Overview

Dataset statistics

Number of variables20
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory177.9 B

Variable types

Numeric15
Text2
Categorical2
DateTime1

Dataset

Description대전광역시 중구 개인서비스(설렁탕,냉면,비빔밥,갈비탕,삼계탕,커피,라면,김밥 등) 가격동향 정보 입니다.
Author대전광역시 중구
URLhttps://www.data.go.kr/data/15008335/fileData.do

Alerts

관리기관 has constant value ""Constant
연락처 has constant value ""Constant
데이터기준일자 has constant value ""Constant
은행선화동 is highly overall correlated with 대흥동 and 3 other fieldsHigh correlation
중촌동 is highly overall correlated with 석교동 and 2 other fieldsHigh correlation
대흥동 is highly overall correlated with 은행선화동 and 1 other fieldsHigh correlation
문창동 is highly overall correlated with 대사동 and 1 other fieldsHigh correlation
석교동 is highly overall correlated with 중촌동 and 2 other fieldsHigh correlation
대사동 is highly overall correlated with 중촌동 and 2 other fieldsHigh correlation
부사동 is highly overall correlated with 용두동High correlation
용두동 is highly overall correlated with 은행선화동 and 7 other fieldsHigh correlation
오류동 is highly overall correlated with 용두동 and 4 other fieldsHigh correlation
태평동 is highly overall correlated with 오류동 and 2 other fieldsHigh correlation
유천동 is highly overall correlated with 은행선화동 and 6 other fieldsHigh correlation
문화동 is highly overall correlated with 오류동 and 2 other fieldsHigh correlation
산성동 is highly overall correlated with 은행선화동 and 5 other fieldsHigh correlation
연번 has unique valuesUnique
품목 has unique valuesUnique
은행선화동 has 7 (15.2%) zerosZeros
목동 has 33 (71.7%) zerosZeros
중촌동 has 24 (52.2%) zerosZeros
대흥동 has 17 (37.0%) zerosZeros
문창동 has 24 (52.2%) zerosZeros
석교동 has 20 (43.5%) zerosZeros
대사동 has 24 (52.2%) zerosZeros
부사동 has 13 (28.3%) zerosZeros
용두동 has 21 (45.7%) zerosZeros
오류동 has 15 (32.6%) zerosZeros
태평동 has 11 (23.9%) zerosZeros
유천동 has 10 (21.7%) zerosZeros
문화동 has 15 (32.6%) zerosZeros
산성동 has 9 (19.6%) zerosZeros

Reproduction

Analysis started2023-12-12 05:59:49.489017
Analysis finished2023-12-12 06:00:12.897652
Duration23.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:12.976894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q112.25
median23.5
Q334.75
95-th percentile43.75
Maximum46
Range45
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.422618
Coefficient of variation (CV)0.57117522
Kurtosis-1.2
Mean23.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1081
Variance180.16667
MonotonicityStrictly increasing
2023-12-12T15:00:13.135152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 1
 
2.2%
36 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
34 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
46 1
2.2%
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%

품목
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-12T15:00:13.398085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7.5
Mean length4.3913043
Min length2

Characters and Unicode

Total characters202
Distinct characters102
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

Unique46 ?
Unique (%)100.0%

Sample

1st row설렁탕
2nd row냉 면
3rd row비빔밥
4th row갈비탕
5th row삼계탕
ValueCountFrequency (%)
2
 
3.9%
설렁탕 1
 
2.0%
영화관람료 1
 
2.0%
1
 
2.0%
의복수선료 1
 
2.0%
공동주택관리비 1
 
2.0%
택배이용료 1
 
2.0%
수영장이용료 1
 
2.0%
볼링장이용료 1
 
2.0%
골프연습장이용료 1
 
2.0%
Other values (40) 40
78.4%
2023-12-12T15:00:13.799181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
9.9%
13
 
6.4%
11
 
5.4%
10
 
5.0%
6
 
3.0%
4
 
2.0%
4
 
2.0%
4
 
2.0%
4
 
2.0%
3
 
1.5%
Other values (92) 123
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
90.6%
Space Separator 13
 
6.4%
Open Punctuation 2
 
1.0%
Close Punctuation 2
 
1.0%
Uppercase Letter 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
20
 
10.9%
11
 
6.0%
10
 
5.5%
6
 
3.3%
4
 
2.2%
4
 
2.2%
4
 
2.2%
4
 
2.2%
3
 
1.6%
3
 
1.6%
Other values (87) 114
62.3%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
90.6%
Common 17
 
8.4%
Latin 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
20
 
10.9%
11
 
6.0%
10
 
5.5%
6
 
3.3%
4
 
2.2%
4
 
2.2%
4
 
2.2%
4
 
2.2%
3
 
1.6%
3
 
1.6%
Other values (87) 114
62.3%
Common
ValueCountFrequency (%)
13
76.5%
( 2
 
11.8%
) 2
 
11.8%
Latin
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
90.6%
ASCII 19
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
20
 
