Overview

Dataset statistics

Number of variables9
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory81.6 B

Variable types

Categorical1
Text1
Numeric7

Dataset

Description역외 소비활동 여부 및 비중을 나타내는 자료(소비한다, 소비하지 않는다, 소비활동 비중 10% 미만, 소비활동 비중 10% 이상~30% 미만 등)입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15066301&srcSe=7661IVAWM27C61E190

Alerts

소비한다(50퍼센트 이상 70퍼센트 미만) is highly overall correlated with 소비한다(70퍼센트 이상 90퍼센트 미만)High correlation
소비한다(70퍼센트 이상 90퍼센트 미만) is highly overall correlated with 소비한다(50퍼센트 이상 70퍼센트 미만)High correlation
특성별(2) has unique valuesUnique
소비한다(10퍼센트 미만) has 1 (2.0%) zerosZeros
소비한다(50퍼센트 이상 70퍼센트 미만) has 1 (2.0%) zerosZeros
소비한다(70퍼센트 이상 90퍼센트 미만) has 5 (10.0%) zerosZeros
소비한다(90퍼센트 이상) has 3 (6.0%) zerosZeros

Reproduction

Analysis started2024-03-18 02:25:47.191690
Analysis finished2024-03-18 02:25:52.235440
Duration5.04 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

2024-03-18T11:25:52.301094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:25:52.449255image/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
2024-03-18T11:25:52.626298image/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%
2024-03-18T11:25:52.936680image/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%

소비한다(10퍼센트 미만)
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.344
Minimum0
Maximum33.1
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T11:25:53.052115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.345
Q19.875
median12.75
Q314.775
95-th percentile27.32
Maximum33.1
Range33.1
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation6.1698464
Coefficient of variation (CV)0.46236858
Kurtosis3.0970323
Mean13.344
Median Absolute Deviation (MAD)2.35
Skewness1.3273914
Sum667.2
Variance38.067004
MonotonicityNot monotonic
2024-03-18T11:25:53.199345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
16.8 2
 
4.0%
9.5 2
 
4.0%
13.7 2
 
4.0%
12.7 2
 
4.0%
13.8 2
 
4.0%
14.8 1
 
2.0%
11.3 1
 
2.0%
14.2 1
 
2.0%
12.1 1
 
2.0%
8.9 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
0.0 1
2.0%
4.7 1
2.0%
5.3 1
2.0%
5.4 1
2.0%
6.7 1
2.0%
6.8 1
2.0%
8.9 1
2.0%
9.0 1
2.0%
9.2 1
2.0%
9.3 1
2.0%
ValueCountFrequency (%)
33.1 1
2.0%
31.6 1
2.0%
28.4 1
2.0%
26.0 1
2.0%
19.7 1
2.0%
19.1 1
2.0%
16.9 1
2.0%
16.8 2
4.0%
16.1 1
2.0%
15.2 1
2.0%

소비한다(10퍼센트 이상 30퍼센트 미만)
Real number (ℝ)

Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.776
Minimum36.2
Maximum75.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T11:25:53.339494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.2
5-th percentile48.26
Q155.25
median56.75
Q359.575
95-th percentile69.485
Maximum75.3
Range39.1
Interquartile range (IQR)4.325

Descriptive statistics

Standard deviation6.6346512
Coefficient of variation (CV)0.11483403
Kurtosis2.6075457
Mean57.776
Median Absolute Deviation (MAD)2.25
Skewness-0.11980834
Sum2888.8
Variance44.018596
MonotonicityNot monotonic
2024-03-18T11:25:53.716797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
57.5 3
 
6.0%
56.5 3
 
6.0%
56.3 2
 
4.0%
62.1 2
 
4.0%
55.9 2
 
4.0%
55.4 2
 
4.0%
54.6 1
 
2.0%
68.0 1
 
2.0%
55.6 1
 
2.0%
54.5 1
 
2.0%
Other values (32) 32
64.0%
ValueCountFrequency (%)
36.2 1
2.0%
43.2 1
2.0%
45.2 1
2.0%
52.0 1
2.0%
52.9 1
2.0%
53.3 1
2.0%
53.6 1
2.0%
54.3 1
2.0%
54.4 1
2.0%
54.5 1
2.0%
ValueCountFrequency (%)
75.3 1
2.0%
71.8 1
2.0%
70.7 1
2.0%
68.0 1
2.0%
67.7 1
2.0%
67.2 1
2.0%
66.8 1
2.0%
62.1 2
4.0%
62.0 1
2.0%
61.1 1
2.0%

