Overview

Dataset statistics

Number of variables12
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory205.2 KiB
Average record size in memory105.1 B

Variable types

Numeric8
Categorical2
Text2

Dataset

Description기준_년분기_코드,서울시_코드,서울시_코드_명,서비스_업종_코드,서비스_업종_코드_명,점포_수,유사_업종_점포_수,개업_율,개업_점포_수,폐업_률,폐업_점포_수,프랜차이즈_점포_수
Author서울신용보증재단
URLhttps://data.seoul.go.kr/dataList/OA-22174/S/1/datasetView.do

Alerts

서울시_코드 has constant value ""Constant
서울시_코드_명 has constant value ""Constant
점포_수 is highly overall correlated with 유사_업종_점포_수 and 3 other fieldsHigh correlation
유사_업종_점포_수 is highly overall correlated with 점포_수 and 3 other fieldsHigh correlation
개업_율 is highly overall correlated with 개업_점포_수High correlation
개업_점포_수 is highly overall correlated with 점포_수 and 4 other fieldsHigh correlation
폐업_률 is highly overall correlated with 폐업_점포_수High correlation
폐업_점포_수 is highly overall correlated with 점포_수 and 4 other fieldsHigh correlation
프랜차이즈_점포_수 is highly overall correlated with 점포_수 and 3 other fieldsHigh correlation
개업_율 has 204 (10.2%) zerosZeros
개업_점포_수 has 67 (3.4%) zerosZeros
폐업_률 has 38 (1.9%) zerosZeros
프랜차이즈_점포_수 has 212 (10.6%) zerosZeros

Reproduction

Analysis started2024-05-04 05:01:51.484046
Analysis finished2024-05-04 05:02:11.228217
Duration19.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년분기_코드
Real number (ℝ)

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20212.5
Minimum20191
Maximum20234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:11.347150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191
5-th percentile20191.95
Q120201.75
median20212.5
Q320223.25
95-th percentile20233.05
Maximum20234
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation14.189809
Coefficient of variation (CV)0.00070203136
Kurtosis-1.2841632
Mean20212.5
Median Absolute Deviation (MAD)11
Skewness0
Sum40425000
Variance201.35068
MonotonicityNot monotonic
2024-05-04T05:02:11.565664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20191 100
 
5.0%
20214 100
 
5.0%
20234 100
 
5.0%
20233 100
 
5.0%
20232 100
 
5.0%
20231 100
 
5.0%
20224 100
 
5.0%
20223 100
 
5.0%
20222 100
 
5.0%
20221 100
 
5.0%
Other values (10) 1000
50.0%
ValueCountFrequency (%)
20191 100
5.0%
20192 100
5.0%
20193 100
5.0%
20194 100
5.0%
20201 100
5.0%
20202 100
5.0%
20203 100
5.0%
20204 100
5.0%
20211 100
5.0%
20212 100
5.0%
ValueCountFrequency (%)
20234 100
5.0%
20233 100
5.0%
20232 100
5.0%
20231 100
5.0%
20224 100
5.0%
20223 100
5.0%
20222 100
5.0%
20221 100
5.0%
20214 100
5.0%
20213 100
5.0%

서울시_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
11
2000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 2000
100.0%

Length

2024-05-04T05:02:12.083935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:02:12.261671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 2000
100.0%

서울시_코드_명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
서울시
2000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울시 2000
100.0%

Length

2024-05-04T05:02:12.450123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:02:12.628035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울시 2000
100.0%

서비스_업종_코드
Text

Distinct100
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-05-04T05:02:13.087744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16000
Distinct characters12
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

Unique0 ?
Unique (%)0.0%

Sample

1st rowCS100002
2nd rowCS100007
3rd rowCS200004
4th rowCS200007
5th rowCS200011
ValueCountFrequency (%)
cs100002 20
 
1.0%
cs300021 20
 
1.0%
cs300008 20
 
1.0%
cs300006 20
 
1.0%
cs300004 20
 
1.0%
cs200024 20
 
1.0%
cs200019 20
 
1.0%
cs200010 20
 
1.0%
cs200002 20
 
1.0%
cs300042 20
 
1.0%
Other values (90) 1800
90.0%
2024-05-04T05:02:13.990602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6720
42.0%
C 2000
 
12.5%
S 2000
 
12.5%
2 1560
 
9.8%
3 1480
 
9.2%
1 840
 
5.2%
4 440
 
2.8%
7 200
 
1.2%
5 200
 
1.2%
6 200
 
1.2%
Other values (2) 360
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12000
75.0%
Uppercase Letter 4000
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6720
56.0%
2 1560
 