10.9%
11
 
6.0%
10
 
5.5%
6
 
3.3%
4
 
2.2%
4
 
2.2%
4
 
2.2%
4
 
2.2%
3
 
1.6%
3
 
1.6%
Other values (87) 114
62.3%
ASCII
ValueCountFrequency (%)
13
68.4%
( 2
 
10.5%
) 2
 
10.5%
C 1
 
5.3%
P 1
 
5.3%
Distinct33
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-12T15:00:14.059032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.3695652
Min length2

Characters and Unicode

Total characters431
Distinct characters130
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)63.0%

Sample

1st row1그릇(대중식당)
2nd row1그릇(대중식당)
3rd row1그릇(대중식당)
4th row1그릇(대중식당)
5th row1그릇(대중식당)
ValueCountFrequency (%)
1그릇(대중식당 7
 
13.7%
1인분(200g 4
 
7.8%
1그릇(중국집 3
 
5.9%
1그릇(분식집 3
 
5.9%
성인1회 1
 
2.0%
1시간(쿠션당구대일반 1
 
2.0%
성인1회(대중목용탕 1
 
2.0%
컷트 1
 
2.0%
1회(성인중급 1
 
2.0%
25평1일1박 1
 
2.0%
Other values (28) 28
54.9%
2023-12-12T15:00:14.481013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 44
 
10.2%
) 38
 
8.8%
( 38
 
8.8%
13
 
3.0%
13
 
3.0%
0 13
 
3.0%
13
 
3.0%
12
 
2.8%
12
 
2.8%
10
 
2.3%
Other values (120) 225
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 263
61.0%
Decimal Number 74
 
17.2%
Close Punctuation 38
 
8.8%
Open Punctuation 38
 
8.8%
Lowercase Letter 9
 
2.1%
Space Separator 5
 
1.2%
Math Symbol 2
 
0.5%
Other Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13
 
4.9%
13
 
4.9%
13
 
4.9%
12
 
4.6%
12
 
4.6%
10
 
3.8%
9
 
3.4%
8
 
3.0%
8
 
3.0%
8
 
3.0%
Other values (103) 157
59.7%
Decimal Number
ValueCountFrequency (%)
1 44
59.5%
0 13
 
17.6%
2 9
 
12.2%
5 4
 
5.4%
3 2
 
2.7%
4 1
 
1.4%
9 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
g 5
55.6%
c 2
 
22.2%
x 1
 
11.1%
k 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
% 1
50.0%
, 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 263
61.0%
Common 159
36.9%
Latin 9
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13
 
4.9%
13
 
4.9%
13
 
4.9%
12
 
4.6%
12
 
4.6%
10
 
3.8%
9
 
3.4%
8
 
3.0%
8
 
3.0%
8
 
3.0%
Other values (103) 157
59.7%
Common
ValueCountFrequency (%)
1 44
27.7%
) 38
23.9%
( 38
23.9%
0 13
 
8.2%
2 9
 
5.7%
5
 
3.1%
5 4
 
2.5%
+ 2
 
1.3%
3 2
 
1.3%
% 1
 
0.6%
Other values (3) 3
 
1.9%
Latin
ValueCountFrequency (%)
g 5
55.6%
c 2
 
22.2%
x 1
 
11.1%
k 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 263
61.0%
ASCII 168
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44
26.2%
) 38
22.6%
( 38
22.6%
0 13
 
7.7%
2 9
 
5.4%
g 5
 
3.0%
5
 
3.0%
5 4
 
2.4%
c 2
 
1.2%
+ 2
 
1.2%
Other values (7) 8
 
4.8%
Hangul
ValueCountFrequency (%)
13
 
4.9%
13
 
4.9%
13
 
4.9%
12
 
4.6%
12
 
4.6%
10
 
3.8%
9
 
3.4%
8
 
3.0%
8
 
3.0%
8
 
3.0%
Other values (103) 157
59.7%

은행선화동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11116.783
Minimum0
Maximum118320
Zeros7
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:14.663112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13307.25
median6984.5
Q312875
95-th percentile22875.25
Maximum118320
Range118320
Interquartile range (IQR)9567.75

Descriptive statistics

Standard deviation19308.471
Coefficient of variation (CV)1.7368758
Kurtosis22.801353
Mean11116.783
Median Absolute Deviation (MAD)4071
Skewness4.5137392
Sum511372
Variance3.7281705 × 108
MonotonicityNot monotonic
2023-12-12T15:00:14.809078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 7
 