소비한다(30퍼센트 이상 50퍼센트 미만)
Real number (ℝ)

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.908
Minimum6.3
Maximum47.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T11:25:53.831425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.3
5-th percentile10.56
Q113.325
median15
Q316.85
95-th percentile21.11
Maximum47.1
Range40.8
Interquartile range (IQR)3.525

Descriptive statistics

Standard deviation5.8249232
Coefficient of variation (CV)0.36616314
Kurtosis17.485301
Mean15.908
Median Absolute Deviation (MAD)1.8
Skewness3.5741193
Sum795.4
Variance33.929731
MonotonicityNot monotonic
2024-03-18T11:25:53.957711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
15.6 4
 
8.0%
16.6 2
 
4.0%
15.0 2
 
4.0%
12.4 2
 
4.0%
14.1 2
 
4.0%
13.0 2
 
4.0%
15.8 2
 
4.0%
18.2 2
 
4.0%
14.6 2
 
4.0%
17.2 2
 
4.0%
Other values (28) 28
56.0%
ValueCountFrequency (%)
6.3 1
2.0%
9.4 1
2.0%
10.2 1
2.0%
11.0 1
2.0%
12.2 1
2.0%
12.4 2
4.0%
12.5 1
2.0%
12.7 1
2.0%
13.0 2
4.0%
13.1 1
2.0%
ValueCountFrequency (%)
47.1 1
2.0%
32.2 1
2.0%
21.2 1
2.0%
21.0 1
2.0%
19.6 1
2.0%
18.6 1
2.0%
18.2 2
4.0%
18.1 1
2.0%
18.0 1
2.0%
17.2 2
4.0%

소비한다(50퍼센트 이상 70퍼센트 미만)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.202
Minimum0
Maximum12.7
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T11:25:54.076150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.305
Q16.75
median8.5
Q39.85
95-th percentile12.31
Maximum12.7
Range12.7
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.7343238
Coefficient of variation (CV)0.33337281
Kurtosis1.0755772
Mean8.202
Median Absolute Deviation (MAD)1.65
Skewness-0.79386776
Sum410.1
Variance7.4765265
MonotonicityNot monotonic
2024-03-18T11:25:54.261297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
8.5 3
 
6.0%
9.3 3
 
6.0%
12.4 2
 
4.0%
8.8 2
 
4.0%
6.6 2
 
4.0%
7.7 2
 
4.0%
8.7 2
 
4.0%
9.2 2
 
4.0%
10.8 2
 
4.0%
5.7 1
 
2.0%
Other values (29) 29
58.0%
ValueCountFrequency (%)
0.0 1
2.0%
1.2 1
2.0%
2.9 1
2.0%
3.8 1
2.0%
4.7 1
2.0%
5.2 1
2.0%
5.4 1
2.0%
5.7 1
2.0%
6.2 1
2.0%
6.5 1
2.0%
ValueCountFrequency (%)
12.7 1
2.0%
12.4 2
4.0%
12.2 1
2.0%
11.8 1
2.0%
11.4 1
2.0%
11.3 1
2.0%
11.0 1
2.0%
10.8 2
4.0%
10.5 1
2.0%
10.2 1
2.0%

소비한다(70퍼센트 이상 90퍼센트 미만)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.778
Minimum0
Maximum8.4
Zeros5
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T11:25:54.424007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.875
median2.7
Q33.4
95-th percentile5.1
Maximum8.4
Range8.4
Interquartile range (IQR)1.525

Descriptive statistics

Standard deviation1.6707923
Coefficient of variation (CV)0.60143712
Kurtosis1.7165288
Mean2.778
Median Absolute Deviation (MAD)0.75
Skewness0.68360098
Sum138.9
Variance2.7915469
MonotonicityNot monotonic
2024-03-18T11:25:54.542729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 5
 
10.0%
3.1 4
 
8.0%
3.3 4
 
8.0%
2.4 3
 
6.0%
2.1 3
 
6.0%
1.6 3
 
6.0%
4.1 2
 
4.0%
3.4 2
 
4.0%
5.1 2
 
4.0%
2.3 2
 
4.0%
Other values (18) 20
40.0%
ValueCountFrequency (%)
0.0 5
10.0%
0.6 1
 
2.0%
0.8 1
 
2.0%
1.2 1
 
2.0%
1.6 3
6.0%
1.7 1
 
2.0%
1.8 1
 
2.0%
2.1 3
6.0%
2.2 2
 
4.0%
2.3 2
 
4.0%
ValueCountFrequency (%)
8.4 1
2.0%
6.5 1
2.0%
5.1 2
4.0%
4.9 1
2.0%
4.8 1
2.0%
4.4 1
2.0%
4.3 1
2.0%
4.1 2
4.0%
4.0 1
2.0%
3.5 1
2.0%