13.0%
3 1480
 
12.3%
1 840
 
7.0%
4 440
 
3.7%
7 200
 
1.7%
5 200
 
1.7%
6 200
 
1.7%
9 180
 
1.5%
8 180
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
C 2000
50.0%
S 2000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12000
75.0%
Latin 4000
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6720
56.0%
2 1560
 
13.0%
3 1480
 
12.3%
1 840
 
7.0%
4 440
 
3.7%
7 200
 
1.7%
5 200
 
1.7%
6 200
 
1.7%
9 180
 
1.5%
8 180
 
1.5%
Latin
ValueCountFrequency (%)
C 2000
50.0%
S 2000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6720
42.0%
C 2000
 
12.5%
S 2000
 
12.5%
2 1560
 
9.8%
3 1480
 
9.2%
1 840
 
5.2%
4 440
 
2.8%
7 200
 
1.2%
5 200
 
1.2%
6 200
 
1.2%
Other values (2) 360
 
2.2%

서비스_업종_코드_명
Text

Distinct100
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-05-04T05:02:14.416384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length4.46
Min length2

Characters and Unicode

Total characters8920
Distinct characters163
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

Unique0 ?
Unique (%)0.0%

Sample

1st row중식음식점
2nd row치킨전문점
3rd row컴퓨터학원
4th row치과의원
5th row변리사사무소
ValueCountFrequency (%)
중식음식점 20
 
1.0%
애완동물 20
 
1.0%
수산물판매 20
 
1.0%
미곡판매 20
 
1.0%
핸드폰 20
 
1.0%
스포츠클럽 20
 
1.0%
pc방 20
 
1.0%
변호사사무소 20
 
1.0%
외국어학원 20
 
1.0%
주유소 20
 
1.0%
Other values (94) 1880
90.4%
2024-05-04T05:02:15.167980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 280
 
3.1%
점 260
 
2.9%
품 240
 
2.7%
기 180
 
2.0%
의 180
 
2.0%
원 180
 
2.0%
식 180
 
2.0%
무 160
 
1.8%
장 160
 
1.8%
소 160
 
1.8%
Other values (153) 6940
77.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8660
97.1%
Uppercase Letter 100
 
1.1%
Space Separator 80
 
0.9%
Dash Punctuation 40
 
0.4%
Other Punctuation 40
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 280
 
3.2%
점 260
 
3.0%
품 240
 
2.8%
기 180
 
2.1%
의 180
 
2.1%
원 180
 
2.1%
식 180
 
2.1%
무 160
 
1.8%
장 160
 
1.8%
소 160
 
1.8%
Other values (146) 6680
77.1%
Uppercase Letter
ValueCountFrequency (%)
D 40
40.0%
C 20
20.0%
P 20
20.0%
V 20
20.0%
Space Separator
ValueCountFrequency (%)
80
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8660
97.1%
Common 160
 
1.8%
Latin 100
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 280
 
3.2%
점 260
 
3.0%
품 240
 
2.8%
기 180
 
2.1%
의 180
 
2.1%
원 180
 
2.1%
식 180
 
2.1%
무 160
 
1.8%
장 160
 
1.8%
소 160
 
1.8%
Other values (146) 6680
77.1%
Latin
ValueCountFrequency (%)
D 40
40.0%
C 20
20.0%
P 20
20.0%
V 20
20.0%
Common
ValueCountFrequency (%)
80
50.0%
- 40
25.0%
/ 40
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8660
97.1%
ASCII 260
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 280
 
3.2%
점 260
 
3.0%
품 240
 
2.8%
기 180
 
2.1%
의 180
 
2.1%
원 180
 
2.1%
식 180
 
2.1%
무 160
 
1.8%
장 160
 
1.8%
소 160
 
1.8%
Other values (146) 6680
77.1%
ASCII
ValueCountFrequency (%)
80
30.8%
- 40
15.4%
D 40
15.4%
/ 40
15.4%
C 20
 
7.7%
P 20
 
7.7%
V 20
 
7.7%

점포_수
Real number (ℝ)

HIGH CORRELATION 

Distinct1717
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5599.8415
Minimum134
Maximum72442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:15.559880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum134
5-th percentile331.8
Q11016
median3328
Q35509.25
95-th percentile17003.7
Maximum72442
Range72308
Interquartile range (IQR)4493.25

