Overview

Dataset statistics

Number of variables14
Number of observations30
Missing cells32
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory120.4 B

Variable types

DateTime2
Categorical6
Text2
Numeric4

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/7ee8c8d3-dbed-48f2-8980-31db8e64e86f

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
등록일자 has constant value ""Constant
작업자명 has constant value ""Constant
업종대분류코드 is highly overall correlated with 업종대분류명High correlation
업종대분류명 is highly overall correlated with 업종대분류코드High correlation
총기업수 is highly overall correlated with 총매출금액High correlation
총매출금액 is highly overall correlated with 총기업수 and 2 other fieldsHigh correlation
지역화폐사용수 is highly overall correlated with 총매출금액 and 1 other fieldsHigh correlation
지역화폐총사용액 is highly overall correlated with 총매출금액 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 행정동명High correlation
행정동명 is highly overall correlated with 시군구명High correlation
지역화폐사용수 has 16 (53.3%) missing valuesMissing
지역화폐총사용액 has 16 (53.3%) missing valuesMissing
총매출금액 has 3 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:42:16.999605
Analysis finished2023-12-10 13:42:23.006125
Duration6.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-01-01 00:00:00
Maximum2021-01-01 00:00:00
2023-12-10T22:42:23.059224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:23.480618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기
30 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 30
100.0%

Length

2023-12-10T22:42:23.660155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:23.800061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 30
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
가평군
19 
고양시 덕양구
11 

Length

Max length7
Median length3
Mean length4.4666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가평군 19
63.3%
고양시 덕양구 11
36.7%

Length

2023-12-10T22:42:23.971622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:24.147576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가평군 19
46.3%
고양시 11
26.8%
덕양구 11
26.8%

행정동명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
청평면
6 
조종면
5 
고양동
4 
북면
3 
관산동
3 
Other values (5)
9 

Length

Max length3
Median length3
Mean length2.8666667
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row북면
2nd row가평읍
3rd row북면
4th row상면
5th row북면

Common Values

ValueCountFrequency (%)
청평면 6
20.0%
조종면 5
16.7%
고양동 4
13.3%
북면 3
10.0%
관산동 3
10.0%
능곡동 3
10.0%
가평읍 2
 
6.7%
설악면 2
 
6.7%
상면 1
 
3.3%
대덕동 1
 
3.3%

Length

2023-12-10T22:42:24.401516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:24.613667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청평면 6
20.0%
조종면 5
16.7%
고양동 4
13.3%
북면 3
10.0%
관산동 3
10.0%
능곡동 3
10.0%
가평읍 2
 
6.7%
설악면 2
 
6.7%
상면 1
 
3.3%
대덕동 1
 
3.3%

업종대분류코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
R
14 
F
13 
S
2 
E
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st rowF
2nd rowR
3rd rowF
4th rowF
5th rowR

Common Values

ValueCountFrequency (%)
R 14
46.7%
F 13
43.3%
S 2
 
6.7%
E 1
 
3.3%

Length

2023-12-10T22:42:24.815712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:24.964196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
r 14
46.7%
f 13
43.3%
s 2
 
6.7%
e 1
 
3.3%

업종중분류코드
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:42:25.211288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st rowF04
2nd rowR27
3rd rowF16
4th rowF15
5th rowR25
ValueCountFrequency (%)
s02 2
 
6.7%
r16 2
 
6.7%
f03 2
 
6.7%
r04 2
 
6.7%
r27 2
 
6.7%
f01 2
 
6.7%
r13 2
 
6.7%
f16 2
 
6.7%
r10 2
 
6.7%
f04 1
 
3.3%
Other values (11) 11
36.7%
2023-12-10T22:42:25.714949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17
18.9%
R 14
15.6%
1 13
14.4%
F 13
14.4%
2 11
12.2%
6 5
 
