Overview

Dataset statistics

Number of variables11
Number of observations131
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory94.0 B

Variable types

Categorical4
Text3
Numeric4

Dataset

Description부산광역시 강서구 동별 대표 음식점의 물가정보 (업소명, 주소, 물품명, 가격, 변동액, 등) 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/3045826/fileData.do

Alerts

전년말 is highly overall correlated with 전월 and 2 other fieldsHigh correlation
전월 is highly overall correlated with 전년말 and 2 other fieldsHigh correlation
현재 is highly overall correlated with 전년말 and 2 other fieldsHigh correlation
업종 is highly overall correlated with 품명High correlation
품명 is highly overall correlated with 전년말 and 4 other fieldsHigh correlation
전월대비 is highly overall correlated with 품명High correlation
전월대비 is highly imbalanced (93.5%)Imbalance
전년말대비 has 37 (28.2%) zerosZeros

Reproduction

Analysis started2023-12-12 19:42:50.295013
Analysis finished2023-12-12 19:42:52.550633
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동
Categorical

Distinct7
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
대저1동
43 
대저2동
28 
명지동
26 
녹산동
17 
강동동
12 
Other values (2)
5 

Length

Max length4
Median length4
Mean length3.5725191
Min length3

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row가덕도동
2nd row가덕도동
3rd row가덕도동
4th row가덕도동
5th row가락동

Common Values

ValueCountFrequency (%)
대저1동 43
32.8%
대저2동 28
21.4%
명지동 26
19.8%
녹산동 17
 
13.0%
강동동 12
 
9.2%
가덕도동 4
 
3.1%
가락동 1
 
0.8%

Length

2023-12-13T04:42:52.611619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:42:52.706762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대저1동 43
32.8%
대저2동 28
21.4%
명지동 26
19.8%
녹산동 17
 
13.0%
강동동 12
 
9.2%
가덕도동 4
 
3.1%
가락동 1
 
0.8%

업종
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
한식
44 
중식
41 
미용
12 
튀김닭
5 
세탁
5 
Other values (10)
24 

Length

Max length3
Median length2
Mean length2.0916031
Min length2

Unique

Unique3 ?
Unique (%)2.3%

Sample

1st row미용
2nd row중식
3rd row중식
4th row중식
5th row미용

Common Values

ValueCountFrequency (%)
한식 44
33.6%
중식 41
31.3%
미용 12
 
9.2%
튀김닭 5
 
3.8%
세탁 5
 
3.8%
이용 5
 
3.8%
노래방 4
 
3.1%
분식 4
 
3.1%
다방 2
 
1.5%
목욕 2
 
1.5%
Other values (5) 7
 
5.3%

Length

2023-12-13T04:42:52.808159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 44
33.6%
중식 41
31.3%
미용 12
 
9.2%
튀김닭 5
 
3.8%
세탁 5
 
3.8%
이용 5
 
3.8%
노래방 4
 
3.1%
분식 4
 
3.1%
다방 2
 
1.5%
목욕 2
 
1.5%
Other values (5) 7
 
5.3%

업소명
Text

Distinct87
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T04:42:53.063689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.5114504
Min length2

Characters and Unicode

Total characters591
Distinct characters177
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)46.6%

Sample

1st row사라미용실
2nd row장춘반점
3rd row장춘반점
4th row장춘반점
5th row태화미용실
ValueCountFrequency (%)
화성관 4
 
3.0%
제일식당 4
 
3.0%
장원성 3
 
2.3%
진시황 3
 
2.3%
삼성반점 3
 
2.3%
신흥각 3
 
2.3%
옥류관 3
 
2.3%
백가네 3
 
2.3%
수목정갈비 3
 
2.3%
장춘반점 3
 
2.3%
Other values (79) 101
75.9%
2023-12-13T04:42:53.451780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 17
 
2.9%
반 14
 
2.4%
수 14
 
2.4%
원 14
 
2.4%
정 14
 
2.4%
점 14
 
2.4%
용 13
 
2.2%
식 13
 
2.2%
장 12
 
2.0%
당 12
 
2.0%
Other values (167) 454
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 585
99.0%
Space Separator 2
 
0.3%
Uppercase Letter 2
 
0.3%
Other Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 17
 
2.9%
반 14
 
2.4%
수 14
 
2.4%
원 14
 
2.4%
정 14
 
2.4%
점 14
 
2.4%
용 13
 
2.2%
식 13
 
2.2%
장 12
 
2.1%
당 12
 
2.1%
Other values (162) 448
76.6%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Decimal Number
ValueCountFrequency (%)
7 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 585
99.0%
Common 4
 
