Overview

Dataset statistics

Number of variables15
Number of observations50
Missing cells14
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory135.6 B

Variable types

Categorical4
Text1
Numeric10

Dataset

Description인천광역시 통근 및 통학 지역과 소요시간(군구별, 성별, 연령별, 학력별, 직업별 등)의 항목을 제공하는 데이터로 구성되어 있습니다.※BASE : 통근 및 통학하는 경우
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15066272&srcSe=7661IVAWM27C61E190

Alerts

통근 및 통학 지역(퍼센트)_강원 is highly overall correlated with 통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트) and 3 other fieldsHigh correlation
특성별(1) is highly overall correlated with 통근 및 통학 지역(퍼센트)_강원High correlation
통근 및 통학 지역(퍼센트)_인천 is highly overall correlated with 통근 및 통학 지역(퍼센트)_경기 and 6 other fieldsHigh correlation
통근 및 통학 지역(퍼센트)_경기 is highly overall correlated with 통근 및 통학 지역(퍼센트)_인천 and 6 other fieldsHigh correlation
통근 및 통학 지역(퍼센트)_서울 is highly overall correlated with 통근 및 통학 지역(퍼센트)_인천 and 6 other fieldsHigh correlation
통근 및 통학 지역(퍼센트)_대전_세종_충북_충남 is highly overall correlated with 통근 및 통학 소요시간(편도)_120분 이상(퍼센트)High correlation
통근 및 통학 소요시간(편도)_30분 미만(퍼센트) is highly overall correlated with 통근 및 통학 지역(퍼센트)_인천 and 6 other fieldsHigh correlation
통근 및 통학 소요시간(편도)_30분 이상_60분 미만(퍼센트) is highly overall correlated with 통근 및 통학 소요시간(편도)_30분 미만(퍼센트)High correlation
통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트) is highly overall correlated with 통근 및 통학 지역(퍼센트)_인천 and 5 other fieldsHigh correlation
통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트) is highly overall correlated with 통근 및 통학 지역(퍼센트)_인천 and 7 other fieldsHigh correlation
통근 및 통학 소요시간(편도)_120분 이상(퍼센트) is highly overall correlated with 통근 및 통학 지역(퍼센트)_인천 and 4 other fieldsHigh correlation
통근 및 통학 소요시간(편도)_평균(분) is highly overall correlated with 통근 및 통학 지역(퍼센트)_인천 and 7 other fieldsHigh correlation
통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남 is highly overall correlated with 통근 및 통학 지역(퍼센트)_경기 and 1 other fieldsHigh correlation
통근 및 통학 지역(퍼센트)_광주_전북_전남 is highly overall correlated with 통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남 and 1 other fieldsHigh correlation
통근 및 통학 지역(퍼센트)_강원 is highly imbalanced (53.8%)Imbalance
통근 및 통학 지역(퍼센트)_서울 has 3 (6.0%) missing valuesMissing
통근 및 통학 지역(퍼센트)_대전_세종_충북_충남 has 7 (14.0%) missing valuesMissing
통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트) has 1 (2.0%) missing valuesMissing
통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트) has 1 (2.0%) missing valuesMissing
통근 및 통학 소요시간(편도)_120분 이상(퍼센트) has 2 (4.0%) missing valuesMissing
특성별(2) has unique valuesUnique
통근 및 통학 소요시간(편도)_120분 이상(퍼센트) has 1 (2.0%) zerosZeros

Reproduction

Analysis started2024-03-18 05:43:42.792064
Analysis finished2024-03-18 05:43:53.182973
Duration10.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

특성별(1)
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
군구별
10 
직업별
월평균소득별
연령별
가구원수별
Other values (4)
13 

Length

Max length7
Median length3
Mean length4.04
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군구별 10
20.0%
직업별 8
16.0%
월평균소득별 8
16.0%
연령별 6
12.0%
가구원수별 5
10.0%
학력별 4
 
8.0%
주거형태별 4
 
8.0%
주거점유형태별 3
 
6.0%
성별 2
 
4.0%

Length

2024-03-18T14:43:53.245385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:43:53.351311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군구별 10
20.0%
직업별 8
16.0%
월평균소득별 8
16.0%
연령별 6
12.0%
가구원수별 5
10.0%
학력별 4
 
8.0%
주거형태별 4
 
8.0%
주거점유형태별 3
 
6.0%
성별 2
 
4.0%

특성별(2)
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2024-03-18T14:43:53.540372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7.5
Mean length4.92
Min length2

