Overview

Dataset statistics

Number of variables11
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory98.7 B

Variable types

Text6
Numeric5

Dataset

Description인천광역시 공중위생관계업소현황 (숙박, 목욕장, 이용, 미용, 손톱, 피부화장분장 등)데이터이며, 군구별 데이터로 구성되어 제공되어 집니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15064869/fileData.do

Alerts

강화군 is highly overall correlated with 옹진군 and 3 other fieldsHigh correlation
옹진군 is highly overall correlated with 강화군 and 2 other fieldsHigh correlation
중구 is highly overall correlated with 강화군 and 3 other fieldsHigh correlation
동구 is highly overall correlated with 강화군 and 3 other fieldsHigh correlation
계양구 is highly overall correlated with 강화군 and 2 other fieldsHigh correlation
구분 has unique valuesUnique
부평구 has unique valuesUnique
강화군 has 6 (26.1%) zerosZeros
옹진군 has 9 (39.1%) zerosZeros
중구 has 2 (8.7%) zerosZeros
동구 has 4 (17.4%) zerosZeros
계양구 has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-16 15:36:29.775153
Analysis finished2023-12-16 15:36:51.159714
Duration21.38 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
2023-12-16T15:36:51.574626image/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%
2023-12-16T15:36:52.993001image/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%

강화군
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.434783
Minimum0
Maximum185
Zeros6
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:36:53.536909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median9
Q330
95-th percentile81.6
Maximum185
Range185
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation42.976554
Coefficient of variation (CV)1.6257578
Kurtosis8.0296457
Mean26.434783
Median Absolute Deviation (MAD)9
Skewness2.6145661
Sum608
Variance1846.9842
MonotonicityNot monotonic
2023-12-16T15:36:54.106696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 6
26.1%
2 2
 
8.7%
65 1
 
4.3%
24 1
 
4.3%
22 1
 
4.3%
185 1
 
4.3%
9 1
 
4.3%
1 1
 
4.3%
4 1
 
4.3%
78 1
 
4.3%
Other values (7) 7
30.4%
ValueCountFrequency (%)
0 6
26.1%
1 1
 
4.3%
2 2
 
8.7%
3 1
 
4.3%
4 1
 
4.3%
9 1
 
4.3%
10 1
 
4.3%
15 1
 
4.3%
22 1
 
4.3%
24 1
 
4.3%
ValueCountFrequency (%)
185 1
4.3%
82 1
4.3%
78 1
4.3%
65 1
4.3%
46 1
4.3%
34 1
4.3%
26 1
4.3%
24 1
4.3%
22 1
4.3%
15 1
4.3%

옹진군
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.652174
Minimum0
Maximum131
Zeros9
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:36:54.568455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.5
95-th percentile50.7
Maximum131
Range131
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation28.756989
Coefficient of variation (CV)2.4679506
Kurtosis14.551623
Mean11.652174
Median Absolute Deviation (MAD)1
Skewness3.6765566
Sum268
Variance826.96443
MonotonicityNot monotonic
2023-12-16T15:36:55.485587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 9
39.1%
1 4
17.4%
4 2
 
8.7%
5 2
 
8.7%
53 1
 
4.3%
131 1
 
4.3%
14 1
 
4.3%
8 1
 
4.3%
30 1
 
4.3%
10 1
 
4.3%
ValueCountFrequency (%)
0 9
39.1%
1 4
17.4%
4 2
 
8.7%
5 2
 
8.7%
8 1
 
4.3%
10 1
 
4.3%
14 1
 
4.3%
30 1
 
4.3%
53 1
 
4.3%
131 1
 
4.3%
ValueCountFrequency (%)
131 1
 
4.3%
53 1
 
4.3%
30 1
 
4.3%
14 1
 
4.3%
10 1
 
4.3%
8 1
 
4.3%
5 2
 
8.7%
4 2
 
8.7%
1 4
17.4%
0 9
39.1%

중구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.217391
Minimum0
Maximum436
Zeros2
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:36:56.568600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q16.5
median18
Q354.5
95-th percentile182.3
Maximum436
Range436
Interquartile range (IQR)48

Descriptive statistics

Standard deviation98.164499
Coefficient of variation (CV)1.6861714
Kurtosis10.001435
Mean58.217391
Median Absolute Deviation (MAD)15
Skewness2.9556558
Sum1339
Variance9636.2688
MonotonicityNot monotonic
2023-12-16T15:36:57.496315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7 2
 