Descriptive statistics

Standard deviation9182.5283
Coefficient of variation (CV)1.6397836
Kurtosis18.721067
Mean5599.8415
Median Absolute Deviation (MAD)2286
Skewness4.0858453
Sum11199683
Variance84318826
MonotonicityNot monotonic
2024-05-04T05:02:16.009319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
818 6
 
0.3%
137 5
 
0.2%
143 5
 
0.2%
141 5
 
0.2%
827 4
 
0.2%
1003 4
 
0.2%
813 4
 
0.2%
145 4
 
0.2%
533 4
 
0.2%
644 4
 
0.2%
Other values (1707) 1955
97.8%
ValueCountFrequency (%)
134 1
 
0.1%
136 3
0.1%
137 5
0.2%
138 3
0.1%
139 1
 
0.1%
140 3
0.1%
141 5
0.2%
142 3
0.1%
143 5
0.2%
144 4
0.2%
ValueCountFrequency (%)
72442 1
0.1%
70739 1
0.1%
68577 1
0.1%
66219 1
0.1%
64200 1
0.1%
61408 1
0.1%
60781 1
0.1%
58652 1
0.1%
57194 1
0.1%
57128 1
0.1%

유사_업종_점포_수
Real number (ℝ)

HIGH CORRELATION 

Distinct1717
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6058.5445
Minimum136
Maximum72606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:16.283445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum136
5-th percentile334.95
Q11063
median3677.5
Q35998.25
95-th percentile18886
Maximum72606
Range72470
Interquartile range (IQR)4935.25

Descriptive statistics

Standard deviation9649.7796
Coefficient of variation (CV)1.5927554
Kurtosis18.20949
Mean6058.5445
Median Absolute Deviation (MAD)2499.5
Skewness3.9956028
Sum12117089
Variance93118247
MonotonicityNot monotonic
2024-05-04T05:02:16.715760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144 8
 
0.4%
143 6
 
0.3%
139 5
 
0.2%
1775 4
 
0.2%
525 4
 
0.2%
601 4
 
0.2%
819 4
 
0.2%
1414 3
 
0.1%
665 3
 
0.1%
4302 3
 
0.1%
Other values (1707) 1956
97.8%
ValueCountFrequency (%)
136 2
 
0.1%
137 3
 
0.1%
138 1
 
0.1%
139 5
0.2%
140 3
 
0.1%
141 1
 
0.1%
142 2
 
0.1%
143 6
0.3%
144 8
0.4%
145 3
 
0.1%
ValueCountFrequency (%)
72606 1
0.1%
70900 1
0.1%
68734 1
0.1%
66373 1
0.1%
64349 1
0.1%
63030 1
0.1%
63009 1
0.1%
62833 1
0.1%
62565 1
0.1%
62393 1
0.1%

개업_율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.707
Minimum0
Maximum16
Zeros204
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:17.096535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0335237
Coefficient of variation (CV)0.75120934
Kurtosis5.6848093
Mean2.707
Median Absolute Deviation (MAD)1
Skewness1.5852157
Sum5414
Variance4.1352186
MonotonicityNot monotonic
2024-05-04T05:02:17.332258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 485
24.2%
1 374
18.7%
3 362
18.1%
4 258
12.9%
0 204
10.2%
5 165
 
8.2%
6 73
 
3.6%
7 33
 
1.7%
8 18
 
0.9%
9 10
 
0.5%
Other values (5) 18
 
0.9%
ValueCountFrequency (%)
0 204
10.2%
1 374
18.7%
2 485
24.2%
3 362
18.1%
4 258
12.9%
5 165
 
8.2%
6 73
 
3.6%
7 33
 
1.7%
8 18
 
0.9%
9 10
 
0.5%
ValueCountFrequency (%)
16 5
 
0.2%
14 1
 
0.1%
12 2
 
0.1%
11 6
 
0.3%
10 4
 
0.2%
9 10
 
0.5%
8 18
 
0.9%
7 33
 
1.7%
6 73
3.6%
5 165
8.2%

개업_점포_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct496
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.9385
Minimum0
Maximum11637
Zeros67
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:17.596896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median64
Q3173
95-th percentile607.15
Maximum11637
Range11637
Interquartile range (IQR)154

Descriptive statistics

Standard deviation492.28541
Coefficient of variation (CV)2.6618871
Kurtosis194.47422
Mean184.9385
Median Absolute Deviation (MAD)54
Skewness11.193629
Sum369877
Variance242344.93
MonotonicityNot monotonic
2024-05-04T05:02:17.874825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
 