5.6%
3 4
 
4.4%
4 4
 
4.4%
S 2
 
2.2%
7 2
 
2.2%
Other values (4) 5
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Uppercase Letter 30
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17
28.3%
1 13
21.7%
2 11
18.3%
6 5
 
8.3%
3 4
 
6.7%
4 4
 
6.7%
7 2
 
3.3%
5 2
 
3.3%
8 1
 
1.7%
9 1
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
R 14
46.7%
F 13
43.3%
S 2
 
6.7%
E 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 60
66.7%
Latin 30
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17
28.3%
1 13
21.7%
2 11
18.3%
6 5
 
8.3%
3 4
 
6.7%
4 4
 
6.7%
7 2
 
3.3%
5 2
 
3.3%
8 1
 
1.7%
9 1
 
1.7%
Latin
ValueCountFrequency (%)
R 14
46.7%
F 13
43.3%
S 2
 
6.7%
E 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17
18.9%
R 14
15.6%
1 13
14.4%
F 13
14.4%
2 11
12.2%
6 5
 
5.6%
3 4
 
4.4%
4 4
 
4.4%
S 2
 
2.2%
7 2
 
2.2%
Other values (4) 5
 
5.6%

업종대분류명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
소매업
14 
음식
13 
서비스
2 
기타
 
1

Length

Max length3
Median length3
Mean length2.5333333
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row음식
2nd row소매업
3rd row음식
4th row음식
5th row소매업

Common Values

ValueCountFrequency (%)
소매업 14
46.7%
음식 13
43.3%
서비스 2
 
6.7%
기타 1
 
3.3%

Length

2023-12-10T22:42:25.956623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:26.167489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소매업 14
46.7%
음식 13
43.3%
서비스 2
 
6.7%
기타 1
 
3.3%

업종중분류명
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:42:26.462340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.6666667
Min length2

Characters and Unicode

Total characters140
Distinct characters60
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

Unique12 ?
Unique (%)40.0%

Sample

1st row분식
2nd row화초 및 식물 소매
3rd row한식
4th row패스트푸드
5th row패션잡화
ValueCountFrequency (%)
정기)납부/대여서비스 2
 
5.6%
식물 2
 
5.6%
서적 2
 
5.6%
한식 2
 
5.6%
완구 2
 
5.6%
간이주점 2
 
5.6%
소매 2
 
5.6%
및 2
 
5.6%
화초 2
 
5.6%
건강식품 2
 
5.6%
Other values (14) 16
44.4%
2023-12-10T22:42:26.957654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 13
 
9.3%
6
 
4.3%
매 5
 
3.6%
/ 5
 
3.6%
소 5
 
3.6%
품 5
 
3.6%
서 5
 
3.6%
화 4
 
2.9%
음 4
 
2.9%
기 4
 
2.9%
Other values (50) 84
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
88.6%
Space Separator 6
 
4.3%
Other Punctuation 5
 
3.6%
Open Punctuation 2
 
1.4%
Close Punctuation 2
 
1.4%
Dash Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 13
 
10.5%
매 5
 
4.0%
소 5
 
4.0%
품 5
 
4.0%
서 5
 
4.0%
화 4
 
3.2%
음 4
 
3.2%
기 4
 
3.2%
한 4
 
3.2%
스 4
 
3.2%
Other values (45) 71
57.3%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
88.6%
Common 16
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 13
 
10.5%
매 5
 
4.0%
소 5
 
4.0%
품 5
 
4.0%
서 5
 
4.0%
화 4
 
3.2%
음 4
 
3.2%
기 4
 
3.2%
한 4
 
3.2%
스 4
 
3.2%
Other values (45) 71
57.3%
Common
ValueCountFrequency (%)
6
37.5%
/ 5
31.2%
( 2
 
12.5%
) 2
 
12.5%
- 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 124
88.6%
ASCII 16
 
11.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 13
 
10.5%
매 5
 
4.0%
소 5
 
4.0%
품 5
 
4.0%
서 5
 
4.0%
화 4
 
3.2%
음 4
 
3.2%
기 4
 
3.2%
한 4
 
3.2%
스 4
 
3.2%
Other values (45) 71
57.3%
ASCII
ValueCountFrequency (%)
6
37.5%
/ 5
31.2%
( 2
 