8.7%
0 2
 
8.7%
4 2
 
8.7%
15 1
 
4.3%
55 1
 
4.3%
42 1
 
4.3%
436 1
 
4.3%
18 1
 
4.3%
10 1
 
4.3%
12 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
0 2
8.7%
3 1
4.3%
4 2
8.7%
6 1
4.3%
7 2
8.7%
10 1
4.3%
12 1
4.3%
15 1
4.3%
18 1
4.3%
25 1
4.3%
ValueCountFrequency (%)
436 1
4.3%
183 1
4.3%
176 1
4.3%
130 1
4.3%
82 1
4.3%
55 1
4.3%
54 1
4.3%
42 1
4.3%
38 1
4.3%
32 1
4.3%

동구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.652174
Minimum0
Maximum180
Zeros4
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:36:58.561724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q327.5
95-th percentile80.5
Maximum180
Range180
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation40.46728
Coefficient of variation (CV)1.8689708
Kurtosis10.954001
Mean21.652174
Median Absolute Deviation (MAD)3
Skewness3.1053091
Sum498
Variance1637.6008
MonotonicityNot monotonic
2023-12-16T15:36:59.281976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 5
21.7%
0 4
17.4%
3 2
 
8.7%
44 1
 
4.3%
6 1
 
4.3%
27 1
 
4.3%
84 1
 
4.3%
49 1
 
4.3%
10 1
 
4.3%
11 1
 
4.3%
Other values (5) 5
21.7%
ValueCountFrequency (%)
0 4
17.4%
1 5
21.7%
2 1
 
4.3%
3 2
 
8.7%
6 1
 
4.3%
10 1
 
4.3%
11 1
 
4.3%
14 1
 
4.3%
27 1
 
4.3%
28 1
 
4.3%
ValueCountFrequency (%)
180 1
4.3%
84 1
4.3%
49 1
4.3%
44 1
4.3%
32 1
4.3%
28 1
4.3%
27 1
4.3%
14 1
4.3%
11 1
4.3%
10 1
4.3%

미추홀구
Text

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-16T15:36:59.850477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.3043478
Min length1

Characters and Unicode

Total characters53
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)82.6%

Sample

1st row175
2nd row12
3rd row35
4th row123
5th row144
ValueCountFrequency (%)
1 2
 
8.7%
13 2
 
8.7%
14 1
 
4.3%
175 1
 
4.3%
44 1
 
4.3%
204 1
 
4.3%
1,475 1
 
4.3%
56 1
 
4.3%
17 1
 
4.3%
8 1
 
4.3%
Other values (11) 11
47.8%
2023-12-16T15:37:01.348462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
32.1%
4 13
24.5%
3 5
 
9.4%
5 4
 
7.5%
7 4
 
7.5%
2 3
 
5.7%
8 2
 
3.8%
6 2
 
3.8%
9 1
 
1.9%
, 1
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52
98.1%
Other Punctuation 1
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
32.7%
4 13
25.0%
3 5
 
9.6%
5 4
 
7.7%
7 4
 
7.7%
2 3
 
5.8%
8 2
 
3.8%
6 2
 
3.8%
9 1
 
1.9%
0 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
32.1%
4 13
24.5%
3 5
 
9.4%
5 4
 
7.5%
7 4
 
7.5%
2 3
 
5.7%
8 2
 
3.8%
6 2
 
3.8%
9 1
 
1.9%
, 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
32.1%
4 13
24.5%
3 5
 
9.4%
5 4
 
7.5%
7 4
 
7.5%
2 3
 
5.7%
8 2
 
3.8%
6 2
 
3.8%
9 1
 
1.9%
, 1
 
1.9%

연수구
Text

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-16T15:37:02.130736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.2173913
Min length1

Characters and Unicode

Total characters51
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)82.6%

Sample

1st row64
2nd row14
3rd row22
4th row65
5th row136
ValueCountFrequency (%)
20 2
 
8.7%
14 2
 
8.7%
31 1
 
4.3%
64 1
 
4.3%
122 1
 
4.3%
1,136 1
 
4.3%
60 1
 
4.3%
21 1
 
4.3%
12 1
 
4.3%
2 1
 
4.3%
Other values (11) 11
47.8%
2023-12-16T15:37:03.296727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13
25.5%
1 12
23.5%
6 7
13.7%
0 5
 
9.8%
4 5
 
9.8%
3 4
 
7.8%
5 2
 
3.9%
7 1
 
2.0%
8 1
 
2.0%
, 1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
98.0%
Other Punctuation 1
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13
26.0%
1 12
24.0%
6 7
14.0%
0 5
 
10.0%
4 5
 
10.0%
3 4
 
8.0%
5 2
 
4.0%
7 1
 
2.0%
8 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13
25.5%
1 12
23.5%
6 7
13.7%
0 5
 