Characters and Unicode

Total characters246
Distinct characters76
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row중구
2nd row동구
3rd row미추홀구
4th row연수구
5th row남동구
ValueCountFrequency (%)
미만 7
 
9.7%
5
 
6.9%
이상 3
 
4.2%
기타 2
 
2.8%
중구 1
 
1.4%
기능노무 1
 
1.4%
4인 1
 
1.4%
학생 1
 
1.4%
주부 1
 
1.4%
무직/기타 1
 
1.4%
Other values (49) 49
68.1%
2024-03-18T14:43:53.848912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
 
13.4%
22
 
8.9%
15
 
6.1%
~ 11
 
4.5%
9
 
3.7%
8
 
3.3%
8
 
3.3%
8
 
3.3%
3 6
 
2.4%
5
 
2.0%
Other values (66) 121
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
57.7%
Decimal Number 69
28.0%
Space Separator 22
 
8.9%
Math Symbol 11
 
4.5%
Other Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15
 
10.6%
9
 
6.3%
8
 
5.6%
8
 
5.6%
8
 
5.6%
5
 
3.5%
5
 
3.5%
4
 
2.8%
4
 
2.8%
4
 
2.8%
Other values (54) 72
50.7%
Decimal Number
ValueCountFrequency (%)
0 33
47.8%
3 6
 
8.7%
9 5
 
7.2%
5 5
 
7.2%
4 5
 
7.2%
2 5
 
7.2%
1 5
 
7.2%
6 3
 
4.3%
7 2
 
2.9%
Space Separator
ValueCountFrequency (%)
22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
57.7%
Common 104
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15
 
10.6%
9
 
6.3%
8
 
5.6%
8
 
5.6%
8
 
5.6%
5
 
3.5%
5
 
3.5%
4
 
2.8%
4
 
2.8%
4
 
2.8%
Other values (54) 72
50.7%
Common
ValueCountFrequency (%)
0 33
31.7%
22
21.2%
~ 11
 
10.6%
3 6
 
5.8%
9 5
 
4.8%
5 5
 
4.8%
4 5
 
4.8%
2 5
 
4.8%
1 5
 
4.8%
6 3
 
2.9%
Other values (2) 4
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
57.7%
ASCII 104
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
31.7%
22
21.2%
~ 11
 
10.6%
3 6
 
5.8%
9 5
 
4.8%
5 5
 
4.8%
4 5
 
4.8%
2 5
 
4.8%
1 5
 
4.8%
6 3
 
2.9%
Other values (2) 4
 
3.8%
Hangul
ValueCountFrequency (%)
15
 
10.6%
9
 
6.3%
8
 
5.6%
8
 
5.6%
8
 
5.6%
5
 
3.5%
5
 
3.5%
4
 
2.8%
4
 
2.8%
4
 
2.8%
Other values (54) 72
50.7%

통근 및 통학 지역(퍼센트)_인천
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.182
Minimum61.3
Maximum99.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:53.967003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61.3
5-th percentile69.585
Q175
median80.2
Q385.75
95-th percentile92.27
Maximum99.7
Range38.4
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.740661
Coefficient of variation (CV)0.096538637
Kurtosis0.46479596
Mean80.182
Median Absolute Deviation (MAD)5.45
Skewness0.31577192
Sum4009.1
Variance59.917833
MonotonicityNot monotonic
2024-03-18T14:43:54.087273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
83.1 2
 
4.0%
75.3 2
 
4.0%
74.1 2
 
4.0%
80.3 2
 
4.0%
78.2 2
 
4.0%
86.0 1
 
2.0%
74.3 1
 
2.0%
80.1 1
 
2.0%
91.5 1
 
2.0%
74.7 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
61.3 1
2.0%
67.5 1
2.0%
69.0 1
2.0%
70.3 1
2.0%
70.5 1
2.0%
71.4 1
2.0%
72.0 1
2.0%
72.6 1
2.0%
74.1 2
4.0%
74.3 1
2.0%
ValueCountFrequency (%)
99.7 1
2.0%
99.6 1
2.0%
92.9 1
2.0%
91.5 1
2.0%
90.3 1
2.0%
89.2 1
2.0%
87.6 1
2.0%
87.5 1
2.0%
86.6 1
2.0%
86.1 1
2.0%

