Overview

Dataset statistics

Number of variables12
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory112.7 B

Variable types

Categorical6
Text1
Numeric5

Dataset

Description대전광역시 서구 행정동별 공중위생관계업소 현황(기준연도, 행정동, 숙박업_일반, 숙박업_생활, 목욕장업, 이용업, 미용업, 세탁업, 건물위생관리업, 기타, 위생처리업, 기타위생용품제조업) 데이터 입니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15095160/fileData.do

Alerts

기준연도 has constant value ""Constant
기타 has constant value ""Constant
숙박업_일반 is highly overall correlated with 이용업 and 2 other fieldsHigh correlation
이용업 is highly overall correlated with 숙박업_일반High correlation
미용업 is highly overall correlated with 세탁업 and 1 other fieldsHigh correlation
세탁업 is highly overall correlated with 미용업 and 1 other fieldsHigh correlation
건물위생관리업 is highly overall correlated with 숙박업_일반 and 3 other fieldsHigh correlation
숙박업_생활 is highly overall correlated with 숙박업_일반High correlation
기타위생용품제조업 is highly overall correlated with 건물위생관리업High correlation
숙박업_생활 is highly imbalanced (53.6%)Imbalance
위생처리업 is highly imbalanced (67.6%)Imbalance
행정동 has unique valuesUnique
숙박업_일반 has 11 (47.8%) zerosZeros
세탁업 has 1 (4.3%) zerosZeros
건물위생관리업 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2024-04-22 00:34:24.533175
Analysis finished2024-04-22 00:34:27.165114
Duration2.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2021
23 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 23
100.0%

Length

2024-04-22T09:34:27.225830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:34:27.307732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 23
100.0%

행정동
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-04-22T09:34:27.455201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Characters and Unicode

Total characters80
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row복수동
2nd row도마1동
3rd row도마2동
4th row정림동
5th row변동
ValueCountFrequency (%)
복수동 1
 
4.3%
내동 1
 
4.3%
관저2동 1
 
4.3%
관저1동 1
 
4.3%
가수원동 1
 
4.3%
만년동 1
 
4.3%
월평3동 1
 
4.3%
월평2동 1
 
4.3%
월평1동 1
 
4.3%
갈마2동 1
 
4.3%
Other values (13) 13
56.5%
2024-04-22T09:34:27.762471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
28.7%
1 5
 
6.2%
2 5
 
6.2%
마 4
 
5.0%
산 3
 
3.8%
둔 3
 
3.8%
평 3
 
3.8%
월 3
 
3.8%
저 2
 
2.5%
갈 2
 
2.5%
Other values (21) 27
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
85.0%
Decimal Number 12
 
15.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
33.8%
마 4
 
5.9%
산 3
 
4.4%
둔 3
 
4.4%
평 3
 
4.4%
월 3
 
4.4%
저 2
 
2.9%
갈 2
 
2.9%
정 2
 
2.9%
수 2
 
2.9%
Other values (18) 21
30.9%
Decimal Number
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
85.0%
Common 12
 
15.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
33.8%
마 4
 
5.9%
산 3
 
4.4%
둔 3
 
4.4%
평 3
 
4.4%
월 3
 
4.4%
저 2
 
2.9%
갈 2
 
2.9%
정 2
 
2.9%
수 2
 
2.9%
Other values (18) 21
30.9%
Common
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
85.0%
ASCII 12
 
15.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
33.8%
마 4
 
5.9%
산 3
 
4.4%
둔 3
 
4.4%
평 3
 
4.4%
월 3
 
4.4%
저 2
 
2.9%
갈 2
 
2.9%
정 2
 
2.9%
수 2
 
2.9%
Other values (18) 21
30.9%
ASCII
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

숙박업_일반
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4782609
Minimum0
Maximum14
Zeros11
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-22T09:34:27.871145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile8.9
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.7763567
Coefficient of variation (CV)1.523793
Kurtosis2.818125
Mean2.4782609
Median Absolute Deviation (MAD)1
Skewness1.7881291
Sum57
Variance14.26087
MonotonicityNot monotonic
2024-04-22T09:34:27.976622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 11
47.8%
1 3
 
13.0%
3 2
 
8.7%
2 2
 
8.7%
14 1
 
4.3%
8 1
 
4.3%
9 1
 
4.3%
6 1
 
4.3%
7 1
 
4.3%
ValueCountFrequency (%)
0 11
47.8%
1 3
 
13.0%
2 2
 
8.7%
3 2
 
8.7%
6 1
 
4.3%
7 1
 
4.3%
8 1
 
4.3%
9 1
 
4.3%
14 1
 
4.3%
ValueCountFrequency (%)
14 1
 
4.3%
9 1
 
4.3%
8 1
 
4.3%
7 1
 
4.3%
6 1
 
4.3%
3 2
 
8.7%
2 2
 
8.7%
1 3
 
13.0%
0 11
47.8%

숙박업_생활
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
19 
1
2 
4
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
82.6%
1 2
 
