Overview

Dataset statistics

Number of variables8
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory72.6 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description대중교통 이용 만족도 중 지하철에 대한 이용여부 만족도를 나타내고 있습니다.* 특성별(1) 특성별(2) 매우 만족 (%) 약간 만족 (%) 보통 (%) 약간 불만족 (%) 매우 불만족 (%) 5점 평균 (점)
Author인천광역시
URLhttps://www.data.go.kr/data/15066424/fileData.do

Alerts

지하철 보통 (퍼센트) is highly overall correlated with 지하철 약간 만족 (퍼센트)High correlation
지하철 약간 만족 (퍼센트) is highly overall correlated with 지하철 보통 (퍼센트)High correlation
지하철 매우 만족 (퍼센트) is highly overall correlated with 지하철 5점 평균 (점)High correlation
지하철 5점 평균 (점) is highly overall correlated with 지하철 매우 만족 (퍼센트)High correlation
특성별(2) has unique valuesUnique

Reproduction

Analysis started2023-12-23 08:01:40.394823
Analysis finished2023-12-23 08:02:01.107621
Duration20.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

특성별(1)
Categorical

Distinct9
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
군구별
10 
직업별
8 
월평균소득별
8 
연령별
6 
가구원수별
5 
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

2023-12-23T08:02:01.651272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T08:02:02.036471image/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
2023-12-23T08:02:02.837052image/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%
2023-12-23T08:02:04.273528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
 
13.4%
22
 
8.9%
만 15
 
6.1%
~ 11
 
4.5%
세 9
 
3.7%
원 8
 
3.3%
구 8
 
3.3%
미 8
 
3.3%
3 6
 
2.4%
인 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 (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
상 4
 
2.8%
기 4
 
2.8%
졸 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 (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
상 4
 
2.8%
기 4
 
2.8%
졸 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 (%)
만 15
 
10.6%
세 9
 
6.3%
원 8
 
5.6%
구 8
 
5.6%
미 8
 
5.6%
인 5
 
3.5%
이 5
 
3.5%
상 4
 
2.8%
기 4
 
2.8%
졸 4
 
2.8%
Other values (54) 72
50.7%

지하철 매우 불만족 (퍼센트)
Real number (ℝ)

Distinct31
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.13
Minimum0.7
Maximum32.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-23T08:02:04.928471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.1
Q12.6
median3.7
Q34.375
95-th percentile6.07
Maximum32.1
Range31.4
Interquartile range (IQR)1.775

Descriptive statistics

Standard deviation4.3422579
Coefficient of variation (CV)1.0513942
Kurtosis36.646262
Mean4.13
Median Absolute Deviation (MAD)0.95
Skewness5.6828153
Sum206.5
Variance18.855204
MonotonicityNot monotonic
2023-12-23T08:02:05.366825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5.3 4
 
8.0%
3.8 3
 
6.0%
3.9 3
 
6.0%
3.7 3
 
6.0%
3.2 2
 
4.0%
2.6 2
 
4.0%
3.4 2
 
4.0%
2.5 2
 
4.0%
2.9 2
 
4.0%
4.8 2
 
4.0%
Other values (21) 25
50.0%
ValueCountFrequency (%)
0.7 2
4.0%
1.1 2
4.0%
1.3 1
2.0%
1.6 1
2.0%
2.0 1
2.0%
2.1 1
2.0%
2.2 1
2.0%
2.3 1
2.0%
2.5 2
4.0%
2.6 2
4.0%
ValueCountFrequency (%)
32.1 1
 
2.0%
10.1 1
 
2.0%
6.7 1
 
2.0%
5.3 4
8.0%
4.9 1
 
2.0%
4.8 2
4.0%
4.6 1
 
2.0%
4.5 1
 
2.0%
4.4 1
 
2.0%
4.3 1
 
2.0%

지하철 약간 불만족 (퍼센트)
Real number (ℝ)