12.5%
) 2
 
12.5%
- 1
 
6.2%

총기업수
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.966667
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:27.173207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316
95-th percentile59.05
Maximum206
Range205
Interquartile range (IQR)14

Descriptive statistics

Standard deviation39.158813
Coefficient of variation (CV)2.0646123
Kurtosis18.885326
Mean18.966667
Median Absolute Deviation (MAD)4
Skewness4.0833113
Sum569
Variance1533.4126
MonotonicityNot monotonic
2023-12-10T22:42:27.349296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 7
23.3%
5 4
13.3%
2 3
10.0%
53 2
 
6.7%
16 2
 
6.7%
4 2
 
6.7%
7 2
 
6.7%
19 1
 
3.3%
26 1
 
3.3%
206 1
 
3.3%
Other values (5) 5
16.7%
ValueCountFrequency (%)
1 7
23.3%
2 3
10.0%
4 2
 
6.7%
5 4
13.3%
7 2
 
6.7%
8 1
 
3.3%
9 1
 
3.3%
12 1
 
3.3%
16 2
 
6.7%
19 1
 
3.3%
ValueCountFrequency (%)
206 1
3.3%
64 1
3.3%
53 2
6.7%
32 1
3.3%
26 1
3.3%
19 1
3.3%
16 2
6.7%
12 1
3.3%
9 1
3.3%
8 1
3.3%

총매출금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6475837 × 108
Minimum0
Maximum1.906944 × 109
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:27.536337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12235250
median21329500
Q31.189455 × 108
95-th percentile8.841583 × 108
Maximum1.906944 × 109
Range1.906944 × 109
Interquartile range (IQR)1.1671025 × 108

Descriptive statistics

Standard deviation4.0726561 × 108
Coefficient of variation (CV)2.4718964
Kurtosis13.020734
Mean1.6475837 × 108
Median Absolute Deviation (MAD)20478500
Skewness3.5601568
Sum4.942751 × 109
Variance1.6586527 × 1017
MonotonicityNot monotonic
2023-12-10T22:42:27.736296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 3
 
10.0%
1020000 1
 
3.3%
8558000 1
 
3.3%
5023000 1
 
3.3%
1740000 1
 
3.3%
1996000 1
 
3.3%
9052000 1
 
3.3%
329852000 1
 
3.3%
130791000 1
 
3.3%
208000 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 3
10.0%
208000 1
 
3.3%
682000 1
 
3.3%
1020000 1
 
3.3%
1740000 1
 
3.3%
1996000 1
 
3.3%
2953000 1
 
3.3%
5023000 1
 
3.3%
5467000 1
 
3.3%
8558000 1
 
3.3%
ValueCountFrequency (%)
1906944000 1
3.3%
1248301000 1
3.3%
439095000 1
3.3%
329852000 1
3.3%
200979000 1
3.3%
192202000 1
3.3%
135289000 1
3.3%
130791000 1
3.3%
83409000 1
3.3%
48713000 1
3.3%

지역화폐사용수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing16
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean497.85714
Minimum2
Maximum4401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:27.926910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13.7
Q134.5
median157.5
Q3344.25
95-th percentile1994.7
Maximum4401
Range4399
Interquartile range (IQR)309.75

Descriptive statistics

Standard deviation1142.0257
Coefficient of variation (CV)2.2938824
Kurtosis12.880086
Mean497.85714
Median Absolute Deviation (MAD)130.5
Skewness3.5396057
Sum6970
Variance1304222.7
MonotonicityNot monotonic
2023-12-10T22:42:28.120123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20 1
 