통근 및 통학 지역(퍼센트)_경기
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.666
Minimum0.3
Maximum17.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:54.202467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile2.255
Q18.1
median9.9
Q312.4
95-th percentile14.465
Maximum17.5
Range17.2
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.694652
Coefficient of variation (CV)0.38223174
Kurtosis0.78317256
Mean9.666
Median Absolute Deviation (MAD)2.25
Skewness-0.67152676
Sum483.3
Variance13.650453
MonotonicityNot monotonic
2024-03-18T14:43:54.324070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
8.4 3
 
6.0%
10.7 2
 
4.0%
9.1 2
 
4.0%
8.1 2
 
4.0%
11.6 2
 
4.0%
12.1 2
 
4.0%
12.5 2
 
4.0%
12.6 2
 
4.0%
5.1 1
 
2.0%
14.6 1
 
2.0%
Other values (31) 31
62.0%
ValueCountFrequency (%)
0.3 1
2.0%
0.4 1
2.0%
0.5 1
2.0%
4.4 1
2.0%
4.9 1
2.0%
5.1 1
2.0%
5.5 1
2.0%
6.3 1
2.0%
6.8 1
2.0%
7.1 1
2.0%
ValueCountFrequency (%)
17.5 1
2.0%
16.1 1
2.0%
14.6 1
2.0%
14.3 1
2.0%
13.7 1
2.0%
13.2 1
2.0%
13.0 1
2.0%
12.8 1
2.0%
12.7 1
2.0%
12.6 2
4.0%

통근 및 통학 지역(퍼센트)_서울
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)83.0%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean10.106383
Minimum3.3
Maximum19.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:54.446377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile5.22
Q17.2
median10
Q312.8
95-th percentile15.91
Maximum19.7
Range16.4
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation3.774307
Coefficient of variation (CV)0.37345774
Kurtosis0.10973326
Mean10.106383
Median Absolute Deviation (MAD)2.8
Skewness0.56593554
Sum475
Variance14.245393
MonotonicityNot monotonic
2024-03-18T14:43:54.594021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
10.0 3
 
6.0%
14.0 2
 
4.0%
11.0 2
 
4.0%
11.2 2
 
4.0%
7.2 2
 
4.0%
19.7 2
 
4.0%
9.0 2
 
4.0%
6.5 1
 
2.0%
6.9 1
 
2.0%
10.1 1
 
2.0%
Other values (29) 29
58.0%
(Missing) 3
 
6.0%
ValueCountFrequency (%)
3.3 1
2.0%
3.9 1
2.0%
5.1 1
2.0%
5.5 1
2.0%
5.7 1
2.0%
6.0 1
2.0%
6.5 1
2.0%
6.6 1
2.0%
6.7 1
2.0%
6.9 1
2.0%
ValueCountFrequency (%)
19.7 2
4.0%
16.0 1
2.0%
15.7 1
2.0%
14.6 1
2.0%
14.1 1
2.0%
14.0 2
4.0%
13.9 1
2.0%
13.4 1
2.0%
13.2 1
2.0%
13.1 1
2.0%

통근 및 통학 지역(퍼센트)_대전_세종_충북_충남
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)37.2%
Missing7
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean0.62325581
Minimum0.1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:54.718230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.11
Q10.2
median0.5
Q30.8
95-th percentile1.49
Maximum2
Range1.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.47501239
Coefficient of variation (CV)0.76214674
Kurtosis0.89334064
Mean0.62325581
Median Absolute Deviation (MAD)0.3
Skewness1.1859411
Sum26.8
Variance0.22563677
MonotonicityNot monotonic
2024-03-18T14:43:54.818488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.2 9
18.0%
0.3 4
8.0%
0.8 4
8.0%
0.6 4
8.0%
0.5 4
8.0%
0.1 3
 
6.0%
0.4 3
 
6.0%
0.7 3
 
6.0%
1.3 2
 
4.0%
2.0 1
 
2.0%
Other values (6) 6
12.0%
(Missing) 7
14.0%
ValueCountFrequency (%)
0.1 3
 
6.0%
0.2 9
18.0%
0.3 4
8.0%
0.4 3
 
6.0%
0.5 4
8.0%
0.6 4
8.0%
0.7 3
 
6.0%
0.8 4
8.0%
1.0 1
 
2.0%
1.1 1
 
2.0%
ValueCountFrequency (%)
2.0 1
 
2.0%
1.8 1
 
2.0%
1.5 1
 
2.0%
1.4 1
 
2.0%
1.3 2
4.0%
1.2 1
 
2.0%
1.1 1
 
2.0%
1.0 1
 
2.0%
0.8 4
8.0%
0.7 3
6.0%
Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
31 
0.1
0.2
0.0
0.3
 