15.2%
9000 1
 
2.2%
19000 1
 
2.2%
4333 1
 
2.2%
4100 1
 
2.2%
6222 1
 
2.2%
3143 1
 
2.2%
118320 1
 
2.2%
8000 1
 
2.2%
4300 1
 
2.2%
Other values (30) 30
65.2%
ValueCountFrequency (%)
0 7
15.2%
400 1
 
2.2%
1075 1
 
2.2%
2786 1
 
2.2%
3041 1
 
2.2%
3143 1
 
2.2%
3800 1
 
2.2%
4100 1
 
2.2%
4300 1
 
2.2%
4333 1
 
2.2%
ValueCountFrequency (%)
118320 1
2.2%
67500 1
2.2%
24167 1
2.2%
19000 1
2.2%
17500 1
2.2%
16175 1
2.2%
16100 1
2.2%
14450 1
2.2%
14408 1
2.2%
13750 1
2.2%

목동
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2739.1304
Minimum0
Maximum25000
Zeros33
Zeros (%)71.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:14.945154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32662.5
95-th percentile14574.75
Maximum25000
Range25000
Interquartile range (IQR)2662.5

Descriptive statistics

Standard deviation5663.2518
Coefficient of variation (CV)2.0675364
Kurtosis5.4379106
Mean2739.1304
Median Absolute Deviation (MAD)0
Skewness2.3716125
Sum126000
Variance32072420
MonotonicityNot monotonic
2023-12-12T15:00:15.102419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 33
71.7%
6500 1
 
2.2%
12000 1
 
2.2%
25000 1
 
2.2%
12900 1
 
2.2%
15133 1
 
2.2%
5667 1
 
2.2%
3500 1
 
2.2%
2250 1
 
2.2%
2800 1
 
2.2%
Other values (4) 4
 
8.7%
ValueCountFrequency (%)
0 33
71.7%
2250 1
 
2.2%
2800 1
 
2.2%
3250 1
 
2.2%
3500 1
 
2.2%
5667 1
 
2.2%
6500 1
 
2.2%
7333 1
 
2.2%
11667 1
 
2.2%
12000 1
 
2.2%
ValueCountFrequency (%)
25000 1
2.2%
18000 1
2.2%
15133 1
2.2%
12900 1
2.2%
12000 1
2.2%
11667 1
2.2%
7333 1
2.2%
6500 1
2.2%
5667 1
2.2%
3500 1
2.2%

중촌동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4225
Minimum0
Maximum30000
Zeros24
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:15.261291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37000
95-th percentile15249.75
Maximum30000
Range30000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation6343.8195
Coefficient of variation (CV)1.5014957
Kurtosis5.1910349
Mean4225
Median Absolute Deviation (MAD)0
Skewness2.0595419
Sum194350
Variance40244046
MonotonicityNot monotonic
2023-12-12T15:00:15.397670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 24
52.2%
7500 2
 
4.3%
10000 2
 
4.3%
6667 2
 
4.3%
7000 2
 
4.3%
3100 1
 
2.2%
9250 1
 
2.2%
1100 1
 
2.2%
30000 1
 
2.2%
3000 1
 
2.2%
Other values (9) 9
 
19.6%
ValueCountFrequency (%)
0 24
52.2%
1100 1
 
2.2%
2500 1
 
2.2%
2900 1
 
2.2%
3000 1
 
2.2%
3100 1
 
2.2%
4000 1
 
2.2%
5667 1
 
2.2%
6667 2
 
4.3%
7000 2
 
4.3%
ValueCountFrequency (%)
30000 1
2.2%
18833 1
2.2%
15333 1
2.2%
15000 1
2.2%
14000 1
2.2%
10000 2
4.3%
9250 1
2.2%
7500 2
4.3%
7333 1
2.2%
7000 2
4.3%

대흥동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9420.6739
Minimum0
Maximum120000
Zeros17
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:15.531393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4700
Q312000
95-th percentile25000.25
Maximum120000
Range120000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation18787.991
Coefficient of variation (CV)1.9943362
Kurtosis27.559345
Mean9420.6739
Median Absolute Deviation (MAD)4700
Skewness4.8343847
Sum433351
Variance3.5298862 × 108
MonotonicityNot monotonic
2023-12-12T15:00:15.691805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 17
37.0%
14500 2
 
4.3%
12000 2
 
4.3%
5500 1
 
2.2%
12667 1
 
2.2%
8000 1
 
2.2%
42500 1
 
2.2%
450 1
 
2.2%
20000 1
 
2.2%
1200 1
 
2.2%
Other values (18) 18
39.1%
ValueCountFrequency (%)
0 17
37.0%
450 1
 
2.2%
1200 1
 
2.2%
2500 1
 
2.2%
3000 1
 
2.2%
3500 1
 
2.2%
3900 1
 
2.2%
5500 1
 
2.2%
5750 1
 
2.2%
6750 1
 
2.2%
ValueCountFrequency (%)
120000 1
2.2%
42500 1
2.2%
26667 1
2.2%
20000 1
2.2%
18900 1
2.2%
16500 1
2.2%
16000 1
2.2%
15000 1
2.2%
14500 2
4.3%
12667 1
2.2%