8.7%
4 1
 
4.3%
5 1
 
4.3%

Length

2024-04-22T09:34:28.096100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:34:28.184139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
82.6%
1 2
 
8.7%
4 1
 
4.3%
5 1
 
4.3%

목욕장업
Categorical

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
1
10 
0
7 
2
3 
3
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 10
43.5%
0 7
30.4%
2 3
 
13.0%
3 3
 
13.0%

Length

2024-04-22T09:34:28.281839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:34:28.379386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10
43.5%
0 7
30.4%
2 3
 
13.0%
3 3
 
13.0%

이용업
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9565217
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-22T09:34:28.477232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q39
95-th percentile11.8
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3639568
Coefficient of variation (CV)0.56475188
Kurtosis-1.1357661
Mean5.9565217
Median Absolute Deviation (MAD)3
Skewness0.28440873
Sum137
Variance11.316206
MonotonicityNot monotonic
2024-04-22T09:34:28.612450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 4
17.4%
9 4
17.4%
5 3
13.0%
2 3
13.0%
12 2
8.7%
8 2
8.7%
4 1
 
4.3%
7 1
 
4.3%
1 1
 
4.3%
10 1
 
4.3%
ValueCountFrequency (%)
1 1
 
4.3%
2 3
13.0%
3 4
17.4%
4 1
 
4.3%
5 3
13.0%
6 1
 
4.3%
7 1
 
4.3%
8 2
8.7%
9 4
17.4%
10 1
 
4.3%
ValueCountFrequency (%)
12 2
8.7%
10 1
 
4.3%
9 4
17.4%
8 2
8.7%
7 1
 
4.3%
6 1
 
4.3%
5 3
13.0%
4 1
 
4.3%
3 4
17.4%
2 3
13.0%

미용업
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.826087
Minimum4
Maximum292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-22T09:34:28.727991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15.4
Q147.5
median68
Q3115
95-th percentile198.2
Maximum292
Range288
Interquartile range (IQR)67.5

Descriptive statistics

Standard deviation65.903271
Coefficient of variation (CV)0.75038378
Kurtosis3.0899373
Mean87.826087
Median Absolute Deviation (MAD)27
Skewness1.5914487
Sum2020
Variance4343.2411
MonotonicityNot monotonic
2024-04-22T09:34:28.842048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
37 2
 
8.7%
65 1
 
4.3%
137 1
 
4.3%
4 1
 
4.3%
126 1
 
4.3%
70 1
 
4.3%
203 1
 
4.3%
41 1
 
4.3%
13 1
 
4.3%
47 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
4 1
4.3%
13 1
4.3%
37 2
8.7%
41 1
4.3%
47 1
4.3%
48 1
4.3%
52 1
4.3%
54 1
4.3%
61 1
4.3%
65 1
4.3%
ValueCountFrequency (%)
292 1
4.3%
203 1
4.3%
155 1
4.3%
152 1
4.3%
137 1
4.3%
126 1
4.3%
104 1
4.3%
95 1
4.3%
83 1
4.3%
76 1
4.3%

세탁업
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10
Minimum0
Maximum21
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-22T09:34:28.947224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q18
median10
Q312
95-th percentile16.8
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.4004132
Coefficient of variation (CV)0.44004132
Kurtosis1.6888467
Mean10
Median Absolute Deviation (MAD)2
Skewness0.12619996
Sum230
Variance19.363636
MonotonicityNot monotonic
2024-04-22T09:34:29.046414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
8 4
17.4%
10 3
13.0%
11 3
13.0%
12 3
13.0%
9 2
8.7%
7 1
 
4.3%
17 1
 
4.3%
13 1
 
4.3%
15 1
 
4.3%
2 1
 
4.3%
Other values (3) 3
13.0%
ValueCountFrequency (%)
0 1
 
4.3%
2 1
 
4.3%
6 1
 
4.3%
7 1
 
4.3%
8 4
17.4%
9 2
8.7%
10 3
13.0%
11 3
13.0%
12 3
13.0%
13 1
 
4.3%
ValueCountFrequency (%)
21 1
 
4.3%
17 1
 
4.3%
15 1
 
4.3%
13 1
 
4.3%
12 3
13.0%
11 3
13.0%
10 3
13.0%
9 2
8.7%
8 4
17.4%
7 1
 
4.3%

건물위생관리업
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.173913
Minimum0
Maximum28
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-22T09:34:29.147124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q312.5
95-th percentile22.9
Maximum28
Range28
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.6077244
Coefficient of variation (CV)0.82927801
Kurtosis0.43116663
Mean9.173913
Median Absolute Deviation (MAD)5
Skewness0.96324778
Sum211
Variance57.87747
MonotonicityNot monotonic
2024-04-22T09:34:29.243950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 3
13.0%
1 3
13.0%
10 2
8.7%
12 2
8.7%
15 2
8.7%
5 2
8.7%
8 2
8.7%
9 1
 