2

Length

Max length4
Median length4
Mean length3.62
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.3
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 31
62.0%
0.1 9
 
18.0%
0.2 4
 
8.0%
0.0 4
 
8.0%
0.3 2
 
4.0%

Length

2024-03-18T14:43:54.921131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:43:55.005998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
62.0%
0.1 9
 
18.0%
0.2 4
 
8.0%
0.0 4
 
8.0%
0.3 2
 
4.0%
Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
32 
0.2
0.1
0.0
0.4
 
1

Length

Max length4
Median length4
Mean length3.64
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row0.2
3rd row<NA>
4th row0.4
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 32
64.0%
0.2 7
 
14.0%
0.1 6
 
12.0%
0.0 4
 
8.0%
0.4 1
 
2.0%

Length

2024-03-18T14:43:55.103820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:43:55.198574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 32
64.0%
0.2 7
 
14.0%
0.1 6
 
12.0%
0.0 4
 
8.0%
0.4 1
 
2.0%

통근 및 통학 지역(퍼센트)_강원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
<NA>
41 
0.1
0.0
 
3
0.2
 
1

Length

Max length4
Median length4
Mean length3.82
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row0.1
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 41
82.0%
0.1 5
 
10.0%
0.0 3
 
6.0%
0.2 1
 
2.0%

Length

2024-03-18T14:43:55.304572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:43:55.407864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 41
82.0%
0.1 5
 
10.0%
0.0 3
 
6.0%
0.2 1
 
2.0%
Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.46
Minimum36.1
Maximum92.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:55.498743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.1
5-th percentile38.645
Q144.025
median47.8
Q354.075
95-th percentile70.575
Maximum92.9
Range56.8
Interquartile range (IQR)10.05

Descriptive statistics

Standard deviation10.497249
Coefficient of variation (CV)0.2080311
Kurtosis4.9703117
Mean50.46
Median Absolute Deviation (MAD)4.45
Skewness1.8935665
Sum2523
Variance110.19224
MonotonicityNot monotonic
2024-03-18T14:43:55.615630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
48.5 2
 
4.0%
46.1 2
 
4.0%
63.2 1
 
2.0%
58.3 1
 
2.0%
51.7 1
 
2.0%
38.7 1
 
2.0%
62.8 1
 
2.0%
56.2 1
 
2.0%
43.0 1
 
2.0%
47.4 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
36.1 1
2.0%
38.2 1
2.0%
38.6 1
2.0%
38.7 1
2.0%
40.6 1
2.0%
40.8 1
2.0%
41.0 1
2.0%
41.5 1
2.0%
42.6 1
2.0%
43.0 1
2.0%
ValueCountFrequency (%)
92.9 1
2.0%
77.2 1
2.0%
70.8 1
2.0%
70.3 1
2.0%
63.2 1
2.0%
62.8 1
2.0%
58.3 1
2.0%
58.0 1
2.0%
56.5 1
2.0%
56.2 1
2.0%
Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.874
Minimum4.6
Maximum51.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:55.740091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile22.295
Q131.25
median35.65
Q337.7
95-th percentile40.055
Maximum51.5
Range46.9
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation7.0364013
Coefficient of variation (CV)0.20772277
Kurtosis6.1497926
Mean33.874
Median Absolute Deviation (MAD)2.55
Skewness-1.6814853
Sum1693.7
Variance49.510943
MonotonicityNot monotonic
2024-03-18T14:43:55.850711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
31.1 2
 
