Overview

Dataset statistics

Number of variables11
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory103.7 B

Variable types

Text1
Numeric10

Dataset

Description인천광역시 공중위생관계업소현황 (숙박, 목욕장, 이용, 미용, 손톱, 피부화장분장 등)데이터이며, 군구별 데이터로 구성되어 제공되어 집니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15064869&srcSe=7661IVAWM27C61E190

Alerts

중구 is highly overall correlated with 동구 and 8 other fieldsHigh correlation
동구 is highly overall correlated with 중구 and 8 other fieldsHigh correlation
미추홀구 is highly overall correlated with 중구 and 8 other fieldsHigh correlation
연수구 is highly overall correlated with 중구 and 8 other fieldsHigh correlation
남동구 is highly overall correlated with 중구 and 8 other fieldsHigh correlation
부평구 is highly overall correlated with 중구 and 8 other fieldsHigh correlation
계양구 is highly overall correlated with 중구 and 6 other fieldsHigh correlation
서구 is highly overall correlated with 중구 and 8 other fieldsHigh correlation
강화군 is highly overall correlated with 중구 and 7 other fieldsHigh correlation
옹진군 is highly overall correlated with 중구 and 7 other fieldsHigh correlation
구분 has unique valuesUnique
미추홀구 has unique valuesUnique
남동구 has unique valuesUnique
중구 has 1 (4.3%) zerosZeros
동구 has 4 (17.4%) zerosZeros
계양구 has 1 (4.3%) zerosZeros
강화군 has 5 (21.7%) zerosZeros
옹진군 has 10 (43.5%) zerosZeros

Reproduction

Analysis started2024-04-17 08:56:04.557926
Analysis finished2024-04-17 08:56:40.809431
Duration36.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-04-17T17:56:40.970702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length12
Mean length10.521739
Min length3

Characters and Unicode

Total characters242
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row숙박업(일반)
2nd row숙박업(생활)
3rd row목욕장업
4th row이용업
5th row미용업
ValueCountFrequency (%)
미용업 8
18.2%
일반미용업 7
15.9%
피부미용업 7
15.9%
네일미용업 7
15.9%
화장ㆍ분장 7
15.9%
숙박업(일반 1
 
2.3%
숙박업(생활 1
 
2.3%
목욕장업 1
 
2.3%
이용업 1
 
2.3%
종합미용업 1
 
2.3%
Other values (3) 3
 
6.8%
2024-04-17T17:56:41.541619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 37
15.3%
용 32
13.2%
미 31
12.8%
21
 
8.7%
일 15
 
6.2%
장 15
 
6.2%
, 14
 
5.8%
반 8
 
3.3%
네 7
 
2.9%
피 7
 
2.9%
Other values (24) 55
22.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 201
83.1%
Space Separator 21
 
8.7%
Other Punctuation 14
 
5.8%
Close Punctuation 3
 
1.2%
Open Punctuation 3
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 37
18.4%
용 32
15.9%
미 31
15.4%
일 15
7.5%
장 15
7.5%
반 8
 
4.0%
네 7
 
3.5%
피 7
 
3.5%
ㆍ 7
 
3.5%
화 7
 
3.5%
Other values (20) 35
17.4%
Space Separator
ValueCountFrequency (%)
21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 201
83.1%
Common 41
 
16.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 37
18.4%
용 32
15.9%
미 31
15.4%
일 15
7.5%
장 15
7.5%
반 8
 
4.0%
네 7
 
3.5%
피 7
 
3.5%
ㆍ 7
 
3.5%
화 7
 
3.5%
Other values (20) 35
17.4%
Common
ValueCountFrequency (%)
21
51.2%
, 14
34.1%
) 3
 
7.3%
( 3
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 194
80.2%
ASCII 41
 
16.9%
Compat Jamo 7
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 37
19.1%
용 32
16.5%
미 31
16.0%
일 15
7.7%
장 15
7.7%
반 8
 
4.1%
네 7
 
3.6%
피 7
 
3.6%
화 7
 
3.6%
분 7
 
3.6%
Other values (19) 28
14.4%
ASCII
ValueCountFrequency (%)
21
51.2%
, 14
34.1%
) 3
 
