variance25.040461
MonotonicityNot monotonic
2023-12-12T09:52:59.300290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.7 4
 
6.3%
72.8 2
 
3.2%
50.2 2
 
3.2%
59.5 2
 
3.2%
64.6 2
 
3.2%
58.6 2
 
3.2%
55.8 2
 
3.2%
57.5 2
 
3.2%
57.8 2
 
3.2%
65.8 1
 
1.6%
Other values (42) 42
66.7%
ValueCountFrequency (%)
48.4 1
1.6%
50.2 2
3.2%
50.8 1
1.6%
50.9 1
1.6%
52.1 1
1.6%
52.2 1
1.6%
53.9 1
1.6%
54.0 1
1.6%
54.4 1
1.6%
54.7 1
1.6%
ValueCountFrequency (%)
72.8 2
3.2%
66.7 1
1.6%
66.3 1
1.6%
65.8 1
1.6%
65.4 1
1.6%
64.9 1
1.6%
64.8 1
1.6%
64.6 2
3.2%
63.9 1
1.6%
63.4 1
1.6%

최고소음예상층 낮 18시
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.203175
Minimum51.6
Maximum73.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:59.509298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.6
5-th percentile52.17
Q155.5
median59.3
Q362.55
95-th percentile66.28
Maximum73.8
Range22.2
Interquartile range (IQR)7.05

Descriptive statistics

Standard deviation4.9889545
Coefficient of variation (CV)0.084268361
Kurtosis0.47998178
Mean59.203175
Median Absolute Deviation (MAD)3.7
Skewness0.6341818
Sum3729.8
Variance24.889667
MonotonicityNot monotonic
2023-12-12T09:52:59.732590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.6 3
 
4.8%
64.7 2
 
3.2%
59.9 2
 
3.2%
65.0 2
 
3.2%
60.4 2
 
3.2%
59.3 2
 
3.2%
60.5 2
 
3.2%
52.8 2
 
3.2%
51.6 2
 
3.2%
61.5 2
 
3.2%
Other values (42) 42
66.7%
ValueCountFrequency (%)
51.6 2
3.2%
52.0 1
1.6%
52.1 1
1.6%
52.8 2
3.2%
52.9 1
1.6%
53.1 1
1.6%
53.2 1
1.6%
53.5 1
1.6%
53.9 1
1.6%
54.1 1
1.6%
ValueCountFrequency (%)
73.8 1
1.6%
73.4 1
1.6%
66.6 1
1.6%
66.3 1
1.6%
66.1 1
1.6%
65.2 1
1.6%
65.0 2
3.2%
64.9 1
1.6%
64.7 2
3.2%
64.5 1
1.6%

최고소음예상층 낮 평균
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.126984
Minimum51
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:52:59.930279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile52
Q155.5
median59
Q362.5
95-th percentile66
Maximum73
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8343264
Coefficient of variation (CV)0.081761762
Kurtosis0.53244087
Mean59.126984
Median Absolute Deviation (MAD)4
Skewness0.54697898
Sum3725
Variance23.370712
MonotonicityNot monotonic
2023-12-12T09:53:00.112377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
59 7
11.1%
60 6
 
9.5%
52 6
 
9.5%
63 5
 
7.9%
55 5
 
7.9%
58 5
 
7.9%
61 4
 
6.3%
57 4
 
6.3%
66 3
 
4.8%
56 3
 
4.8%
Other values (7) 15
23.8%
ValueCountFrequency (%)
51 1
 
1.6%
52 6
9.5%
53 2
 
3.2%
54 2
 
3.2%
55 5
7.9%
56 3
4.8%
57 4
6.3%
58 5
7.9%
59 7
11.1%
60 6
9.5%
ValueCountFrequency (%)
73 2
 
3.2%
66 3
4.8%
65 3
4.8%
64 3
4.8%
63 5
7.9%
62 2
 
3.2%
61 4
6.3%
60 6
9.5%
59 7
11.1%
58 5
7.9%

최고소음도예상층 밤 22시
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.671429
Minimum37.9
Maximum74.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-12T09:53:00.298239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.9
5-th percentile47
Q152.85
median57.2
Q360.5
95-th percentile64
Maximum74.7
Range36.8
Interquartile range (IQR)7.65

Descriptive statistics

Standard deviation6.2841436
Coefficient of variation (CV)0.11088733
Kurtosis1.2045299
Mean56.671429
Median Absolute Deviation (MAD)4.2
Skewness-0.088508211
Sum3570.3
Variance39.490461
MonotonicityNot monotonic
2023-12-12T09:53:00.518859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
56.0 4
 