소비한다(90퍼센트 이상)
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.972
Minimum0
Maximum4.4
Zeros3
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T11:25:54.653439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.045
Q11.4
median1.9
Q32.575
95-th percentile3.6
Maximum4.4
Range4.4
Interquartile range (IQR)1.175

Descriptive statistics

Standard deviation1.0734343
Coefficient of variation (CV)0.54433789
Kurtosis-0.17601224
Mean1.972
Median Absolute Deviation (MAD)0.6
Skewness0.14708509
Sum98.6
Variance1.1522612
MonotonicityNot monotonic
2024-03-18T11:25:54.766577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2.3 5
 
10.0%
0.0 3
 
6.0%
1.9 3
 
6.0%
1.4 3
 
6.0%
1.6 3
 
6.0%
2.7 2
 
4.0%
2.4 2
 
4.0%
3.0 2
 
4.0%
4.4 2
 
4.0%
1.7 2
 
4.0%
Other values (17) 23
46.0%
ValueCountFrequency (%)
0.0 3
6.0%
0.1 1
 
2.0%
0.5 1
 
2.0%
0.7 2
4.0%
0.8 2
4.0%
1.1 1
 
2.0%
1.2 1
 
2.0%
1.3 1
 
2.0%
1.4 3
6.0%
1.5 2
4.0%
ValueCountFrequency (%)
4.4 2
4.0%
3.6 2
4.0%
3.5 1
2.0%
3.3 2
4.0%
3.2 1
2.0%
3.0 2
4.0%
2.7 2
4.0%
2.6 1
2.0%
2.5 1
2.0%
2.4 2
4.0%

소비하지 않는다(소계)
Real number (ℝ)

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.116
Minimum64.3
Maximum92.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2024-03-18T11:25:54.889388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64.3
5-th percentile66.31
Q173.2
median79.2
Q385.85
95-th percentile91.045
Maximum92.2
Range27.9
Interquartile range (IQR)12.65

Descriptive statistics

Standard deviation7.8729026
Coefficient of variation (CV)0.099510878
Kurtosis-0.9913318
Mean79.116
Median Absolute Deviation (MAD)6.3
Skewness-0.051767889
Sum3955.8
Variance61.982596
MonotonicityNot monotonic
2024-03-18T11:25:55.009843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
83.4 2
 
4.0%
68.6 2
 
4.0%
79.7 2
 
4.0%
75.9 2
 
4.0%
72.9 2
 
4.0%
73.2 2
 
4.0%
88.8 1
 
2.0%
85.9 1
 
2.0%
91.9 1
 
2.0%
88.7 1
 
2.0%
Other values (34) 34
68.0%
ValueCountFrequency (%)
64.3 1
2.0%
65.0 1
2.0%
65.5 1
2.0%
67.3 1
2.0%
68.6 2
4.0%
69.1 1
2.0%
69.6 1
2.0%
71.2 1
2.0%
71.6 1
2.0%
72.9 2
4.0%
ValueCountFrequency (%)
92.2 1
2.0%
92.1 1
2.0%
91.9 1
2.0%
90.0 1
2.0%
89.6 1
2.0%
89.3 1
2.0%
88.9 1
2.0%
88.8 1
2.0%
88.7 1
2.0%
87.9 1
2.0%