9.8%
4 5
 
9.8%
3 4
 
7.8%
5 2
 
3.9%
7 1
 
2.0%
8 1
 
2.0%
, 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13
25.5%
1 12
23.5%
6 7
13.7%
0 5
 
9.8%
4 5
 
9.8%
3 4
 
7.8%
5 2
 
3.9%
7 1
 
2.0%
8 1
 
2.0%
, 1
 
2.0%

남동구
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-16T15:37:04.011120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2608696
Min length1

Characters and Unicode

Total characters52
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st row78
2nd row7
3rd row34
4th row115
5th row269
ValueCountFrequency (%)
1 2
 
8.7%
78 1
 
4.3%
7 1
 
4.3%
181 1
 
4.3%
1,797 1
 
4.3%
61 1
 
4.3%
18 1
 
4.3%
8 1
 
4.3%
50 1
 
4.3%
22 1
 
4.3%
Other values (12) 12
52.2%
2023-12-16T15:37:05.489497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
26.9%
2 7
13.5%
7 6
11.5%
8 6
11.5%
4 6
11.5%
5 4
 
7.7%
6 3
 
5.8%
9 2
 
3.8%
0 2
 
3.8%
3 1
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51
98.1%
Other Punctuation 1
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
27.5%
2 7
13.7%
7 6
11.8%
8 6
11.8%
4 6
11.8%
5 4
 
7.8%
6 3
 
5.9%
9 2
 
3.9%
0 2
 
3.9%
3 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
26.9%
2 7
13.5%
7 6
11.5%
8 6
11.5%
4 6
11.5%
5 4
 
7.7%
6 3
 
5.8%
9 2
 
3.8%
0 2
 
3.8%
3 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
26.9%
2 7
13.5%
7 6
11.5%
8 6
11.5%
4 6
11.5%
5 4
 
7.7%
6 3
 
5.8%
9 2
 
3.8%
0 2
 
3.8%
3 1
 
1.9%

부평구
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-16T15:37:06.352088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2173913
Min length1

Characters and Unicode

Total characters51
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row160
2nd row17
3rd row23
4th row126
5th row331
ValueCountFrequency (%)
160 1
 
4.3%
15 1
 
4.3%
183 1
 
4.3%
1,739 1
 
4.3%
30 1
 
4.3%
38 1
 
4.3%
5 1
 
4.3%
4 1
 
4.3%
1 1
 
4.3%
71 1
 
4.3%
Other values (13) 13
56.5%
2023-12-16T15:37:07.492136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10
19.6%
3 9
17.6%
7 6
11.8%
2 6
11.8%
8 5
9.8%
6 4
 
7.8%
0 3
 
5.9%
5 3
 
5.9%
4 2
 
3.9%
9 2
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
98.0%
Other Punctuation 1
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
20.0%
3 9
18.0%
7 6
12.0%
2 6
12.0%
8 5
10.0%
6 4
 
8.0%
0 3
 
6.0%
5 3
 
6.0%
4 2
 
4.0%
9 2
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
19.6%
3 9
17.6%
7 6
11.8%
2 6
11.8%
8 5
9.8%
6 4
 
7.8%
0 3
 
5.9%
5 3
 
5.9%
4 2
 
3.9%
9 2
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
19.6%
3 9
17.6%
7 6
11.8%
2 6
11.8%
8 5
9.8%
6 4
 
7.8%
0 3
 
5.9%
5 3
 
5.9%
4 2
 
3.9%
9 2
 
3.9%

계양구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.521739
Minimum0
Maximum935
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:37:08.020482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median19
Q363
95-th percentile556
Maximum935
Range935
Interquartile range (IQR)56

Descriptive statistics

Standard deviation221.04254
Coefficient of variation (CV)2.3140549
Kurtosis10.676547
Mean95.521739
Median Absolute Deviation (MAD)15
Skewness3.2942872
Sum2197
Variance48859.806
MonotonicityNot monotonic
2023-12-16T15:37:08.581498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 2
 
8.7%
60 1
 
4.3%
5 1
 
4.3%
69 1
 
4.3%
115 1
 
4.3%
935 1
 
4.3%
10 1
 
4.3%
16 1
 
4.3%
4 1
 
4.3%
0 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 1
4.3%
3 2
8.7%
4 1
4.3%
5 1
4.3%
6 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
16 1
4.3%
17 1
4.3%
ValueCountFrequency (%)
935 1
4.3%
605 1
4.3%
115 1
4.3%
108 1
4.3%
69 1
4.3%
65 1
4.3%
61 1
4.3%
60 1
4.3%
28 1
4.3%
26 1
4.3%