3.3%
33 1
 
3.3%
157 1
 
3.3%
365 1
 
3.3%
498 1
 
3.3%
39 1
 
3.3%
699 1
 
3.3%
4401 1
 
3.3%
219 1
 
3.3%
158 1
 
3.3%
Other values (4) 4
 
13.3%
(Missing) 16
53.3%
ValueCountFrequency (%)
2 1
3.3%
20 1
3.3%
21 1
3.3%
33 1
3.3%
39 1
3.3%
76 1
3.3%
157 1
3.3%
158 1
3.3%
219 1
3.3%
282 1
3.3%
ValueCountFrequency (%)
4401 1
3.3%
699 1
3.3%
498 1
3.3%
365 1
3.3%
282 1
3.3%
219 1
3.3%
158 1
3.3%
157 1
3.3%
76 1
3.3%
39 1
3.3%

지역화폐총사용액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing16
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean7861533.6
Minimum35000
Maximum64642050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:42:28.277957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35000
5-th percentile196167.5
Q1842100
median2935045
Q35456362.5
95-th percentile29888740
Maximum64642050
Range64607050
Interquartile range (IQR)4614262.5

Descriptive statistics

Standard deviation16676810
Coefficient of variation (CV)2.1213176
Kurtosis12.63193
Mean7861533.6
Median Absolute Deviation (MAD)2308145
Skewness3.4944869
Sum1.1006147 × 108
Variance2.7811598 × 1014
MonotonicityNot monotonic
2023-12-10T22:42:28.475052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
564000 1
 
3.3%
1299000 1
 
3.3%
3920570 1
 
3.3%
3603890 1
 
3.3%
11175420 1
 
3.3%
689800 1
 
3.3%
9060640 1
 
3.3%
64642050 1
 
3.3%
2118500 1
 
3.3%
4692450 1
 
3.3%
Other values (4) 4
 
13.3%
(Missing) 16
53.3%
ValueCountFrequency (%)
35000 1
3.3%
282950 1
3.3%
564000 1
3.3%
689800 1
3.3%
1299000 1
3.3%
2118500 1
3.3%
2266200 1
3.3%
3603890 1
3.3%
3920570 1
3.3%
4692450 1
3.3%
ValueCountFrequency (%)
64642050 1
3.3%
11175420 1
3.3%
9060640 1
3.3%
5711000 1
3.3%
4692450 1
3.3%
3920570 1
3.3%
3603890 1
3.3%
2266200 1
3.3%
2118500 1
3.3%
1299000 1
3.3%

등록일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-11-25 00:00:00
Maximum2021-11-25 00:00:00
2023-12-10T22:42:28.671370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:28.822892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

작업자명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
KEDSYSTEM
30 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KEDSYSTEM 30
100.0%

Length

2023-12-10T22:42:28.997519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:29.167759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsystem 30
100.0%

Interactions

2023-12-10T22:42:21.572620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.472593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.212893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.851315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.791771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.606140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.369676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.009169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.025860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.740649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.540784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.186464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:22.209177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:19.957368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:20.676204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:42:21.377710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:42:29.290328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명총기업수총매출금액지역화폐사용수지역화폐총사용액
시군구명1.0001.0000.0000.0000.0000.0000.0000.0000.1630.121
행정동명1.0001.0000.6970.6960.6970.6960.0000.0000.0000.000
업종대분류코드0.0000.6971.0001.0001.0001.0000.0000.0000.0000.022
업종중분류코드0.0000.6961.0001.0001.0001.0000.0000.5810.8130.817
업종대분류명0.0000.6971.0001.0001.0001.0000.0000.0000.0000.022
업종중분류명0.0000.6961.0001.0001.0001.0000.0000.5810.8130.817
총기업수0.0000.0000.0000.0000.0000.0001.0000.9770.7610.757
총매출금액0.0000.0000.0000.5810.0000.5810.9771.0000.7610.757
지역화폐사용수0.1630.0000.0000.8130.0000.8130.7610.7611.0001.000
지역화폐총사용액0.1210.0000.0220.8170.0220.8170.7570.7571.0001.000
2023-12-10T22:42:29.482697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류코드업종대분류명시군구명행정동명
업종대분류코드1.0001.0000.0000.427
업종대분류명1.0001.0000.0000.427
시군구명0.0000.0001.0000.845
행정동명0.4270.4270.8451.000
2023-12-10T22:42:29.658150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총기업수총매출금액지역화폐사용수지역화폐총사용액시군구명행정동명업종대분류코드업종대분류명
총기업수1.0000.8400.3240.3850.0000.0000.0000.000
총매출금액0.8401.0000.5870.6920.0000.0000.0000.000
지역화폐사용수0.3240.5871.0000.9250.2340.0000.0000.000
지역화폐총사용액0.3850.6920.9251.0000.2340.0000.0000.000
시군구명0.0000.0000.2340.2341.0000.8450.0000.000
행정동명0.0000.0000.0000.0000.8451.0000.4270.427
업종대분류코드0.0000.0000.0000.0000.0000.4271.0001.000
업종대분류명0.0000.0000.0000.0000.0000.4271.0001.000