Interactions

2024-03-18T11:25:51.500792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:47.465131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.411415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.006544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.677862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.393090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.936332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.566394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:47.537992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.488629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.104337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.758336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.469291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.007974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.640821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:47.637501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.576688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.238029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.884516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.544803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.087589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.713432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:47.757673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.673836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.344967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.995869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.622240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.170319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.779468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:47.858864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.756399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.443305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.080848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.697771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.242782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.842359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:47.965272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.830975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.528573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.208725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.789080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.342490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.904881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.057002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:48.912929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:49.599363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.309944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:50.861225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:25:51.435342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:25:55.098019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특성별(1)특성별(2)소비한다(10퍼센트 미만)소비한다(10퍼센트 이상 30퍼센트 미만)소비한다(30퍼센트 이상 50퍼센트 미만)소비한다(50퍼센트 이상 70퍼센트 미만)소비한다(70퍼센트 이상 90퍼센트 미만)소비한다(90퍼센트 이상)소비하지 않는다(소계)
특성별(1)1.0001.0000.0000.0000.0000.0000.0000.0000.070
특성별(2)1.0001.0001.0001.0001.0001.0001.0001.0001.000
소비한다(10퍼센트 미만)0.0001.0001.0000.9030.7880.7530.7550.0000.152
소비한다(10퍼센트 이상 30퍼센트 미만)0.0001.0000.9031.0000.8630.6950.7680.5390.456
소비한다(30퍼센트 이상 50퍼센트 미만)0.0001.0000.7880.8631.0000.5730.0000.4940.288
소비한다(50퍼센트 이상 70퍼센트 미만)0.0001.0000.7530.6950.5731.0000.4390.2670.000
소비한다(70퍼센트 이상 90퍼센트 미만)0.0001.0000.7550.7680.0000.4391.0000.2900.369
소비한다(90퍼센트 이상)0.0001.0000.0000.5390.4940.2670.2901.0000.620
소비하지 않는다(소계)0.0701.0000.1520.4560.2880.0000.3690.6201.000
2024-03-18T11:25:55.209109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소비한다(10퍼센트 미만)소비한다(10퍼센트 이상 30퍼센트 미만)소비한다(30퍼센트 이상 50퍼센트 미만)소비한다(50퍼센트 이상 70퍼센트 미만)소비한다(70퍼센트 이상 90퍼센트 미만)소비한다(90퍼센트 이상)소비하지 않는다(소계)특성별(1)
소비한다(10퍼센트 미만)1.000-0.416-0.398-0.279-0.0620.208-0.3140.000
소비한다(10퍼센트 이상 30퍼센트 미만)-0.4161.000-0.335-0.268-0.238-0.1460.0420.000
소비한다(30퍼센트 이상 50퍼센트 미만)-0.398-0.3351.0000.3030.128-0.1240.0910.000
소비한다(50퍼센트 이상 70퍼센트 미만)-0.279-0.2680.3031.0000.5090.146-0.1060.000
소비한다(70퍼센트 이상 90퍼센트 미만)-0.062-0.2380.1280.5091.0000.020-0.0840.000
소비한다(90퍼센트 이상)0.208-0.146-0.1240.1460.0201.000-0.4120.000
소비하지 않는다(소계)-0.3140.0420.091-0.106-0.084-0.4121.0000.000
특성별(1)0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-18T11:25:52.013060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:25:52.183780image/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)소비한다(10퍼센트 미만)소비한다(10퍼센트 이상 30퍼센트 미만)소비한다(30퍼센트 이상 50퍼센트 미만)소비한다(50퍼센트 이상 70퍼센트 미만)소비한다(70퍼센트 이상 90퍼센트 미만)소비한다(90퍼센트 이상)소비하지 않는다(소계)
0군구별중구10.575.39.42.90.81.188.8
1군구별동구10.767.713.34.72.21.480.6
2군구별미추홀구5.454.321.012.75.11.590.0
3군구별연수구26.043.212.57.48.42.683.4
4군구별남동구6.766.821.25.40.00.087.6
5군구별부평구4.759.617.212.24.31.968.6
6군구별계양구33.154.46.31.21.73.364.3
7군구별서구10.456.217.211.81.82.568.6
8군구별강화군9.270.710.26.20.03.679.7
9군구별옹진군0.045.247.17.70.00.092.1
특성별(1)특성별(2)소비한다(10퍼센트 미만)소비한다(10퍼센트 이상 30퍼센트 미만)소비한다(30퍼센트 이상 50퍼센트 미만)소비한다(50퍼센트 이상 70퍼센트 미만)소비한다(70퍼센트 이상 90퍼센트 미만)소비한다(90퍼센트 이상)소비하지 않는다(소계)
40주거형태별연립/다세대주택16.956.313.09.32.71.881.3
41주거형태별기타6.855.918.211.36.51.382.6
42주거점유형태별자가13.857.015.08.83.41.977.4
43주거점유형태별전세13.257.616.77.71.23.577.0
44주거점유형태별월세 및 기타12.759.914.08.83.11.483.4
45가구원수별1인9.061.113.89.32.44.486.5
46가구원수별2인9.562.016.96.64.10.885.7
47가구원수별3인13.155.216.010.83.21.775.5
48가구원수별4인16.857.514.66.72.12.473.2
49가구원수별5인 이상13.755.913.79.74.03.072.9