4.0%
35.6 2
 
4.0%
38.0 2
 
4.0%
33.9 2
 
4.0%
37.9 2
 
4.0%
37.4 2
 
4.0%
33.1 2
 
4.0%
35.8 2
 
4.0%
37.1 1
 
2.0%
35.7 1
 
2.0%
Other values (32) 32
64.0%
ValueCountFrequency (%)
4.6 1
2.0%
15.9 1
2.0%
21.8 1
2.0%
22.9 1
2.0%
25.6 1
2.0%
27.3 1
2.0%
28.6 1
2.0%
29.4 1
2.0%
29.6 1
2.0%
29.9 1
2.0%
ValueCountFrequency (%)
51.5 1
2.0%
42.1 1
2.0%
40.1 1
2.0%
40.0 1
2.0%
39.9 1
2.0%
39.7 1
2.0%
39.0 1
2.0%
38.4 1
2.0%
38.0 2
4.0%
37.9 2
4.0%
Distinct42
Distinct (%)85.7%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean10.573469
Minimum1.6
Maximum17.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:55.959084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.56
Q19
median10.3
Q312.4
95-th percentile16.46
Maximum17.9
Range16.3
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation3.5888006
Coefficient of variation (CV)0.33941561
Kurtosis0.54288079
Mean10.573469
Median Absolute Deviation (MAD)2
Skewness-0.25287179
Sum518.1
Variance12.87949
MonotonicityNot monotonic
2024-03-18T14:43:56.075729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10.0 3
 
6.0%
10.9 2
 
4.0%
9.1 2
 
4.0%
1.6 2
 
4.0%
12.4 2
 
4.0%
12.8 2
 
4.0%
6.5 1
 
2.0%
11.5 1
 
2.0%
17.9 1
 
2.0%
8.4 1
 
2.0%
Other values (32) 32
64.0%
ValueCountFrequency (%)
1.6 2
4.0%
3.8 1
2.0%
5.7 1
2.0%
6.3 1
2.0%
6.5 1
2.0%
7.3 1
2.0%
7.8 1
2.0%
7.9 1
2.0%
8.0 1
2.0%
8.3 1
2.0%
ValueCountFrequency (%)
17.9 1
2.0%
17.7 1
2.0%
16.5 1
2.0%
16.4 1
2.0%
15.6 1
2.0%
15.2 1
2.0%
14.7 1
2.0%
14.4 1
2.0%
13.6 1
2.0%
13.4 1
2.0%
Distinct31
Distinct (%)63.3%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.6183673
Minimum0.3
Maximum12.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:56.485024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1.36
Q12.6
median3.5
Q34.5
95-th percentile5.4
Maximum12.8
Range12.5
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.8626452
Coefficient of variation (CV)0.51477505
Kurtosis11.530137
Mean3.6183673
Median Absolute Deviation (MAD)1
Skewness2.3031093
Sum177.3
Variance3.4694473
MonotonicityNot monotonic
2024-03-18T14:43:56.631545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4.2 4
 
8.0%
5.0 3
 
6.0%
3.1 3
 
6.0%
2.7 3
 
6.0%
1.7 3
 
6.0%
4.6 2
 
4.0%
5.1 2
 
4.0%
3.5 2
 
4.0%
2.2 2
 
4.0%
2.5 2
 
4.0%
Other values (21) 23
46.0%
ValueCountFrequency (%)
0.3 1
 
2.0%
0.4 1
 
2.0%
1.2 1
 
2.0%
1.6 1
 
2.0%
1.7 3
6.0%
2.2 2
4.0%
2.4 1
 
2.0%
2.5 2
4.0%
2.6 1
 
2.0%
2.7 3
6.0%
ValueCountFrequency (%)
12.8 1
 
2.0%
5.8 1
 
2.0%
5.6 1
 
2.0%
5.1 2
4.0%
5.0 3
6.0%
4.8 1
 
2.0%
4.6 2
4.0%
4.5 2
4.0%
4.4 1
 
2.0%
4.3 1
 
2.0%

통근 및 통학 소요시간(편도)_120분 이상(퍼센트)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)52.1%
Missing2
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1.8354167
Minimum0
Maximum4.2
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:56.802381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11.475
median1.8
Q32.225
95-th percentile3.165
Maximum4.2
Range4.2
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.83576684
Coefficient of variation (CV)0.45535537
Kurtosis0.90588241
Mean1.8354167
Median Absolute Deviation (MAD)0.4
Skewness0.15382138
Sum88.1
Variance0.69850621
MonotonicityNot monotonic
2024-03-18T14:43:56.935068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.0 6
 