Distinct36
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.952
Minimum1.6
Maximum15.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-23T08:02:05.858769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.435
Q15.825
median6.65
Q37.7
95-th percentile9.665
Maximum15.7
Range14.1
Interquartile range (IQR)1.875

Descriptive statistics

Standard deviation2.2391908
Coefficient of variation (CV)0.32209304
Kurtosis6.347166
Mean6.952
Median Absolute Deviation (MAD)0.9
Skewness1.679455
Sum347.6
Variance5.0139755
MonotonicityNot monotonic
2023-12-23T08:02:06.633564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
7.2 4
 
8.0%
6.1 2
 
4.0%
5.9 2
 
4.0%
8.0 2
 
4.0%
6.7 2
 
4.0%
6.6 2
 
4.0%
7.0 2
 
4.0%
5.8 2
 
4.0%
5.7 2
 
4.0%
7.7 2
 
4.0%
Other values (26) 28
56.0%
ValueCountFrequency (%)
1.6 1
2.0%
3.2 1
2.0%
4.3 1
2.0%
4.6 1
2.0%
4.9 1
2.0%
5.2 1
2.0%
5.3 1
2.0%
5.5 1
2.0%
5.6 1
2.0%
5.7 2
4.0%
ValueCountFrequency (%)
15.7 1
2.0%
14.5 1
2.0%
9.8 1
2.0%
9.5 1
2.0%
8.8 1
2.0%
8.6 1
2.0%
8.5 1
2.0%
8.3 1
2.0%
8.0 2
4.0%
7.9 1
2.0%

지하철 보통 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.344
Minimum17.2
Maximum53.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-23T08:02:07.240220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.2
5-th percentile28.37
Q131.275
median33.15
Q335.625
95-th percentile38.12
Maximum53.7
Range36.5
Interquartile range (IQR)4.35

Descriptive statistics

Standard deviation4.6131648
Coefficient of variation (CV)0.13835067
Kurtosis9.3315105
Mean33.344
Median Absolute Deviation (MAD)2.05
Skewness0.88380154
Sum1667.2
Variance21.28129
MonotonicityNot monotonic
2023-12-23T08:02:08.095872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
31.1 3
 
6.0%
34.4 2
 
4.0%
32.2 2
 
4.0%
31.8 2
 
4.0%
29.3 2
 
4.0%
32.5 2
 
4.0%
36.4 2
 
4.0%
32.9 2
 
4.0%
33.3 2
 
4.0%
38.3 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
17.2 1
 
2.0%
28.0 1
 
2.0%
28.1 1
 
2.0%
28.7 1
 
2.0%
28.9 1
 
2.0%
29.0 1
 
2.0%
29.3 2
4.0%
30.0 1
 
2.0%
30.8 1
 
2.0%
31.1 3
6.0%
ValueCountFrequency (%)
53.7 1
2.0%
38.5 1
2.0%
38.3 1
2.0%
37.9 1
2.0%
37.0 1
2.0%
36.9 1
2.0%
36.7 1
2.0%
36.5 1
2.0%
36.4 2
4.0%
36.3 1
2.0%

지하철 약간 만족 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.354
Minimum7.9
Maximum43.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-23T08:02:08.774317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile30.165
Q133
median35
Q336.45
95-th percentile41.465
Maximum43.9
Range36
Interquartile range (IQR)3.45

Descriptive statistics

Standard deviation5.1051479
Coefficient of variation (CV)0.14860418
Kurtosis14.78576
Mean34.354
Median Absolute Deviation (MAD)1.9
Skewness-2.8748896
Sum1717.7
Variance26.062535
MonotonicityNot monotonic
2023-12-23T08:02:09.335531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
34.6 3
 