0.7%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 17
 
2.9%
반 14
 
2.4%
수 14
 
2.4%
원 14
 
2.4%
정 14
 
2.4%
점 14
 
2.4%
용 13
 
2.2%
식 13
 
2.2%
장 12
 
2.1%
당 12
 
2.1%
Other values (162) 448
76.6%
Common
ValueCountFrequency (%)
2
50.0%
& 1
25.0%
7 1
25.0%
Latin
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 585
99.0%
ASCII 6
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 17
 
2.9%
반 14
 
2.4%
수 14
 
2.4%
원 14
 
2.4%
정 14
 
2.4%
점 14
 
2.4%
용 13
 
2.2%
식 13
 
2.2%
장 12
 
2.1%
당 12
 
2.1%
Other values (162) 448
76.6%
ASCII
ValueCountFrequency (%)
2
33.3%
C 1
16.7%
& 1
16.7%
P 1
16.7%
7 1
16.7%

주소
Text

Distinct85
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T04:42:53.773726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length10.557252
Min length4

Characters and Unicode

Total characters1383
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)42.0%

Sample

1st row성북동 346
2nd row동선길 129
3rd row동선길 129
4th row동선길 129
5th row가락대로 1432-11
ValueCountFrequency (%)
대저로 12
 
4.4%
공항로811번길 11
 
4.1%
제도로 8
 
3.0%
12 7
 
2.6%
대저로274번길 7
 
2.6%
16 6
 
2.2%
공항로1309번길 6
 
2.2%
47 6
 
2.2%
6 5
 
1.8%
공항앞길 5
 
1.8%
Other values (108) 198
73.1%
2023-12-13T04:42:54.245342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
156
 
11.3%
1 127
 
9.2%
로 94
 
6.8%
길 88
 
6.4%
2 86
 
6.2%
번 63
 
4.6%
4 55
 
4.0%
7 49
 
3.5%
3 48
 
3.5%
대 41
 
3.0%
Other values (51) 576
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 672
48.6%
Decimal Number 537
38.8%
Space Separator 156
 
11.3%
Dash Punctuation 13
 
0.9%
Other Punctuation 4
 
0.3%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 94
14.0%
길 88
13.1%
번 63
 
9.4%
대 41
 
6.1%
저 40
 
6.0%
동 33
 
4.9%
공 28
 
4.2%
항 28
 
4.2%
지 26
 
3.9%
명 22
 
3.3%
Other values (37) 209
31.1%
Decimal Number
ValueCountFrequency (%)
1 127
23.6%
2 86
16.0%
4 55
10.2%
7 49
 
9.1%
3 48
 
8.9%
8 37
 
6.9%
6 35
 
6.5%
9 34
 
6.3%
5 33
 
6.1%
0 33
 
6.1%
Space Separator
ValueCountFrequency (%)
156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 711
51.4%
Hangul 672
48.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 94
14.0%
길 88
13.1%
번 63
 
9.4%
대 41
 
6.1%
저 40
 
6.0%
동 33
 
4.9%
공 28
 
4.2%
항 28
 
4.2%
지 26
 
3.9%
명 22
 
3.3%
Other values (37) 209
31.1%
Common
ValueCountFrequency (%)
156
21.9%
1 127
17.9%
2 86
12.1%
4 55
 
7.7%
7 49
 
6.9%
3 48
 
6.8%
8 37
 
5.2%
6 35
 
4.9%
9 34
 
4.8%
5 33
 
4.6%
Other values (4) 51
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 711
51.4%
Hangul 672
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156
21.9%
1 127
17.9%
2 86
12.1%
4 55
 
7.7%
7 49
 
6.9%
3 48
 
6.8%
8 37
 
5.2%
6 35
 
4.9%
9 34
 
4.8%
5 33
 
4.6%
Other values (4) 51
 
7.2%
Hangul
ValueCountFrequency (%)
로 94
14.0%
길 88
13.1%
번 63
 
9.4%
대 41
 
6.1%
저 40
 
6.0%
동 33
 
4.9%
공 28
 
4.2%
항 28
 
4.2%
지 26
 
3.9%
명 22
 
3.3%
Other values (37) 209
31.1%

전화번호
Text

Distinct88
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T04:42:54.523819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.007634
Min length12

Characters and Unicode

Total characters1573
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

Unique62 ?
Unique (%)47.3%

Sample

1st row051-972-1819
2nd row051-972-2250
3rd row051-972-2250
4th row051-972-2250
5th row051-972-3943
ValueCountFrequency (%)
051-971-0815 4
 