7.3%
( 3
 
7.3%
Compat Jamo
ValueCountFrequency (%)
ㆍ 7
100.0%

중구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.26087
Minimum0
Maximum374
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:41.774182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q14.5
median17
Q349
95-th percentile184
Maximum374
Range374
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation86.268723
Coefficient of variation (CV)1.6507326
Kurtosis8.6365579
Mean52.26087
Median Absolute Deviation (MAD)15
Skewness2.7613572
Sum1202
Variance7442.2925
MonotonicityNot monotonic
2024-04-17T17:56:42.084829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 2
 
8.7%
44 2
 
8.7%
4 2
 
8.7%
8 1
 
4.3%
54 1
 
4.3%
374 1
 
4.3%
17 1
 
4.3%
0 1
 
4.3%
1 1
 
4.3%
10 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
0 1
4.3%
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 2
8.7%
5 1
4.3%
6 2
8.7%
8 1
4.3%
10 1
4.3%
17 1
4.3%
ValueCountFrequency (%)
374 1
4.3%
188 1
4.3%
148 1
4.3%
110 1
4.3%
85 1
4.3%
54 1
4.3%
44 2
8.7%
35 1
4.3%
31 1
4.3%
23 1
4.3%

동구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.478261
Minimum0
Maximum180
Zeros4
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:42.368869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q332
95-th percentile87.9
Maximum180
Range180
Interquartile range (IQR)31

Descriptive statistics

Standard deviation41.499036
Coefficient of variation (CV)1.8461853
Kurtosis9.4964911
Mean22.478261
Median Absolute Deviation (MAD)3
Skewness2.8978494
Sum517
Variance1722.17
MonotonicityNot monotonic
2024-04-17T17:56:42.662633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 6
26.1%
0 4
17.4%
32 2
 
8.7%
50 1
 
4.3%
8 1
 
4.3%
92 1
 
4.3%
51 1
 
4.3%
10 1
 
4.3%
6 1
 
4.3%
3 1
 
4.3%
Other values (4) 4
17.4%
ValueCountFrequency (%)
0 4
17.4%
1 6
26.1%
2 1
 
4.3%
3 1
 
4.3%
6 1
 
4.3%
8 1
 
4.3%
10 1
 
4.3%
11 1
 
4.3%
32 2
 
8.7%
34 1
 
4.3%
ValueCountFrequency (%)
180 1
4.3%
92 1
4.3%
51 1
4.3%
50 1
4.3%
34 1
4.3%
32 2
8.7%
11 1
4.3%
10 1
4.3%
8 1
4.3%
6 1
4.3%

미추홀구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.95652
Minimum1
Maximum1390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:42.932826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q110
median35
Q3127.5
95-th percentile727.5
Maximum1390
Range1389
Interquartile range (IQR)117.5

Descriptive statistics

Standard deviation316.16631
Coefficient of variation (CV)2.1083865
Kurtosis11.679896
Mean149.95652
Median Absolute Deviation (MAD)32
Skewness3.3539385
Sum3449
Variance99961.134
MonotonicityNot monotonic
2024-04-17T17:56:43.223702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
187 1
 
4.3%
7 1
 
4.3%
108 1
 
4.3%
210 1
 
4.3%
1390 1
 
4.3%
40 1
 
4.3%
16 1
 
4.3%
6 1
 
4.3%
2 1
 
4.3%
1 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
12 1
4.3%
13 1
4.3%
16 1
4.3%
20 1
4.3%
ValueCountFrequency (%)
1390 1
4.3%
785 1
4.3%
210 1
4.3%
187 1
4.3%
151 1
4.3%
132 1
4.3%
123 1
4.3%
122 1
4.3%
108 1
4.3%
44 1
4.3%

연수구
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.04348
Minimum1
Maximum1043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:43.523722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q113.5
median28
Q377
95-th percentile415.5
Maximum1043
Range1042
Interquartile range (IQR)63.5

Descriptive statistics

Standard deviation225.36243
Coefficient of variation (CV)2.1454205
Kurtosis14.796206
Mean105.04348
Median Absolute Deviation (MAD)21
Skewness3.7142555
Sum2416
Variance50788.225
MonotonicityNot monotonic
2024-04-17T17:56:43.820047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20 2
 