6.0%
35.0 3
 
6.0%
35.3 2
 
4.0%
36.8 2
 
4.0%
33.0 2
 
4.0%
33.6 2
 
4.0%
35.7 2
 
4.0%
32.9 2
 
4.0%
33.1 2
 
4.0%
37.1 2
 
4.0%
Other values (28) 28
56.0%
ValueCountFrequency (%)
7.9 1
2.0%
22.6 1
2.0%
29.4 1
2.0%
31.1 1
2.0%
31.2 1
2.0%
31.3 1
2.0%
31.6 1
2.0%
31.9 1
2.0%
32.1 1
2.0%
32.7 1
2.0%
ValueCountFrequency (%)
43.9 1
2.0%
42.8 1
2.0%
42.5 1
2.0%
40.2 1
2.0%
37.6 1
2.0%
37.3 1
2.0%
37.2 1
2.0%
37.1 2
4.0%
37.0 1
2.0%
36.8 2
4.0%

지하철 매우 만족 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.236
Minimum9.1
Maximum34.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-23T08:02:10.078460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.1
5-th percentile14.08
Q120.125
median21.65
Q322.675
95-th percentile24.355
Maximum34.6
Range25.5
Interquartile range (IQR)2.55

Descriptive statistics

Standard deviation3.6884685
Coefficient of variation (CV)0.17368942
Kurtosis5.5382792
Mean21.236
Median Absolute Deviation (MAD)1.25
Skewness-0.0027955564
Sum1061.8
Variance13.6048
MonotonicityNot monotonic
2023-12-23T08:02:10.880151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
22.9 3
 
6.0%
21.7 2
 
4.0%
20.2 2
 
4.0%
18.7 2
 
4.0%
22.1 2
 
4.0%
22.8 2
 
4.0%
21.6 2
 
4.0%
19.9 2
 
4.0%
20.4 2
 
4.0%
21.8 2
 
4.0%
Other values (26) 29
58.0%
ValueCountFrequency (%)
9.1 1
2.0%
13.0 2
4.0%
15.4 1
2.0%
18.7 2
4.0%
18.8 1
2.0%
19.3 1
2.0%
19.7 1
2.0%
19.9 2
4.0%
20.1 2
4.0%
20.2 2
4.0%
ValueCountFrequency (%)
34.6 1
 
2.0%
29.9 1
 
2.0%
24.4 1
 
2.0%
24.3 1
 
2.0%
23.8 1
 
2.0%
23.4 1
 
2.0%
23.2 1
 
2.0%
22.9 3
6.0%
22.8 2
4.0%
22.7 1
 
2.0%

지하철 5점 평균 (점)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.614
Minimum2.8
Maximum4.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-23T08:02:11.667570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.445
Q13.6
median3.6
Q33.7
95-th percentile3.7
Maximum4.1
Range1.3
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1565052
Coefficient of variation (CV)0.043305257
Kurtosis16.099767
Mean3.614
Median Absolute Deviation (MAD)0.05
Skewness-2.4042585
Sum180.7
Variance0.024493878
MonotonicityNot monotonic
2023-12-23T08:02:12.446555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3.6 25
50.0%
3.7 16
32.0%
3.5 4
 