3.1%
051-971-7601 4
 
3.1%
051-972-1646 3
 
2.3%
051-973-4773 3
 
2.3%
051-973-0765 3
 
2.3%
051-832-0030 3
 
2.3%
051-971-3800 3
 
2.3%
051-972-2250 3
 
2.3%
051-973-1593 3
 
2.3%
051-971-5391 3
 
2.3%
Other values (78) 99
75.6%
2023-12-13T04:42:55.046930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 262
16.7%
1 243
15.4%
0 218
13.9%
5 171
10.9%
7 154
9.8%
9 137
8.7%
3 107
6.8%
2 107
6.8%
6 62
 
3.9%
4 60
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1311
83.3%
Dash Punctuation 262
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 243
18.5%
0 218
16.6%
5 171
13.0%
7 154
11.7%
9 137
10.5%
3 107
8.2%
2 107
8.2%
6 62
 
4.7%
4 60
 
4.6%
8 52
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 262
16.7%
1 243
15.4%
0 218
13.9%
5 171
10.9%
7 154
9.8%
9 137
8.7%
3 107
6.8%
2 107
6.8%
6 62
 
3.9%
4 60
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 262
16.7%
1 243
15.4%
0 218
13.9%
5 171
10.9%
7 154
9.8%
9 137
8.7%
3 107
6.8%
2 107
6.8%
6 62
 
3.9%
4 60
 
3.8%

품명
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
자장면
16 
짬뽕
13 
미용료
12 
탕수육
11 
김치찌개
 
7
Other values (34)
72 

Length

Max length7
Median length3
Mean length3.2442748
Min length2

Unique

Unique16 ?
Unique (%)12.2%

Sample

1st row미용료
2nd row자장면
3rd row짬뽕
4th row탕수육
5th row미용료

Common Values

ValueCountFrequency (%)
자장면 16
 
12.2%
짬뽕 13
 
9.9%
미용료 12
 
9.2%
탕수육 11
 
8.4%
김치찌개 7
 
5.3%
된장찌개 6
 
4.6%
치킨 5
 
3.8%
이용료 5
 
3.8%
세탁료 4
 
3.1%
노래방이용료 4
 
3.1%
Other values (29) 48
36.6%

Length

2023-12-13T04:42:55.214192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자장면 16
 
12.2%
짬뽕 13
 
9.9%
미용료 12
 
9.2%
탕수육 11
 
8.4%
김치찌개 7
 
5.3%
된장찌개 6
 
4.6%
치킨 5
 
3.8%
이용료 5
 
3.8%
칼국수 4
 
3.1%
노래방이용료 4
 
3.1%
Other values (29) 48
36.6%

전년말
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8043.5115
Minimum300
Maximum25000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T04:42:55.368207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile3000
Q15000
median6000
Q39500
95-th percentile18000
Maximum25000
Range24700
Interquartile range (IQR)4500

Descriptive statistics

Standard deviation4996.7389
Coefficient of variation (CV)0.62121363
Kurtosis1.3670031
Mean8043.5115
Median Absolute Deviation (MAD)1500
Skewness1.4311132
Sum1053700
Variance24967400
MonotonicityNot monotonic
2023-12-13T04:42:55.557055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
6000 29
22.1%
5000 20
15.3%
7000 10
 
7.6%
8000 8
 
6.1%
10000 8
 
6.1%
18000 7
 
5.3%
4500 7
 
5.3%
17000 5
 
3.8%
4000 5
 
3.8%
5500 4
 
3.1%
Other values (18) 28
21.4%
ValueCountFrequency (%)
300 1
 
0.8%
1400 1
 
0.8%
2000 1
 
0.8%
2200 1
 
0.8%
2500 1
 
0.8%
3000 4
 
3.1%
3500 2
 
1.5%
4000 5
 
3.8%
4500 7
 
5.3%
5000 20
15.3%
ValueCountFrequency (%)
25000 2
 
1.5%
20000 1
 
0.8%
19900 1
 
0.8%
18000 7
5.3%
17000 5
3.8%
16000 2
 
1.5%
15000 4
3.1%
14000 1
 
0.8%
13000 1
 
0.8%
12000 1
 
0.8%

전월
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9352.6718
Minimum500
Maximum28000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T04:42:55.707688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3250
Q16000
median8000
Q310000
95-th percentile20000
Maximum28000
Range27500
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation5157.8071
Coefficient of variation (CV)0.55147953
Kurtosis1.4479573
Mean9352.6718
Median Absolute Deviation (MAD)2000
Skewness1.2813253
Sum1225200
Variance26602974
MonotonicityNot monotonic
2023-12-13T04:42:55.844227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8000 18
13.7%
7000 18
13.7%
10000 17
13.0%
6000 15
11.5%
5000 9
 