8.7%
65 1
 
4.3%
17 1
 
4.3%
46 1
 
4.3%
129 1
 
4.3%
1043 1
 
4.3%
37 1
 
4.3%
7 1
 
4.3%
2 1
 
4.3%
1 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
10 1
4.3%
17 1
4.3%
20 2
8.7%
21 1
4.3%
25 1
4.3%
ValueCountFrequency (%)
1043 1
4.3%
444 1
4.3%
159 1
4.3%
143 1
4.3%
129 1
4.3%
89 1
4.3%
65 1
4.3%
61 1
4.3%
46 1
4.3%
37 1
4.3%

남동구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.82609
Minimum1
Maximum1764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:44.082872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q115
median43
Q3154
95-th percentile767.7
Maximum1764
Range1763
Interquartile range (IQR)139

Descriptive statistics

Standard deviation387.26097
Coefficient of variation (CV)2.1655731
Kurtosis13.792607
Mean178.82609
Median Absolute Deviation (MAD)35
Skewness3.5950528
Sum4113
Variance149971.06
MonotonicityNot monotonic
2024-04-17T17:56:44.381629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
79 1
 
4.3%
8 1
 
4.3%
151 1
 
4.3%
193 1
 
4.3%
1764 1
 
4.3%
54 1
 
4.3%
18 1
 
4.3%
13 1
 
4.3%
2 1
 
4.3%
1 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
8 1
4.3%
9 1
4.3%
12 1
4.3%
13 1
4.3%
17 1
4.3%
18 1
4.3%
23 1
4.3%
32 1
4.3%
ValueCountFrequency (%)
1764 1
4.3%
822 1
4.3%
279 1
4.3%
230 1
4.3%
193 1
4.3%
157 1
4.3%
151 1
4.3%
122 1
4.3%
79 1
4.3%
54 1
4.3%

부평구
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.56522
Minimum2
Maximum1690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:44.664329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.2
Q114
median40
Q3166
95-th percentile672.4
Maximum1690
Range1688
Interquartile range (IQR)152

Descriptive statistics

Standard deviation366.30909
Coefficient of variation (CV)2.1104983
Kurtosis14.387445
Mean173.56522
Median Absolute Deviation (MAD)34
Skewness3.6410308
Sum3992
Variance134182.35
MonotonicityNot monotonic
2024-04-17T17:56:44.946375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
166 2
 
8.7%
26 2
 
8.7%
28 1
 
4.3%
86 1
 
4.3%
191 1
 
4.3%
1690 1
 
4.3%
35 1
 
4.3%
6 1
 
4.3%
4 1
 
4.3%
2 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
2 1
4.3%
4 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
11 1
4.3%
17 1
4.3%
26 2
8.7%
28 1
4.3%
35 1
4.3%
ValueCountFrequency (%)
1690 1
4.3%
707 1
4.3%
361 1
4.3%
191 1
4.3%
178 1
4.3%
166 2
8.7%
126 1
4.3%
86 1
4.3%
69 1
4.3%
42 1
4.3%

계양구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.521739
Minimum0
Maximum920
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:45.235224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q18
median15
Q365
95-th percentile551.4
Maximum920
Range920
Interquartile range (IQR)57

Descriptive statistics

Standard deviation218.06398
Coefficient of variation (CV)2.3070246
Kurtosis10.542012
Mean94.521739
Median Absolute Deviation (MAD)12
Skewness3.2733061
Sum2174
Variance47551.897
MonotonicityNot monotonic
2024-04-17T17:56:45.512615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
65 2
 
8.7%
4 2
 
8.7%
13 2
 
8.7%
61 1
 
4.3%
7 1
 
4.3%
123 1
 
4.3%
920 1
 
4.3%
11 1
 
4.3%
1 1
 
4.3%
0 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
0 1
4.3%
1 1
4.3%
3 1
4.3%
4 2
8.7%
7 1
4.3%
9 1
4.3%
11 1
4.3%
13 2
8.7%
14 1
4.3%
15 1
4.3%
ValueCountFrequency (%)
920 1
4.3%
599 1
4.3%
123 1
4.3%
106 1
4.3%
72 1
4.3%
65 2
8.7%
61 1
4.3%
29 1
4.3%
23 1
4.3%
17 1
4.3%

서구
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.26087
Minimum1
Maximum1639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:45.790012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110.5
median32
Q3127.5
95-th percentile775.3
Maximum1639
Range1638
Interquartile range (IQR)117