12.0%
1.8 6
 
12.0%
2.1 3
 
6.0%
2.3 3
 
6.0%
1.0 3
 
6.0%
1.7 3
 
6.0%
1.5 3
 
6.0%
2.4 2
 
4.0%
1.3 2
 
4.0%
0.4 2
 
4.0%
Other values (15) 15
30.0%
(Missing) 2
 
4.0%
ValueCountFrequency (%)
0.0 1
 
2.0%
0.1 1
 
2.0%
0.4 2
4.0%
0.5 1
 
2.0%
1.0 3
6.0%
1.1 1
 
2.0%
1.3 2
4.0%
1.4 1
 
2.0%
1.5 3
6.0%
1.6 1
 
2.0%
ValueCountFrequency (%)
4.2 1
 
2.0%
3.5 1
 
2.0%
3.2 1
 
2.0%
3.1 1
 
2.0%
3.0 1
 
2.0%
2.9 1
 
2.0%
2.6 1
 
2.0%
2.4 2
4.0%
2.3 3
6.0%
2.2 1
 
2.0%

통근 및 통학 소요시간(편도)_평균(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.666
Minimum14
Maximum40.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T14:43:57.066717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20.4
Q129.25
median32.85
Q334.675
95-th percentile38.485
Maximum40.9
Range26.9
Interquartile range (IQR)5.425

Descriptive statistics

Standard deviation5.3171501
Coefficient of variation (CV)0.16791354
Kurtosis2.4109575
Mean31.666
Median Absolute Deviation (MAD)2.25
Skewness-1.2742448
Sum1583.3
Variance28.272086
MonotonicityNot monotonic
2024-03-18T14:43:57.222715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
34.2 2
 
4.0%
27.7 2
 
4.0%
35.0 2
 
4.0%
33.1 2
 
4.0%
31.3 2
 
4.0%
33.4 1
 
2.0%
31.7 1
 
2.0%
33.2 1
 
2.0%
25.5 1
 
2.0%
40.9 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
14.0 1
2.0%
17.8 1
2.0%
18.6 1
2.0%
22.6 1
2.0%
25.5 1
2.0%
26.5 1
2.0%
27.1 1
2.0%
27.7 2
4.0%
28.0 1
2.0%
28.5 1
2.0%
ValueCountFrequency (%)
40.9 1
2.0%
40.1 1
2.0%
38.8 1
2.0%
38.1 1
2.0%
37.5 1
2.0%
37.0 1
2.0%
36.6 1
2.0%
35.9 1
2.0%
35.2 1
2.0%
35.0 2
4.0%