문창동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4951.087
Minimum0
Maximum37500
Zeros24
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:15.818947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37500
95-th percentile13562.5
Maximum37500
Range37500
Interquartile range (IQR)7500

Descriptive statistics

Standard deviation7730.8221
Coefficient of variation (CV)1.5614394
Kurtosis7.8359119
Mean4951.087
Median Absolute Deviation (MAD)0
Skewness2.5256236
Sum227750
Variance59765610
MonotonicityNot monotonic
2023-12-12T15:00:15.967046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 24
52.2%
13000 4
 
8.7%
8000 3
 
6.5%
7000 3
 
6.5%
7500 2
 
4.3%
4000 2
 
4.3%
6000 2
 
4.3%
3000 2
 
4.3%
8500 1
 
2.2%
30000 1
 
2.2%
Other values (2) 2
 
4.3%
ValueCountFrequency (%)
0 24
52.2%
3000 2
 
4.3%
4000 2
 
4.3%
6000 2
 
4.3%
7000 3
 
6.5%
7500 2
 
4.3%
8000 3
 
6.5%
8500 1
 
2.2%
13000 4
 
8.7%
13750 1
 
2.2%
ValueCountFrequency (%)
37500 1
 
2.2%
30000 1
 
2.2%
13750 1
 
2.2%
13000 4
8.7%
8500 1
 
2.2%
8000 3
6.5%
7500 2
4.3%
7000 3
6.5%
6000 2
4.3%
4000 2
4.3%

석교동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8627.5435
Minimum0
Maximum170000
Zeros20
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:16.095913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3650
Q38000
95-th percentile18750
Maximum170000
Range170000
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation25103.707
Coefficient of variation (CV)2.9097167
Kurtosis40.162699
Mean8627.5435
Median Absolute Deviation (MAD)3650
Skewness6.1609589
Sum396867
Variance6.301961 × 108
MonotonicityNot monotonic
2023-12-12T15:00:16.228377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 20
43.5%
6000 4
 
8.7%
8000 3
 
6.5%
15000 2
 
4.3%
4000 2
 
4.3%
3800 1
 
2.2%
9000 1
 
2.2%
1000 1
 
2.2%
25000 1
 
2.2%
170000 1
 
2.2%
Other values (10) 10
21.7%
ValueCountFrequency (%)
0 20
43.5%
1000 1
 
2.2%
2250 1
 
2.2%
3500 1
 
2.2%
3800 1
 
2.2%
4000 2
 
4.3%
6000 4
 
8.7%
6750 1
 
2.2%
7000 1
 
2.2%
7500 1
 
2.2%
ValueCountFrequency (%)
170000 1
 
2.2%
25000 1
 
2.2%
19000 1
 
2.2%
18000 1
 
2.2%
15000 2
4.3%
13900 1
 
2.2%
13667 1
 
2.2%
10500 1
 
2.2%
9000 1
 
2.2%
8000 3
6.5%

대사동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4637.3043
Minimum0
Maximum35000
Zeros24
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:16.336285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37000
95-th percentile18000
Maximum35000
Range35000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation7085.7476
Coefficient of variation (CV)1.5279885
Kurtosis6.7461282
Mean4637.3043
Median Absolute Deviation (MAD)0
Skewness2.3037553
Sum213316
Variance50207819
MonotonicityNot monotonic
2023-12-12T15:00:16.483524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 24
52.2%
7000 3
 
6.5%
7333 2
 
4.3%
15000 2
 
4.3%
3150 1
 
2.2%
8000 1
 
2.2%
10000 1
 
2.2%
35000 1
 
2.2%
20000 1
 
2.2%
2667 1
 
2.2%
Other values (9) 9
 
19.6%
ValueCountFrequency (%)
0 24
52.2%
2667 1
 
2.2%
3000 1
 
2.2%
3150 1
 
2.2%
3500 1
 
2.2%
5000 1
 
2.2%
5750 1
 
2.2%
6000 1
 
2.2%
6333 1
 
2.2%
7000 3
 
6.5%
ValueCountFrequency (%)
35000 1
 
2.2%
20000 1
 
2.2%
19000 1
 
2.2%
15000 2
4.3%
13000 1
 
2.2%
10000 1
 
2.2%
8000 1
 
2.2%
7333 2
4.3%
7250 1
 
2.2%
7000 3
6.5%

부사동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7020.2826
Minimum0
Maximum40000
Zeros13
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:16.639953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5500
Q39750
95-th percentile19500
Maximum40000
Range40000
Interquartile range (IQR)9750

Descriptive statistics

Standard deviation8307.8731
Coefficient of variation (CV)1.1834101
Kurtosis5.1398156
Mean7020.2826
Median Absolute Deviation (MAD)5500
Skewness1.9913919
Sum322933
Variance69020755
MonotonicityNot monotonic
2023-12-12T15:00:16.827730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 13
28.3%
13000 4
 