Descriptive statistics

Standard deviation365.6326
Coefficient of variation (CV)2.2259264
Kurtosis13.068345
Mean164.26087
Median Absolute Deviation (MAD)29
Skewness3.534171
Sum3778
Variance133687.2
MonotonicityNot monotonic
2024-04-17T17:56:46.070819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 3
 
13.0%
29 2
 
8.7%
55 1
 
4.3%
106 1
 
4.3%
188 1
 
4.3%
1639 1
 
4.3%
19 1
 
4.3%
32 1
 
4.3%
1 1
 
4.3%
65 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
1 1
 
4.3%
3 3
13.0%
6 1
 
4.3%
9 1
 
4.3%
12 1
 
4.3%
19 1
 
4.3%
27 1
 
4.3%
29 2
8.7%
32 1
 
4.3%
55 1
 
4.3%
ValueCountFrequency (%)
1639 1
4.3%
839 1
4.3%
202 1
4.3%
188 1
4.3%
186 1
4.3%
149 1
4.3%
106 1
4.3%
103 1
4.3%
73 1
4.3%
65 1
4.3%

강화군
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.652174
Minimum0
Maximum166
Zeros5
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:46.351497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q329
95-th percentile82.6
Maximum166
Range166
Interquartile range (IQR)28

Descriptive statistics

Standard deviation39.675512
Coefficient of variation (CV)1.6094123
Kurtosis6.7818045
Mean24.652174
Median Absolute Deviation (MAD)8
Skewness2.4513028
Sum567
Variance1574.1462
MonotonicityNot monotonic
2024-04-17T17:56:46.619512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 5
21.7%
1 3
13.0%
19 2
 
8.7%
3 2
 
8.7%
79 1
 
4.3%
60 1
 
4.3%
17 1
 
4.3%
37 1
 
4.3%
83 1
 
4.3%
35 1
 
4.3%
Other values (5) 5
21.7%
ValueCountFrequency (%)
0 5
21.7%
1 3
13.0%
2 1
 
4.3%
3 2
 
8.7%
8 1
 
4.3%
10 1
 
4.3%
17 1
 
4.3%
19 2
 
8.7%
23 1
 
4.3%
35 1
 
4.3%
ValueCountFrequency (%)
166 1
4.3%
83 1
4.3%
79 1
4.3%
60 1
4.3%
37 1
4.3%
35 1
4.3%
23 1
4.3%
19 2
8.7%
17 1
4.3%
10 1
4.3%

옹진군
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.347826
Minimum0
Maximum122
Zeros10
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-04-17T17:56:46.898503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile50
Maximum122
Range122
Interquartile range (IQR)7

Descriptive statistics

Standard deviation27.081709
Coefficient of variation (CV)2.3865108
Kurtosis13.538899
Mean11.347826
Median Absolute Deviation (MAD)1
Skewness3.5384751
Sum261
Variance733.41897
MonotonicityNot monotonic
2024-04-17T17:56:47.188680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 10
43.5%
3 3
 