6.3%
53.0 3
 
4.8%
58.0 3
 
4.8%
64.0 3
 
4.8%
63.0 2
 
3.2%
47.0 2
 
3.2%
50.0 2
 
3.2%
58.3 2
 
3.2%
62.0 2
 
3.2%
57.0 2
 
3.2%
Other values (34) 38
60.3%
ValueCountFrequency (%)
37.9 1
1.6%
45.0 2
3.2%
47.0 2
3.2%
48.4 1
1.6%
48.8 1
1.6%
50.0 2
3.2%
50.5 1
1.6%
50.9 1
1.6%
51.1 1
1.6%
51.3 1
1.6%
ValueCountFrequency (%)
74.7 1
 
1.6%
72.0 1
 
1.6%
64.9 1
 
1.6%
64.0 3
4.8%
63.6 1
 
1.6%
63.0 2
3.2%
62.9 1
 
1.6%
62.0 2
3.2%
61.7 1
 
1.6%
61.0 2
3.2%

Interactions

2023-12-12T09:52:53.689620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:45.412293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.475503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.498966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.496084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.372288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:50.307186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.635755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.622827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:53.819435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:45.528077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.592649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.644564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.590711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.456576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:50.400213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.776185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.733522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:53.947418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:45.664934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.726213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.757135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.700957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.545133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:50.518262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.892008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.854479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:54.065412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:45.802667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.864372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.894370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.805452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.638861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:50.631558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.996283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.982067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:54.195911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:45.926975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.996872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.999391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.902456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.725670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:50.740535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.085838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:53.077931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:54.307934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.032911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.108917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.097489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.013417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.838897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.200771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.176835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:53.199100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:54.419386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.146769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.204649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.186992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.099986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.931455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.306289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.276337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:53.327258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:54.537918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.246742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.294167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.272233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.190458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:50.022884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.411616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.376506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:53.426711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:54.672219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:46.353365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:47.390111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:48.380787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:49.267978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:50.186718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:51.511742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:52.501527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:52:53.551445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:53:00.719402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번권역대표노선용도구분측정지점반기지면위 낮 11시지면위 낮 18시지면위 낮 평균지면위 밤22시최고소음예상층 낮 11시최고소음예상층 낮 18시최고소음예상층 낮 평균최고소음도예상층 밤 22시
순번1.0000.8980.2750.0000.0000.9990.2970.4510.6450.0000.0000.0000.3480.182
권역0.8981.0000.9480.6341.0000.0000.5120.6950.7110.5020.2940.3170.4460.243
대표노선0.2750.9481.0000.8431.0000.0000.6330.5730.2220.6150.7700.5670.8010.705
용도구분0.0000.6340.8431.0001.0000.0000.7700.8450.7500.5520.6780.6860.7160.678
측정지점0.0001.0001.0001.0001.0000.0000.8540.9140.8090.8890.9180.8660.9280.856
반기0.9990.0000.0000.0000.0001.0000.0000.0870.0000.0000.0000.0000.0000.313
지면위 낮 11시0.2970.5120.6330.7700.8540.0001.0000.8250.9090.6120.5760.6160.6390.550
지면위 낮 18시0.4510.6950.5730.8450.9140.0870.8251.0000.9390.7190.6650.7170.6730.654
지면위 낮 평균0.6450.7110.2220.7500.8090.0000.9090.9391.0000.6790.5980.6470.5780.619
지면위 밤22시0.0000.5020.6150.5520.8890.0000.6120.7190.6791.0000.6720.5220.4540.893
최고소음예상층 낮 11시0.0000.2940.7700.6780.9180.0000.5760.6650.5980.6721.0000.8220.8620.911
최고소음예상층 낮 18시0.0000.3170.5670.6860.8660.0000.6160.7170.6470.5220.8221.0000.9660.766
최고소음예상층 낮 평균0.3480.4460.8010.7160.9280.0000.6390.6730.5780.4540.8620.9661.0000.772
최고소음도예상층 밤 22시0.1820.2430.7050.6780.8560.3130.5500.6540.6190.8930.9110.7660.7721.000
2023-12-12T09:53:00.946026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도구분반기대표노선권역측정지점
용도구분1.0000.0000.5650.2810.743
반기0.0001.0000.0000.0000.000
대표노선0.5650.0001.0000.7570.825
권역0.2810.0000.7571.0000.743
측정지점0.7430.0000.8250.7431.000
2023-12-12T09:53:01.112816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번지면위 낮 11시지면위 낮 18시지면위 낮 평균지면위 밤22시최고소음예상층 낮 11시최고소음예상층 낮 18시최고소음예상층 낮 평균최고소음도예상층 밤 22시권역대표노선용도구분측정지점반기
순번1.000-0.091-0.095-0.076-0.039-0.169-0.057-0.141-0.1490.5520.0000.0000.0000.932
지면위 낮 11시-0.0911.0000.8750.9610.8590.5710.6260.6190.6350.2410.2770.4020.3890.000
지면위 낮 18시-0.0950.8751.0000.9620.8490.4400.6340.5620.5990.3410.2420.4840.4830.039
지면위 낮 평균-0.0760.9610.9621.0000.8960.5150.6550.6090.6430.3530.0500.3870.3350.000
지면위 밤22시-0.0390.8590.8490.8961.0000.5290.6850.6310.7780.3050.2810.3440.4520.000
최고소음예상층 낮 11시-0.1690.5710.4400.5150.5291.0000.8530.9540.8100.1620.4200.4580.5040.000
최고소음예상층 낮 18시-0.0570.6260.6340.6550.6850.8531.0000.9590.9040.1790.1960.4930.4010.000
최고소음예상층 낮 평균-0.1410.6190.5620.6090.6310.9540.9591.0000.8920.2830.3810.5310.5290.000
최고소음도예상층 밤 22시-0.1490.6350.5990.6430.7780.8100.9040.8921.0000.1300.3530.4580.4030.291
권역0.5520.2410.3410.3530.3050.1620.1790.2830.1301.0000.7570.2810.7430.000
대표노선0.0000.2770.2420.0500.2810.4200.1960.3810.3530.7571.0000.5650.8250.000
용도구분0.0000.4020.4840.3870.3440.4580.4930.5310.4580.2810.5651.0000.7430.000
측정지점0.0000.3890.4830.3350.4520.5040.4010.5290.4030.7430.8250.7431.0000.000
반기0.9320.0000.0390.0000.0000.0000.0000.0000.2910.0000.0000.0000.0001.000