3.4%
14 32
 
1.6%
19 29
 
1.5%
9 29
 
1.5%
3 28
 
1.4%
1 28
 
1.4%
10 25
 
1.2%
2 25
 
1.2%
11 24
 
1.2%
4 24
 
1.2%
Other values (486) 1689
84.5%
ValueCountFrequency (%)
0 67
3.4%
1 28
1.4%
2 25
 
1.2%
3 28
1.4%
4 24
 
1.2%
5 22
 
1.1%
6 17
 
0.9%
7 23
 
1.1%
8 16
 
0.8%
9 29
1.5%
ValueCountFrequency (%)
11637 1
0.1%
6893 1
0.1%
6590 1
0.1%
6254 1
0.1%
3315 1
0.1%
3096 1
0.1%
2932 1
0.1%
2842 1
0.1%
2811 1
0.1%
2765 1
0.1%

폐업_률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.352
Minimum0
Maximum11
Zeros38
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:18.209253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5047352
Coefficient of variation (CV)0.63976838
Kurtosis2.4967315
Mean2.352
Median Absolute Deviation (MAD)1
Skewness1.3687884
Sum4704
Variance2.2642281
MonotonicityNot monotonic
2024-05-04T05:02:18.436217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 640
32.0%
2 600
30.0%
3 351
17.5%
4 193
 
9.7%
5 98
 
4.9%
0 38
 
1.9%
6 36
 
1.8%
7 27
 
1.4%
8 13
 
0.7%
10 2
 
0.1%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 38
 
1.9%
1 640
32.0%
2 600
30.0%
3 351
17.5%
4 193
 
9.7%
5 98
 
4.9%
6 36
 
1.8%
7 27
 
1.4%
8 13
 
0.7%
9 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
10 2
 
0.1%
9 1
 
0.1%
8 13
 
0.7%
7 27
 
1.4%
6 36
 
1.8%
5 98
 
4.9%
4 193
 
9.7%
3 351
17.5%
2 600
30.0%

폐업_점포_수
Real number (ℝ)

HIGH CORRELATION 

Distinct470
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.012
Minimum0
Maximum3480
Zeros7
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:18.677610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q120
median60
Q3147
95-th percentile598.05
Maximum3480
Range3480
Interquartile range (IQR)127

Descriptive statistics

Standard deviation306.7034
Coefficient of variation (CV)2.0044402
Kurtosis33.337651
Mean153.012
Median Absolute Deviation (MAD)47
Skewness5.1547301
Sum306024
Variance94066.978
MonotonicityNot monotonic
2024-05-04T05:02:19.042213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 40
 
2.0%
10 39
 
1.9%
6 36
 
1.8%
12 35
 
1.8%
9 34
 
1.7%
8 34
 
1.7%
15 29
 
1.5%
11 27
 
1.4%
3 26
 
1.3%
5 26
 
1.3%
Other values (460) 1674
83.7%
ValueCountFrequency (%)
0 7
 
0.4%
1 11
 
0.5%
2 13
 
0.7%
3 26
1.3%
4 25
1.2%
5 26
1.3%
6 36
1.8%
7 40
2.0%
8 34
1.7%
9 34
1.7%
ValueCountFrequency (%)
3480 1
0.1%
3041 1
0.1%
2477 1
0.1%
2430 1
0.1%
2419 1
0.1%
2417 1
0.1%
2393 1
0.1%
2367 1
0.1%
2332 1
0.1%
2271 2
0.1%

프랜차이즈_점포_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct541
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458.703
Minimum0
Maximum10580
Zeros212
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-05-04T05:02:19.379983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median14
Q3169
95-th percentile2663.35
Maximum10580
Range10580
Interquartile range (IQR)166

Descriptive statistics

Standard deviation1387.277
Coefficient of variation (CV)3.0243468
Kurtosis27.046846
Mean458.703
Median Absolute Deviation (MAD)14
Skewness4.8820934
Sum917406
Variance1924537.4
MonotonicityNot monotonic
2024-05-04T05:02:19.667792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212
 
10.6%
1 134
 
6.7%
2 133
 
6.7%
3 98
 
4.9%
8 58
 
2.9%
11 53
 
2.6%
5 53
 
2.6%
9 45
 
2.2%
4 42
 
2.1%
6 42
 
2.1%
Other values (531) 1130
56.5%
ValueCountFrequency (%)
0 212
10.6%
1 134
6.7%
2 133
6.7%
3 98
4.9%
4 42
 