8.7%
6500 3
 
6.5%
6000 2
 
4.3%
7000 2
 
4.3%
17000 2
 
4.3%
3000 2
 
4.3%
9000 1
 
2.2%
3800 1
 
2.2%
10000 1
 
2.2%
Other values (15) 15
32.6%
ValueCountFrequency (%)
0 13
28.3%
400 1
 
2.2%
1000 1
 
2.2%
2750 1
 
2.2%
3000 2
 
4.3%
3100 1
 
2.2%
3800 1
 
2.2%
4000 1
 
2.2%
4300 1
 
2.2%
5000 1
 
2.2%
ValueCountFrequency (%)
40000 1
 
2.2%
30000 1
 
2.2%
20000 1
 
2.2%
18000 1
 
2.2%
17000 2
4.3%
13000 4
8.7%
12000 1
 
2.2%
10000 1
 
2.2%
9000 1
 
2.2%
7750 1
 
2.2%

용두동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5634.0652
Minimum0
Maximum35000
Zeros21
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:16.970530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2125
Q38562.5
95-th percentile19375
Maximum35000
Range35000
Interquartile range (IQR)8562.5

Descriptive statistics

Standard deviation7770.2411
Coefficient of variation (CV)1.3791536
Kurtosis3.7442939
Mean5634.0652
Median Absolute Deviation (MAD)2125
Skewness1.8098878
Sum259167
Variance60376647
MonotonicityNot monotonic
2023-12-12T15:00:17.106545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 21
45.7%
7000 3
 
6.5%
9000 2
 
4.3%
6500 2
 
4.3%
2000 1
 
2.2%
12000 1
 
2.2%
10000 1
 
2.2%
35000 1
 
2.2%
1000 1
 
2.2%
20000 1
 
2.2%
Other values (12) 12
26.1%
ValueCountFrequency (%)
0 21
45.7%
1000 1
 
2.2%
2000 1
 
2.2%
2250 1
 
2.2%
2667 1
 
2.2%
3000 1
 
2.2%
6000 1
 
2.2%
6250 1
 
2.2%
6500 2
 
4.3%
7000 3
 
6.5%
ValueCountFrequency (%)
35000 1
2.2%
25000 1
2.2%
20000 1
2.2%
17500 1
2.2%
16500 1
2.2%
15000 1
2.2%
13500 1
2.2%
12250 1
2.2%
12000 1
2.2%
10000 1
2.2%

오류동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13811.87
Minimum0
Maximum200000
Zeros15
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:17.249249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6000
Q311500
95-th percentile40625.25
Maximum200000
Range200000
Interquartile range (IQR)11500

Descriptive statistics

Standard deviation33611.696
Coefficient of variation (CV)2.433537
Kurtosis22.970819
Mean13811.87
Median Absolute Deviation (MAD)6000
Skewness4.6079657
Sum635346
Variance1.1297461 × 109
MonotonicityNot monotonic
2023-12-12T15:00:17.402892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 15
32.6%
6000 2
 
4.3%
12000 2
 
4.3%
7250 2
 
4.3%
7000 2
 
4.3%
7500 2
 
4.3%
20000 2
 
4.3%
10000 2
 
4.3%
3000 1
 
2.2%
8000 1
 
2.2%
Other values (15) 15
32.6%
ValueCountFrequency (%)
0 15
32.6%
1000 1
 
2.2%
1500 1
 
2.2%
3000 1
 
2.2%
3250 1
 
2.2%
3500 1
 
2.2%
4500 1
 
2.2%
5500 1
 
2.2%
6000 2
 
4.3%
7000 2
 
4.3%
ValueCountFrequency (%)
200000 1
2.2%
116829 1
2.2%
41667 1
2.2%
37500 1
2.2%
20000 2
4.3%
16500 1
2.2%
15400 1
2.2%
14200 1
2.2%
14000 1
2.2%
12000 2
4.3%

태평동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15647.087
Minimum0
Maximum214000
Zeros11
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:17.556410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1625
median6125
Q313750
95-th percentile43500
Maximum214000
Range214000
Interquartile range (IQR)13125

Descriptive statistics

Standard deviation37592.814
Coefficient of variation (CV)2.402544
Kurtosis20.640008
Mean15647.087
Median Absolute Deviation (MAD)6125
Skewness4.4599088
Sum719766
Variance1.4132197 × 109
MonotonicityNot monotonic
2023-12-12T15:00:17.701497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 11
23.9%
3000 2
 