6.9%
20000 5
 
3.8%
18000 5
 
3.8%
17000 4
 
3.1%
15000 4
 
3.1%
5500 4
 
3.1%
Other values (17) 32
24.4%
ValueCountFrequency (%)
500 1
 
0.8%
1400 1
 
0.8%
2500 3
 
2.3%
3000 2
 
1.5%
3500 1
 
0.8%
4000 1
 
0.8%
4500 3
 
2.3%
5000 9
6.9%
5500 4
 
3.1%
6000 15
11.5%
ValueCountFrequency (%)
28000 1
 
0.8%
25000 2
 
1.5%
20000 5
3.8%
19900 1
 
0.8%
18000 5
3.8%
17000 4
3.1%
16000 1
 
0.8%
15000 4
3.1%
14000 1
 
0.8%
13000 4
3.1%

현재
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9356.4885
Minimum500
Maximum28000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T04:42:55.971293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile3250
Q16000
median8000
Q310000
95-th percentile20000
Maximum28000
Range27500
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation5155.8645
Coefficient of variation (CV)0.55104695
Kurtosis1.4504497
Mean9356.4885
Median Absolute Deviation (MAD)2000
Skewness1.2810998
Sum1225700
Variance26582938
MonotonicityNot monotonic
2023-12-13T04:42:56.127002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7000 19
14.5%
8000 18
13.7%
10000 17
13.0%
6000 15
11.5%
5000 9
 
6.9%
18000 5
 
3.8%
20000 5
 
3.8%
12000 4
 
3.1%
17000 4
 
3.1%
15000 4
 
3.1%
Other values (17) 31
23.7%
ValueCountFrequency (%)
500 1
 
0.8%
1400 1
 
0.8%
2500 3
 
2.3%
3000 2
 
1.5%
3500 1
 
0.8%
4000 1
 
0.8%
4500 3
 
2.3%
5000 9
6.9%
5500 4
 
3.1%
6000 15
11.5%
ValueCountFrequency (%)
28000 1
 
0.8%
25000 2
 
1.5%
20000 5
3.8%
19900 1
 
0.8%
18000 5
3.8%
17000 4
3.1%
16000 1
 
0.8%
15000 4
3.1%
14000 1
 
0.8%
13000 4
3.1%

전년말대비
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.894656
Minimum-16.7
Maximum66.7
Zeros37
Zeros (%)28.2%
Negative1
Negative (%)0.8%
Memory size1.3 KiB
2023-12-13T04:42:56.279830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-16.7
5-th percentile0
Q10
median16.7
Q333.3
95-th percentile66.7
Maximum66.7
Range83.4
Interquartile range (IQR)33.3

Descriptive statistics

Standard deviation20.824517
Coefficient of variation (CV)0.99664319
Kurtosis-0.25378717
Mean20.894656
Median Absolute Deviation (MAD)16.6
Skewness0.82894961
Sum2737.2
Variance433.66051
MonotonicityNot monotonic
2023-12-13T04:42:56.436317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 37
28.2%
66.7 10
 
7.6%
16.7 10
 
7.6%
33.3 10
 
7.6%
50.0 9
 
6.9%
11.1 9
 
6.9%
20.0 7
 
5.3%
14.3 5
 
3.8%
40.0 4
 
3.1%
30.0 4
 
3.1%
Other values (17) 26
19.8%
ValueCountFrequency (%)
-16.7 1
 
0.8%
0.0 37
28.2%
5.9 1
 
0.8%
8.3 1
 
0.8%
9.1 2
 
1.5%
10.0 3
 
2.3%
11.1 9
 
6.9%
12.0 1
 
0.8%
12.5 2
 
1.5%
13.3 1
 
0.8%
ValueCountFrequency (%)
66.7 10
7.6%
60.0 2
 
1.5%
50.0 9
6.9%
42.9 2
 
1.5%
40.0 4
 
3.1%
33.3 10
7.6%
30.0 4
 
3.1%
28.6 1
 
0.8%
27.3 2
 
1.5%
25.0 3
 
2.3%

전월대비
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0.0
130 
7.7
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 130
99.2%
7.7 1
 
0.8%

Length

2023-12-13T04:42:56.609487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:42:56.755642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 130
99.2%
7.7 1
 