2.1%
5 53
 
2.6%
6 42
 
2.1%
7 25
 
1.2%
8 58
 
2.9%
9 45
 
2.2%
ValueCountFrequency (%)
10580 1
0.1%
10550 1
0.1%
10544 1
0.1%
10488 1
0.1%
10485 1
0.1%
10429 1
0.1%
10411 1
0.1%
10338 1
0.1%
10334 1
0.1%
10331 1
0.1%

Interactions

2024-05-04T05:02:08.597257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:53.140950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:55.155366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:57.184178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:59.285421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:01.481323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:04.097984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:06.420832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:08.947650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:53.305342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:55.426424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:57.437092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:59.551457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:01.741958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:04.410618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:06.641363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:09.220058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:53.576083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:55.705791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:57.724521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:59.815916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:02.158988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:04.697049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:06.929211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:09.502949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:53.867878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:55.987831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:58.006359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:00.086673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:02.510612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:04.986169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:07.210843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:09.785136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:54.092122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:56.183912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:58.273719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:00.376904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:02.815955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:05.283877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:07.501987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:09.989488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:54.360786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:56.638598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:58.458124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:00.654244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:03.115669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:05.564415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:07.777246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:10.176703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:54.623834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:56.813455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:58.758855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:00.928295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:03.423242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:05.927789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:08.050194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:10.388306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:54.894456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:57.006613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:01:59.006662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:01.210856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:03.791846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:06.203585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:02:08.328316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T05:02:19.893395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드서비스_업종_코드서비스_업종_코드_명점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수
기준_년분기_코드1.0000.0000.0000.0000.0000.3170.0770.1240.0000.000
서비스_업종_코드0.0001.0001.0000.9600.9630.7840.7840.8000.8530.991
서비스_업종_코드_명0.0001.0001.0000.9600.9630.7840.7840.8000.8530.991
점포_수0.0000.9600.9601.0000.9940.5220.8390.2260.8840.643
유사_업종_점포_수0.0000.9630.9630.9941.0000.5080.8660.2740.8790.693
개업_율0.3170.7840.7840.5220.5081.0000.7830.5580.1950.268
개업_점포_수0.0770.7840.7840.8390.8660.7831.0000.1880.7190.509
폐업_률0.1240.8000.8000.2260.2740.5580.1881.0000.4190.409
폐업_점포_수0.0000.8530.8530.8840.8790.1950.7190.4191.0000.639
프랜차이즈_점포_수0.0000.9910.9910.6430.6930.2680.5090.4090.6391.000
2024-05-04T05:02:20.212800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수
기준_년분기_코드1.0000.0220.020-0.350-0.193-0.081-0.031-0.007
점포_수0.0221.0000.9540.1060.7640.0970.8380.578
유사_업종_점포_수0.0200.9541.0000.1680.8280.1710.9020.677
개업_율-0.3500.1060.1681.0000.6330.4410.3310.383
개업_점포_수-0.1930.7640.8280.6331.0000.3510.8480.714
폐업_률-0.0810.0970.1710.4410.3511.0000.5250.479
폐업_점포_수-0.0310.8380.9020.3310.8480.5251.0000.774
프랜차이즈_점포_수-0.0070.5780.6770.3830.7140.4790.7741.000

Missing values

2024-05-04T05:02:10.699549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T05:02:11.101201image/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

기준_년분기_코드서울시_코드서울시_코드_명서비스_업종_코드서비스_업종_코드_명점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수
02019111서울시CS100002중식음식점4838518352384216345
12019111서울시CS100007치킨전문점22755904526652633629
22019111서울시CS200004컴퓨터학원3283324145164
32019111서울시CS200007치과의원491949202792731
42019111서울시CS200011변리사사무소652655423013
52019111서울시CS200012법무사사무소222222222352380
62019111서울시CS200014회계사사무소936936181130
72019111서울시CS200031세탁소4078503431523126956
82019111서울시CS200045비디오/서적임대25330941241256
92019111서울시CS300007육류판매67366745317732079
기준_년분기_코드서울시_코드서울시_코드_명서비스_업종_코드서비스_업종_코드_명점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수
19902023411서울시CS200024스포츠클럽2143226508358122
19912023411서울시CS200026자동차미용1556158823722932
19922023411서울시CS200027모터사이클수리740741322191
19932023411서울시CS200046의류임대466468003152
19942023411서울시CS200047가정용품임대63463400170
19952023411서울시CS300018의약품8854939421981124540
19962023411서울시CS300024운동/경기용품416141681531567
19972023411서울시CS300025자전거 및 기타운송장비81581502180
19982023411서울시CS300041예술품213321353671212
19992023411서울시CS300043전자상거래업552065533805431514132