4.3%
5500 2
 
4.3%
11500 1
 
2.2%
4500 1
 
2.2%
6250 1
 
2.2%
214000 1
 
2.2%
8000 1
 
2.2%
4000 1
 
2.2%
150000 1
 
2.2%
Other values (24) 24
52.2%
ValueCountFrequency (%)
0 11
23.9%
500 1
 
2.2%
1000 1
 
2.2%
2650 1
 
2.2%
3000 2
 
4.3%
4000 1
 
2.2%
4167 1
 
2.2%
4500 1
 
2.2%
5333 1
 
2.2%
5500 2
 
4.3%
ValueCountFrequency (%)
214000 1
2.2%
150000 1
2.2%
48000 1
2.2%
30000 1
2.2%
20000 1
2.2%
18000 1
2.2%
17500 1
2.2%
16333 1
2.2%
15450 1
2.2%
14333 1
2.2%

유천동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12310.87
Minimum0
Maximum170000
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:17.842266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11225
median6900
Q314699.75
95-th percentile25375
Maximum170000
Range170000
Interquartile range (IQR)13474.75

Descriptive statistics

Standard deviation25599.174
Coefficient of variation (CV)2.0793961
Kurtosis33.492894
Mean12310.87
Median Absolute Deviation (MAD)6600
Skewness5.4736961
Sum566300
Variance6.553177 × 108
MonotonicityNot monotonic
2023-12-12T15:00:18.003220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 10
21.7%
18000 3
 
6.5%
7000 3
 
6.5%
2500 2
 
4.3%
9500 1
 
2.2%
3500 1
 
2.2%
4000 1
 
2.2%
5000 1
 
2.2%
170000 1
 
2.2%
800 1
 
2.2%
Other values (22) 22
47.8%
ValueCountFrequency (%)
0 10
21.7%
600 1
 
2.2%
800 1
 
2.2%
2500 2
 
4.3%
3000 1
 
2.2%
3500 1
 
2.2%
4000 1
 
2.2%
4250 1
 
2.2%
5000 1
 
2.2%
6000 1
 
2.2%
ValueCountFrequency (%)
170000 1
 
2.2%
50000 1
 
2.2%
25500 1
 
2.2%
25000 1
 
2.2%
21667 1
 
2.2%
18000 3
6.5%
17667 1
 
2.2%
17000 1
 
2.2%
15000 1
 
2.2%
14933 1
 
2.2%

문화동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8948.8261
Minimum0
Maximum123368
Zeros15
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:18.145926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6627
Q310916.75
95-th percentile19250
Maximum123368
Range123368
Interquartile range (IQR)10916.75

Descriptive statistics

Standard deviation18636.022
Coefficient of variation (CV)2.0825102
Kurtosis32.997071
Mean8948.8261
Median Absolute Deviation (MAD)6373
Skewness5.4052549
Sum411646
Variance3.4730132 × 108
MonotonicityNot monotonic
2023-12-12T15:00:18.276844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 15
32.6%
7000 3
 
6.5%
13000 2
 
4.3%
15000 2
 
4.3%
3000 2
 
4.3%
11000 2
 
4.3%
6833 1
 
2.2%
35000 1
 
2.2%
8833 1
 
2.2%
10667 1
 
2.2%
Other values (16) 16
34.8%
ValueCountFrequency (%)
0 15
32.6%
400 1
 
2.2%
2500 1
 
2.2%
2933 1
 
2.2%
3000 2
 
4.3%
4000 1
 
2.2%
5000 1
 
2.2%
6540 1
 
2.2%
6714 1
 
2.2%
6750 1
 
2.2%
ValueCountFrequency (%)
123368 1
2.2%
35000 1
2.2%
20000 1
2.2%
17000 1
2.2%
15333 1
2.2%
15000 2
4.3%
13000 2
4.3%
12375 1
2.2%
11000 2
4.3%
10667 1
2.2%

산성동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9983.5652
Minimum0
Maximum126720
Zeros9
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:00:18.430726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12625
median6708.5
Q311030
95-th percentile25250
Maximum126720
Range126720
Interquartile range (IQR)8405

Descriptive statistics

Standard deviation18903.217
Coefficient of variation (CV)1.8934335
Kurtosis33.785652
Mean9983.5652
Median Absolute Deviation (MAD)4311.5
Skewness5.4704204
Sum459244
Variance3.5733161 × 108
MonotonicityNot monotonic
2023-12-12T15:00:18.588841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 9
19.6%
13000 3
 