서구
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-16T15:37:09.079667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2173913
Min length1

Characters and Unicode

Total characters51
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st row74
2nd row4
3rd row28
4th row102
5th row185
ValueCountFrequency (%)
5 2
 
8.7%
74 1
 
4.3%
13 1
 
4.3%
180 1
 
4.3%
1,871 1
 
4.3%
20 1
 
4.3%
40 1
 
4.3%
2 1
 
4.3%
3 1
 
4.3%
73 1
 
4.3%
Other values (12) 12
52.2%
2023-12-16T15:37:10.424684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
23.5%
2 7
13.7%
4 6
11.8%
7 5
9.8%
0 5
9.8%
3 5
9.8%
5 4
 
7.8%
8 4
 
7.8%
9 2
 
3.9%
, 1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
98.0%
Other Punctuation 1
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
24.0%
2 7
14.0%
4 6
12.0%
7 5
10.0%
0 5
10.0%
3 5
10.0%
5 4
 
8.0%
8 4
 
8.0%
9 2
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
23.5%
2 7
13.7%
4 6
11.8%
7 5
9.8%
0 5
9.8%
3 5
9.8%
5 4
 
7.8%
8 4
 
7.8%
9 2
 
3.9%
, 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
23.5%
2 7
13.7%
4 6
11.8%
7 5
9.8%
0 5
9.8%
3 5
9.8%
5 4
 
7.8%
8 4
 
7.8%
9 2
 
3.9%
, 1
 
2.0%

Interactions

2023-12-16T15:36:47.630703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:36.598906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:39.489520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:42.586289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:45.019186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:48.076802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:37.129868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:40.195524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:43.322678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:46.010076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:48.505208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:37.651266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:41.266689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:43.786244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:46.606542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:48.885432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:38.145881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:41.565219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:44.225019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:47.003607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:49.366625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:38.490697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:42.331764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:44.596325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:36:47.308466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:37:10.780653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분강화군옹진군중구동구미추홀구연수구남동구부평구계양구서구
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
강화군1.0001.0000.8900.9100.8841.0000.7181.0001.0000.9641.000
옹진군1.0000.8901.0000.9790.9661.0000.0001.0001.0000.5231.000
중구1.0000.9100.9791.0000.9591.0000.0001.0001.0000.7171.000
동구1.0000.8840.9660.9591.0001.0001.0001.0001.0000.7171.000
미추홀구1.0001.0001.0001.0001.0001.0000.9671.0001.0001.0000.953
연수구1.0000.7180.0000.0001.0000.9671.0000.9531.0001.0000.974
남동구1.0001.0001.0001.0001.0001.0000.9531.0001.0001.0000.985
부평구1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계양구1.0000.9640.5230.7170.7171.0001.0001.0001.0001.0001.000
서구1.0001.0001.0001.0001.0000.9530.9740.9851.0001.0001.000
2023-12-16T15:37:11.398043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강화군옹진군중구동구계양구
강화군1.0000.8470.8740.7740.506
옹진군0.8471.0000.8980.7500.424
중구0.8740.8981.0000.7890.660
동구0.7740.7500.7891.0000.596
계양구0.5060.4240.6600.5961.000

Missing values

2023-12-16T15:36:49.912922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:36:50.769254image/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

구분강화군옹진군중구동구미추홀구연수구남동구부평구계양구서구
0숙박업(일반)78531834417564781606074
1숙박업(생활)651311301121471734
2목욕장업154256352234231928
3이용업34532271236511512661102
4미용업821482841441362693316185
5일반미용업46817649791476844734605954
6피부미용업2615410141180225202108224
7네일미용업101381113410216217265173
8일반미용업, 피부미용업303141415685
9일반미용업, 네일미용업2040135179911
구분강화군옹진군중구동구미추홀구연수구남동구부평구계양구서구
13피부미용업, 화장ㆍ분장 미용업0171142022281741
14네일미용업, 화장ㆍ분장 미용업00120463150712573
15일반미용업, 피부미용업, 네일미용업0000111103
16일반미용업, 피부미용업, 화장ㆍ분장 미용업0001121432
17일반미용업, 네일미용업, 화장ㆍ분장 미용업20438128545
18피부미용업, 네일미용업, 화장ㆍ분장 미용업01101172118381640
19종합미용업941814566061301020
20미용업(총계)185304361801,4751,1361,7971,7399351,871
21세탁업2254228204122181183115180
22건물위생관리업24105532114621708869117