8.0%
3.4 2
 
4.0%
3.8 1
 
2.0%
4.1 1
 
2.0%
2.8 1
 
2.0%
ValueCountFrequency (%)
2.8 1
 
2.0%
3.4 2
 
4.0%
3.5 4
 
8.0%
3.6 25
50.0%
3.7 16
32.0%
3.8 1
 
2.0%
4.1 1
 
2.0%
ValueCountFrequency (%)
4.1 1
 
2.0%
3.8 1
 
2.0%
3.7 16
32.0%
3.6 25
50.0%
3.5 4
 
8.0%
3.4 2
 
4.0%
2.8 1
 
2.0%

Interactions

2023-12-23T08:01:55.811617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:41.384275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:44.258624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:46.825719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:49.336480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:53.005713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:56.499688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:41.847211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:44.646464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:47.364778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:49.758203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:53.477289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:57.012988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:42.382831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:45.154530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:47.765426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:50.305697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:53.777498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:57.800385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:42.894270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:45.524775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:48.064490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:50.948627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:54.243634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:58.315139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:43.408025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:45.908165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:48.437831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:51.888792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:54.695711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:58.818376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:43.790404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:46.339982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:48.933552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:52.517276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T08:01:55.363565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T08:02:12.902941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특성별(1)특성별(2)지하철 매우 불만족 (퍼센트)지하철 약간 불만족 (퍼센트)지하철 보통 (퍼센트)지하철 약간 만족 (퍼센트)지하철 매우 만족 (퍼센트)지하철 5점 평균 (점)
특성별(1)1.0001.0000.0000.0000.0000.0000.0000.000
특성별(2)1.0001.0001.0001.0001.0001.0001.0001.000
지하철 매우 불만족 (퍼센트)0.0001.0001.0000.6840.5580.7090.0000.758
지하철 약간 불만족 (퍼센트)0.0001.0000.6841.0000.6860.9380.7690.771
지하철 보통 (퍼센트)0.0001.0000.5580.6861.0000.7480.7900.883
지하철 약간 만족 (퍼센트)0.0001.0000.7090.9380.7481.0000.8070.735
지하철 매우 만족 (퍼센트)0.0001.0000.0000.7690.7900.8071.0000.838
지하철 5점 평균 (점)0.0001.0000.7580.7710.8830.7350.8381.000
2023-12-23T08:02:13.523711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지하철 매우 불만족 (퍼센트)지하철 약간 불만족 (퍼센트)지하철 보통 (퍼센트)지하철 약간 만족 (퍼센트)지하철 매우 만족 (퍼센트)지하철 5점 평균 (점)특성별(1)
지하철 매우 불만족 (퍼센트)1.0000.001-0.219-0.161-0.081-0.4360.000
지하철 약간 불만족 (퍼센트)0.0011.000-0.1870.006-0.345-0.2920.000
지하철 보통 (퍼센트)-0.219-0.1871.000-0.683-0.356-0.2880.000
지하철 약간 만족 (퍼센트)-0.1610.006-0.6831.0000.0280.3660.000
지하철 매우 만족 (퍼센트)-0.081-0.345-0.3560.0281.0000.7220.000
지하철 5점 평균 (점)-0.436-0.292-0.2880.3660.7221.0000.000
특성별(1)0.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-23T08:01:59.802055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T08:02:00.812404image/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

특성별(1)특성별(2)지하철 매우 불만족 (퍼센트)지하철 약간 불만족 (퍼센트)지하철 보통 (퍼센트)지하철 약간 만족 (퍼센트)지하철 매우 만족 (퍼센트)지하철 5점 평균 (점)
0군구별중구3.215.728.143.99.13.4
1군구별동구6.79.536.431.915.43.4
2군구별미추홀구0.73.253.729.413.03.5
3군구별연수구5.35.529.337.622.33.7
4군구별남동구1.18.836.131.122.93.7
5군구별부평구2.04.632.537.123.83.8
6군구별계양구1.14.317.242.834.64.1
7군구별서구10.18.528.033.120.43.5
8군구별강화군32.11.638.57.919.92.8
9군구별옹진군4.114.528.922.629.93.6
특성별(1)특성별(2)지하철 매우 불만족 (퍼센트)지하철 약간 불만족 (퍼센트)지하철 보통 (퍼센트)지하철 약간 만족 (퍼센트)지하철 매우 만족 (퍼센트)지하철 5점 평균 (점)
40주거형태별연립/다세대주택2.26.232.937.021.73.7
41주거형태별기타0.75.235.236.822.13.7
42주거점유형태별자가3.96.432.535.022.23.7
43주거점유형태별전세4.07.033.235.720.13.6
44주거점유형태별월세 및 기타2.37.836.433.619.93.6
45가구원수별1인1.38.636.535.018.73.6
46가구원수별2인2.67.234.433.022.93.7
47가구원수별3인3.96.431.836.121.83.7
48가구원수별4인5.35.731.136.221.83.6
49가구원수별5인 이상3.77.736.332.120.23.6