6.5%
9000 2
 
4.3%
19000 2
 
4.3%
3000 2
 
4.3%
7000 2
 
4.3%
3500 2
 
4.3%
2500 1
 
2.2%
7800 1
 
2.2%
15000 1
 
2.2%
Other values (21) 21
45.7%
ValueCountFrequency (%)
0 9
19.6%
500 1
 
2.2%
1000 1
 
2.2%
2500 1
 
2.2%
3000 2
 
4.3%
3150 1
 
2.2%
3500 2
 
4.3%
3800 1
 
2.2%
4500 1
 
2.2%
5250 1
 
2.2%
ValueCountFrequency (%)
126720 1
 
2.2%
27500 1
 
2.2%
27000 1
 
2.2%
20000 1
 
2.2%
19000 2
4.3%
15000 1
 
2.2%
13000 3
6.5%
12000 1
 
2.2%
11040 1
 
2.2%
11000 1
 
2.2%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
대전광역시 중구청 일자리경제과
46 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시 중구청 일자리경제과
2nd row대전광역시 중구청 일자리경제과
3rd row대전광역시 중구청 일자리경제과
4th row대전광역시 중구청 일자리경제과
5th row대전광역시 중구청 일자리경제과

Common Values

ValueCountFrequency (%)
대전광역시 중구청 일자리경제과 46
100.0%

Length

2023-12-12T15:00:18.722591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:00:18.811534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 46
33.3%
중구청 46
33.3%
일자리경제과 46
33.3%

연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
042-606-7224
46 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row042-606-7224
2nd row042-606-7224
3rd row042-606-7224
4th row042-606-7224
5th row042-606-7224

Common Values

ValueCountFrequency (%)
042-606-7224 46
100.0%

Length

2023-12-12T15:00:18.892592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:00:18.968122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
042-606-7224 46
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
Minimum2023-11-30 00:00:00
Maximum2023-11-30 00:00:00
2023-12-12T15:00:19.029424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:19.328610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T15:00:10.801426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:50.132374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T14:59:58.982984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:00:00.654546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:00:11.969427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:51.322724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:52.605556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:53.927784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.533476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.155390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.771588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:00.269831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:02.137734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:03.703597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:05.070352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:06.360396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:07.926013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.442829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.626959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.055684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:51.399568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:52.686394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.020096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.912360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.242040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.861681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:00.375764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:02.262836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:03.792445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:05.170468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:06.459103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.017348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.530961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.705011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:00:19.395814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번품목조사기준은행선화동목동중촌동대흥동문창동석교동대사동부사동용두동오류동태평동유천동문화동산성동
연번1.0001.0000.9600.4110.2240.2220.0000.4620.0000.3540.0000.2740.2990.2990.2800.4900.356
품목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
조사기준0.9601.0001.0000.9650.5610.0000.9620.6920.9510.0000.8370.6460.8520.8520.9650.0000.601
은행선화동0.4111.0000.9651.0000.0000.0000.9790.6630.2730.6780.6930.7510.9470.9470.8890.6960.813
목동0.2241.0000.5610.0001.0000.9010.0000.0000.2140.6340.4790.9040.0000.0000.0000.0000.000
중촌동0.2221.0000.0000.0000.9011.0000.6300.7220.7450.7660.7580.9210.0000.0000.0000.0000.000
대흥동0.0001.0000.9620.9790.0000.6301.0000.6590.4700.4260.6980.6940.8050.8050.8790.6770.736
문창동0.4621.0000.6920.6630.0000.7220.6591.0000.6080.7860.8620.8880.5540.5540.0770.1200.694
석교동0.0001.0000.9510.2730.2140.7450.4700.6081.0000.6680.5910.6520.6940.6940.3830.3420.104
대사동0.3541.0000.0000.6780.6340.7660.4260.7860.6681.0000.9180.7830.5430.5430.4810.1880.511
부사동0.0001.0000.8370.6930.4790.7580.6980.8620.5910.9181.0000.8660.5580.5580.4150.3060.648
용두동0.2741.0000.6460.7510.9040.9210.6940.8880.6520.7830.8661.0000.5310.5310.5050.5580.792
오류동0.2991.0000.8520.9470.0000.0000.8050.5540.6940.5430.5580.5311.0001.0000.6700.8290.828
태평동0.2991.0000.8520.9470.0000.0000.8050.5540.6940.5430.5580.5311.0001.0000.6700.8290.828
유천동0.2801.0000.9650.8890.0000.0000.8790.0770.3830.4810.4150.5050.6700.6701.0000.9260.932
문화동0.4901.0000.0000.6960.0000.0000.6770.1200.3420.1880.3060.5580.8290.8290.9261.0000.970
산성동0.3561.0000.6010.8130.0000.0000.7360.6940.1040.5110.6480.7920.8280.8280.9320.9701.000
2023-12-12T15:00:19.535138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번은행선화동목동중촌동대흥동문창동석교동대사동부사동용두동오류동태평동유천동문화동산성동
연번1.000-0.191-0.126-0.224-0.146-0.209-0.151-0.134-0.154-0.173-0.091-0.275-0.214-0.233-0.140
은행선화동-0.1911.0000.2150.2270.7360.3750.2880.4210.4570.5190.4530.4940.7900.3980.576
목동-0.1260.2151.0000.3540.3700.0260.3150.0970.2910.3350.0830.0670.2030.1230.234
중촌동-0.2240.2270.3541.0000.2540.4190.6050.5250.3440.5260.1940.2070.2940.4030.198
대흥동-0.1460.7360.3700.2541.0000.1800.2980.1410.3340.4220.3730.3260.7390.4370.364
문창동-0.2090.3750.0260.4190.1801.0000.4130.5680.4790.6900.4500.3910.2950.2540.461
석교동-0.1510.2880.3150.6050.2980.4131.0000.5150.2800.5090.4440.4990.2830.3960.371
대사동-0.1340.4210.0970.5250.1410.5680.5151.0000.4580.4320.4890.4290.3300.2440.322
부사동-0.1540.4570.2910.3440.3340.4790.2800.4581.0000.5650.2990.2520.3530.2370.375
용두동-0.1730.5190.3350.5260.4220.6900.5090.4320.5651.0000.5510.3870.5080.4850.564
오류동-0.0910.4530.0830.1940.3730.4500.4440.4890.2990.5511.0000.7130.6250.5610.571
태평동-0.2750.4940.0670.2070.3260.3910.4990.4290.2520.3870.7131.0000.5690.3630.583
유천동-0.2140.7900.2030.2940.7390.2950.2830.3300.3530.5080.6250.5691.0000.5730.584
문화동-0.2330.3980.1230.4030.4370.2540.3960.2440.2370.4850.5610.3630.5731.0000.577
산성동-0.1400.5760.2340.1980.3640.4610.3710.3220.3750.5640.5710.5830.5840.5771.000