Interactions

2024-03-18T14:43:51.574392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.320136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.203701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.110558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.942630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.043505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.866919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.728304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.658164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.590098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:51.967565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.399933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.290610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.189699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.022774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.129209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.953526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.862817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.753581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.686059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.044946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.480643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.371194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.274854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.101147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.212843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.059998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.948666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.847109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.782328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.122349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.563029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.469756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.358939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.397297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.290921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.147716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.027804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.940219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.881902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.203852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.663270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.554379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.443100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.474130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.372257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.233578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.135286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.033643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:51.019659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.275473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.768255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.646542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.514195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.563678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.449664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.307277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.239541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.126598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:51.104219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.357966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.848540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.744164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.591057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.666656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.519483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.376979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.326211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.237777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:51.206645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.455402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:43.952012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.843826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.684244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.780151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.613726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.464421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.410977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.342027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:51.316372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.526904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.046720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.933695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.763737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.862671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.702635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.547453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.493161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.424989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:51.412300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:52.593698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:44.128536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.023058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:45.849714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:46.952852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:47.787647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:48.626948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:49.571093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:50.510476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:43:51.482784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:43:57.355014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특성별(1)특성별(2)통근 및 통학 지역(퍼센트)_인천통근 및 통학 지역(퍼센트)_경기통근 및 통학 지역(퍼센트)_서울통근 및 통학 지역(퍼센트)_대전_세종_충북_충남통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남통근 및 통학 지역(퍼센트)_광주_전북_전남통근 및 통학 지역(퍼센트)_강원통근 및 통학 소요시간(편도)_30분 미만(퍼센트)통근 및 통학 소요시간(편도)_30분 이상_60분 미만(퍼센트)통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트)통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트)통근 및 통학 소요시간(편도)_120분 이상(퍼센트)통근 및 통학 소요시간(편도)_평균(분)
특성별(1)1.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.3390.0000.0000.000
특성별(2)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
통근 및 통학 지역(퍼센트)_인천0.0001.0001.0000.8150.8680.5510.2560.0000.5680.7810.7060.9230.7340.7310.867
통근 및 통학 지역(퍼센트)_경기0.0001.0000.8151.0000.5320.3810.8530.1670.5680.7000.5100.7290.5530.5590.839
통근 및 통학 지역(퍼센트)_서울0.0001.0000.8680.5321.0000.4830.0000.0000.0000.6040.3870.8150.5800.2290.771
통근 및 통학 지역(퍼센트)_대전_세종_충북_충남0.0001.0000.5510.3810.4831.0000.7060.5390.0000.4630.2210.3080.7460.8490.505
통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남0.0001.0000.2560.8530.0000.7061.0000.6410.0000.5150.7350.5360.2720.2800.000
통근 및 통학 지역(퍼센트)_광주_전북_전남0.0001.0000.0000.1670.0000.5390.6411.0001.0000.7720.1170.0000.7200.0000.000
통근 및 통학 지역(퍼센트)_강원1.0001.0000.5680.5680.0000.0000.0001.0001.0000.5680.6550.0000.9420.8530.657
통근 및 통학 소요시간(편도)_30분 미만(퍼센트)0.0001.0000.7810.7000.6040.4630.5150.7720.5681.0000.9530.8290.6250.5040.876
통근 및 통학 소요시간(편도)_30분 이상_60분 미만(퍼센트)0.0001.0000.7060.5100.3870.2210.7350.1170.6550.9531.0000.7430.5990.4770.814
통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트)0.3391.0000.9230.7290.8150.3080.5360.0000.0000.8290.7431.0000.5960.2570.840
통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트)0.0001.0000.7340.5530.5800.7460.2720.7200.9420.6250.5990.5961.0000.5170.764
통근 및 통학 소요시간(편도)_120분 이상(퍼센트)0.0001.0000.7310.5590.2290.8490.2800.0000.8530.5040.4770.2570.5171.0000.414
통근 및 통학 소요시간(편도)_평균(분)0.0001.0000.8670.8390.7710.5050.0000.0000.6570.8760.8140.8400.7640.4141.000
2024-03-18T14:43:57.576699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통근 및 통학 지역(퍼센트)_광주_전북_전남통근 및 통학 지역(퍼센트)_강원통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남특성별(1)
통근 및 통학 지역(퍼센트)_광주_전북_전남1.0000.5770.5570.000
통근 및 통학 지역(퍼센트)_강원0.5771.0000.0001.000
통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남0.5570.0001.0000.000
특성별(1)0.0001.0000.0001.000
2024-03-18T14:43:57.730124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통근 및 통학 지역(퍼센트)_인천통근 및 통학 지역(퍼센트)_경기통근 및 통학 지역(퍼센트)_서울통근 및 통학 지역(퍼센트)_대전_세종_충북_충남통근 및 통학 소요시간(편도)_30분 미만(퍼센트)통근 및 통학 소요시간(편도)_30분 이상_60분 미만(퍼센트)통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트)통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트)통근 및 통학 소요시간(편도)_120분 이상(퍼센트)통근 및 통학 소요시간(편도)_평균(분)특성별(1)통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남통근 및 통학 지역(퍼센트)_광주_전북_전남통근 및 통학 지역(퍼센트)_강원
통근 및 통학 지역(퍼센트)_인천1.000-0.885-0.935-0.3180.837-0.368-0.901-0.769-0.555-0.9370.0000.1680.0000.491
통근 및 통학 지역(퍼센트)_경기-0.8851.0000.6850.271-0.7750.4970.7470.6050.4200.7970.0000.6620.0000.491
통근 및 통학 지역(퍼센트)_서울-0.9350.6851.0000.221-0.7420.2370.8360.7520.5040.8690.0000.0000.0000.000
통근 및 통학 지역(퍼센트)_대전_세종_충북_충남-0.3180.2710.2211.000-0.305-0.0470.2880.3060.6630.3990.0000.4930.3170.000
통근 및 통학 소요시간(편도)_30분 미만(퍼센트)0.837-0.775-0.742-0.3051.000-0.687-0.883-0.726-0.464-0.9170.0000.4150.3980.491
통근 및 통학 소요시간(편도)_30분 이상_60분 미만(퍼센트)-0.3680.4970.237-0.047-0.6871.0000.4190.2860.1070.4390.0000.3610.0190.258
통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트)-0.9010.7470.8360.288-0.8830.4191.0000.6880.4970.9180.1450.3310.0000.000
통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트)-0.7690.6050.7520.306-0.7260.2860.6881.0000.5830.8560.0000.2330.3450.692
통근 및 통학 소요시간(편도)_120분 이상(퍼센트)-0.5550.4200.5040.663-0.4640.1070.4970.5831.0000.6290.0000.0620.0000.292
통근 및 통학 소요시간(편도)_평균(분)-0.9370.7970.8690.399-0.9170.4390.9180.8560.6291.0000.0000.0000.0000.599
특성별(1)0.0000.0000.0000.0000.0000.0000.1450.0000.0000.0001.0000.0000.0001.000
통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남0.1680.6620.0000.4930.4150.3610.3310.2330.0620.0000.0001.0000.5570.000
통근 및 통학 지역(퍼센트)_광주_전북_전남0.0000.0000.0000.3170.3980.0190.0000.3450.0000.0000.0000.5571.0000.577
통근 및 통학 지역(퍼센트)_강원0.4910.4910.0000.0000.4910.2580.0000.6920.2920.5991.0000.0000.5771.000