0.8%

Interactions

2023-12-13T04:42:52.060133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.163274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.491343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.775779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:52.124252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.248383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.565783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.850298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:52.189676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.329348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.635018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.925890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:52.261012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.412173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.704355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:42:51.996068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:42:56.847958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동업종업소명주소전화번호품명전년말전월현재전년말대비전월대비
법정동1.0000.2431.0001.0001.0000.0000.0880.1330.1330.3950.073
업종0.2431.0001.0000.9931.0000.9980.7050.7300.7300.5700.000
업소명1.0001.0001.0001.0001.0000.9080.8070.0000.0000.8100.000
주소1.0000.9931.0001.0001.0000.8310.8090.0000.0000.7040.000
전화번호1.0001.0001.0001.0001.0000.8940.8020.0000.0000.8090.000
품명0.0000.9980.9080.8310.8941.0000.9110.9110.9110.4861.000
전년말0.0880.7050.8070.8090.8020.9111.0000.8890.8890.1890.000
전월0.1330.7300.0000.0000.0000.9110.8891.0001.0000.4980.000
현재0.1330.7300.0000.0000.0000.9110.8891.0001.0000.4980.000
전년말대비0.3950.5700.8100.7040.8090.4860.1890.4980.4981.0000.000
전월대비0.0730.0000.0000.0000.0001.0000.0000.0000.0000.0001.000
2023-12-13T04:42:57.033173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품명법정동업종전월대비
품명1.0000.0000.8660.844
법정동0.0001.0000.1050.074
업종0.8660.1051.0000.000
전월대비0.8440.0740.0001.000
2023-12-13T04:42:57.194767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전년말전월현재전년말대비법정동업종품명전월대비
전년말1.0000.9210.921-0.3350.0000.3710.5480.000
전월0.9211.0001.000-0.0180.0660.3550.5350.000
현재0.9211.0001.000-0.0180.0660.3550.5350.000
전년말대비-0.335-0.018-0.0181.0000.2140.2770.1640.000
법정동0.0000.0660.0660.2141.0000.1050.0000.074
업종0.3710.3550.3550.2770.1051.0000.8660.000
품명0.5480.5350.5350.1640.0000.8661.0000.844
전월대비0.0000.0000.0000.0000.0740.0000.8441.000

Missing values

2023-12-13T04:42:52.366399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:42:52.490736image/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

법정동업종업소명주소전화번호품명전년말전월현재전년말대비전월대비
0가덕도동미용사라미용실성북동 346051-972-1819미용료6000100001000066.70.0
1가덕도동중식장춘반점동선길 129051-972-2250자장면45006000600033.30.0
2가덕도동중식장춘반점동선길 129051-972-2250짬뽕60007000700016.70.0
3가덕도동중식장춘반점동선길 129051-972-2250탕수육1800018000180000.00.0
4가락동미용태화미용실가락대로 1432-11051-972-3943미용료6000100001000066.70.0
5강동동미용육일머리방낙동북로 59051-972-0465미용료600050005000-16.70.0
6강동동중식대성루제도로 1199051-971-8686자장면4500450045000.00.0
7강동동중식대성루제도로 1199051-971-8686짬뽕5000500050000.00.0
8강동동중식대성루제도로 1199051-971-8686탕수육1700017000170000.00.0
9강동동중식오복반점낙동북로 21051-972-8741자장면45005000500011.10.0
법정동업종업소명주소전화번호품명전년말전월현재전년말대비전월대비
121명지동한식명지돼지국밥명지오션시티8로6번길 13051-271-4235돼지국밥60007000700016.70.0
122명지동한식명지숯불갈비명지새동네14번길 16051-271-1890된장찌개60007000700016.70.0
123명지동한식서울깍두기명지새동네길7번길 18051-271-0328설렁탕7000700070000.00.0
124명지동한식수미가칼국수영강길 157051-271-3352칼국수60008000800033.30.0
125명지동한식신원두막식당새진목길18번길 12051-271-1336김치찌개5000500050000.00.0
126명지동한식신원두막식당새진목길18번길 12051-271-1336동태찌개5000500050000.00.0
127명지동한식이조김밥명지오션시티2로 71051-271-0203김밥2500250025000.00.0
128명지동한식이조냉면명지새동네길14번길 3051-271-0838냉면8000800080000.00.0
129명지동한식평창면옥명지새동네길13번길 3051-271-1224칼국수5000500050000.00.0
130명지동한식한성규돈까스&육개장명지오션시티10로 126051-271-1399돈가스7000700070000.00.0