Missing values

2023-12-12T15:00:12.211849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:00:12.809184image/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

연번품목조사기준은행선화동목동중촌동대흥동문창동석교동대사동부사동용두동오류동태평동유천동문화동산성동관리기관연락처데이터기준일자
01설렁탕1그릇(대중식당)90000008000070009000009500009000대전광역시 중구청 일자리경제과042-606-72242023-11-30
12냉 면1그릇(대중식당)70406500700086678000007500700055006000730000대전광역시 중구청 일자리경제과042-606-72242023-11-30
23비빔밥1그릇(대중식당)71110007000600057506500600060005500650070005250대전광역시 중구청 일자리경제과042-606-72242023-11-30
34갈비탕1그릇(대중식당)7500001450013000001300090001200014000100001100013000대전광역시 중구청 일자리경제과042-606-72242023-11-30
45삼계탕1그릇(대중식당)125001200015000110000105000013500014333150001100012000대전광역시 중구청 일자리경제과042-606-72242023-11-30
56김치찌개백반1그릇(대중식당)69170750007500800072506833700072507750680071677667대전광역시 중구청 일자리경제과042-606-72242023-11-30
67된장찌개백반1그릇(대중식당)69290750007000700073336500650072507500700068336667대전광역시 중구청 일자리경제과042-606-72242023-11-30
78불고기1인분(200g)7700010000120000001300000017000130000대전광역시 중구청 일자리경제과042-606-72242023-11-30
89등심1인분(200g)0000000003750048000255003500027000대전광역시 중구청 일자리경제과042-606-72242023-11-30
910돼지갈비1인분(200g)130000016000130001500000150001420012500140001300013000대전광역시 중구청 일자리경제과042-606-72242023-11-30
연번품목조사기준은행선화동목동중촌동대흥동문창동석교동대사동부사동용두동오류동태평동유천동문화동산성동관리기관연락처데이터기준일자
3637사진촬영료증명판17500180000200000002000020000200000180001500020000대전광역시 중구청 일자리경제과042-606-72242023-11-30
3738사진인화료3x5(24매기준)현상인화료포함4000045000040010001000500600400500대전광역시 중구청 일자리경제과042-606-72242023-11-30
3839숙박료(호텔)1박(관광호텔2급2종)67500004250000000005000000대전광역시 중구청 일자리경제과042-606-72242023-11-30
3940숙박료(여관)1박(독방,욕탕부설)24167000375000350004000035000416673000021667027500대전광역시 중구청 일자리경제과042-606-72242023-11-30
4041콘도이용료25평1일1박00000000000000대전광역시 중구청 일자리경제과042-606-72242023-11-30
4142이용료1회(성인중급)983301000080008000150001000012000100001000013000112001066711040대전광역시 중구청 일자리경제과042-606-72242023-11-30
4243미용료컷트13750116679250126671375090007000100001200095001100010333883311000대전광역시 중구청 일자리경제과042-606-72242023-11-30
4344목 욕 료성인1회(대중목용탕)60000700007000800080000080007000700070007500대전광역시 중구청 일자리경제과042-606-72242023-11-30
4445찜질방이용료성인1회00000000000009000대전광역시 중구청 일자리경제과042-606-72242023-11-30
4546운동경기관람료1회( 프로야구축구일반내야)000000013000000000대전광역시 중구청 일자리경제과042-606-72242023-11-30