4.3%
22 1
 
4.3%
28 1
 
4.3%
Other values (4) 4
17.4%
ValueCountFrequency (%)
0 1
 
4.3%
1 3
13.0%
3 3
13.0%
4 1
 
4.3%
5 2
8.7%
8 2
8.7%
9 1
 
4.3%
10 2
8.7%
12 2
8.7%
13 1
 
4.3%
ValueCountFrequency (%)
28 1
4.3%
23 1
4.3%
22 1
4.3%
15 2
8.7%
13 1
4.3%
12 2
8.7%
10 2
8.7%
9 1
4.3%
8 2
8.7%
5 2
8.7%

기타
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
23 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23
100.0%

Length

2024-04-22T09:34:29.359262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:34:29.443296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
100.0%

위생처리업
Categorical

IMBALANCE 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
21 
1
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21
91.3%
1 1
 
4.3%
2 1
 
4.3%

Length

2024-04-22T09:34:29.529789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:34:29.630344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
91.3%
1 1
 
4.3%
2 1
 
4.3%

기타위생용품제조업
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
19 
1
3 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
82.6%
1 3
 
13.0%
2 1
 
4.3%

Length

2024-04-22T09:34:29.721454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:34:29.811727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
82.6%
1 3
 
13.0%
2 1
 
4.3%

Interactions

2024-04-22T09:34:26.524390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:24.888153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.295506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.711425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.123247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.604289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:24.974141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.376652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.803435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.203643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.683563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.053166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.454314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.887080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.288621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.763243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.136710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.538200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.968685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.367791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.838595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.212217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:25.615958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.045158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:34:26.448866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T09:34:29.882231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동숙박업_일반숙박업_생활목욕장업이용업미용업세탁업건물위생관리업위생처리업기타위생용품제조업
행정동1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
숙박업_일반1.0001.0000.7560.3270.0600.4810.3560.7350.0000.000
숙박업_생활1.0000.7561.0000.7530.0000.6480.0000.5320.0000.000
목욕장업1.0000.3270.7531.0000.5140.0000.0000.3730.0000.130
이용업1.0000.0600.0000.5141.0000.6090.6210.3630.0000.000
미용업1.0000.4810.6480.0000.6091.0000.8080.7910.0000.620
세탁업1.0000.3560.0000.0000.6210.8081.0000.8250.0000.875
건물위생관리업1.0000.7350.5320.3730.3630.7910.8251.0000.0000.894
위생처리업1.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
기타위생용품제조업1.0000.0000.0000.1300.0000.6200.8750.8940.0001.000
2024-04-22T09:34:30.001944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생처리업숙박업_생활목욕장업기타위생용품제조업
위생처리업1.0000.0000.0000.000
숙박업_생활0.0001.0000.3830.000
목욕장업0.0000.3831.0000.089
기타위생용품제조업0.0000.0000.0891.000
2024-04-22T09:34:30.107343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
숙박업_일반이용업미용업세탁업건물위생관리업숙박업_생활목욕장업위생처리업기타위생용품제조업
숙박업_일반1.0000.5380.1810.0810.5490.7330.3390.0000.000
이용업0.5381.0000.4240.3980.4730.0000.2400.0000.000
미용업0.1810.4241.0000.7230.6000.2730.0000.0000.411
세탁업0.0810.3980.7231.0000.6600.0000.0000.0000.491
건물위생관리업0.5490.4730.6000.6601.0000.2920.1660.0000.516
숙박업_생활0.7330.0000.2730.0000.2921.0000.3830.0000.000
목욕장업0.3390.2400.0000.0000.1660.3831.0000.0000.089
위생처리업0.0000.0000.0000.0000.0000.0000.0001.0000.000
기타위생용품제조업0.0000.0000.4110.4910.5160.0000.0890.0001.000

Missing values

2024-04-22T09:34:26.947961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T09:34:27.103498image/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

기준연도행정동숙박업_일반숙박업_생활목욕장업이용업미용업세탁업건물위생관리업기타위생처리업기타위생용품제조업
02021복수동001365109000
12021도마1동30112951110000
22021도마2동11095493000
32021정림동00244873000
42021변동2015371222000
52021용문동1401561812001
62021탄방동213122921228000
72021둔산1동842983910000
82021둔산2동95251551015000
92021둔산3동000352110000
기준연도행정동숙박업_일반숙박업_생활목욕장업이용업미용업세탁업건물위생관리업기타위생처리업기타위생용품제조업
132021갈마1동00181371312001
142021갈마2동000176128020
152021월평1동70181041523000
162021월평2동301104784000
172021월평3동00021321000
182021만년동00124168000
192021가수원동00162032113001
202021관저1동00337081000
212021관저2동0003126103000
222021기성동1002401002