Missing values

2023-12-10T22:42:22.458004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:42:22.749587image/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.
2023-12-10T22:42:22.939710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준년월시도명시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명총기업수총매출금액지역화폐사용수지역화폐총사용액등록일자작업자명
02021-01경기가평군북면FF04음식분식11020000205640002021-11-25KEDSYSTEM
12021-01경기가평군가평읍RR27소매업화초 및 식물 소매503312990002021-11-25KEDSYSTEM
22021-01경기가평군북면FF16음식한식53200979000<NA><NA>2021-11-25KEDSYSTEM
32021-01경기가평군상면FF15음식패스트푸드25467000<NA><NA>2021-11-25KEDSYSTEM
42021-01경기가평군북면RR25소매업패션잡화1948713000<NA><NA>2021-11-25KEDSYSTEM
52021-01경기가평군설악면RR16소매업음식료품소매2613528900015739205702021-11-25KEDSYSTEM
62021-01경기가평군설악면RR27소매업화초 및 식물 소매52953000<NA><NA>2021-11-25KEDSYSTEM
72021-01경기가평군조종면FF03음식떡/한과53277800036536038902021-11-25KEDSYSTEM
82021-01경기가평군조종면FF08음식유흥주점1641224000<NA><NA>2021-11-25KEDSYSTEM
92021-01경기가평군조종면RR04소매업건강식품511742000<NA><NA>2021-11-25KEDSYSTEM
기준년월시도명시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명총기업수총매출금액지역화폐사용수지역화폐총사용액등록일자작업자명
202021-01경기고양시 덕양구고양동FF10음식일식719220200015846924502021-11-25KEDSYSTEM
212021-01경기고양시 덕양구고양동RR26소매업화장품/미용1238304000212829502021-11-25KEDSYSTEM
222021-01경기고양시 덕양구고양동SS02서비스(정기)납부/대여서비스22080002350002021-11-25KEDSYSTEM
232021-01경기고양시 덕양구관산동FF01음식간이주점321307910007622662002021-11-25KEDSYSTEM
242021-01경기고양시 덕양구관산동FF12음식중식832985200028257110002021-11-25KEDSYSTEM
252021-01경기고양시 덕양구능곡동RR04소매업건강식품49052000<NA><NA>2021-11-25KEDSYSTEM
262021-01경기고양시 덕양구관산동RR10소매업서적11996000<NA><NA>2021-11-25KEDSYSTEM
272021-01경기고양시 덕양구능곡동RR10소매업서적21740000<NA><NA>2021-11-25KEDSYSTEM
282021-01경기고양시 덕양구능곡동RR13소매업완구10<NA><NA>2021-11-25KEDSYSTEM
292021-01경기고양시 덕양구대덕동EE02기타기업75023000<NA><NA>2021-11-25KEDSYSTEM