Missing values

2024-03-18T14:43:52.709761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:43:52.908549image/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.
2024-03-18T14:43:53.069935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

특성별(1)특성별(2)통근 및 통학 지역(퍼센트)_인천통근 및 통학 지역(퍼센트)_경기통근 및 통학 지역(퍼센트)_서울통근 및 통학 지역(퍼센트)_대전_세종_충북_충남통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남통근 및 통학 지역(퍼센트)_광주_전북_전남통근 및 통학 지역(퍼센트)_강원통근 및 통학 소요시간(편도)_30분 미만(퍼센트)통근 및 통학 소요시간(편도)_30분 이상_60분 미만(퍼센트)통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트)통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트)통근 및 통학 소요시간(편도)_120분 이상(퍼센트)통근 및 통학 소요시간(편도)_평균(분)
0군구별중구86.05.17.21.40.3<NA><NA>63.222.96.54.23.230.0
1군구별동구85.86.87.00.2<NA>0.2<NA>41.540.111.25.12.234.2
2군구별미추홀구83.78.47.70.1<NA><NA><NA>49.338.08.33.11.332.0
3군구별연수구79.410.79.00.3<NA>0.40.150.033.010.94.21.933.6
4군구별남동구81.711.26.01.1<NA><NA><NA>43.842.19.53.01.732.5
5군구별부평구61.317.519.71.5<NA><NA><NA>38.633.917.75.64.240.1
6군구별계양구82.39.08.50.20.1<NA><NA>49.239.79.11.70.429.4
7군구별서구74.111.614.00.10.2<NA><NA>44.435.014.74.21.634.6
8군구별강화군90.36.33.3<NA><NA><NA><NA>77.215.93.83.00.118.6
9군구별옹진군99.60.4<NA><NA><NA><NA><NA>92.94.61.60.40.414.0
특성별(1)특성별(2)통근 및 통학 지역(퍼센트)_인천통근 및 통학 지역(퍼센트)_경기통근 및 통학 지역(퍼센트)_서울통근 및 통학 지역(퍼센트)_대전_세종_충북_충남통근 및 통학 지역(퍼센트)_부산_대구_울산_경북_경남통근 및 통학 지역(퍼센트)_광주_전북_전남통근 및 통학 지역(퍼센트)_강원통근 및 통학 소요시간(편도)_30분 미만(퍼센트)통근 및 통학 소요시간(편도)_30분 이상_60분 미만(퍼센트)통근 및 통학 소요시간(편도)_60분 이상_90분 미만(퍼센트)통근 및 통학 소요시간(편도)_90분 이상_120분 미만(퍼센트)통근 및 통학 소요시간(편도)_120분 이상(퍼센트)통근 및 통학 소요시간(편도)_평균(분)
40주거형태별연립/다세대주택80.78.910.00.5<NA><NA><NA>46.939.010.03.11.031.8
41주거형태별기타78.210.311.20.3<NA><NA><NA>54.130.69.92.72.631.3
42주거점유형태별자가77.111.410.70.80.10.0<NA>46.535.412.43.72.033.7
43주거점유형태별전세75.311.013.20.2<NA>0.20.147.136.510.94.11.533.3
44주거점유형태별월세 및 기타83.19.57.20.2<NA><NA><NA>51.135.67.93.52.031.2
45가구원수별1인87.65.56.70.2<NA><NA><NA>54.035.97.81.70.527.7
46가구원수별2인83.18.18.40.30.0<NA><NA>52.633.110.02.51.830.4
47가구원수별3인74.112.612.20.70.2<NA>0.144.736.013.43.62.335.0
48가구원수별4인75.313.011.00.5<NA>0.2<NA>43.737.912.34.41.734.7
49가구원수별5인 이상75.610.712.51.2<NA>0.0<NA>49.531.710.15.82.934.2