Missing values

2023-12-12T09:52:54.854438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:52:55.099378image/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

순번권역대표노선용도구분측정지점연도반기지면위 낮 11시지면위 낮 18시지면위 낮 평균지면위 밤22시최고소음예상층 낮 11시최고소음예상층 낮 18시최고소음예상층 낮 평균최고소음도예상층 밤 22시
01수도권혼합선(수도권)준공업지역영등포 센터플러스2022상반기65.566.36664.072.873.47372.0
12영남경부선주거지역한국산업인력공단2022상반기55.556.95652.062.561.56262.0
23영남동해남부선주거지역해운대구 삼환아파트2022상반기51.550.65148.061.160.36159.0
34영남경부선주거지역모라동 동원아파트2022상반기54.355.15552.057.656.85755.0
45수도권공항철도(KTX)녹지지역논골경로당2022상반기56.456.65755.063.163.06361.0
56호남광주선주거지역운암동 광주성도교회2022상반기50.851.75150.057.555.85756.0
67호남호남선주거지역소촌동 모아아파트2022상반기53.149.35142.058.952.05545.0
78대전경부선녹지지역판암동 아름다운교회2022상반기64.560.76356.058.759.95957.0
89대전호남선주거지역도마동 효성타운2022상반기52.848.75146.066.355.66153.0
910대전경부선주거지역읍내동 백송아파트2022상반기60.957.95958.055.853.55554.0
순번권역대표노선용도구분측정지점연도반기지면위 낮 11시지면위 낮 18시지면위 낮 평균지면위 밤22시최고소음예상층 낮 11시최고소음예상층 낮 18시최고소음예상층 낮 평균최고소음도예상층 밤 22시
5354호남장항선녹지지역통사리2022하반기47.850.64935.348.452.95137.9
5455호남호남선주거지역평화동2022하반기49.648.34947.258.757.75857.5
5556호남호남선일반주거지역신흥장미아파트2022하반기55.454.35557.958.657.65858.3
5657호남호남선녹지지역완주 상관면2022하반기62.763.56356.763.964.76458.0
5758호남경전선주거지역오천동 건우주택아파트2022하반기51.150.95145.252.152.85248.8
5859호남호남선주거지역죽림동 삼성아파트2022하반기52.854.85446.255.955.95651.1
5960호남전라선주거지역곡성읍 읍내리2022하반기52.153.15348.354.453.95451.3
6061영남경부(KTX전용)주거지역지좌동 대양가든빌라2022하반기59.159.35956.459.260.46056.6
6162영남대구중앙선주거지역금노동2022하반기50.752.45248.250.852.85248.4
6263영남경부선주거지역옥곡동 동화프라임아파트2022하반기56.657.35751.258.159.65953.3