13.0%
5 2
 
8.7%
1 2
 
8.7%
52 1
 
4.3%
122 1
 
4.3%
15 1
 
4.3%
9 1
 
4.3%
32 1
 
4.3%
10 1
 
4.3%
ValueCountFrequency (%)
0 10
43.5%
1 2
 
8.7%
3 3
 
13.0%
5 2
 
8.7%
9 1
 
4.3%
10 1
 
4.3%
15 1
 
4.3%
32 1
 
4.3%
52 1
 
4.3%
122 1
 
4.3%
ValueCountFrequency (%)
122 1
 
4.3%
52 1
 
4.3%
32 1
 
4.3%
15 1
 
4.3%
10 1
 
4.3%
9 1
 
4.3%
5 2
 
8.7%
3 3
 
13.0%
1 2
 
8.7%
0 10
43.5%

Interactions

2024-04-17T17:56:37.869232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:17.750047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:20.130256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:22.629531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:25.120135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:27.058283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:29.086126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:31.382218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:33.238628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:35.313193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:38.119750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:18.041005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:20.429153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:22.975063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:25.315170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:27.262703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:29.300070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:31.573966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:33.442961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:35.534217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:38.415248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:18.285634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:20.677735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:23.213436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:25.533006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:27.471630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:29.510719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:31.774388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:33.653111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:35.748637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:38.676216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:18.527776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:20.984398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:23.448792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:25.721061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:27.689673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:29.717825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:31.967432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:33.860901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:35.972608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:38.890946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:18.759531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:21.232699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:23.671115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:25.912345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:27.882191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:29.906340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:32.141814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:34.058537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:36.595720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:39.122697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:19.017460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:21.548584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:23.886668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:26.114121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:28.086747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:30.140465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:32.333768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:34.263237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:36.829730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:39.324890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:19.266975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:21.780367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:24.113002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:26.310660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:28.294715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:30.396249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:32.522340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:34.457561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:37.069253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:39.517053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:19.486429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:21.964914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:24.297184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:26.486944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:28.483708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:30.665214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:32.689165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:34.646276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:37.255224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:39.760401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:19.699517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:22.177297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:24.713971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:26.683621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:28.689344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:30.920077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:32.878780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:34.839969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:37.472568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:39.961913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:19.903561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:22.405708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:24.920045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:26.868698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:28.884019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:31.179685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:33.060790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:35.055214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T17:56:37.666904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T17:56:47.429228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분중구동구미추홀구연수구남동구부평구계양구서구강화군옹진군
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중구1.0001.0000.8700.9760.9760.9760.9010.9850.9760.9410.890
동구1.0000.8701.0000.8240.7790.7790.9710.7170.7790.9030.966
미추홀구1.0000.9760.8241.0000.9910.9910.8850.9810.9910.8620.730
연수구1.0000.9760.7790.9911.0001.0001.0000.9971.0000.8450.637
남동구1.0000.9760.7790.9911.0001.0001.0000.9971.0000.8450.637
부평구1.0000.9010.9710.8851.0001.0001.0001.0001.0000.8720.930
계양구1.0000.9850.7170.9810.9970.9971.0001.0000.9970.8130.523
서구1.0000.9760.7790.9911.0001.0001.0000.9971.0000.8450.637
강화군1.0000.9410.9030.8620.8450.8450.8720.8130.8451.0001.000
옹진군1.0000.8900.9660.7300.6370.6370.9300.5230.6371.0001.000
2024-04-17T17:56:47.750854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중구동구미추홀구연수구남동구부평구계양구서구강화군옹진군
중구1.0000.8150.8370.8240.7810.8340.6270.7560.8940.914
동구0.8151.0000.8370.8290.8280.7810.5880.7590.7750.745
미추홀구0.8370.8371.0000.9560.9470.9510.8300.9280.7420.680
연수구0.8240.8290.9561.0000.9780.9680.8150.9520.7210.637
남동구0.7810.8280.9470.9781.0000.9550.8190.9620.7030.601
부평구0.8340.7810.9510.9680.9551.0000.8180.9800.6990.646
계양구0.6270.5880.8300.8150.8190.8181.0000.8320.4990.409
서구0.7560.7590.9280.9520.9620.9800.8321.0000.6210.570
강화군0.8940.7750.7420.7210.7030.6990.4990.6211.0000.884
옹진군0.9140.7450.6800.6370.6010.6460.4090.5700.8841.000

Missing values

2024-04-17T17:56:40.287016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T17:56:40.663515image/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

구분중구동구미추홀구연수구남동구부평구계양구서구강화군옹진군
0숙박업(일반)18850187657916661737952
1숙박업(생활)1101788173360122
2목욕장업238342132261727173
3이용업35321236112212665103375
4미용업859215114327936142028315
5일반미용업14851785444822707599839359
6피부미용업4410122159230178106186191
7네일미용업316132891571667214983
8일반미용업, 피부미용업203101279630
9일반미용업, 네일미용업31126178131210
구분중구동구미추홀구연수구남동구부평구계양구서구강화군옹진군
13피부미용업, 화장ㆍ분장 미용업418202328142900
14네일미용업, 화장ㆍ분장 미용업10135354469236510
15일반미용업, 피부미용업, 네일미용업1011120300
16일반미용업, 피부미용업, 화장ㆍ분장 미용업0122241100
17일반미용업, 네일미용업, 화장ㆍ분장 미용업41671364320
18피부미용업, 네일미용업, 화장ㆍ분장 미용업6016201835133201
19종합미용업1711403754261119103
20미용업(총계)3741801390104317641690920163916632
21세탁업4434210129193191123188235
22건물위생관리업54321084615186651061910